diff --git a/.buildinfo b/.buildinfo new file mode 100644 index 0000000..92139d9 --- /dev/null +++ b/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: a44a3b5e49715f78f8dd1c4affa19a43 +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/ColabFold/AlphaFold2.html b/ColabFold/AlphaFold2.html new file mode 100644 index 0000000..fbac0b1 --- /dev/null +++ b/ColabFold/AlphaFold2.html @@ -0,0 +1,1254 @@ + + + + + + + + + + + + News — Tutorials + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Contents

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Open In Colab

+https://raw.githubusercontent.com/sokrypton/ColabFold/main/.github/ColabFold_Marv_Logo_Small.png +

##ColabFold v1.5.3: AlphaFold2 using MMseqs2

+

Easy to use protein structure and complex prediction using AlphaFold2 and Alphafold2-multimer. Sequence alignments/templates are generated through MMseqs2 and HHsearch. For more details, see bottom of the notebook, checkout the ColabFold GitHub and read our manuscript. +Old versions: v1.4, v1.5.1, v1.5.2

+

Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all. +Nature Methods, 2022

+
+
+

News#

+
    +

  • 2023/07/31: The ColabFold MSA server is back to normal. It was using older DB (UniRef30 2202/PDB70 220313) from 27th ~8:30 AM CEST to 31st ~11:10 AM CEST.

    • +

  • 2023/06/12: New databases! UniRef30 updated to 2023_02 and PDB to 230517. We now use PDB100 instead of PDB70 (see notes).

    • +

  • 2023/06/12: We introduced a new default pairing strategy: Previously, for multimer predictions with more than 2 chains, we only pair if all sequences taxonomically match (“complete” pairing). The new default “greedy” strategy pairs any taxonomically matching subsets.

    • +
+
+
+
# @title Input protein sequence(s), then hit `Runtime` -> `Run all` { display-mode: "form" }
+from google.colab import files
+import os
+import re
+import hashlib
+import random
+
+from sys import version_info
+python_version = f"{version_info.major}.{version_info.minor}"
+
+def add_hash(x,y):
+  return x+"_"+hashlib.sha1(y.encode()).hexdigest()[:5]
+
+query_sequence = 'PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASK' #@param {type:"string"}
+#@markdown  - Use `:` to specify inter-protein chainbreaks for **modeling complexes** (supports homo- and hetro-oligomers). For example **PI...SK:PI...SK** for a homodimer
+jobname = 'test' #@param {type:"string"}
+# number of models to use
+num_relax = 0 #@param [0, 1, 5] {type:"raw"}
+#@markdown - specify how many of the top ranked structures to relax using amber
+template_mode = "none" #@param ["none", "pdb100","custom"]
+#@markdown - `none` = no template information is used. `pdb100` = detect templates in pdb100 (see [notes](#pdb100)). `custom` - upload and search own templates (PDB or mmCIF format, see [notes](#custom_templates))
+
+use_amber = num_relax > 0
+
+# remove whitespaces
+query_sequence = "".join(query_sequence.split())
+
+basejobname = "".join(jobname.split())
+basejobname = re.sub(r'\W+', '', basejobname)
+jobname = add_hash(basejobname, query_sequence)
+
+# check if directory with jobname exists
+def check(folder):
+  if os.path.exists(folder):
+    return False
+  else:
+    return True
+if not check(jobname):
+  n = 0
+  while not check(f"{jobname}_{n}"): n += 1
+  jobname = f"{jobname}_{n}"
+
+# make directory to save results
+os.makedirs(jobname, exist_ok=True)
+
+# save queries
+queries_path = os.path.join(jobname, f"{jobname}.csv")
+with open(queries_path, "w") as text_file:
+  text_file.write(f"id,sequence\n{jobname},{query_sequence}")
+
+if template_mode == "pdb100":
+  use_templates = True
+  custom_template_path = None
+elif template_mode == "custom":
+  custom_template_path = os.path.join(jobname,f"template")
+  os.makedirs(custom_template_path, exist_ok=True)
+  uploaded = files.upload()
+  use_templates = True
+  for fn in uploaded.keys():
+    os.rename(fn,os.path.join(custom_template_path,fn))
+else:
+  custom_template_path = None
+  use_templates = False
+
+print("jobname",jobname)
+print("sequence",query_sequence)
+print("length",len(query_sequence.replace(":","")))
+
+
+
+
+
jobname test_a5e17
+sequence PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASK
+length 59
+
+
+
+
+
+
+
# @title Install dependencies { display-mode: "form" }
+%%time
+import os
+USE_AMBER = use_amber
+USE_TEMPLATES = use_templates
+PYTHON_VERSION = python_version
+
+if not os.path.isfile("COLABFOLD_READY"):
+  print("installing colabfold...")
+  os.system("pip install -q --no-warn-conflicts 'colabfold[alphafold-minus-jax] @ git+https://github.com/sokrypton/ColabFold'")
+  os.system("pip install --upgrade dm-haiku")
+  os.system("ln -s /usr/local/lib/python3.*/dist-packages/colabfold colabfold")
+  os.system("ln -s /usr/local/lib/python3.*/dist-packages/alphafold alphafold")
+  # patch for jax > 0.3.25
+  os.system("sed -i 's/weights = jax.nn.softmax(logits)/logits=jnp.clip(logits,-1e8,1e8);weights=jax.nn.softmax(logits)/g' alphafold/model/modules.py")
+  os.system("touch COLABFOLD_READY")
+
+if USE_AMBER or USE_TEMPLATES:
+  if not os.path.isfile("CONDA_READY"):
+    print("installing conda...")
+    os.system("wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh")
+    os.system("bash Mambaforge-Linux-x86_64.sh -bfp /usr/local")
+    os.system("mamba config --set auto_update_conda false")
+    os.system("touch CONDA_READY")
+
+if USE_TEMPLATES and not os.path.isfile("HH_READY") and USE_AMBER and not os.path.isfile("AMBER_READY"):
+  print("installing hhsuite and amber...")
+  os.system(f"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer")
+  os.system("touch HH_READY")
+  os.system("touch AMBER_READY")
+else:
+  if USE_TEMPLATES and not os.path.isfile("HH_READY"):
+    print("installing hhsuite...")
+    os.system(f"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 python='{PYTHON_VERSION}'")
+    os.system("touch HH_READY")
+  if USE_AMBER and not os.path.isfile("AMBER_READY"):
+    print("installing amber...")
+    os.system(f"mamba install -y -c conda-forge openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer")
+    os.system("touch AMBER_READY")
+
+
+
+
+
installing colabfold...
+CPU times: user 114 ms, sys: 27.2 ms, total: 142 ms
+Wall time: 42.9 s
+
+
+
+
+
+
+
#@markdown ### MSA options (custom MSA upload, single sequence, pairing mode)
+msa_mode = "mmseqs2_uniref_env" #@param ["mmseqs2_uniref_env", "mmseqs2_uniref","single_sequence","custom"]
+pair_mode = "unpaired_paired" #@param ["unpaired_paired","paired","unpaired"] {type:"string"}
+#@markdown - "unpaired_paired" = pair sequences from same species + unpaired MSA, "unpaired" = seperate MSA for each chain, "paired" - only use paired sequences.
+
+# decide which a3m to use
+if "mmseqs2" in msa_mode:
+  a3m_file = os.path.join(jobname,f"{jobname}.a3m")
+
+elif msa_mode == "custom":
+  a3m_file = os.path.join(jobname,f"{jobname}.custom.a3m")
+  if not os.path.isfile(a3m_file):
+    custom_msa_dict = files.upload()
+    custom_msa = list(custom_msa_dict.keys())[0]
+    header = 0
+    import fileinput
+    for line in fileinput.FileInput(custom_msa,inplace=1):
+      if line.startswith(">"):
+         header = header + 1
+      if not line.rstrip():
+        continue
+      if line.startswith(">") == False and header == 1:
+         query_sequence = line.rstrip()
+      print(line, end='')
+
+    os.rename(custom_msa, a3m_file)
+    queries_path=a3m_file
+    print(f"moving {custom_msa} to {a3m_file}")
+
+else:
+  a3m_file = os.path.join(jobname,f"{jobname}.single_sequence.a3m")
+  with open(a3m_file, "w") as text_file:
+    text_file.write(">1\n%s" % query_sequence)
+
+
+
+
+
+
+
# @title  { display-mode: "form" }
+#@markdown ### Advanced settings
+model_type = "auto" #@param ["auto", "alphafold2_ptm", "alphafold2_multimer_v1", "alphafold2_multimer_v2", "alphafold2_multimer_v3"]
+#@markdown - if `auto` selected, will use `alphafold2_ptm` for monomer prediction and `alphafold2_multimer_v3` for complex prediction.
+#@markdown Any of the mode_types can be used (regardless if input is monomer or complex).
+num_recycles = "3" #@param ["auto", "0", "1", "3", "6", "12", "24", "48"]
+#@markdown - if `auto` selected, will use `num_recycles=20` if `model_type=alphafold2_multimer_v3`, else `num_recycles=3` .
+recycle_early_stop_tolerance = "auto" #@param ["auto", "0.0", "0.5", "1.0"]
+#@markdown - if `auto` selected, will use `tol=0.5` if `model_type=alphafold2_multimer_v3` else `tol=0.0`.
+relax_max_iterations = 200 #@param [0, 200, 2000] {type:"raw"}
+#@markdown - max amber relax iterations, `0` = unlimited (AlphaFold2 default, can take very long)
+pairing_strategy = "greedy" #@param ["greedy", "complete"] {type:"string"}
+#@markdown - `greedy` = pair any taxonomically matching subsets, `complete` = all sequences have to match in one line.
+
+
+#@markdown #### Sample settings
+#@markdown -  enable dropouts and increase number of seeds to sample predictions from uncertainty of the model.
+#@markdown -  decrease `max_msa` to increase uncertainity
+max_msa = "auto" #@param ["auto", "512:1024", "256:512", "64:128", "32:64", "16:32"]
+num_seeds = 1 #@param [1,2,4,8,16] {type:"raw"}
+use_dropout = False #@param {type:"boolean"}
+
+num_recycles = None if num_recycles == "auto" else int(num_recycles)
+recycle_early_stop_tolerance = None if recycle_early_stop_tolerance == "auto" else float(recycle_early_stop_tolerance)
+if max_msa == "auto": max_msa = None
+
+#@markdown #### Save settings
+save_all = False #@param {type:"boolean"}
+save_recycles = False #@param {type:"boolean"}
+save_to_google_drive = False #@param {type:"boolean"}
+#@markdown -  if the save_to_google_drive option was selected, the result zip will be uploaded to your Google Drive
+dpi = 200 #@param {type:"integer"}
+#@markdown - set dpi for image resolution
+
+if save_to_google_drive:
+  from pydrive.drive import GoogleDrive
+  from pydrive.auth import GoogleAuth
+  from google.colab import auth
+  from oauth2client.client import GoogleCredentials
+  auth.authenticate_user()
+  gauth = GoogleAuth()
+  gauth.credentials = GoogleCredentials.get_application_default()
+  drive = GoogleDrive(gauth)
+  print("You are logged into Google Drive and are good to go!")
+
+#@markdown Don't forget to hit `Runtime` -> `Run all` after updating the form.
+
+
+
+
+
+
+
#@title Run Prediction
+display_images = True #@param {type:"boolean"}
+
+import sys
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+from Bio import BiopythonDeprecationWarning
+warnings.simplefilter(action='ignore', category=BiopythonDeprecationWarning)
+from pathlib import Path
+from colabfold.download import download_alphafold_params, default_data_dir
+from colabfold.utils import setup_logging
+from colabfold.batch import get_queries, run, set_model_type
+from colabfold.plot import plot_msa_v2
+
+import os
+import numpy as np
+try:
+  K80_chk = os.popen('nvidia-smi | grep "Tesla K80" | wc -l').read()
+except:
+  K80_chk = "0"
+  pass
+if "1" in K80_chk:
+  print("WARNING: found GPU Tesla K80: limited to total length < 1000")
+  if "TF_FORCE_UNIFIED_MEMORY" in os.environ:
+    del os.environ["TF_FORCE_UNIFIED_MEMORY"]
+  if "XLA_PYTHON_CLIENT_MEM_FRACTION" in os.environ:
+    del os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]
+
+from colabfold.colabfold import plot_protein
+from pathlib import Path
+import matplotlib.pyplot as plt
+
+# For some reason we need that to get pdbfixer to import
+if use_amber and f"/usr/local/lib/python{python_version}/site-packages/" not in sys.path:
+    sys.path.insert(0, f"/usr/local/lib/python{python_version}/site-packages/")
+
+def input_features_callback(input_features):
+  if display_images:
+    plot_msa_v2(input_features)
+    plt.show()
+    plt.close()
+
+def prediction_callback(protein_obj, length,
+                        prediction_result, input_features, mode):
+  model_name, relaxed = mode
+  if not relaxed:
+    if display_images:
+      fig = plot_protein(protein_obj, Ls=length, dpi=150)
+      plt.show()
+      plt.close()
+
+result_dir = jobname
+log_filename = os.path.join(jobname,"log.txt")
+setup_logging(Path(log_filename))
+
+queries, is_complex = get_queries(queries_path)
+model_type = set_model_type(is_complex, model_type)
+
+if "multimer" in model_type and max_msa is not None:
+  use_cluster_profile = False
+else:
+  use_cluster_profile = True
+
+download_alphafold_params(model_type, Path("."))
+results = run(
+    queries=queries,
+    result_dir=result_dir,
+    use_templates=use_templates,
+    custom_template_path=custom_template_path,
+    num_relax=num_relax,
+    msa_mode=msa_mode,
+    model_type=model_type,
+    num_models=5,
+    num_recycles=num_recycles,
+    relax_max_iterations=relax_max_iterations,
+    recycle_early_stop_tolerance=recycle_early_stop_tolerance,
+    num_seeds=num_seeds,
+    use_dropout=use_dropout,
+    model_order=[1,2,3,4,5],
+    is_complex=is_complex,
+    data_dir=Path("."),
+    keep_existing_results=False,
+    rank_by="auto",
+    pair_mode=pair_mode,
+    pairing_strategy=pairing_strategy,
+    stop_at_score=float(100),
+    prediction_callback=prediction_callback,
+    dpi=dpi,
+    zip_results=False,
+    save_all=save_all,
+    max_msa=max_msa,
+    use_cluster_profile=use_cluster_profile,
+    input_features_callback=input_features_callback,
+    save_recycles=save_recycles,
+    user_agent="colabfold/google-colab-main",
+)
+results_zip = f"{jobname}.result.zip"
+os.system(f"zip -r {results_zip} {jobname}")
+
+
+
+
+
Downloading alphafold2 weights to .: 100%|██████████| 3.47G/3.47G [02:40<00:00, 23.2MB/s]
+
+
+
2023-12-07 22:02:42,821 Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: "rocm". Available platform names are: CUDA
+2023-12-07 22:02:42,823 Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory
+2023-12-07 22:02:44,726 Running on GPU
+2023-12-07 22:02:44,904 Found 4 citations for tools or databases
+2023-12-07 22:02:44,904 Query 1/1: test_a5e17 (length 59)
+
+
+
COMPLETE: 100%|██████████| 150/150 [elapsed: 00:01 remaining: 00:00]
+
+
+../_images/9994329806f45a5389c0debb2644aa89065e9c2ac09ed381afb2155d0ef75450.png +
2023-12-07 22:02:47,182 Setting max_seq=512, max_extra_seq=5120
+
+
+
---------------------------------------------------------------------------
+KeyboardInterrupt                         Traceback (most recent call last)
+<ipython-input-5-06098a2c69e9> in <cell line: 65>()
+     63 
+     64 download_alphafold_params(model_type, Path("."))
+---> 65 results = run(
+     66     queries=queries,
+     67     result_dir=result_dir,
+
+/content/colabfold/batch.py in run(queries, result_dir, num_models, is_complex, num_recycles, recycle_early_stop_tolerance, model_order, num_ensemble, model_type, msa_mode, use_templates, custom_template_path, num_relax, relax_max_iterations, relax_tolerance, relax_stiffness, relax_max_outer_iterations, keep_existing_results, rank_by, pair_mode, pairing_strategy, data_dir, host_url, user_agent, random_seed, num_seeds, recompile_padding, zip_results, prediction_callback, save_single_representations, save_pair_representations, save_all, save_recycles, use_dropout, use_gpu_relax, stop_at_score, dpi, max_seq, max_extra_seq, pdb_hit_file, local_pdb_path, use_cluster_profile, feature_dict_callback, **kwargs)
+   1568                     first_job = False
+   1569 
+-> 1570                 results = predict_structure(
+   1571                     prefix=jobname,
+   1572                     result_dir=result_dir,
+
+/content/colabfold/batch.py in predict_structure(prefix, result_dir, feature_dict, is_complex, use_templates, sequences_lengths, pad_len, model_type, model_runner_and_params, num_relax, relax_max_iterations, relax_tolerance, relax_stiffness, relax_max_outer_iterations, rank_by, random_seed, num_seeds, stop_at_score, prediction_callback, use_gpu_relax, save_all, save_single_representations, save_pair_representations, save_recycles)
+    419             # predict
+    420             result, recycles = \
+--> 421             model_runner.predict(input_features,
+    422                 random_seed=seed,
+    423                 return_representations=return_representations,
+
+/content/alphafold/model/model.py in predict(self, feat, random_seed, return_representations, callback)
+    183         # run
+    184         key, sub_key = jax.random.split(key)
+--> 185         result, prev = run(sub_key, sub_feat, prev)
+    186 
+    187         if return_representations:
+
+/content/alphafold/model/model.py in run(key, feat, prev)
+    163             x[k] = np.asarray(v,np.float16)
+    164         return x
+--> 165       result = _jnp_to_np(self.apply(self.params, key, {**feat, "prev":prev}))
+    166       prev = result.pop("prev")
+    167       return result, prev
+
+    [... skipping hidden 1 frame]
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/pjit.py in cache_miss(*args, **kwargs)
+    254   @api_boundary
+    255   def cache_miss(*args, **kwargs):
+--> 256     outs, out_flat, out_tree, args_flat, jaxpr = _python_pjit_helper(
+    257         fun, infer_params_fn, *args, **kwargs)
+    258     executable = _read_most_recent_pjit_call_executable(jaxpr)
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/pjit.py in _python_pjit_helper(fun, infer_params_fn, *args, **kwargs)
+    165     dispatch.check_arg(arg)
+    166   try:
+--> 167     out_flat = pjit_p.bind(*args_flat, **params)
+    168   except pxla.DeviceAssignmentMismatchError as e:
+    169     fails, = e.args
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/core.py in bind(self, *args, **params)
+   2654     top_trace = (top_trace if not axis_main or axis_main.level < top_trace.level
+   2655                  else axis_main.with_cur_sublevel())
+-> 2656     return self.bind_with_trace(top_trace, args, params)
+   2657 
+   2658 
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/core.py in bind_with_trace(self, trace, args, params)
+    386 
+    387   def bind_with_trace(self, trace, args, params):
+--> 388     out = trace.process_primitive(self, map(trace.full_raise, args), params)
+    389     return map(full_lower, out) if self.multiple_results else full_lower(out)
+    390 
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/core.py in process_primitive(self, primitive, tracers, params)
+    866 
+    867   def process_primitive(self, primitive, tracers, params):
+--> 868     return primitive.impl(*tracers, **params)
+    869 
+    870   def process_call(self, primitive, f, tracers, params):
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/pjit.py in _pjit_call_impl(jaxpr, in_shardings, out_shardings, resource_env, donated_invars, name, keep_unused, inline, *args)
+   1210   has_explicit_sharding = _pjit_explicit_sharding(
+   1211       in_shardings, out_shardings, None, None)
+-> 1212   return xc._xla.pjit(name, f, call_impl_cache_miss, [], [], donated_argnums,
+   1213                       tree_util.dispatch_registry,
+   1214                       _get_cpp_global_cache(has_explicit_sharding))(*args)
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/pjit.py in call_impl_cache_miss(*args_, **kwargs_)
+   1194                     donated_invars, name, keep_unused, inline):
+   1195   def call_impl_cache_miss(*args_, **kwargs_):
+-> 1196     out_flat, compiled = _pjit_call_impl_python(
+   1197         *args, jaxpr=jaxpr, in_shardings=in_shardings,
+   1198         out_shardings=out_shardings, resource_env=resource_env,
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/pjit.py in _pjit_call_impl_python(jaxpr, in_shardings, out_shardings, resource_env, donated_invars, name, keep_unused, inline, *args)
+   1127       resource_env.physical_mesh if resource_env is not None else None)
+   1128 
+-> 1129   compiled = _pjit_lower(
+   1130       jaxpr, in_shardings, out_shardings, resource_env,
+   1131       donated_invars, name, keep_unused, inline,
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/pjit.py in _pjit_lower(jaxpr, in_shardings, out_shardings, *args, **kwargs)
+   1258   in_shardings = SameDeviceAssignmentTuple(tuple(in_shardings), da)
+   1259   out_shardings = SameDeviceAssignmentTuple(tuple(out_shardings), da)
+-> 1260   return _pjit_lower_cached(jaxpr, in_shardings, out_shardings, *args, **kwargs)
+   1261 
+   1262 
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/pjit.py in _pjit_lower_cached(jaxpr, sdat_in_shardings, sdat_out_shardings, resource_env, donated_invars, name, keep_unused, inline, lowering_parameters)
+   1297       lowering_parameters=lowering_parameters)
+   1298   else:
+-> 1299     return pxla.lower_sharding_computation(
+   1300         jaxpr, api_name, name, in_shardings, out_shardings,
+   1301         tuple(donated_invars), tuple(jaxpr.in_avals),
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/profiler.py in wrapper(*args, **kwargs)
+    338   def wrapper(*args, **kwargs):
+    339     with TraceAnnotation(name, **decorator_kwargs):
+--> 340       return func(*args, **kwargs)
+    341     return wrapper
+    342   return wrapper
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/pxla.py in lower_sharding_computation(fun_or_jaxpr, api_name, fun_name, in_shardings, out_shardings, donated_invars, global_in_avals, keep_unused, inline, devices_from_context, lowering_parameters)
+   2029   semantic_out_shardings = SemanticallyEqualShardings(out_shardings)
+   2030   (module, keepalive, host_callbacks, unordered_effects, ordered_effects,
+-> 2031    nreps, tuple_args, shape_poly_state) = _cached_lowering_to_hlo(
+   2032        closed_jaxpr, api_name, fun_name, backend, semantic_in_shardings,
+   2033        semantic_out_shardings, da_object,
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/pxla.py in _cached_lowering_to_hlo(closed_jaxpr, api_name, fun_name, backend, semantic_in_shardings, semantic_out_shardings, da_object, donated_invars, name_stack, all_default_mem_kind, lowering_parameters)
+   1830         "Finished jaxpr to MLIR module conversion {fun_name} in {elapsed_time} sec",
+   1831         fun_name=str(name_stack), event=dispatch.JAXPR_TO_MLIR_MODULE_EVENT):
+-> 1832     lowering_result = mlir.lower_jaxpr_to_module(
+   1833         module_name,
+   1834         closed_jaxpr,
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py in lower_jaxpr_to_module(module_name, jaxpr, ordered_effects, backend_or_name, platforms, axis_context, name_stack, donated_args, replicated_args, arg_shardings, result_shardings, arg_names, result_names, num_replicas, num_partitions, all_default_mem_kind, lowering_parameters)
+    804     attrs["mhlo.num_partitions"] = i32_attr(num_partitions)
+    805     replace_tokens_with_dummy = lowering_parameters.replace_tokens_with_dummy
+--> 806     lower_jaxpr_to_fun(
+    807         ctx, "main", jaxpr, ordered_effects, public=True,
+    808         create_tokens=replace_tokens_with_dummy,
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py in lower_jaxpr_to_fun(ctx, name, jaxpr, effects, create_tokens, public, replace_tokens_with_dummy, replicated_args, arg_shardings, result_shardings, use_sharding_annotations, input_output_aliases, num_output_tokens, api_name, arg_names, result_names, arg_memory_kinds, result_memory_kinds)
+   1211     callee_name_stack = ctx.name_stack.extend(util.wrap_name(name, api_name))
+   1212     consts = [ir_constants(xla.canonicalize_dtype(x)) for x in jaxpr.consts]
+-> 1213     out_vals, tokens_out = jaxpr_subcomp(
+   1214         ctx.replace(name_stack=callee_name_stack), jaxpr.jaxpr, tokens_in,
+   1215         consts, *args, dim_var_values=dim_var_values)
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py in jaxpr_subcomp(ctx, jaxpr, tokens, consts, dim_var_values, *args)
+   1429       if len(ctx.platforms) == 1:
+   1430         # Classic, single-platform lowering
+-> 1431         ans = rule(rule_ctx, *rule_inputs, **eqn.params)
+   1432       else:
+   1433         ans = lower_multi_platform(rule_ctx, str(eqn), rules,
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py in f_lowered(ctx, *args, **params)
+   1626       # TODO(frostig,mattjj): check ctx.avals_out against jaxpr avals out?
+   1627 
+-> 1628     out, tokens = jaxpr_subcomp(
+   1629         ctx.module_context, jaxpr, ctx.tokens_in, _ir_consts(consts),
+   1630         *map(wrap_singleton_ir_values, args), dim_var_values=ctx.dim_var_values)
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py in jaxpr_subcomp(ctx, jaxpr, tokens, consts, dim_var_values, *args)
+   1429       if len(ctx.platforms) == 1:
+   1430         # Classic, single-platform lowering
+-> 1431         ans = rule(rule_ctx, *rule_inputs, **eqn.params)
+   1432       else:
+   1433         ans = lower_multi_platform(rule_ctx, str(eqn), rules,
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/lax/control_flow/loops.py in _while_lowering(ctx, cond_jaxpr, body_jaxpr, cond_nconsts, body_nconsts, *args)
+   1670     body_consts = [mlir.ir_constants(xla.canonicalize_dtype(x))
+   1671                    for x in body_jaxpr.consts]
+-> 1672     new_z, tokens_out = mlir.jaxpr_subcomp(body_ctx, body_jaxpr.jaxpr,
+   1673         tokens_in, body_consts, *(y + z), dim_var_values=ctx.dim_var_values)
+   1674     out_tokens = [tokens_out.get(eff) for eff in body_effects]
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py in jaxpr_subcomp(ctx, jaxpr, tokens, consts, dim_var_values, *args)
+   1429       if len(ctx.platforms) == 1:
+   1430         # Classic, single-platform lowering
+-> 1431         ans = rule(rule_ctx, *rule_inputs, **eqn.params)
+   1432       else:
+   1433         ans = lower_multi_platform(rule_ctx, str(eqn), rules,
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py in f_lowered(ctx, *args, **params)
+   1626       # TODO(frostig,mattjj): check ctx.avals_out against jaxpr avals out?
+   1627 
+-> 1628     out, tokens = jaxpr_subcomp(
+   1629         ctx.module_context, jaxpr, ctx.tokens_in, _ir_consts(consts),
+   1630         *map(wrap_singleton_ir_values, args), dim_var_values=ctx.dim_var_values)
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py in jaxpr_subcomp(ctx, jaxpr, tokens, consts, dim_var_values, *args)
+   1429       if len(ctx.platforms) == 1:
+   1430         # Classic, single-platform lowering
+-> 1431         ans = rule(rule_ctx, *rule_inputs, **eqn.params)
+   1432       else:
+   1433         ans = lower_multi_platform(rule_ctx, str(eqn), rules,
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/lax/control_flow/loops.py in _while_lowering(ctx, cond_jaxpr, body_jaxpr, cond_nconsts, body_nconsts, *args)
+   1632         mlir.ir_constants(xla.canonicalize_dtype(x)) for x in cond_jaxpr.consts
+   1633     ]
+-> 1634     ((pred,),), _ = mlir.jaxpr_subcomp(
+   1635         cond_ctx,
+   1636         cond_jaxpr.jaxpr,
+
+/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py in jaxpr_subcomp(ctx, jaxpr, tokens, consts, dim_var_values, *args)
+   1313   return func_op
+   1314 
+-> 1315 def jaxpr_subcomp(ctx: ModuleContext, jaxpr: core.Jaxpr,
+   1316                   tokens: TokenSet,
+   1317                   consts: Sequence[Sequence[ir.Value]],
+
+KeyboardInterrupt: 
+
+
+
+
+
+
+
#@title Display 3D structure {run: "auto"}
+import py3Dmol
+import glob
+import matplotlib.pyplot as plt
+from colabfold.colabfold import plot_plddt_legend
+from colabfold.colabfold import pymol_color_list, alphabet_list
+rank_num = 1 #@param ["1", "2", "3", "4", "5"] {type:"raw"}
+color = "lDDT" #@param ["chain", "lDDT", "rainbow"]
+show_sidechains = False #@param {type:"boolean"}
+show_mainchains = False #@param {type:"boolean"}
+
+tag = results["rank"][0][rank_num - 1]
+jobname_prefix = ".custom" if msa_mode == "custom" else ""
+pdb_filename = f"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb"
+pdb_file = glob.glob(pdb_filename)
+
+def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color="lDDT"):
+  model_name = f"rank_{rank_num}"
+  view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)
+  view.addModel(open(pdb_file[0],'r').read(),'pdb')
+
+  if color == "lDDT":
+    view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})
+  elif color == "rainbow":
+    view.setStyle({'cartoon': {'color':'spectrum'}})
+  elif color == "chain":
+    chains = len(queries[0][1]) + 1 if is_complex else 1
+    for n,chain,color in zip(range(chains),alphabet_list,pymol_color_list):
+       view.setStyle({'chain':chain},{'cartoon': {'color':color}})
+
+  if show_sidechains:
+    BB = ['C','O','N']
+    view.addStyle({'and':[{'resn':["GLY","PRO"],'invert':True},{'atom':BB,'invert':True}]},
+                        {'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+    view.addStyle({'and':[{'resn':"GLY"},{'atom':'CA'}]},
+                        {'sphere':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+    view.addStyle({'and':[{'resn':"PRO"},{'atom':['C','O'],'invert':True}]},
+                        {'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+  if show_mainchains:
+    BB = ['C','O','N','CA']
+    view.addStyle({'atom':BB},{'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+
+  view.zoomTo()
+  return view
+
+show_pdb(rank_num, show_sidechains, show_mainchains, color).show()
+if color == "lDDT":
+  plot_plddt_legend().show()
+
+
+
+
+
+
+
#@title Plots {run: "auto"}
+from IPython.display import display, HTML
+import base64
+from html import escape
+
+# see: https://stackoverflow.com/a/53688522
+def image_to_data_url(filename):
+  ext = filename.split('.')[-1]
+  prefix = f'data:image/{ext};base64,'
+  with open(filename, 'rb') as f:
+    img = f.read()
+  return prefix + base64.b64encode(img).decode('utf-8')
+
+pae = image_to_data_url(os.path.join(jobname,f"{jobname}{jobname_prefix}_pae.png"))
+cov = image_to_data_url(os.path.join(jobname,f"{jobname}{jobname_prefix}_coverage.png"))
+plddt = image_to_data_url(os.path.join(jobname,f"{jobname}{jobname_prefix}_plddt.png"))
+display(HTML(f"""
+<style>
+  img {{
+    float:left;
+  }}
+  .full {{
+    max-width:100%;
+  }}
+  .half {{
+    max-width:50%;
+  }}
+  @media (max-width:640px) {{
+    .half {{
+      max-width:100%;
+    }}
+  }}
+</style>
+<div style="max-width:90%; padding:2em;">
+  <h1>Plots for {escape(jobname)}</h1>
+  <img src="{pae}" class="full" />
+  <img src="{cov}" class="half" />
+  <img src="{plddt}" class="half" />
+</div>
+"""))
+
+
+
+
+
+
+
#@title Package and download results
+#@markdown If you are having issues downloading the result archive, try disabling your adblocker and run this cell again. If that fails click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \"Download\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).
+
+if msa_mode == "custom":
+  print("Don't forget to cite your custom MSA generation method.")
+
+files.download(f"{jobname}.result.zip")
+
+if save_to_google_drive == True and drive:
+  uploaded = drive.CreateFile({'title': f"{jobname}.result.zip"})
+  uploaded.SetContentFile(f"{jobname}.result.zip")
+  uploaded.Upload()
+  print(f"Uploaded {jobname}.result.zip to Google Drive with ID {uploaded.get('id')}")
+
+
+
+
+
+
+

Instructions #

+

Quick start

+
    +

  1. Paste your protein sequence(s) in the input field.

    2. +

  2. Press “Runtime” -> “Run all”.

    3. +

  3. The pipeline consists of 5 steps. The currently running step is indicated by a circle with a stop sign next to it.

    4. +
+

Result zip file contents

+
    +

  1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if use_amber is enabled).

    2. +

  2. Plots of the model quality.

    3. +

  3. Plots of the MSA coverage.

    4. +

  4. Parameter log file.

    5. +

  5. A3M formatted input MSA.

    6. +

  6. A predicted_aligned_error_v1.json using AlphaFold-DB’s format and a scores.json for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.

    7. +

  7. BibTeX file with citations for all used tools and databases.

    8. +
+

At the end of the job a download modal box will pop up with a jobname.result.zip file. Additionally, if the save_to_google_drive option was selected, the jobname.result.zip will be uploaded to your Google Drive.

+

MSA generation for complexes

+

For the complex prediction we use unpaired and paired MSAs. Unpaired MSA is generated the same way as for the protein structures prediction by searching the UniRef100 and environmental sequences three iterations each.

+

The paired MSA is generated by searching the UniRef100 database and pairing the best hits sharing the same NCBI taxonomic identifier (=species or sub-species). We only pair sequences if all of the query sequences are present for the respective taxonomic identifier.

+

Using a custom MSA as input

+

To predict the structure with a custom MSA (A3M formatted): (1) Change the msa_mode: to “custom”, (2) Wait for an upload box to appear at the end of the “MSA options …” box. Upload your A3M. The first fasta entry of the A3M must be the query sequence without gaps.

+

It is also possilbe to proide custom MSAs for complex predictions. Read more about the format here.

+

As an alternative for MSA generation the HHblits Toolkit server can be used. After submitting your query, click “Query Template MSA” -> “Download Full A3M”. Download the A3M file and upload it in this notebook.

+

PDB100

+

As of 23/06/08, we have transitioned from using the PDB70 to a 100% clustered PDB, the PDB100. The construction methodology of PDB100 differs from that of PDB70.

+

The PDB70 was constructed by running each PDB70 representative sequence through HHblits against the Uniclust30. On the other hand, the PDB100 is built by searching each PDB100 representative structure with Foldseek against the AlphaFold Database.

+

To maintain compatibility with older Notebook versions and local installations, the generated files and API responses will continue to be named “PDB70”, even though we’re now using the PDB100.

+

Using custom templates

+

To predict the structure with a custom template (PDB or mmCIF formatted): (1) change the template_mode to “custom” in the execute cell and (2) wait for an upload box to appear at the end of the “Input Protein” box. Select and upload your templates (multiple choices are possible).

+
    +

  • Templates must follow the four letter PDB naming with lower case letters.

    • +

  • Templates in mmCIF format must contain _entity_poly_seq. An error is thrown if this field is not present. The field _pdbx_audit_revision_history.revision_date is automatically generated if it is not present.

    • +

  • Templates in PDB format are automatically converted to the mmCIF format. _entity_poly_seq and _pdbx_audit_revision_history.revision_date are automatically generated.

    • +
+

If you encounter problems, please report them to this issue.

+

Comparison to the full AlphaFold2 and AlphaFold2 Colab

+

This notebook replaces the homology detection and MSA pairing of AlphaFold2 with MMseqs2. For a comparison against the AlphaFold2 Colab and the full AlphaFold2 system read our paper.

+

Troubleshooting

+
    +

  • Check that the runtime type is set to GPU at “Runtime” -> “Change runtime type”.

    • +

  • Try to restart the session “Runtime” -> “Factory reset runtime”.

    • +

  • Check your input sequence.

    • +
+

Known issues

+
    +

  • Google Colab assigns different types of GPUs with varying amount of memory. Some might not have enough memory to predict the structure for a long sequence.

    • +

  • Your browser can block the pop-up for downloading the result file. You can choose the save_to_google_drive option to upload to Google Drive instead or manually download the result file: Click on the little folder icon to the left, navigate to file: jobname.result.zip, right-click and select “Download” (see screenshot).

    • +
+

Limitations

+
    +

  • Computing resources: Our MMseqs2 API can handle ~20-50k requests per day.

    • +

  • MSAs: MMseqs2 is very precise and sensitive but might find less hits compared to HHblits/HMMer searched against BFD or MGnify.

    • +

  • We recommend to additionally use the full AlphaFold2 pipeline.

    • +
+

Description of the plots

+
    +

  • Number of sequences per position - We want to see at least 30 sequences per position, for best performance, ideally 100 sequences.

    • +

  • Predicted lDDT per position - model confidence (out of 100) at each position. The higher the better.

    • +

  • Predicted Alignment Error - For homooligomers, this could be a useful metric to assess how confident the model is about the interface. The lower the better.

    • +
+

Bugs

+ +

License

+

The source code of ColabFold is licensed under MIT. Additionally, this notebook uses the AlphaFold2 source code and its parameters licensed under Apache 2.0 and CC BY 4.0 respectively. Read more about the AlphaFold license here.

+

Acknowledgments

+
    +

  • We thank the AlphaFold team for developing an excellent model and open sourcing the software.

    • +

  • KOBIC and Söding Lab for providing the computational resources for the MMseqs2 MSA server.

    • +

  • Richard Evans for helping to benchmark the ColabFold’s Alphafold-multimer support.

    • +

  • David Koes for his awesome py3Dmol plugin, without whom these notebooks would be quite boring!

    • +

  • Do-Yoon Kim for creating the ColabFold logo.

    • +

  • A colab by Sergey Ovchinnikov (@sokrypton), Milot Mirdita (@milot_mirdita) and Martin Steinegger (@thesteinegger).

    • +
+
+ + + + +
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+ + + +
+ +
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+ Contents +
+
+ +
+ + +
+ + + +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/ColabFold/Tutorial_Monomer.html b/ColabFold/Tutorial_Monomer.html new file mode 100644 index 0000000..9ddf06e --- /dev/null +++ b/ColabFold/Tutorial_Monomer.html @@ -0,0 +1,1339 @@ + + + + + + + + + + + + 1. ColabFold Tutorial: Predicting the structure of Bax protein — Tutorials + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + Ctrl+K +
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+ + + +
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+ +
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+ +
+ +
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+ + + +
+

1. ColabFold Tutorial: Predicting the structure of Bax protein

+ +
+ +
+
+ + + + +
+ +

Open In Colab

+
+
+
%%time
+
+# @title **Install Python Packages** { run: "auto", display-mode: "form" }
+
+import os
+from sys import version_info
+PYTHON_VERSION = f"{version_info.major}.{version_info.minor}"
+
+if not os.path.isfile("COLABFOLD_READY"):
+  print("installing colabfold...")
+  os.system("pip install -q --no-warn-conflicts 'colabfold[alphafold-minus-jax] @ git+https://github.com/sokrypton/ColabFold'")
+  os.system("pip install --upgrade dm-haiku")
+  os.system("ln -s /usr/local/lib/python3.*/dist-packages/colabfold colabfold")
+  os.system("ln -s /usr/local/lib/python3.*/dist-packages/alphafold alphafold")
+  # patch for jax > 0.3.25
+  os.system("sed -i 's/weights = jax.nn.softmax(logits)/logits=jnp.clip(logits,-1e8,1e8);weights=jax.nn.softmax(logits)/g' alphafold/model/modules.py")
+  os.system("touch COLABFOLD_READY")
+
+
+if not os.path.isfile("CONDA_READY"):
+  print("installing conda...")
+  os.system("wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh")
+  os.system("bash Miniforge3-Linux-x86_64.sh -bfp /usr/local")
+  os.system("conda config --set auto_update_conda false")
+  os.system("touch CONDA_READY")
+
+
+if not os.path.isfile("CONDA_READY"):
+  print("installing conda...")
+  os.system("wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh")
+  os.system("bash Mambaforge-Linux-x86_64.sh -bfp /usr/local")
+  os.system("mamba config --set auto_update_conda false")
+  os.system("touch CONDA_READY")
+
+
+if not os.path.isfile("HH_READY") and not os.path.isfile("AMBER_READY"):
+  print("installing hhsuite and amber...")
+  os.system(f"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer")
+  os.system("touch HH_READY")
+  os.system("touch AMBER_READY")
+else:
+  if not os.path.isfile("HH_READY"):
+    print("installing hhsuite...")
+    os.system(f"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 python='{PYTHON_VERSION}'")
+    os.system("touch HH_READY")
+  if not os.path.isfile("AMBER_READY"):
+    print("installing amber...")
+    os.system(f"mamba install -y -c conda-forge openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer")
+    os.system("touch AMBER_READY")
+
+
+
+
+
installing colabfold...
+installing conda...
+installing hhsuite and amber...
+CPU times: user 269 ms, sys: 52.3 ms, total: 321 ms
+Wall time: 1min 24s
+
+
+
+
+
+

1. ColabFold Tutorial: Predicting the structure of Bax protein#

+

Let’s get familar with the workflow for predicting a monomer structure. This example looks at Bax, an apoptosis regulating protein.

+

We will only use the default options set by Steinegger et al. which excludes Amber minimization and structure templates.

+

a. Background

+
    +

  • Encoded by the BAX gene in the Bcl-2 family

    • +

  • Highly conserved DNA sequence and structural motifs

    • +

  • Different regulatory function in anti-apoptosis or pro-apoptosis

    • +

  • Permeation of the mitochondrial outer membrane

    • +
+

b. Pro-death signals triggers conformational shift

+
    +

  • Bax adopts a globular state when inactive

    • +

  • Apoptotic signals displaces N-terminal \(\alpha\)-helicies

    • +

  • Becomes membrane-associated

    • +

  • Oligomers form, resulting in a pore and cell death

    • +
+

Dysregulation of Bax is implicated in cancer progression, and suggests some contributions towards in Alzheimer’s and Parkinson’s disease.

+

Experiments have shown point-mutations near opposite of the binding site region, has been shown to affect activity. This is also near the loop region between \(\alpha\)-helix1 and \(\alpha\)-helicies2, which has been difficult to structurally resolve. Does this loop play a role in membrane insertion mechanism?

+
+
+
#@title # **2. Notebook Setup** { display-mode: "form" }
+
+from google.colab import files
+import os
+import re
+import hashlib
+import random
+
+from sys import version_info
+python_version = f"{version_info.major}.{version_info.minor}"
+
+def add_hash(x,y):
+  return x+"_"+hashlib.sha1(y.encode()).hexdigest()[:5]
+
+#@markdown \
+#@markdown ##a. Protein Sequence for Structure Prediction
+#@markdown For bax (human), the amino acid sequence was obtained from
+#@markdown UniProt ([Q07812](https://www.uniprot.org/uniprotkb/Q07812/entry))
+query_sequence = 'MDGSGEQPRGGGPTSSEQIMKTGALLLQGFIQDRAGRMGGEAPELALDPVPQDASTKKLSECLKRIGDELDSNMELQRMIAAVDTDSPREVFFRVAADMFSDGNFNWGRVVALFYFASKLVLKALCTKVPELIRTIMGWTLDFLRERLLGWIQDQGGWDGLLSYFGTPTWQTVTIFVAGVLTASLTIWKKMG' #@param {type:"string"}
+#@markdown **Note:** For modeling complexes, use `:` to specify inter-protein chainbreaks
+#@markdown (supports homo- and hetro-oligomers).
+#@markdown For example, **PI...SK:PI...SK** for a homodimer.
+
+#@markdown \
+#@markdown ## b. Job Name (also name of saving directory)
+jobname = 'colabfold-bax-1f16' #@param {type:"string"}
+
+#@markdown \
+#@markdown  ## c. Number of predicted models to relax using Amber
+num_relax = 0 #@param [0, 1, 5] {type:"raw"}
+
+#@markdown \
+#@markdown ## **d. Option for Structure Template**
+#@markdown  Search for PDB structure (xyz-coordinates)
+template_mode = "none" #@param ["none", "pdb100","custom"]
+#@markdown `none` = no template information is used. \
+#@markdown `pdb100` = detect templates in pdb100 (see [notes](#pdb100)). \
+#@markdown `custom` - upload and search own templates (PDB or mmCIF format, see [notes](#custom_templates))
+
+use_amber = num_relax > 0
+
+query_sequence = "".join(query_sequence.split()) # remove whitespaces
+
+basejobname = "".join(jobname.split())
+basejobname = re.sub(r'\W+', '', basejobname)
+jobname = add_hash(basejobname, query_sequence)
+
+# check if directory with jobname exists
+def check(folder):
+  if os.path.exists(folder):
+    return False
+  else:
+    return True
+if not check(jobname):
+  n = 0
+  while not check(f"{jobname}_{n}"): n += 1
+  jobname = f"{jobname}_{n}"
+
+# make directory to save results
+os.makedirs(jobname, exist_ok=True)
+
+# save queries
+queries_path = os.path.join(jobname, f"{jobname}.csv")
+with open(queries_path, "w") as text_file:
+  text_file.write(f"id,sequence\n{jobname},{query_sequence}")
+
+if template_mode == "pdb100":
+  use_templates = True
+  custom_template_path = None
+elif template_mode == "custom":
+  custom_template_path = os.path.join(jobname,f"template")
+  os.makedirs(custom_template_path, exist_ok=True)
+  uploaded = files.upload()
+  use_templates = True
+  for fn in uploaded.keys():
+    os.rename(fn,os.path.join(custom_template_path,fn))
+else:
+  custom_template_path = None
+  use_templates = False
+
+
+
+
+
+
+
#@title # **3. Multiple Sequence Alignment (MSA)**
+
+#@markdown ## a. MSA options
+msa_mode = "mmseqs2_uniref" #@param ["mmseqs2_uniref_env", "mmseqs2_uniref","single_sequence","custom"]
+#@markdown - pairing mode
+#@markdown - single sequence
+#@markdown - custom MSA upload
+
+#@markdown \
+pair_mode = "unpaired_paired" #@param ["unpaired_paired","unpaired","paired"] {type:"string"}
+#@markdown - "unpaired_paired" - pair sequences from same species + unpaired MSA
+#@markdown - "unpaired" - seperate MSA for each chain
+#@markdown - "paired" - only use paired sequences
+
+# decide which a3m to use
+if "mmseqs2" in msa_mode:
+  a3m_file = os.path.join(jobname,f"{jobname}.a3m")
+
+elif msa_mode == "custom":
+  a3m_file = os.path.join(jobname,f"{jobname}.custom.a3m")
+  if not os.path.isfile(a3m_file):
+    custom_msa_dict = files.upload()
+    custom_msa = list(custom_msa_dict.keys())[0]
+    header = 0
+    import fileinput
+    for line in fileinput.FileInput(custom_msa,inplace=1):
+      if line.startswith(">"):
+         header = header + 1
+      if not line.rstrip():
+        continue
+      if line.startswith(">") == False and header == 1:
+         query_sequence = line.rstrip()
+      print(line, end='')
+
+    os.rename(custom_msa, a3m_file)
+    queries_path=a3m_file
+    print(f"moving {custom_msa} to {a3m_file}")
+
+else:
+  a3m_file = os.path.join(jobname,f"{jobname}.single_sequence.a3m")
+  with open(a3m_file, "w") as text_file:
+    text_file.write(">1\n%s" % query_sequence)
+
+
+
+
+
+
+
# @title # **4. Advanced Settings** { run: "auto", display-mode: "form" }
+
+#@markdown ## a. ML Model
+model_type = "auto" #@param ["auto", "alphafold2_ptm", "alphafold2_multimer_v1", "alphafold2_multimer_v2", "alphafold2_multimer_v3"]
+#@markdown **Note:** If `auto` selected, will use `alphafold2_ptm` for monomer prediction
+#@markdown and `alphafold2_multimer_v3` for complex prediction.
+#@markdown Any of the `mode_types` can be used (regardless if input is monomer or complex).
+
+#@markdown \
+#@markdown ## b. Re-Train with Predicted Structures
+num_recycles = "3" #@param ["auto", "0", "1", "3", "6", "12", "24", "48"]
+#@markdown **Note:**
+#@markdown - If `auto` selected, will use `num_recycles=20`,
+#@markdown - If `model_type=alphafold2_multimer_v3`, else `num_recycles=3`
+
+#@markdown \
+recycle_early_stop_tolerance = "auto" #@param ["auto", "0.0", "0.5", "1.0"]
+#@markdown - if `auto` selected, will use `tol=0.5`
+#@markdown - If `model_type=alphafold2_multimer_v3`,  else `tol=0.0`.
+
+#@markdown \
+#@markdown ## c. Amber Relaxation
+relax_max_iterations = 200 #@param [0, 200, 2000] {type:"raw"}
+#@markdown - max amber relax iterations
+#@markdown -  `0` = unlimited (AlphaFold2 default, can take very long)
+
+#@markdown \
+#@markdown ## d. MSA Pairing
+pairing_strategy = "greedy" #@param ["greedy", "complete"] {type:"string"}
+#@markdown - `greedy` = pair any taxonomically matching subsets,
+#@markdown - `complete` = all sequences have to match in one line.
+
+#@markdown \
+#@markdown ## e. Sample settings
+#@markdown -  enable dropouts and increase number of seeds to sample predictions from uncertainty of the model.
+#@markdown -  decrease `max_msa` to increase uncertainity
+max_msa = "auto" #@param ["auto", "512:1024", "256:512", "64:128", "32:64", "16:32"]
+num_seeds = 1 #@param [1,2,4,8,16] {type:"raw"}
+use_dropout = False #@param {type:"boolean"}
+
+num_recycles = None if num_recycles == "auto" else int(num_recycles)
+recycle_early_stop_tolerance = None if recycle_early_stop_tolerance == "auto" else float(recycle_early_stop_tolerance)
+if max_msa == "auto": max_msa = None
+
+#@markdown \
+#@markdown # **5. Save settings**
+#@markdown This will ask for your credentials, to save the outputs.
+save_all = False #@param {type:"boolean"}
+save_recycles = False #@param {type:"boolean"}
+save_to_google_drive = False #@param {type:"boolean"}
+#@markdown -  if the save_to_google_drive option was selected, the result zip will be uploaded to your Google Drive
+dpi = 200 #@param {type:"integer"}
+#@markdown - set dpi for image resolution
+
+if save_to_google_drive:
+  from pydrive.drive import GoogleDrive
+  from pydrive.auth import GoogleAuth
+  from google.colab import auth
+  from oauth2client.client import GoogleCredentials
+  auth.authenticate_user()
+  gauth = GoogleAuth()
+  gauth.credentials = GoogleCredentials.get_application_default()
+  drive = GoogleDrive(gauth)
+  print("You are logged into Google Drive and are good to go!")
+
+#@markdown Don't forget to hit `Runtime` -> `Run all` after updating the form.
+
+
+
+
+
+
+
#@title # **6. Run Prediction** { display-mode: "form" }
+
+display_images = True #@param {type:"boolean"}
+
+
+import sys
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+from Bio import BiopythonDeprecationWarning
+warnings.simplefilter(action='ignore', category=BiopythonDeprecationWarning)
+from pathlib import Path
+from colabfold.download import download_alphafold_params, default_data_dir
+from colabfold.utils import setup_logging
+from colabfold.batch import get_queries, run, set_model_type
+from colabfold.plot import plot_msa_v2
+
+import os
+import numpy as np
+try:
+  K80_chk = os.popen('nvidia-smi | grep "Tesla K80" | wc -l').read()
+except:
+  K80_chk = "0"
+  pass
+if "1" in K80_chk:
+  print("WARNING: found GPU Tesla K80: limited to total length < 1000")
+  if "TF_FORCE_UNIFIED_MEMORY" in os.environ:
+    del os.environ["TF_FORCE_UNIFIED_MEMORY"]
+  if "XLA_PYTHON_CLIENT_MEM_FRACTION" in os.environ:
+    del os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]
+
+from colabfold.colabfold import plot_protein
+from pathlib import Path
+import matplotlib.pyplot as plt
+
+# For some reason we need that to get pdbfixer to import
+if use_amber and f"/usr/local/lib/python{python_version}/site-packages/" not in sys.path:
+    sys.path.insert(0, f"/usr/local/lib/python{python_version}/site-packages/")
+
+def input_features_callback(input_features):
+  if display_images:
+    plot_msa_v2(input_features)
+    plt.show()
+    plt.close()
+
+def prediction_callback(protein_obj, length,
+                        prediction_result, input_features, mode):
+  model_name, relaxed = mode
+  if not relaxed:
+    if display_images:
+      fig = plot_protein(protein_obj, Ls=length, dpi=150)
+      plt.show()
+      plt.close()
+
+result_dir = jobname
+log_filename = os.path.join(jobname,"log.txt")
+setup_logging(Path(log_filename))
+
+queries, is_complex = get_queries(queries_path)
+model_type = set_model_type(is_complex, model_type)
+
+if "multimer" in model_type and max_msa is not None:
+  use_cluster_profile = False
+else:
+  use_cluster_profile = True
+
+download_alphafold_params(model_type, Path("."))
+results = run(
+    queries=queries,
+    result_dir=result_dir,
+    use_templates=use_templates,
+    custom_template_path=custom_template_path,
+    num_relax=num_relax,
+    msa_mode=msa_mode,
+    model_type=model_type,
+    num_models=5,
+    num_recycles=num_recycles,
+    relax_max_iterations=relax_max_iterations,
+    recycle_early_stop_tolerance=recycle_early_stop_tolerance,
+    num_seeds=num_seeds,
+    use_dropout=use_dropout,
+    model_order=[1,2,3,4,5],
+    is_complex=is_complex,
+    data_dir=Path("."),
+    keep_existing_results=False,
+    rank_by="auto",
+    pair_mode=pair_mode,
+    pairing_strategy=pairing_strategy,
+    stop_at_score=float(100),
+    prediction_callback=prediction_callback,
+    dpi=dpi,
+    zip_results=False,
+    save_all=save_all,
+    max_msa=max_msa,
+    use_cluster_profile=use_cluster_profile,
+    input_features_callback=input_features_callback,
+    save_recycles=save_recycles,
+    user_agent="colabfold/google-colab-main",
+)
+results_zip = f"{jobname}.result.zip"
+os.system(f"zip -r {results_zip} {jobname}")
+
+
+
+
+
Downloading alphafold2 weights to .: 100%|██████████| 3.47G/3.47G [01:33<00:00, 39.9MB/s]
+
+
+
2023-12-08 01:03:26,095 Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: "rocm". Available platform names are: CUDA
+2023-12-08 01:03:26,097 Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory
+2023-12-08 01:03:28,068 Running on GPU
+2023-12-08 01:03:28,251 Found 3 citations for tools or databases
+2023-12-08 01:03:28,252 Query 1/1: colabfoldbax1f16_f85cf (length 192)
+
+
+
COMPLETE: 100%|██████████| 150/150 [elapsed: 00:02 remaining: 00:00]
+
+
+../_images/51afc86871184df45c2b11a63d853044193ea9104d12bf9adf0d571de06bceb2.png +
2023-12-08 01:03:32,044 Setting max_seq=512, max_extra_seq=1703
+2023-12-08 01:04:07,724 alphafold2_ptm_model_1_seed_000 recycle=0 pLDDT=70.2 pTM=0.694
+2023-12-08 01:04:13,490 alphafold2_ptm_model_1_seed_000 recycle=1 pLDDT=80.9 pTM=0.793 tol=1.07
+2023-12-08 01:04:19,257 alphafold2_ptm_model_1_seed_000 recycle=2 pLDDT=78.4 pTM=0.775 tol=0.281
+2023-12-08 01:04:25,019 alphafold2_ptm_model_1_seed_000 recycle=3 pLDDT=81.1 pTM=0.798 tol=0.165
+2023-12-08 01:04:25,021 alphafold2_ptm_model_1_seed_000 took 39.0s (3 recycles)
+
+
+../_images/f65c685391c095fc6944b7e34723544d79938dec21f0d4d5f9f930b0c1266b97.png +
2023-12-08 01:04:31,023 alphafold2_ptm_model_2_seed_000 recycle=0 pLDDT=68.3 pTM=0.67
+2023-12-08 01:04:36,783 alphafold2_ptm_model_2_seed_000 recycle=1 pLDDT=79.3 pTM=0.783 tol=1.21
+2023-12-08 01:04:42,548 alphafold2_ptm_model_2_seed_000 recycle=2 pLDDT=78.2 pTM=0.779 tol=0.353
+2023-12-08 01:04:48,310 alphafold2_ptm_model_2_seed_000 recycle=3 pLDDT=79.4 pTM=0.789 tol=0.121
+2023-12-08 01:04:48,311 alphafold2_ptm_model_2_seed_000 took 23.1s (3 recycles)
+
+
+../_images/056fe209adc8b6f3e35c1a5161f46f7a9ad34b5190df977a7ba43c55fded4685.png +
2023-12-08 01:04:54,270 alphafold2_ptm_model_3_seed_000 recycle=0 pLDDT=74.4 pTM=0.721
+2023-12-08 01:05:00,044 alphafold2_ptm_model_3_seed_000 recycle=1 pLDDT=83.6 pTM=0.822 tol=0.98
+2023-12-08 01:05:05,823 alphafold2_ptm_model_3_seed_000 recycle=2 pLDDT=84.2 pTM=0.828 tol=0.401
+2023-12-08 01:05:11,599 alphafold2_ptm_model_3_seed_000 recycle=3 pLDDT=84 pTM=0.821 tol=0.224
+2023-12-08 01:05:11,600 alphafold2_ptm_model_3_seed_000 took 23.1s (3 recycles)
+
+
+../_images/90772f57f1ec3fe79cbbc8c18bdf73cc975282b5b48acff8eab5f014fec562e8.png +
2023-12-08 01:05:17,555 alphafold2_ptm_model_4_seed_000 recycle=0 pLDDT=71 pTM=0.702
+2023-12-08 01:05:23,318 alphafold2_ptm_model_4_seed_000 recycle=1 pLDDT=78.8 pTM=0.778 tol=0.867
+2023-12-08 01:05:29,082 alphafold2_ptm_model_4_seed_000 recycle=2 pLDDT=78 pTM=0.763 tol=0.439
+2023-12-08 01:05:34,846 alphafold2_ptm_model_4_seed_000 recycle=3 pLDDT=76.6 pTM=0.736 tol=0.257
+2023-12-08 01:05:34,847 alphafold2_ptm_model_4_seed_000 took 23.1s (3 recycles)
+
+
+../_images/1044d6c4f48649fb7224c0e3b6dbfdc327df4ebf04fde37b437647f254b50063.png +
2023-12-08 01:05:40,809 alphafold2_ptm_model_5_seed_000 recycle=0 pLDDT=75.6 pTM=0.776
+2023-12-08 01:05:46,584 alphafold2_ptm_model_5_seed_000 recycle=1 pLDDT=81.9 pTM=0.828 tol=1.01
+2023-12-08 01:05:52,360 alphafold2_ptm_model_5_seed_000 recycle=2 pLDDT=81.6 pTM=0.823 tol=0.38
+2023-12-08 01:05:58,134 alphafold2_ptm_model_5_seed_000 recycle=3 pLDDT=81.8 pTM=0.822 tol=0.216
+2023-12-08 01:05:58,135 alphafold2_ptm_model_5_seed_000 took 23.1s (3 recycles)
+
+
+../_images/45c21eb98d5e1c04f68d6357455e20d8e855a9242c8d18230861eb03a1f8edb3.png +
2023-12-08 01:05:58,341 reranking models by 'plddt' metric
+2023-12-08 01:05:58,341 rank_001_alphafold2_ptm_model_3_seed_000 pLDDT=84 pTM=0.821
+2023-12-08 01:05:58,342 rank_002_alphafold2_ptm_model_5_seed_000 pLDDT=81.8 pTM=0.822
+2023-12-08 01:05:58,343 rank_003_alphafold2_ptm_model_1_seed_000 pLDDT=81.1 pTM=0.798
+2023-12-08 01:05:58,343 rank_004_alphafold2_ptm_model_2_seed_000 pLDDT=79.4 pTM=0.789
+2023-12-08 01:05:58,344 rank_005_alphafold2_ptm_model_4_seed_000 pLDDT=76.6 pTM=0.736
+2023-12-08 01:06:00,018 Done
+
+
+
0
+
+
+
+
+
+
+
# @title # **7. Visualize 3D Structure of ColabFold Prediction** { run: "auto", display-mode: "form" }
+
+#@markdown Rank number/score for the **monomer model is the average pLDDT**,
+#@markdown where a ranking of 1 correlates better local structure confidence.
+#@markdown An acceptable model prediction should average $\ge 85$.
+#@markdown The number of ranked models was set in step `6. Run Prediction` (step 6, line 75.).
+
+#@markdown \
+#@markdown Here, you can change the value for `rank_num` to visualize the 5 predictions.
+
+import py3Dmol
+import glob
+import matplotlib.pyplot as plt
+from colabfold.colabfold import plot_plddt_legend
+from colabfold.colabfold import pymol_color_list, alphabet_list
+
+rank_num = 1 #@param ["1", "2", "3", "4", "5"] {type:"raw"}
+color = "rainbow" #@param ["chain", "lDDT", "rainbow"]
+show_sidechains = False #@param {type:"boolean"}
+show_mainchains = False #@param {type:"boolean"}
+
+tag = results["rank"][0][rank_num - 1]
+jobname_prefix = ".custom" if msa_mode == "custom" else ""
+pdb_filename = f"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb"
+pdb_file = glob.glob(pdb_filename)
+
+def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color="lDDT"):
+  model_name = f"rank_{rank_num}"
+  view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)
+  view.addModel(open(pdb_file[0],'r').read(),'pdb')
+
+
+  if color == "lDDT":
+    view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})
+  elif color == "rainbow":
+    view.setStyle({'cartoon': {'color':'spectrum'}})
+  elif color == "chain":
+    chains = len(queries[0][1]) + 1 if is_complex else 1
+    for n,chain,color in zip(range(chains),alphabet_list,pymol_color_list):
+       view.setStyle({'chain':chain},{'cartoon': {'color':color}})
+
+  if show_sidechains:
+    BB = ['C','O','N']
+    view.addStyle({'and':[{'resn':["GLY","PRO"],'invert':True},{'atom':BB,'invert':True}]},
+                        {'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+    view.addStyle({'and':[{'resn':"GLY"},{'atom':'CA'}]},
+                        {'sphere':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+    view.addStyle({'and':[{'resn':"PRO"},{'atom':['C','O'],'invert':True}]},
+                        {'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+  if show_mainchains:
+    BB = ['C','O','N','CA']
+    view.addStyle({'atom':BB},{'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}}, viewer=(0,0))
+
+  view.zoomTo()
+  return view
+
+print('Prediction: Rank %d' % rank_num)
+show_pdb(rank_num, show_sidechains, show_mainchains, color).show()
+if color == "lDDT":
+  plot_plddt_legend().show()
+
+
+#@markdown \
+
+
+
+
+
Prediction: Rank 1
+
+
+
+

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
+ jupyter labextension install jupyterlab_3dmol

+
+
+
+
+
+
# @title # **8. Align PDBs** { run: "auto", display-mode: "form" }
+#@markdown We can compare the predictions with experiment.
+#@markdown Example: PDB 1F16 was obtained from NMR, so there is an ensemble of structures.
+
+import Bio.PDB
+
+pdb_code = "1f16" #@param{type:"string"}
+#@markdown - Reference PDB (Experiment)
+cpred = ""#@param{type:"string"}
+#@markdown - Color for Reference PDB (Experiment)
+os.system("wget -qnc https://files.rcsb.org/download/%s.pdb.gz; gunzip %s.pdb.gz" % (pdb_code,pdb_code))
+
+start_id = 1
+end_id = len(query_sequence)
+atoms_to_be_aligned = range(start_id, end_id + 1)
+
+# Start the parser
+pdb_parser = Bio.PDB.PDBParser(QUIET = True)
+
+# Get the structures
+ref_structure = pdb_parser.get_structure("reference", "%s.pdb" % pdb_code)
+sample_structure = pdb_parser.get_structure("sample", pdb_file[0])
+
+# Use the first model in the pdb-files for alignment
+# Change the number 0 if you want to align to another structure
+ref_model    = ref_structure[0]
+sample_model = sample_structure[0]
+
+# Make a list of the atoms (in the structures) you wish to align.
+# In this case we use CA atoms whose index is in the specified range
+ref_atoms = []
+sample_atoms = []
+
+# Iterate of all chains in the model in order to find all residues
+for ref_chain in ref_model:
+  # Iterate of all residues in each model in order to find proper atoms
+  for ref_res in ref_chain:
+    # Check if residue number ( .get_id() ) is in the list
+    if ref_res.get_id()[1] in atoms_to_be_aligned:
+      # Append CA atom to list
+      ref_atoms.append(ref_res['CA'])
+
+# Do the same for the sample structure
+for sample_chain in sample_model:
+  for sample_res in sample_chain:
+    if sample_res.get_id()[1] in atoms_to_be_aligned:
+      sample_atoms.append(sample_res['CA'])
+
+# Now we initiate the superimposer:
+super_imposer = Bio.PDB.Superimposer()
+super_imposer.set_atoms(ref_atoms, sample_atoms)
+super_imposer.apply(sample_model.get_atoms())
+
+# Print RMSD:
+print('The calculated RMSD is: %.3f Å' % super_imposer.rms)
+
+# Save the aligned version of one of the chains of 6ANE
+io = Bio.PDB.PDBIO()
+io.set_structure(sample_structure)
+io.save("%s-aligned.pdb" % jobname)
+print('Saved Aligned PDB.')
+
+# rank_num = 1 #@param ["1", "2", "3", "4", "5"] {type:"raw"}
+# #@markdown - Predicted PDB (AF/CF)
+# color = "lDDT" #@param ["chain", "lDDT", "rainbow"]
+# #@markdown - Color Predicted PDB (Default: lDDT)
+# tag = results["rank"][0][rank_num - 1]
+# jobname_prefix = ".custom" if msa_mode == "custom" else ""
+# pdb_filename = f"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb"
+# pdb_file = glob.glob(pdb_filename)
+
+# how_sidechains = False #@param {type:"boolean"}
+# show_mainchains = False #@param {type:"boolean"}
+
+view=py3Dmol.view()
+view.addModel(open("%s-aligned.pdb" % jobname).read(),'pdb')
+# view.addModel(open("%s.pdb" % pdb_code).read())
+view.zoomTo()
+view.setBackgroundColor('white')
+view.setStyle({'chain':'A'},{'cartoon': {'color': 'red'}})
+view.setStyle({'chain':'B'},{'cartoon': {'color': 'blue'}})
+view.show()
+
+# show_pdb(rank_num, show_sidechains, show_mainchains, color).show()
+# if color == "lDDT":
+#   plot_plddt_legend().show()
+
+
+
+
+
The calculated RMSD is: 5.711 Å
+Saved Aligned PDB.
+
+
+
+

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
+ jupyter labextension install jupyterlab_3dmol

+
+
+
+
+
+
#@title **9. Package and Download**
+
+#@markdown If you are having issues downloading the result archive, try disabling your adblocker and run this cell again. If that fails click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \"Download\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).
+
+if msa_mode == "custom":
+  print("Don't forget to cite your custom MSA generation method.")
+
+files.download(f"{jobname}.result.zip")
+
+if save_to_google_drive == True and drive:
+  uploaded = drive.CreateFile({'title': f"{jobname}.result.zip"})
+  uploaded.SetContentFile(f"{jobname}.result.zip")
+  uploaded.Upload()
+  print(f"Uploaded {jobname}.result.zip to Google Drive with ID {uploaded.get('id')}")
+
+
+
+
+
+
+

Colab notebooks are adapted from:

+ +

From the original notebook:

+
+
https://raw.githubusercontent.com/sokrypton/ColabFold/main/.github/ColabFold_Marv_Logo_Small.png +
+
+

ColabFold v1.5.3: AlphaFold2 using MMseqs2#

+

Easy to use protein structure and complex prediction using AlphaFold2 and Alphafold2-multimer. Sequence alignments/templates are generated through MMseqs2 and HHsearch. For more details, see bottom of the notebook, checkout the ColabFold GitHub and read our manuscript.

+

Old versions: v1.4, v1.5.1, v1.5.2

+

Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all. +Nature Methods, 2022

+
+
+

News#

+
    +

  • 2023/07/31: The ColabFold MSA server is back to normal. It was using older DB (UniRef30 2202/PDB70 220313) from 27th ~8:30 AM CEST to 31st ~11:10 AM CEST.

    • +

  • 2023/06/12: New databases! UniRef30 updated to 2023_02 and PDB to 230517. We now use PDB100 instead of PDB70 (see notes).

    • +

  • 2023/06/12: We introduced a new default pairing strategy: Previously, for multimer predictions with more than 2 chains, we only pair if all sequences taxonomically match (“complete” pairing). The new default “greedy” strategy pairs any taxonomically matching subsets.

    • +
+
+
+
+
+

Instructions #

+

Quick start

+
    +

  1. Paste your protein sequence(s) in the input field.

    2. +

  2. Press “Runtime” -> “Run all”.

    3. +

  3. The pipeline consists of 5 steps. The currently running step is indicated by a circle with a stop sign next to it.

    4. +
+

Result zip file contents

+
    +

  1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if use_amber is enabled).

    2. +

  2. Plots of the model quality.

    3. +

  3. Plots of the MSA coverage.

    4. +

  4. Parameter log file.

    5. +

  5. A3M formatted input MSA.

    6. +

  6. A predicted_aligned_error_v1.json using AlphaFold-DB’s format and a scores.json for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.

    7. +

  7. BibTeX file with citations for all used tools and databases.

    8. +
+

At the end of the job a download modal box will pop up with a jobname.result.zip file. Additionally, if the save_to_google_drive option was selected, the jobname.result.zip will be uploaded to your Google Drive.

+

License +githubusercontent.com/sokrypton/ColabFold/main/LICENSE). Additionally, this > notebook uses the AlphaFold2 source code and its parameters licensed under Apache 2.0 > and CC BY 4.0 respectively. Read more about the AlphaFold license [here](deepmind/> alphafold).

+

Acknowledgments

+
    +

  • We thank the AlphaFold team for developing an excellent model and open > sourcing the software.

    • +

  • KOBIC and Söding Lab for providing the computational resources for the MMseqs2 MSA server.

    • +

  • Richard Evans for helping to benchmark the ColabFold’s Alphafold-multimer support.

    • +

  • David Koes for his awesome py3Dmol plugin, without whom these notebooks would be quite boring!

    • +

  • Do-Yoon Kim for creating the ColabFold logo.

    • +

  • A colab by Sergey Ovchinnikov (@sokrypton), Milot Mirdita (@milot_mirdita) and Martin Steinegger (@thesteinegger).

    • +
+
+ + + + +
+ + + + +
+ +
+ +
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+ +
+ +
+ +
+ + + +
+ +
+
+ Contents +
+
+ +
+ + +
+ + + +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/ColabFold/Tutorial_Multimer.html b/ColabFold/Tutorial_Multimer.html new file mode 100644 index 0000000..260a620 --- /dev/null +++ b/ColabFold/Tutorial_Multimer.html @@ -0,0 +1,1038 @@ + + + + + + + + + + + + 1. ColabFold Tutorial: Predicting Protein-Protein Complexes of TA Systems — Tutorials + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+ + + Ctrl+K +
+
+ + + +
+
+ +
+ + + +
+ + + + +
+ +
+
+ + +
+
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+ + +
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+ + + + +
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+ + +
+ + + + + + + + + + + +
+ +
+ +
+
+ + + +
+

1. ColabFold Tutorial: Predicting Protein-Protein Complexes of TA Systems

+ +
+ +
+
+ + + + +
+ +

Open In Colab

+
+
+
%%time
+
+# @title **Install Python Packages** { run: "auto", display-mode: "form" }
+
+import os
+from sys import version_info
+PYTHON_VERSION = f"{version_info.major}.{version_info.minor}"
+
+if not os.path.isfile("COLABFOLD_READY"):
+  print("installing colabfold...")
+  os.system("pip install -q --no-warn-conflicts 'colabfold[alphafold-minus-jax] @ git+https://github.com/sokrypton/ColabFold'")
+  os.system("pip install --upgrade dm-haiku")
+  os.system("ln -s /usr/local/lib/python3.*/dist-packages/colabfold colabfold")
+  os.system("ln -s /usr/local/lib/python3.*/dist-packages/alphafold alphafold")
+  # patch for jax > 0.3.25
+  os.system("sed -i 's/weights = jax.nn.softmax(logits)/logits=jnp.clip(logits,-1e8,1e8);weights=jax.nn.softmax(logits)/g' alphafold/model/modules.py")
+  os.system("touch COLABFOLD_READY")
+
+
+if not os.path.isfile("CONDA_READY"):
+  print("installing conda...")
+  os.system("wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh")
+  os.system("bash Miniforge3-Linux-x86_64.sh -bfp /usr/local")
+  os.system("conda config --set auto_update_conda false")
+  os.system("touch CONDA_READY")
+
+
+if not os.path.isfile("CONDA_READY"):
+  print("installing conda...")
+  os.system("wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh")
+  os.system("bash Mambaforge-Linux-x86_64.sh -bfp /usr/local")
+  os.system("mamba config --set auto_update_conda false")
+  os.system("touch CONDA_READY")
+
+
+if not os.path.isfile("HH_READY") and not os.path.isfile("AMBER_READY"):
+  print("installing hhsuite and amber...")
+  os.system(f"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer")
+  os.system("touch HH_READY")
+  os.system("touch AMBER_READY")
+else:
+  if not os.path.isfile("HH_READY"):
+    print("installing hhsuite...")
+    os.system(f"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 python='{PYTHON_VERSION}'")
+    os.system("touch HH_READY")
+  if not os.path.isfile("AMBER_READY"):
+    print("installing amber...")
+    os.system(f"mamba install -y -c conda-forge openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer")
+    os.system("touch AMBER_READY")
+
+
+
+
+
+

1. ColabFold Tutorial: Predicting Protein-Protein Complexes of TA Systems#

+

Like the monomer example, we will use the default options set by Steinegger et al. which excludes Amber minimization and structure templates.

+

Predicting the structure of protein complexes can be investigated using the AlphaFold oligomers

+

There are subtle differences when running the AF-Multimer externsion:

+
    +

  • Model confidence metric is iPTM

    +
      +

    • Interference predicted template modelling score

      • +
    +
    • +

  • Better for homomeric interfaces than for heteromeric interfaces (high conservation)

    • +

  • COM between chains are added to the loss function

    • +
+

This example focuses on a toxin-antitoxin (TA) protein system, prevalent in bacterial systems. Toxins, or over-expressed proteins, become accumulated in the cell causing disturbing other cellular processes. Yet, their natural role aids in infections and apoptosis. Antitoxin, protein or RNA, can neutralize the toxin by forming stable complexes. How does the protein-protein interactions maintianing a balance between the toxin and antitoxin.

+

We will predict the TA complex in Vibrio cholerae, specifically, VCParD1/2 (antitoxin) and VcParE (toxin)1/2.

+

a. Background

+
    +

  • TA systems are essential in maintaining cellular homeostasis

    • +

  • Low sequence similariy to maintain specificity towards cognate receptor

    • +

  • ParDE was the first TA system to be identified as a stabilizing element upon plasmisd loss

    • +

  • Potential in biotechnological applications

    • +
+

b. ParE attenuates Toxicity Protecting Cells

+
    +

  • ParE can inhibit DNA gyrase supercoiling

    • +

  • Protects from anti-gyrase antibiotitcs

    • +

  • At the same time ParD is degraded

    • +

  • C-terminal domain is key to formation of PaParDE complexes

    • +
+

The C-terminal residues of ParD was found to be crucial in formation and stabilizatino of toxin-antitoxin complexes. This resulted in protection from proteases and attenuates toxicitiy from antibiotics in bacterial systems.

+

Interestingly, structure-based studies have resolved several oligomeric TA complexes. Despite low sequence similarity, their structure is highly conserved. Let’s investivate VcParDE and later compare it with PaParDE.

+
+
+
#@title # **2. Notebook Setup** { display-mode: "form" }
+
+from google.colab import files
+import os
+import re
+import hashlib
+import random
+
+from sys import version_info
+python_version = f"{version_info.major}.{version_info.minor}"
+
+def add_hash(x,y):
+  return x+"_"+hashlib.sha1(y.encode()).hexdigest()[:5]
+
+#@markdown \
+#@markdown ##a. Protein Sequence for Structure Prediction
+#@markdown For bax (human), the amino acid sequence was obtained from
+#@markdown UniProt ([Q07812](https://www.uniprot.org/uniprotkb/Q07812/entry))
+query_sequence = 'MHTLTANDAKRNFGELLLSAQREPVIISKNSKNTVVVMSIKDFEELEAMKLDYLKHCFESAQKDLDSGKTVDGATFLNTL:MQNKQYKLSQLAQEHLLKIKHYTIENFAEAQWQKYKSTLLSGFQTLADNPGLGKSCEDIYQNGFYFPVGKHMAYYTKEANFILIVAVLGQSQLPQKHLKQSRFVS:MAKNTSITLGEHFDGFITSQIQSGRYGSASEVIRSALRLLENQETKLQSLRQLLIEGEQSGDADYDLDSFINELDSENI:MKPFNLTVAAKADLRDIALFTQRRWGKEQRNVYLKQFDDSFWLLAENPDIGKSCDEIREGYRKFPQGSHVIFYQQTGSQQIRVIRILHKSMDVNPIFGA ' #@param {type:"string"}
+#@markdown **Note:** For modeling complexes, use `:` to specify inter-protein chainbreaks
+#@markdown (supports homo- and hetro-oligomers).
+#@markdown For example, **PI...SK:PI...SK** for a homodimer.
+
+#@markdown \
+#@markdown ## b. Job Name (also name of saving directory)
+jobname = '' #@param {type:"string"}
+
+#@markdown \
+#@markdown  ## c. Number of predicted models to relax using Amber
+num_relax = 5 #@param [0, 1, 5] {type:"raw"}
+
+#@markdown \
+#@markdown ## **d. Option for Structure Template**
+#@markdown  Search for PDB structure (xyz-coordinates)
+template_mode = "none" #@param ["none", "pdb100","custom"]
+#@markdown `none` = no template information is used. \
+#@markdown `pdb100` = detect templates in pdb100 (see [notes](#pdb100)). \
+#@markdown `custom` - upload and search own templates (PDB or mmCIF format, see [notes](#custom_templates))
+
+use_amber = num_relax > 0
+
+query_sequence = "".join(query_sequence.split()) # remove whitespaces
+
+basejobname = "".join(jobname.split())
+basejobname = re.sub(r'\W+', '', basejobname)
+jobname = add_hash(basejobname, query_sequence)
+
+# check if directory with jobname exists
+def check(folder):
+  if os.path.exists(folder):
+    return False
+  else:
+    return True
+if not check(jobname):
+  n = 0
+  while not check(f"{jobname}_{n}"): n += 1
+  jobname = f"{jobname}_{n}"
+
+# make directory to save results
+os.makedirs(jobname, exist_ok=True)
+
+# save queries
+queries_path = os.path.join(jobname, f"{jobname}.csv")
+with open(queries_path, "w") as text_file:
+  text_file.write(f"id,sequence\n{jobname},{query_sequence}")
+
+if template_mode == "pdb100":
+  use_templates = True
+  custom_template_path = None
+elif template_mode == "custom":
+  custom_template_path = os.path.join(jobname,f"template")
+  os.makedirs(custom_template_path, exist_ok=True)
+  uploaded = files.upload()
+  use_templates = True
+  for fn in uploaded.keys():
+    os.rename(fn,os.path.join(custom_template_path,fn))
+else:
+  custom_template_path = None
+  use_templates = False
+
+
+
+
+
+
+
#@title # **3. Multiple Sequence Alignment (MSA)**
+
+#@markdown ## a. MSA options
+msa_mode = "mmseqs2_uniref_env" #@param ["mmseqs2_uniref_env", "mmseqs2_uniref","single_sequence","custom"]
+#@markdown - pairing mode
+#@markdown - single sequence
+#@markdown - custom MSA upload
+
+#@markdown \
+pair_mode = "unpaired_paired" #@param ["unpaired_paired","unpaired","paired"] {type:"string"}
+#@markdown - "unpaired_paired" - pair sequences from same species + unpaired MSA
+#@markdown - "unpaired" - seperate MSA for each chain
+#@markdown - "paired" - only use paired sequences
+
+# decide which a3m to use
+if "mmseqs2" in msa_mode:
+  a3m_file = os.path.join(jobname,f"{jobname}.a3m")
+
+elif msa_mode == "custom":
+  a3m_file = os.path.join(jobname,f"{jobname}.custom.a3m")
+  if not os.path.isfile(a3m_file):
+    custom_msa_dict = files.upload()
+    custom_msa = list(custom_msa_dict.keys())[0]
+    header = 0
+    import fileinput
+    for line in fileinput.FileInput(custom_msa,inplace=1):
+      if line.startswith(">"):
+         header = header + 1
+      if not line.rstrip():
+        continue
+      if line.startswith(">") == False and header == 1:
+         query_sequence = line.rstrip()
+      print(line, end='')
+
+    os.rename(custom_msa, a3m_file)
+    queries_path=a3m_file
+    print(f"moving {custom_msa} to {a3m_file}")
+
+else:
+  a3m_file = os.path.join(jobname,f"{jobname}.single_sequence.a3m")
+  with open(a3m_file, "w") as text_file:
+    text_file.write(">1\n%s" % query_sequence)
+
+
+
+
+
+
+
# @title # **4. Advanced Settings** { run: "auto", display-mode: "form" }
+
+#@markdown ## a. ML Model
+model_type = "auto" #@param ["auto", "alphafold2_ptm", "alphafold2_multimer_v1", "alphafold2_multimer_v2", "alphafold2_multimer_v3"]
+#@markdown **Note:** If `auto` selected, will use `alphafold2_ptm` for monomer prediction
+#@markdown and `alphafold2_multimer_v3` for complex prediction.
+#@markdown Any of the `mode_types` can be used (regardless if input is monomer or complex).
+
+#@markdown \
+#@markdown ## b. Re-Train with Predicted Structures
+num_recycles = "0" #@param ["auto", "0", "1", "3", "6", "12", "24", "48"]
+#@markdown **Note:**
+#@markdown - If `auto` selected, will use `num_recycles=20`,
+#@markdown - If `model_type=alphafold2_multimer_v3`, else `num_recycles=3`
+
+#@markdown \
+recycle_early_stop_tolerance = "auto" #@param ["auto", "0.0", "0.5", "1.0"]
+#@markdown - if `auto` selected, will use `tol=0.5`
+#@markdown - If `model_type=alphafold2_multimer_v3`,  else `tol=0.0`.
+
+#@markdown \
+#@markdown ## c. Amber Relaxation
+relax_max_iterations = 200 #@param [0, 200, 2000] {type:"raw"}
+#@markdown - max amber relax iterations
+#@markdown -  `0` = unlimited (AlphaFold2 default, can take very long)
+
+#@markdown \
+#@markdown ## d. MSA Pairing
+pairing_strategy = "greedy" #@param ["greedy", "complete"] {type:"string"}
+#@markdown - `greedy` = pair any taxonomically matching subsets,
+#@markdown - `complete` = all sequences have to match in one line.
+
+#@markdown \
+#@markdown ## e. Sample settings
+#@markdown -  enable dropouts and increase number of seeds to sample predictions from uncertainty of the model.
+#@markdown -  decrease `max_msa` to increase uncertainity
+max_msa = "auto" #@param ["auto", "512:1024", "256:512", "64:128", "32:64", "16:32"]
+num_seeds = 1 #@param [1,2,4,8,16] {type:"raw"}
+use_dropout = False #@param {type:"boolean"}
+
+num_recycles = None if num_recycles == "auto" else int(num_recycles)
+recycle_early_stop_tolerance = None if recycle_early_stop_tolerance == "auto" else float(recycle_early_stop_tolerance)
+if max_msa == "auto": max_msa = None
+
+#@markdown \
+#@markdown # **5. Save settings**
+#@markdown This will ask for your credentials, to save the outputs.
+save_all = False #@param {type:"boolean"}
+save_recycles = False #@param {type:"boolean"}
+save_to_google_drive = False #@param {type:"boolean"}
+#@markdown -  if the save_to_google_drive option was selected, the result zip will be uploaded to your Google Drive
+dpi = 200 #@param {type:"integer"}
+#@markdown - set dpi for image resolution
+
+if save_to_google_drive:
+  from pydrive.drive import GoogleDrive
+  from pydrive.auth import GoogleAuth
+  from google.colab import auth
+  from oauth2client.client import GoogleCredentials
+  auth.authenticate_user()
+  gauth = GoogleAuth()
+  gauth.credentials = GoogleCredentials.get_application_default()
+  drive = GoogleDrive(gauth)
+  print("You are logged into Google Drive and are good to go!")
+
+#@markdown Don't forget to hit `Runtime` -> `Run all` after updating the form.
+
+
+
+
+
+
+
#@title # **6. Run Prediction** { display-mode: "form" }
+
+display_images = True #@param {type:"boolean"}
+
+
+import sys
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+from Bio import BiopythonDeprecationWarning
+warnings.simplefilter(action='ignore', category=BiopythonDeprecationWarning)
+from pathlib import Path
+from colabfold.download import download_alphafold_params, default_data_dir
+from colabfold.utils import setup_logging
+from colabfold.batch import get_queries, run, set_model_type
+from colabfold.plot import plot_msa_v2
+
+import os
+import numpy as np
+try:
+  K80_chk = os.popen('nvidia-smi | grep "Tesla K80" | wc -l').read()
+except:
+  K80_chk = "0"
+  pass
+if "1" in K80_chk:
+  print("WARNING: found GPU Tesla K80: limited to total length < 1000")
+  if "TF_FORCE_UNIFIED_MEMORY" in os.environ:
+    del os.environ["TF_FORCE_UNIFIED_MEMORY"]
+  if "XLA_PYTHON_CLIENT_MEM_FRACTION" in os.environ:
+    del os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]
+
+from colabfold.colabfold import plot_protein
+from pathlib import Path
+import matplotlib.pyplot as plt
+
+# For some reason we need that to get pdbfixer to import
+if use_amber and f"/usr/local/lib/python{python_version}/site-packages/" not in sys.path:
+    sys.path.insert(0, f"/usr/local/lib/python{python_version}/site-packages/")
+
+def input_features_callback(input_features):
+  if display_images:
+    plot_msa_v2(input_features)
+    plt.show()
+    plt.close()
+
+def prediction_callback(protein_obj, length,
+                        prediction_result, input_features, mode):
+  model_name, relaxed = mode
+  if not relaxed:
+    if display_images:
+      fig = plot_protein(protein_obj, Ls=length, dpi=150)
+      plt.show()
+      plt.close()
+
+result_dir = jobname
+log_filename = os.path.join(jobname,"log.txt")
+setup_logging(Path(log_filename))
+
+queries, is_complex = get_queries(queries_path)
+model_type = set_model_type(is_complex, model_type)
+
+if "multimer" in model_type and max_msa is not None:
+  use_cluster_profile = False
+else:
+  use_cluster_profile = True
+
+download_alphafold_params(model_type, Path("."))
+results = run(
+    queries=queries,
+    result_dir=result_dir,
+    use_templates=use_templates,
+    custom_template_path=custom_template_path,
+    num_relax=num_relax,
+    msa_mode=msa_mode,
+    model_type=model_type,
+    num_models=5,
+    num_recycles=num_recycles,
+    relax_max_iterations=relax_max_iterations,
+    recycle_early_stop_tolerance=recycle_early_stop_tolerance,
+    num_seeds=num_seeds,
+    use_dropout=use_dropout,
+    model_order=[1,2,3,4,5],
+    is_complex=is_complex,
+    data_dir=Path("."),
+    keep_existing_results=False,
+    rank_by="auto",
+    pair_mode=pair_mode,
+    pairing_strategy=pairing_strategy,
+    stop_at_score=float(100),
+    prediction_callback=prediction_callback,
+    dpi=dpi,
+    zip_results=False,
+    save_all=save_all,
+    max_msa=max_msa,
+    use_cluster_profile=use_cluster_profile,
+    input_features_callback=input_features_callback,
+    save_recycles=save_recycles,
+    user_agent="colabfold/google-colab-main",
+)
+results_zip = f"{jobname}.result.zip"
+os.system(f"zip -r {results_zip} {jobname}")
+
+
+
+
+
2023-12-08 03:34:55,968 alphafold2_multimer_v3_model_2_seed_000 recycle=18 pLDDT=85.8 pTM=0.479 ipTM=0.372 tol=0.629
+2023-12-08 03:35:09,602 alphafold2_multimer_v3_model_2_seed_000 recycle=19 pLDDT=85.5 pTM=0.478 ipTM=0.374 tol=1.08
+
+
+
+
+
+
+
# @title # **7. Visualize 3D Structure of ColabFold Prediction** { run: "auto", display-mode: "form" }
+
+#@markdown Rank number/score for the **monomer model is the average pLDDT**,
+#@markdown where a ranking of 1 correlates better local structure confidence.
+#@markdown An acceptable model prediction should average $\ge 85$.
+#@markdown The number of ranked models was set in step `6. Run Prediction` (step 6, line 75.).
+
+#@markdown \
+#@markdown Here, you can change the value for `rank_num` to visualize the 5 predictions.
+
+import py3Dmol
+import glob
+import matplotlib.pyplot as plt
+from colabfold.colabfold import plot_plddt_legend
+from colabfold.colabfold import pymol_color_list, alphabet_list
+
+rank_num = 1 #@param ["1", "2", "3", "4", "5"] {type:"raw"}
+color = "rainbow" #@param ["chain", "lDDT", "rainbow"]
+show_sidechains = False #@param {type:"boolean"}
+show_mainchains = False #@param {type:"boolean"}
+
+tag = results["rank"][0][rank_num - 1]
+jobname_prefix = ".custom" if msa_mode == "custom" else ""
+pdb_filename = f"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb"
+pdb_file = glob.glob(pdb_filename)
+
+def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color="lDDT"):
+  model_name = f"rank_{rank_num}"
+  view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)
+  view.addModel(open(pdb_file[0],'r').read(),'pdb')
+
+
+  if color == "lDDT":
+    view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})
+  elif color == "rainbow":
+    view.setStyle({'cartoon': {'color':'spectrum'}})
+  elif color == "chain":
+    chains = len(queries[0][1]) + 1 if is_complex else 1
+    for n,chain,color in zip(range(chains),alphabet_list,pymol_color_list):
+       view.setStyle({'chain':chain},{'cartoon': {'color':color}})
+
+  if show_sidechains:
+    BB = ['C','O','N']
+    view.addStyle({'and':[{'resn':["GLY","PRO"],'invert':True},{'atom':BB,'invert':True}]},
+                        {'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+    view.addStyle({'and':[{'resn':"GLY"},{'atom':'CA'}]},
+                        {'sphere':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+    view.addStyle({'and':[{'resn':"PRO"},{'atom':['C','O'],'invert':True}]},
+                        {'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+  if show_mainchains:
+    BB = ['C','O','N','CA']
+    view.addStyle({'atom':BB},{'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}}, viewer=(0,0))
+
+  view.zoomTo()
+  return view
+
+print('Prediction: Rank %d' % rank_num)
+show_pdb(rank_num, show_sidechains, show_mainchains, color).show()
+if color == "lDDT":
+  plot_plddt_legend().show()
+
+
+#@markdown \
+
+
+
+
+
+
+
#@title **8. Package and Download**
+
+#@markdown If you are having issues downloading the result archive, try disabling your adblocker and run this cell again. If that fails click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \"Download\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).
+
+if msa_mode == "custom":
+  print("Don't forget to cite your custom MSA generation method.")
+
+files.download(f"{jobname}.result.zip")
+
+if save_to_google_drive == True and drive:
+  uploaded = drive.CreateFile({'title': f"{jobname}.result.zip"})
+  uploaded.SetContentFile(f"{jobname}.result.zip")
+  uploaded.Upload()
+  print(f"Uploaded {jobname}.result.zip to Google Drive with ID {uploaded.get('id')}")
+
+
+
+
+

Colab notebooks are adapted from:

+ +

From the original notebook:

+
+
https://raw.githubusercontent.com/sokrypton/ColabFold/main/.github/ColabFold_Marv_Logo_Small.png +
+
+

ColabFold v1.5.3: AlphaFold2 using MMseqs2#

+

Easy to use protein structure and complex prediction using AlphaFold2 and Alphafold2-multimer. Sequence alignments/templates are generated through MMseqs2 and HHsearch. For more details, see bottom of the notebook, checkout the ColabFold GitHub and read our manuscript.

+

Old versions: v1.4, v1.5.1, v1.5.2

+

Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all. +Nature Methods, 2022

+
+
+

News#

+
    +

  • 2023/07/31: The ColabFold MSA server is back to normal. It was using older DB (UniRef30 2202/PDB70 220313) from 27th ~8:30 AM CEST to 31st ~11:10 AM CEST.

    • +

  • 2023/06/12: New databases! UniRef30 updated to 2023_02 and PDB to 230517. We now use PDB100 instead of PDB70 (see notes).

    • +

  • 2023/06/12: We introduced a new default pairing strategy: Previously, for multimer predictions with more than 2 chains, we only pair if all sequences taxonomically match (“complete” pairing). The new default “greedy” strategy pairs any taxonomically matching subsets.

    • +
+
+
+
+
+

Instructions #

+

Quick start

+
    +

  1. Paste your protein sequence(s) in the input field.

    2. +

  2. Press “Runtime” -> “Run all”.

    3. +

  3. The pipeline consists of 5 steps. The currently running step is indicated by a circle with a stop sign next to it.

    4. +
+

Result zip file contents

+
    +

  1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if use_amber is enabled).

    2. +

  2. Plots of the model quality.

    3. +

  3. Plots of the MSA coverage.

    4. +

  4. Parameter log file.

    5. +

  5. A3M formatted input MSA.

    6. +

  6. A predicted_aligned_error_v1.json using AlphaFold-DB’s format and a scores.json for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.

    7. +

  7. BibTeX file with citations for all used tools and databases.

    8. +
+

At the end of the job a download modal box will pop up with a jobname.result.zip file. Additionally, if the save_to_google_drive option was selected, the jobname.result.zip will be uploaded to your Google Drive.

+

License +githubusercontent.com/sokrypton/ColabFold/main/LICENSE). Additionally, this > notebook uses the AlphaFold2 source code and its parameters licensed under Apache 2.0 > and CC BY 4.0 respectively. Read more about the AlphaFold license [here](deepmind/> alphafold).

+

Acknowledgments

+
    +

  • We thank the AlphaFold team for developing an excellent model and open > sourcing the software.

    • +

  • KOBIC and Söding Lab for providing the computational resources for the MMseqs2 MSA server.

    • +

  • Richard Evans for helping to benchmark the ColabFold’s Alphafold-multimer support.

    • +

  • David Koes for his awesome py3Dmol plugin, without whom these notebooks would be quite boring!

    • +

  • Do-Yoon Kim for creating the ColabFold logo.

    • +

  • A colab by Sergey Ovchinnikov (@sokrypton), Milot Mirdita (@milot_mirdita) and Martin Steinegger (@thesteinegger).

    • +
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+ + +
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+ + + + + + + + \ No newline at end of file diff --git a/ColabFold/old/ColabFold_AF2_mmseqs2.html b/ColabFold/old/ColabFold_AF2_mmseqs2.html new file mode 100644 index 0000000..e8248b3 --- /dev/null +++ b/ColabFold/old/ColabFold_AF2_mmseqs2.html @@ -0,0 +1,1155 @@ + + + + + + + + + + + + Instructions — Tutorials + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+
+
+ + + Ctrl+K +
+
+ + + +
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+ +
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+ +
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+ +
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+ + + +
+

Instructions

+ +
+
+ +
+
+
+ + + + +
+ +

Open In Colab

+https://raw.githubusercontent.com/sokrypton/ColabFold/main/.github/ColabFold_Marv_Logo_Small.png +

##ColabFold: AlphaFold2 using MMseqs2

+

Easy to use protein structure and complex prediction using AlphaFold2 and Alphafold2-multimer. Sequence alignments/templates are generated through MMseqs2 and HHsearch. For more details, see bottom of the notebook, checkout the ColabFold GitHub and read our manuscript. Old versions: v1.0, v1.1, v1.2, v1.3

+

Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all. +Nature Methods, 2022

+
+
+
#@title Input protein sequence(s), then hit `Runtime` -> `Run all`
+from google.colab import files
+import os.path
+import re
+import hashlib
+import random
+
+def add_hash(x,y):
+  return x+"_"+hashlib.sha1(y.encode()).hexdigest()[:5]
+
+#@markdown Example query sequence is for bax (obtained from 00_monomer_pred/bax.fasta).
+#@markdown - Use `:` to specify inter-protein chainbreaks for **modeling complexes** (supports homo- and hetro-oligomers). 
+#@markdown   - For example **MDG...KMG:MDF...KMG** for a bax homodimer.
+query_sequence = 'MDGSGEQPRGGGPTSSEQIMKTGALLLQGFIQDRAGRMGGEAPELALDPVPQDASTKKLSECLKRIGDELDSNMELQRMIAAVDTDSPREVFFRVAADMFSDGNFNWGRVVALFYFASKLVLKALCTKVPELIRTIMGWTLDFLRERLLGWIQDQGGWDGLLSYFGTPTWQTVTIFVAGVLTASLTIWKKMG' #@param {type:"string"}
+
+# remove whitespaces
+query_sequence = "".join(query_sequence.split())
+
+jobname = 'bax' #@param {type:"string"}
+# remove whitespaces
+basejobname = "".join(jobname.split())
+basejobname = re.sub(r'\W+', '', basejobname)
+jobname = add_hash(basejobname, query_sequence)
+while os.path.isfile(f"{jobname}.csv"):
+  jobname = add_hash(basejobname, ''.join(random.sample(query_sequence,len(query_sequence))))
+
+with open(f"{jobname}.csv", "w") as text_file:
+    text_file.write(f"id,sequence\n{jobname},{query_sequence}")
+
+queries_path=f"{jobname}.csv"
+
+#@markdown Amber minimization option.
+# number of models to use
+use_amber = True #@param {type:"boolean"}
+#@markdown Template mode refers to usage of PDB templates from RCSB.
+
+#@markdown 3 options:
+#@markdown - "none" = no template information is used
+#@markdown - "pdb70" = detect templates in pdb70
+#@markdown - "custom" - upload and search own templates (PDB or mmCIF format, see [notes below](#custom_templates)).
+template_mode = "none" #@param ["none", "pdb70","custom"]
+
+
+if template_mode == "pdb70":
+  use_templates = True
+  custom_template_path = None
+elif template_mode == "custom":
+  custom_template_path = f"{jobname}_template"
+  os.mkdir(custom_template_path)
+  uploaded = files.upload()
+  use_templates = True
+  for fn in uploaded.keys():
+    os.rename(fn, f"{jobname}_template/{fn}")
+else:
+  custom_template_path = None
+  use_templates = False
+
+
+
+
+
---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 2
+      1 #@title Input protein sequence(s), then hit `Runtime` -> `Run all`
+----> 2 from google.colab import files
+      3 import os.path
+      4 import re
+
+ModuleNotFoundError: No module named 'google'
+
+
+
+
+
+
+
#@markdown ### MSA options (custom MSA upload, single sequence, pairing mode)
+#@markdown 4 options:
+#@markdown - "MMseqs2 (UniRef+Environmental)"
+#@markdown - "MMseqs2 (Uniref only)"
+#@markdown - "single sequence"
+#@markdown - "custom"
+msa_mode = "MMseqs2 (UniRef+Environmental)" #@param ["MMseqs2 (UniRef+Environmental)", "MMseqs2 (UniRef only)","single_sequence","custom"]
+
+#@markdown 3 options:
+#@markdown - "unpaired+paired" = pair sequences from same species + unpaired MSA
+#@markdown - "unpaired" = seperate MSA for each chain
+#@markdown - "paired" = only use paired sequences
+pair_mode = "unpaired+paired" #@param ["unpaired+paired","paired","unpaired"] {type:"string"}
+
+# decide which a3m to use
+if msa_mode.startswith("MMseqs2"):
+  a3m_file = f"{jobname}.a3m"
+elif msa_mode == "custom":
+  a3m_file = f"{jobname}.custom.a3m"
+  if not os.path.isfile(a3m_file):
+    custom_msa_dict = files.upload()
+    custom_msa = list(custom_msa_dict.keys())[0]
+    header = 0
+    import fileinput
+    for line in fileinput.FileInput(custom_msa,inplace=1):
+      if line.startswith(">"):
+         header = header + 1
+      if not line.rstrip():
+        continue
+      if line.startswith(">") == False and header == 1:
+         query_sequence = line.rstrip()
+      print(line, end='')
+
+    os.rename(custom_msa, a3m_file)
+    queries_path=a3m_file
+    print(f"moving {custom_msa} to {a3m_file}")
+else:
+  a3m_file = f"{jobname}.single_sequence.a3m"
+  with open(a3m_file, "w") as text_file:
+    text_file.write(">1\n%s" % query_sequence)
+
+
+
+
+
+
+
#@markdown ### Advanced settings
+#@markdown - "auto" = protein structure prediction using "AlphaFold2-ptm" and complex prediction "AlphaFold-multimer-v2". 
+#@markdown - For complexes "AlphaFold-multimer-v[1,2]" and "AlphaFold-ptm" can be used.
+model_type = "auto" #@param ["auto", "AlphaFold2-ptm", "AlphaFold2-multimer-v1", "AlphaFold2-multimer-v2"]
+
+num_recycles = 3 #@param [1,3,6,12,24,48] {type:"raw"}
+
+#@markdown -  The save_to_google_drive option will upload zip file to your Google Drive
+save_to_google_drive = False #@param {type:"boolean"}
+
+dpi = 300 #@param {type:"integer"}
+#@markdown - set dpi for image resolution
+
+#@markdown Don't forget to hit `Runtime` -> `Run all` after updating the form.
+
+
+if save_to_google_drive:
+  from pydrive.drive import GoogleDrive
+  from pydrive.auth import GoogleAuth
+  from google.colab import auth
+  from oauth2client.client import GoogleCredentials
+  auth.authenticate_user()
+  gauth = GoogleAuth()
+  gauth.credentials = GoogleCredentials.get_application_default()
+  drive = GoogleDrive(gauth)
+  print("You are logged into Google Drive and are good to go!")
+
+
+
+
+
+
+
#@title Install dependencies
+%%bash -s $use_amber $use_templates
+
+set -e
+
+USE_AMBER=$1
+USE_TEMPLATES=$2
+
+if [ ! -f COLABFOLD_READY ]; then
+  # install dependencies
+  # We have to use "--no-warn-conflicts" because colab already has a lot preinstalled with requirements different to ours
+  pip install -q --no-warn-conflicts "colabfold[alphafold-minus-jax] @ git+https://github.com/sokrypton/ColabFold"
+  # high risk high gain
+  pip install -q "jax[cuda11_cudnn805]>=0.3.8,<0.4" -f https://storage.googleapis.com/jax-releases/jax_releases.html
+  touch COLABFOLD_READY
+fi
+
+# setup conda
+if [ ${USE_AMBER} == "True" ] || [ ${USE_TEMPLATES} == "True" ]; then
+  if [ ! -f CONDA_READY ]; then
+    wget -qnc https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+    bash Miniconda3-latest-Linux-x86_64.sh -bfp /usr/local 2>&1 1>/dev/null
+    rm Miniconda3-latest-Linux-x86_64.sh
+    touch CONDA_READY
+  fi
+fi
+# setup template search
+if [ ${USE_TEMPLATES} == "True" ] && [ ! -f HH_READY ]; then
+  conda install -y -q -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 python=3.7 2>&1 1>/dev/null
+  touch HH_READY
+fi
+# setup openmm for amber refinement
+if [ ${USE_AMBER} == "True" ] && [ ! -f AMBER_READY ]; then
+  conda install -y -q -c conda-forge openmm=7.5.1 python=3.7 pdbfixer 2>&1 1>/dev/null
+  touch AMBER_READY
+fi
+
+
+
+
+
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+Extracting : zlib-1.2.12-h7f8727e_1.conda:   2%|▎         | 1/40 [00:00<00:02, 15.82it/s]    
+Extracting : _libgcc_mutex-0.1-main.conda:   5%|▌         | 2/40 [00:00<00:01, 27.74it/s]
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+Extracting : pycosat-0.6.3-py39h27cfd23_0.conda:  12%|█▎        | 5/40 [00:00<00:02, 16.85it/s]
+Extracting : pycparser-2.21-pyhd3eb1b0_0.conda:  12%|█▎        | 5/40 [00:00<00:02, 16.85it/s] 
+Extracting : cffi-1.15.0-py39hd667e15_1.conda:  15%|█▌        | 6/40 [00:00<00:02, 16.85it/s] 
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+Extracting : wheel-0.37.1-pyhd3eb1b0_0.conda:  20%|██        | 8/40 [00:00<00:01, 16.85it/s]
+Extracting : libffi-3.3-he6710b0_2.conda:  22%|██▎       | 9/40 [00:00<00:01, 16.85it/s]    
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+Extracting : ca-certificates-2022.3.29-h06a4308_1.conda:  28%|██▊       | 11/40 [00:00<00:01, 16.85it/s]
+Extracting : xz-5.2.5-h7b6447c_0.conda:  30%|███       | 12/40 [00:00<00:01, 16.85it/s]                 
+Extracting : pip-21.2.4-py39h06a4308_0.conda:  32%|███▎      | 13/40 [00:00<00:01, 16.85it/s]
+Extracting : pip-21.2.4-py39h06a4308_0.conda:  35%|███▌      | 14/40 [00:00<00:00, 33.28it/s]
+Extracting : tqdm-4.63.0-pyhd3eb1b0_0.conda:  35%|███▌      | 14/40 [00:00<00:00, 33.28it/s] 
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+                                                                                             
+
+
+
+
+
+
+
#@title Run Prediction
+import sys
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+from colabfold.download import download_alphafold_params, default_data_dir
+from colabfold.utils import setup_logging
+from colabfold.batch import get_queries, run, set_model_type
+K80_chk = !nvidia-smi | grep "Tesla K80" | wc -l
+if "1" in K80_chk:
+  print("WARNING: found GPU Tesla K80: limited to total length < 1000")
+  if "TF_FORCE_UNIFIED_MEMORY" in os.environ:
+    del os.environ["TF_FORCE_UNIFIED_MEMORY"]
+  if "XLA_PYTHON_CLIENT_MEM_FRACTION" in os.environ:
+    del os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]
+
+from colabfold.colabfold import plot_protein
+from pathlib import Path
+import matplotlib.pyplot as plt
+
+
+# For some reason we need that to get pdbfixer to import
+if use_amber and '/usr/local/lib/python3.7/site-packages/' not in sys.path:
+    sys.path.insert(0, '/usr/local/lib/python3.7/site-packages/')
+
+def prediction_callback(unrelaxed_protein, length, prediction_result, input_features, type):
+  fig = plot_protein(unrelaxed_protein, Ls=length, dpi=150)
+  plt.show()
+  plt.close()
+
+result_dir="."
+if 'logging_setup' not in globals():
+    setup_logging(Path(".").joinpath("log.txt"))
+    logging_setup = True
+
+queries, is_complex = get_queries(queries_path)
+model_type = set_model_type(is_complex, model_type)
+download_alphafold_params(model_type, Path("."))
+run(
+    queries=queries,
+    result_dir=result_dir,
+    use_templates=use_templates,
+    custom_template_path=custom_template_path,
+    use_amber=use_amber,
+    msa_mode=msa_mode,    
+    model_type=model_type,
+    num_models=5,
+    num_recycles=num_recycles,
+    model_order=[1, 2, 3, 4, 5],
+    is_complex=is_complex,
+    data_dir=Path("."),
+    keep_existing_results=False,
+    recompile_padding=1.0,
+    rank_by="auto",
+    pair_mode=pair_mode,
+    stop_at_score=float(100),
+    prediction_callback=prediction_callback,
+    dpi=dpi
+)
+
+
+
+
+
Downloading alphafold2 weights to .: 100%|██████████| 3.47G/3.47G [00:22<00:00, 162MB/s]
+
+
+
2022-09-14 19:50:01,569 Found 6 citations for tools or databases
+2022-09-14 19:50:07,414 Query 1/1: bax_f85cf (length 192)
+
+
+
COMPLETE: 100%|██████████| 150/150 [elapsed: 00:01 remaining: 00:00]
+
+
+
2022-09-14 19:50:09,525 Running model_1
+2022-09-14 19:51:48,517 model_1 took 94.4s (3 recycles) with pLDDT 80.6 and ptmscore 0.793
+
+
+../../_images/c2a76a87adf318087a2bc6981edf712e273efffc99393ff6992fe1b943838835.png +
2022-09-14 19:53:42,597 Relaxation took 78.4s
+
+
+../../_images/5ba7fb9f5aa0115877c4e750271cd6596d70f7ec021781b68dbbd01b78ca826a.png +
2022-09-14 19:53:42,884 Running model_2
+2022-09-14 19:54:46,588 model_2 took 61.5s (3 recycles) with pLDDT 80.4 and ptmscore 0.803
+
+
+../../_images/0eeb61c92a8a8369c280106ca142d01a7468bb04b642f553d2ddffd6e53bbb06.png +
2022-09-14 19:56:06,648 Relaxation took 71.9s
+
+
+../../_images/92e7e51893be363f2476d175090f73cfedcef8f7cc8f4be891e8a204be562827.png +
2022-09-14 19:56:06,938 Running model_3
+2022-09-14 19:57:10,632 model_3 took 61.5s (3 recycles) with pLDDT 85.1 and ptmscore 0.831
+
+
+../../_images/1c9a112d326603b6a145d3d380e85a1e63ac7617f257de92664452c9410b9e4b.png +
2022-09-14 19:58:27,707 Relaxation took 69.0s
+
+
+../../_images/3a7713484cd113db8a04c3f2186e4fade8d3bb9ab75323d72ef1d4178c84aa0a.png +
2022-09-14 19:58:27,969 Running model_4
+2022-09-14 19:59:31,845 model_4 took 61.7s (3 recycles) with pLDDT 78 and ptmscore 0.757
+
+
+../../_images/1c1081c0c1a13efbb0cddb25af0b1876d8ac443475716485d7736ad5f37a2cd3.png +
2022-09-14 20:00:44,946 Relaxation took 64.8s
+
+
+../../_images/b1654eb5bde2180c42268a8f437445d8ec15158f73abf78755a7a5090445d6b0.png +
2022-09-14 20:00:45,235 Running model_5
+2022-09-14 20:01:49,152 model_5 took 61.7s (3 recycles) with pLDDT 83.5 and ptmscore 0.832
+
+
+../../_images/19da840fc8691bde82401616cf390a239f40ce6d4c01b8a1a95e1df22daab2c3.png +
2022-09-14 20:03:01,405 Relaxation took 64.0s
+
+
+../../_images/0c4540f054eb50af50ff5ed1ce0ca2d38d8d3a85cb91c6b031ae0c2d82cfdc75.png +
2022-09-14 20:03:01,711 reranking models by plddt
+2022-09-14 20:03:04,582 Done
+
+
+
+
+
+
+
#@title Display 3D structure {run: "auto"}
+import py3Dmol
+import glob
+import matplotlib.pyplot as plt
+from colabfold.colabfold import plot_plddt_legend
+rank_num = 1 #@param ["1", "2", "3", "4", "5"] {type:"raw"}
+color = "lDDT" #@param ["chain", "lDDT", "rainbow"]
+show_sidechains = False #@param {type:"boolean"}
+show_mainchains = False #@param {type:"boolean"}
+
+jobname_prefix = ".custom" if msa_mode == "custom" else ""
+if use_amber:
+  pdb_filename = f"{jobname}{jobname_prefix}_relaxed_rank_{rank_num}_model_*.pdb"
+else:
+  pdb_filename = f"{jobname}{jobname_prefix}_unrelaxed_rank_{rank_num}_model_*.pdb"
+
+pdb_file = glob.glob(pdb_filename)
+
+def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color="lDDT"):
+  model_name = f"rank_{rank_num}"
+  view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)
+  view.addModel(open(pdb_file[0],'r').read(),'pdb')
+
+  if color == "lDDT":
+    view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})
+  elif color == "rainbow":
+    view.setStyle({'cartoon': {'color':'spectrum'}})
+  elif color == "chain":
+    chains = len(queries[0][1]) + 1 if is_complex else 1
+    for n,chain,color in zip(range(chains),list("ABCDEFGH"),
+                     ["lime","cyan","magenta","yellow","salmon","white","blue","orange"]):
+      view.setStyle({'chain':chain},{'cartoon': {'color':color}})
+  if show_sidechains:
+    BB = ['C','O','N']
+    view.addStyle({'and':[{'resn':["GLY","PRO"],'invert':True},{'atom':BB,'invert':True}]},
+                        {'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+    view.addStyle({'and':[{'resn':"GLY"},{'atom':'CA'}]},
+                        {'sphere':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+    view.addStyle({'and':[{'resn':"PRO"},{'atom':['C','O'],'invert':True}]},
+                        {'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}})  
+  if show_mainchains:
+    BB = ['C','O','N','CA']
+    view.addStyle({'atom':BB},{'stick':{'colorscheme':f"WhiteCarbon",'radius':0.3}})
+
+  view.zoomTo()
+  return view
+
+
+show_pdb(rank_num,show_sidechains, show_mainchains, color).show()
+if color == "lDDT":
+  plot_plddt_legend().show() 
+
+
+
+
+
+

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
+ jupyter labextension install jupyterlab_3dmol

+
+
../../_images/881216eaf59076a500ab959ea9ac5d26abf22a1d489ce07303016bf35e6ea465.png +
+
+
+
+
#@title Plots {run: "auto"}
+from IPython.display import display, HTML
+import base64
+from html import escape
+
+# see: https://stackoverflow.com/a/53688522
+def image_to_data_url(filename):
+  ext = filename.split('.')[-1]
+  prefix = f'data:image/{ext};base64,'
+  with open(filename, 'rb') as f:
+    img = f.read()
+  return prefix + base64.b64encode(img).decode('utf-8')
+
+pae = image_to_data_url(f"{jobname}{jobname_prefix}_PAE.png")
+cov = image_to_data_url(f"{jobname}{jobname_prefix}_coverage.png")
+plddt = image_to_data_url(f"{jobname}{jobname_prefix}_plddt.png")
+display(HTML(f"""
+<style>
+  img {{
+    float:left;
+  }}
+  .full {{
+    max-width:100%;
+  }}
+  .half {{
+    max-width:50%;
+  }}
+  @media (max-width:640px) {{
+    .half {{
+      max-width:100%;
+    }}
+  }}
+</style>
+<div style="max-width:90%; padding:2em;">
+  <h1>Plots for {escape(jobname)}</h1>
+  <img src="{pae}" class="full" />
+  <img src="{cov}" class="half" />
+  <img src="{plddt}" class="half" />
+</div>
+"""))
+
+
+
+
+
+ +
+

Plots for bax_f85cf

+ + + +
+
+
+
+
+
#@title Package and download results
+#@markdown If you are having issues downloading the result archive, try disabling your adblocker and run this cell again. If that fails click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \"Download\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).
+
+if msa_mode == "custom":
+  print("Don't forget to cite your custom MSA generation method.")
+
+!zip -FSr $jobname".result.zip" config.json $jobname*".json" $jobname*".a3m" $jobname*"relaxed_rank_"*".pdb" "cite.bibtex" $jobname*".png"
+files.download(f"{jobname}.result.zip")
+
+if save_to_google_drive == True and drive:
+  uploaded = drive.CreateFile({'title': f"{jobname}.result.zip"})
+  uploaded.SetContentFile(f"{jobname}.result.zip")
+  uploaded.Upload()
+  print(f"Uploaded {jobname}.result.zip to Google Drive with ID {uploaded.get('id')}")
+
+
+
+
+
  adding: config.json (deflated 49%)
+  adding: bax_f85cf_predicted_aligned_error_v1.json (deflated 89%)
+  adding: bax_f85cf_unrelaxed_rank_1_model_3_scores.json (deflated 70%)
+  adding: bax_f85cf_unrelaxed_rank_2_model_5_scores.json (deflated 70%)
+  adding: bax_f85cf_unrelaxed_rank_3_model_1_scores.json (deflated 69%)
+  adding: bax_f85cf_unrelaxed_rank_4_model_2_scores.json (deflated 70%)
+  adding: bax_f85cf_unrelaxed_rank_5_model_4_scores.json (deflated 69%)
+  adding: bax_f85cf.a3m (deflated 66%)
+  adding: bax_f85cf_relaxed_rank_1_model_3.pdb (deflated 78%)
+  adding: bax_f85cf_relaxed_rank_2_model_5.pdb (deflated 78%)
+  adding: bax_f85cf_relaxed_rank_3_model_1.pdb (deflated 78%)
+  adding: bax_f85cf_relaxed_rank_4_model_2.pdb (deflated 78%)
+  adding: bax_f85cf_relaxed_rank_5_model_4.pdb (deflated 78%)
+  adding: bax_f85cf_unrelaxed_rank_1_model_3.pdb (deflated 78%)
+  adding: bax_f85cf_unrelaxed_rank_2_model_5.pdb (deflated 78%)
+  adding: bax_f85cf_unrelaxed_rank_3_model_1.pdb (deflated 78%)
+  adding: bax_f85cf_unrelaxed_rank_4_model_2.pdb (deflated 78%)
+  adding: bax_f85cf_unrelaxed_rank_5_model_4.pdb (deflated 78%)
+  adding: cite.bibtex (deflated 52%)
+  adding: bax_f85cf_coverage.png (deflated 8%)
+  adding: bax_f85cf_PAE.png (deflated 1%)
+  adding: bax_f85cf_plddt.png (deflated 7%)
+
+
+
+
+
+

Instructions #

+

Quick start

+
    +

  1. Paste your protein sequence(s) in the input field.

    2. +

  2. Press “Runtime” -> “Run all”.

    3. +

  3. The pipeline consists of 5 steps. The currently running step is indicated by a circle with a stop sign next to it.

    4. +
+

Result zip file contents

+
    +

  1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if use_amber is enabled).

    2. +

  2. Plots of the model quality.

    3. +

  3. Plots of the MSA coverage.

    4. +

  4. Parameter log file.

    5. +

  5. A3M formatted input MSA.

    6. +

  6. A predicted_aligned_error_v1.json using AlphaFold-DB’s format and a scores.json for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.

    7. +

  7. BibTeX file with citations for all used tools and databases.

    8. +
+

At the end of the job a download modal box will pop up with a jobname.result.zip file. Additionally, if the save_to_google_drive option was selected, the jobname.result.zip will be uploaded to your Google Drive.

+

MSA generation for complexes

+

For the complex prediction we use unpaired and paired MSAs. Unpaired MSA is generated the same way as for the protein structures prediction by searching the UniRef100 and environmental sequences three iterations each.

+

The paired MSA is generated by searching the UniRef100 database and pairing the best hits sharing the same NCBI taxonomic identifier (=species or sub-species). We only pair sequences if all of the query sequences are present for the respective taxonomic identifier.

+

Using a custom MSA as input

+

To predict the structure with a custom MSA (A3M formatted): (1) Change the msa_mode: to “custom”, (2) Wait for an upload box to appear at the end of the “MSA options …” box. Upload your A3M. The first fasta entry of the A3M must be the query sequence without gaps.

+

It is also possilbe to proide custom MSAs for complex predictions. Read more about the format here.

+

As an alternative for MSA generation the HHblits Toolkit server can be used. After submitting your query, click “Query Template MSA” -> “Download Full A3M”. Download the A3M file and upload it in this notebook.

+

Using custom templates

+

To predict the structure with a custom template (PDB or mmCIF formatted): (1) change the template_mode to “custom” in the execute cell and (2) wait for an upload box to appear at the end of the “Input Protein” box. Select and upload your templates (multiple choices are possible).

+
    +

  • Templates must follow the four letter PDB naming with lower case letters.

    • +

  • Templates in mmCIF format must contain _entity_poly_seq. An error is thrown if this field is not present. The field _pdbx_audit_revision_history.revision_date is automatically generated if it is not present.

    • +

  • Templates in PDB format are automatically converted to the mmCIF format. _entity_poly_seq and _pdbx_audit_revision_history.revision_date are automatically generated.

    • +
+

If you encounter problems, please report them to this issue.

+

Comparison to the full AlphaFold2 and Alphafold2 colab

+

This notebook replaces the homology detection and MSA pairing of AlphaFold2 with MMseqs2. For a comparison against the AlphaFold2 Colab and the full AlphaFold2 system read our preprint.

+

Troubleshooting

+
    +

  • Check that the runtime type is set to GPU at “Runtime” -> “Change runtime type”.

    • +

  • Try to restart the session “Runtime” -> “Factory reset runtime”.

    • +

  • Check your input sequence.

    • +
+

Known issues

+
    +

  • Google Colab assigns different types of GPUs with varying amount of memory. Some might not have enough memory to predict the structure for a long sequence.

    • +

  • Your browser can block the pop-up for downloading the result file. You can choose the save_to_google_drive option to upload to Google Drive instead or manually download the result file: Click on the little folder icon to the left, navigate to file: jobname.result.zip, right-click and select “Download” (see screenshot).

    • +
+

Limitations

+
    +

  • Computing resources: Our MMseqs2 API can handle ~20-50k requests per day.

    • +

  • MSAs: MMseqs2 is very precise and sensitive but might find less hits compared to HHblits/HMMer searched against BFD or MGnify.

    • +

  • We recommend to additionally use the full AlphaFold2 pipeline.

    • +
+

Description of the plots

+
    +

  • Number of sequences per position - We want to see at least 30 sequences per position, for best performance, ideally 100 sequences.

    • +

  • Predicted lDDT per position - model confidence (out of 100) at each position. The higher the better.

    • +

  • Predicted Alignment Error - For homooligomers, this could be a useful metric to assess how confident the model is about the interface. The lower the better.

    • +
+

Bugs

+ +

License

+

The source code of ColabFold is licensed under MIT. Additionally, this notebook uses the AlphaFold2 source code and its parameters licensed under Apache 2.0 and CC BY 4.0 respectively. Read more about the AlphaFold license here.

+

Acknowledgments

+
    +

  • We thank the AlphaFold team for developing an excellent model and open sourcing the software.

    • +

  • KOBIC and Söding Lab for providing the computational resources for the MMseqs2 MSA server.

    • +

  • Richard Evans for helping to benchmark the ColabFold’s Alphafold-multimer support.

    • +

  • David Koes for his awesome py3Dmol plugin, without whom these notebooks would be quite boring!

    • +

  • Do-Yoon Kim for creating the ColabFold logo.

    • +

  • A colab by Sergey Ovchinnikov (@sokrypton), Milot Mirdita (@milot_mirdita) and Martin Steinegger (@thesteinegger).

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b/_images/f65c685391c095fc6944b7e34723544d79938dec21f0d4d5f9f930b0c1266b97.png differ diff --git a/_sources/ColabFold/AlphaFold2.ipynb b/_sources/ColabFold/AlphaFold2.ipynb new file mode 100644 index 0000000..1c67ded --- /dev/null +++ b/_sources/ColabFold/AlphaFold2.ipynb @@ -0,0 +1,759 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [], + "machine_shape": "hm", + "gpuType": "T4", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G4yBrceuFbf3" + }, + "source": [ + "\n", + "\n", + "##ColabFold v1.5.3: AlphaFold2 using MMseqs2\n", + "\n", + "Easy to use protein structure and complex prediction using [AlphaFold2](https://www.nature.com/articles/s41586-021-03819-2) and [Alphafold2-multimer](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1). Sequence alignments/templates are generated through [MMseqs2](mmseqs.com) and [HHsearch](https://github.com/soedinglab/hh-suite). For more details, see bottom of the notebook, checkout the [ColabFold GitHub](https://github.com/sokrypton/ColabFold) and read our manuscript.\n", + "Old versions: [v1.4](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.4.0/AlphaFold2.ipynb), [v1.5.1](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.1/AlphaFold2.ipynb), [v1.5.2](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.2/AlphaFold2.ipynb)\n", + "\n", + "[Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all.\n", + "*Nature Methods*, 2022](https://www.nature.com/articles/s41592-022-01488-1)\n", + "\n", + "-----------\n", + "\n", + "### News\n", + "- 2023/07/31: The ColabFold MSA server is back to normal. It was using older DB (UniRef30 2202/PDB70 220313) from 27th ~8:30 AM CEST to 31st ~11:10 AM CEST.\n", + "- 2023/06/12: New databases! UniRef30 updated to 2023_02 and PDB to 230517. We now use PDB100 instead of PDB70 (see [notes](#pdb100)).\n", + "- 2023/06/12: We introduced a new default pairing strategy: Previously, for multimer predictions with more than 2 chains, we only pair if all sequences taxonomically match (\"complete\" pairing). The new default \"greedy\" strategy pairs any taxonomically matching subsets." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kOblAo-xetgx", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fd5d6895-cd56-4678-8e88-cb82cca50871", + "collapsed": true + }, + "source": [ + "# @title Input protein sequence(s), then hit `Runtime` -> `Run all` { display-mode: \"form\" }\n", + "from google.colab import files\n", + "import os\n", + "import re\n", + "import hashlib\n", + "import random\n", + "\n", + "from sys import version_info\n", + "python_version = f\"{version_info.major}.{version_info.minor}\"\n", + "\n", + "def add_hash(x,y):\n", + " return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n", + "\n", + "query_sequence = 'PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASK' #@param {type:\"string\"}\n", + "#@markdown - Use `:` to specify inter-protein chainbreaks for **modeling complexes** (supports homo- and hetro-oligomers). For example **PI...SK:PI...SK** for a homodimer\n", + "jobname = 'test' #@param {type:\"string\"}\n", + "# number of models to use\n", + "num_relax = 0 #@param [0, 1, 5] {type:\"raw\"}\n", + "#@markdown - specify how many of the top ranked structures to relax using amber\n", + "template_mode = \"none\" #@param [\"none\", \"pdb100\",\"custom\"]\n", + "#@markdown - `none` = no template information is used. `pdb100` = detect templates in pdb100 (see [notes](#pdb100)). `custom` - upload and search own templates (PDB or mmCIF format, see [notes](#custom_templates))\n", + "\n", + "use_amber = num_relax > 0\n", + "\n", + "# remove whitespaces\n", + "query_sequence = \"\".join(query_sequence.split())\n", + "\n", + "basejobname = \"\".join(jobname.split())\n", + "basejobname = re.sub(r'\\W+', '', basejobname)\n", + "jobname = add_hash(basejobname, query_sequence)\n", + "\n", + "# check if directory with jobname exists\n", + "def check(folder):\n", + " if os.path.exists(folder):\n", + " return False\n", + " else:\n", + " return True\n", + "if not check(jobname):\n", + " n = 0\n", + " while not check(f\"{jobname}_{n}\"): n += 1\n", + " jobname = f\"{jobname}_{n}\"\n", + "\n", + "# make directory to save results\n", + "os.makedirs(jobname, exist_ok=True)\n", + "\n", + "# save queries\n", + "queries_path = os.path.join(jobname, f\"{jobname}.csv\")\n", + "with open(queries_path, \"w\") as text_file:\n", + " text_file.write(f\"id,sequence\\n{jobname},{query_sequence}\")\n", + "\n", + "if template_mode == \"pdb100\":\n", + " use_templates = True\n", + " custom_template_path = None\n", + "elif template_mode == \"custom\":\n", + " custom_template_path = os.path.join(jobname,f\"template\")\n", + " os.makedirs(custom_template_path, exist_ok=True)\n", + " uploaded = files.upload()\n", + " use_templates = True\n", + " for fn in uploaded.keys():\n", + " os.rename(fn,os.path.join(custom_template_path,fn))\n", + "else:\n", + " custom_template_path = None\n", + " use_templates = False\n", + "\n", + "print(\"jobname\",jobname)\n", + "print(\"sequence\",query_sequence)\n", + "print(\"length\",len(query_sequence.replace(\":\",\"\")))\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "jobname test_a5e17\n", + "sequence PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASK\n", + "length 59\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# @title Install dependencies { display-mode: \"form\" }\n", + "%%time\n", + "import os\n", + "USE_AMBER = use_amber\n", + "USE_TEMPLATES = use_templates\n", + "PYTHON_VERSION = python_version\n", + "\n", + "if not os.path.isfile(\"COLABFOLD_READY\"):\n", + " print(\"installing colabfold...\")\n", + " os.system(\"pip install -q --no-warn-conflicts 'colabfold[alphafold-minus-jax] @ git+https://github.com/sokrypton/ColabFold'\")\n", + " os.system(\"pip install --upgrade dm-haiku\")\n", + " os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/colabfold colabfold\")\n", + " os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/alphafold alphafold\")\n", + " # patch for jax > 0.3.25\n", + " os.system(\"sed -i 's/weights = jax.nn.softmax(logits)/logits=jnp.clip(logits,-1e8,1e8);weights=jax.nn.softmax(logits)/g' alphafold/model/modules.py\")\n", + " os.system(\"touch COLABFOLD_READY\")\n", + "\n", + "if USE_AMBER or USE_TEMPLATES:\n", + " if not os.path.isfile(\"CONDA_READY\"):\n", + " print(\"installing conda...\")\n", + " os.system(\"wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh\")\n", + " os.system(\"bash Mambaforge-Linux-x86_64.sh -bfp /usr/local\")\n", + " os.system(\"mamba config --set auto_update_conda false\")\n", + " os.system(\"touch CONDA_READY\")\n", + "\n", + "if USE_TEMPLATES and not os.path.isfile(\"HH_READY\") and USE_AMBER and not os.path.isfile(\"AMBER_READY\"):\n", + " print(\"installing hhsuite and amber...\")\n", + " os.system(f\"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer\")\n", + " os.system(\"touch HH_READY\")\n", + " os.system(\"touch AMBER_READY\")\n", + "else:\n", + " if USE_TEMPLATES and not os.path.isfile(\"HH_READY\"):\n", + " print(\"installing hhsuite...\")\n", + " os.system(f\"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 python='{PYTHON_VERSION}'\")\n", + " os.system(\"touch HH_READY\")\n", + " if USE_AMBER and not os.path.isfile(\"AMBER_READY\"):\n", + " print(\"installing amber...\")\n", + " os.system(f\"mamba install -y -c conda-forge openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer\")\n", + " os.system(\"touch AMBER_READY\")" + ], + "metadata": { + "id": "AzIKiDiCaHAn", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5b6b9ab0-af96-45ef-c903-43ae13525af0", + "collapsed": true + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "installing colabfold...\n", + "CPU times: user 114 ms, sys: 27.2 ms, total: 142 ms\n", + "Wall time: 42.9 s\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#@markdown ### MSA options (custom MSA upload, single sequence, pairing mode)\n", + "msa_mode = \"mmseqs2_uniref_env\" #@param [\"mmseqs2_uniref_env\", \"mmseqs2_uniref\",\"single_sequence\",\"custom\"]\n", + "pair_mode = \"unpaired_paired\" #@param [\"unpaired_paired\",\"paired\",\"unpaired\"] {type:\"string\"}\n", + "#@markdown - \"unpaired_paired\" = pair sequences from same species + unpaired MSA, \"unpaired\" = seperate MSA for each chain, \"paired\" - only use paired sequences.\n", + "\n", + "# decide which a3m to use\n", + "if \"mmseqs2\" in msa_mode:\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.a3m\")\n", + "\n", + "elif msa_mode == \"custom\":\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.custom.a3m\")\n", + " if not os.path.isfile(a3m_file):\n", + " custom_msa_dict = files.upload()\n", + " custom_msa = list(custom_msa_dict.keys())[0]\n", + " header = 0\n", + " import fileinput\n", + " for line in fileinput.FileInput(custom_msa,inplace=1):\n", + " if line.startswith(\">\"):\n", + " header = header + 1\n", + " if not line.rstrip():\n", + " continue\n", + " if line.startswith(\">\") == False and header == 1:\n", + " query_sequence = line.rstrip()\n", + " print(line, end='')\n", + "\n", + " os.rename(custom_msa, a3m_file)\n", + " queries_path=a3m_file\n", + " print(f\"moving {custom_msa} to {a3m_file}\")\n", + "\n", + "else:\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.single_sequence.a3m\")\n", + " with open(a3m_file, \"w\") as text_file:\n", + " text_file.write(\">1\\n%s\" % query_sequence)" + ], + "metadata": { + "id": "C2_sh2uAonJH", + "cellView": "form" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# @title { display-mode: \"form\" }\n", + "#@markdown ### Advanced settings\n", + "model_type = \"auto\" #@param [\"auto\", \"alphafold2_ptm\", \"alphafold2_multimer_v1\", \"alphafold2_multimer_v2\", \"alphafold2_multimer_v3\"]\n", + "#@markdown - if `auto` selected, will use `alphafold2_ptm` for monomer prediction and `alphafold2_multimer_v3` for complex prediction.\n", + "#@markdown Any of the mode_types can be used (regardless if input is monomer or complex).\n", + "num_recycles = \"3\" #@param [\"auto\", \"0\", \"1\", \"3\", \"6\", \"12\", \"24\", \"48\"]\n", + "#@markdown - if `auto` selected, will use `num_recycles=20` if `model_type=alphafold2_multimer_v3`, else `num_recycles=3` .\n", + "recycle_early_stop_tolerance = \"auto\" #@param [\"auto\", \"0.0\", \"0.5\", \"1.0\"]\n", + "#@markdown - if `auto` selected, will use `tol=0.5` if `model_type=alphafold2_multimer_v3` else `tol=0.0`.\n", + "relax_max_iterations = 200 #@param [0, 200, 2000] {type:\"raw\"}\n", + "#@markdown - max amber relax iterations, `0` = unlimited (AlphaFold2 default, can take very long)\n", + "pairing_strategy = \"greedy\" #@param [\"greedy\", \"complete\"] {type:\"string\"}\n", + "#@markdown - `greedy` = pair any taxonomically matching subsets, `complete` = all sequences have to match in one line.\n", + "\n", + "\n", + "#@markdown #### Sample settings\n", + "#@markdown - enable dropouts and increase number of seeds to sample predictions from uncertainty of the model.\n", + "#@markdown - decrease `max_msa` to increase uncertainity\n", + "max_msa = \"auto\" #@param [\"auto\", \"512:1024\", \"256:512\", \"64:128\", \"32:64\", \"16:32\"]\n", + "num_seeds = 1 #@param [1,2,4,8,16] {type:\"raw\"}\n", + "use_dropout = False #@param {type:\"boolean\"}\n", + "\n", + "num_recycles = None if num_recycles == \"auto\" else int(num_recycles)\n", + "recycle_early_stop_tolerance = None if recycle_early_stop_tolerance == \"auto\" else float(recycle_early_stop_tolerance)\n", + "if max_msa == \"auto\": max_msa = None\n", + "\n", + "#@markdown #### Save settings\n", + "save_all = False #@param {type:\"boolean\"}\n", + "save_recycles = False #@param {type:\"boolean\"}\n", + "save_to_google_drive = False #@param {type:\"boolean\"}\n", + "#@markdown - if the save_to_google_drive option was selected, the result zip will be uploaded to your Google Drive\n", + "dpi = 200 #@param {type:\"integer\"}\n", + "#@markdown - set dpi for image resolution\n", + "\n", + "if save_to_google_drive:\n", + " from pydrive.drive import GoogleDrive\n", + " from pydrive.auth import GoogleAuth\n", + " from google.colab import auth\n", + " from oauth2client.client import GoogleCredentials\n", + " auth.authenticate_user()\n", + " gauth = GoogleAuth()\n", + " gauth.credentials = GoogleCredentials.get_application_default()\n", + " drive = GoogleDrive(gauth)\n", + " print(\"You are logged into Google Drive and are good to go!\")\n", + "\n", + "#@markdown Don't forget to hit `Runtime` -> `Run all` after updating the form." + ], + "metadata": { + "id": "ADDuaolKmjGW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#@title Run Prediction\n", + "display_images = True #@param {type:\"boolean\"}\n", + "\n", + "import sys\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)\n", + "from Bio import BiopythonDeprecationWarning\n", + "warnings.simplefilter(action='ignore', category=BiopythonDeprecationWarning)\n", + "from pathlib import Path\n", + "from colabfold.download import download_alphafold_params, default_data_dir\n", + "from colabfold.utils import setup_logging\n", + "from colabfold.batch import get_queries, run, set_model_type\n", + "from colabfold.plot import plot_msa_v2\n", + "\n", + "import os\n", + "import numpy as np\n", + "try:\n", + " K80_chk = os.popen('nvidia-smi | grep \"Tesla K80\" | wc -l').read()\n", + "except:\n", + " K80_chk = \"0\"\n", + " pass\n", + "if \"1\" in K80_chk:\n", + " print(\"WARNING: found GPU Tesla K80: limited to total length < 1000\")\n", + " if \"TF_FORCE_UNIFIED_MEMORY\" in os.environ:\n", + " del os.environ[\"TF_FORCE_UNIFIED_MEMORY\"]\n", + " if \"XLA_PYTHON_CLIENT_MEM_FRACTION\" in os.environ:\n", + " del os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"]\n", + "\n", + "from colabfold.colabfold import plot_protein\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# For some reason we need that to get pdbfixer to import\n", + "if use_amber and f\"/usr/local/lib/python{python_version}/site-packages/\" not in sys.path:\n", + " sys.path.insert(0, f\"/usr/local/lib/python{python_version}/site-packages/\")\n", + "\n", + "def input_features_callback(input_features):\n", + " if display_images:\n", + " plot_msa_v2(input_features)\n", + " plt.show()\n", + " plt.close()\n", + "\n", + "def prediction_callback(protein_obj, length,\n", + " prediction_result, input_features, mode):\n", + " model_name, relaxed = mode\n", + " if not relaxed:\n", + " if display_images:\n", + " fig = plot_protein(protein_obj, Ls=length, dpi=150)\n", + " plt.show()\n", + " plt.close()\n", + "\n", + "result_dir = jobname\n", + "log_filename = os.path.join(jobname,\"log.txt\")\n", + "setup_logging(Path(log_filename))\n", + "\n", + "queries, is_complex = get_queries(queries_path)\n", + "model_type = set_model_type(is_complex, model_type)\n", + "\n", + "if \"multimer\" in model_type and max_msa is not None:\n", + " use_cluster_profile = False\n", + "else:\n", + " use_cluster_profile = True\n", + "\n", + "download_alphafold_params(model_type, Path(\".\"))\n", + "results = run(\n", + " queries=queries,\n", + " result_dir=result_dir,\n", + " use_templates=use_templates,\n", + " custom_template_path=custom_template_path,\n", + " num_relax=num_relax,\n", + " msa_mode=msa_mode,\n", + " model_type=model_type,\n", + " num_models=5,\n", + " num_recycles=num_recycles,\n", + " relax_max_iterations=relax_max_iterations,\n", + " recycle_early_stop_tolerance=recycle_early_stop_tolerance,\n", + " num_seeds=num_seeds,\n", + " use_dropout=use_dropout,\n", + " model_order=[1,2,3,4,5],\n", + " is_complex=is_complex,\n", + " data_dir=Path(\".\"),\n", + " keep_existing_results=False,\n", + " rank_by=\"auto\",\n", + " pair_mode=pair_mode,\n", + " pairing_strategy=pairing_strategy,\n", + " stop_at_score=float(100),\n", + " prediction_callback=prediction_callback,\n", + " dpi=dpi,\n", + " zip_results=False,\n", + " save_all=save_all,\n", + " max_msa=max_msa,\n", + " use_cluster_profile=use_cluster_profile,\n", + " input_features_callback=input_features_callback,\n", + " save_recycles=save_recycles,\n", + " user_agent=\"colabfold/google-colab-main\",\n", + ")\n", + "results_zip = f\"{jobname}.result.zip\"\n", + "os.system(f\"zip -r {results_zip} {jobname}\")" + ], + "metadata": { + "id": "mbaIO9pWjaN0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "9beb72b5-c2a3-4e28-a80e-f9583095f4ff", + "collapsed": true, + "cellView": "form" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Downloading alphafold2 weights to .: 100%|██████████| 3.47G/3.47G [02:40<00:00, 23.2MB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-12-07 22:02:42,821 Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: \"rocm\". Available platform names are: CUDA\n", + "2023-12-07 22:02:42,823 Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n", + "2023-12-07 22:02:44,726 Running on GPU\n", + "2023-12-07 22:02:44,904 Found 4 citations for tools or databases\n", + "2023-12-07 22:02:44,904 Query 1/1: test_a5e17 (length 59)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "COMPLETE: 100%|██████████| 150/150 [elapsed: 00:01 remaining: 00:00]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-12-07 22:02:47,182 Setting max_seq=512, max_extra_seq=5120\n" + ] + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0mdownload_alphafold_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m results = run(\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0mqueries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqueries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mresult_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/content/colabfold/batch.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(queries, result_dir, num_models, is_complex, num_recycles, recycle_early_stop_tolerance, model_order, num_ensemble, model_type, msa_mode, use_templates, custom_template_path, num_relax, relax_max_iterations, relax_tolerance, relax_stiffness, relax_max_outer_iterations, keep_existing_results, rank_by, pair_mode, pairing_strategy, data_dir, host_url, user_agent, random_seed, num_seeds, recompile_padding, zip_results, prediction_callback, save_single_representations, save_pair_representations, save_all, save_recycles, use_dropout, use_gpu_relax, stop_at_score, dpi, max_seq, max_extra_seq, pdb_hit_file, local_pdb_path, use_cluster_profile, feature_dict_callback, **kwargs)\u001b[0m\n\u001b[1;32m 1568\u001b[0m \u001b[0mfirst_job\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1570\u001b[0;31m results = predict_structure(\n\u001b[0m\u001b[1;32m 1571\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjobname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1572\u001b[0m \u001b[0mresult_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/content/colabfold/batch.py\u001b[0m in \u001b[0;36mpredict_structure\u001b[0;34m(prefix, result_dir, feature_dict, is_complex, use_templates, sequences_lengths, pad_len, model_type, model_runner_and_params, num_relax, relax_max_iterations, relax_tolerance, relax_stiffness, relax_max_outer_iterations, rank_by, random_seed, num_seeds, stop_at_score, prediction_callback, use_gpu_relax, save_all, save_single_representations, save_pair_representations, save_recycles)\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0;31m# predict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecycles\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m model_runner.predict(input_features,\n\u001b[0m\u001b[1;32m 422\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 423\u001b[0m \u001b[0mreturn_representations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_representations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/content/alphafold/model/model.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, feat, random_seed, return_representations, callback)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;31m# run\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msub_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjax\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprev\u001b[0m \u001b[0;34m=\u001b[0m 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name, jaxpr, effects, create_tokens, public, replace_tokens_with_dummy, replicated_args, arg_shardings, result_shardings, use_sharding_annotations, input_output_aliases, num_output_tokens, api_name, arg_names, result_names, arg_memory_kinds, result_memory_kinds)\u001b[0m\n\u001b[1;32m 1211\u001b[0m \u001b[0mcallee_name_stack\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrap_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapi_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1212\u001b[0m \u001b[0mconsts\u001b[0m \u001b[0;34m=\u001b[0m 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consts, *args, dim_var_values=dim_var_values)\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py\u001b[0m in \u001b[0;36mjaxpr_subcomp\u001b[0;34m(ctx, jaxpr, tokens, consts, dim_var_values, *args)\u001b[0m\n\u001b[1;32m 1429\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplatforms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1430\u001b[0m \u001b[0;31m# Classic, single-platform lowering\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1431\u001b[0;31m \u001b[0mans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrule_ctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrule_inputs\u001b[0m\u001b[0;34m,\u001b[0m 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lowering\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1431\u001b[0;31m \u001b[0mans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrule_ctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrule_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0meqn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1432\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1433\u001b[0m ans = lower_multi_platform(rule_ctx, str(eqn), rules,\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/jax/_src/lax/control_flow/loops.py\u001b[0m in \u001b[0;36m_while_lowering\u001b[0;34m(ctx, cond_jaxpr, body_jaxpr, cond_nconsts, body_nconsts, *args)\u001b[0m\n\u001b[1;32m 1670\u001b[0m body_consts = [mlir.ir_constants(xla.canonicalize_dtype(x))\n\u001b[1;32m 1671\u001b[0m for x in body_jaxpr.consts]\n\u001b[0;32m-> 1672\u001b[0;31m new_z, tokens_out = mlir.jaxpr_subcomp(body_ctx, body_jaxpr.jaxpr,\n\u001b[0m\u001b[1;32m 1673\u001b[0m tokens_in, body_consts, *(y + z), dim_var_values=ctx.dim_var_values)\n\u001b[1;32m 1674\u001b[0m \u001b[0mout_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtokens_out\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meff\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0meff\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbody_effects\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py\u001b[0m in \u001b[0;36mjaxpr_subcomp\u001b[0;34m(ctx, jaxpr, tokens, consts, dim_var_values, *args)\u001b[0m\n\u001b[1;32m 1429\u001b[0m \u001b[0;32mif\u001b[0m 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lower_multi_platform(rule_ctx, str(eqn), rules,\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py\u001b[0m in \u001b[0;36mf_lowered\u001b[0;34m(ctx, *args, **params)\u001b[0m\n\u001b[1;32m 1626\u001b[0m \u001b[0;31m# TODO(frostig,mattjj): check ctx.avals_out against jaxpr avals out?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1628\u001b[0;31m out, tokens = jaxpr_subcomp(\n\u001b[0m\u001b[1;32m 1629\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule_context\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokens_in\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ir_consts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconsts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1630\u001b[0m *map(wrap_singleton_ir_values, args), dim_var_values=ctx.dim_var_values)\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py\u001b[0m in \u001b[0;36mjaxpr_subcomp\u001b[0;34m(ctx, jaxpr, tokens, consts, dim_var_values, *args)\u001b[0m\n\u001b[1;32m 1429\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplatforms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1430\u001b[0m \u001b[0;31m# Classic, single-platform lowering\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1431\u001b[0;31m \u001b[0mans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrule_ctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrule_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0meqn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1432\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1433\u001b[0m ans = lower_multi_platform(rule_ctx, str(eqn), rules,\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/jax/_src/lax/control_flow/loops.py\u001b[0m in \u001b[0;36m_while_lowering\u001b[0;34m(ctx, cond_jaxpr, body_jaxpr, cond_nconsts, body_nconsts, *args)\u001b[0m\n\u001b[1;32m 1632\u001b[0m \u001b[0mmlir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mir_constants\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxla\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanonicalize_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcond_jaxpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconsts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1633\u001b[0m ]\n\u001b[0;32m-> 1634\u001b[0;31m ((pred,),), _ = mlir.jaxpr_subcomp(\n\u001b[0m\u001b[1;32m 1635\u001b[0m \u001b[0mcond_ctx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1636\u001b[0m \u001b[0mcond_jaxpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/jax/_src/interpreters/mlir.py\u001b[0m in \u001b[0;36mjaxpr_subcomp\u001b[0;34m(ctx, jaxpr, tokens, consts, dim_var_values, *args)\u001b[0m\n\u001b[1;32m 1313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1315\u001b[0;31m def jaxpr_subcomp(ctx: ModuleContext, jaxpr: core.Jaxpr,\n\u001b[0m\u001b[1;32m 1316\u001b[0m \u001b[0mtokens\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTokenSet\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1317\u001b[0m \u001b[0mconsts\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mValue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KK7X9T44pWb7", + "cellView": "form" + }, + "source": [ + "#@title Display 3D structure {run: \"auto\"}\n", + "import py3Dmol\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "from colabfold.colabfold import plot_plddt_legend\n", + "from colabfold.colabfold import pymol_color_list, alphabet_list\n", + "rank_num = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n", + "color = \"lDDT\" #@param [\"chain\", \"lDDT\", \"rainbow\"]\n", + "show_sidechains = False #@param {type:\"boolean\"}\n", + "show_mainchains = False #@param {type:\"boolean\"}\n", + "\n", + "tag = results[\"rank\"][0][rank_num - 1]\n", + "jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n", + "pdb_filename = f\"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb\"\n", + "pdb_file = glob.glob(pdb_filename)\n", + "\n", + "def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color=\"lDDT\"):\n", + " model_name = f\"rank_{rank_num}\"\n", + " view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)\n", + " view.addModel(open(pdb_file[0],'r').read(),'pdb')\n", + "\n", + " if color == \"lDDT\":\n", + " view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})\n", + " elif color == \"rainbow\":\n", + " view.setStyle({'cartoon': {'color':'spectrum'}})\n", + " elif color == \"chain\":\n", + " chains = len(queries[0][1]) + 1 if is_complex else 1\n", + " for n,chain,color in zip(range(chains),alphabet_list,pymol_color_list):\n", + " view.setStyle({'chain':chain},{'cartoon': {'color':color}})\n", + "\n", + " if show_sidechains:\n", + " BB = ['C','O','N']\n", + " view.addStyle({'and':[{'resn':[\"GLY\",\"PRO\"],'invert':True},{'atom':BB,'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"GLY\"},{'atom':'CA'}]},\n", + " {'sphere':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"PRO\"},{'atom':['C','O'],'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " if show_mainchains:\n", + " BB = ['C','O','N','CA']\n", + " view.addStyle({'atom':BB},{'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + "\n", + " view.zoomTo()\n", + " return view\n", + "\n", + "show_pdb(rank_num, show_sidechains, show_mainchains, color).show()\n", + "if color == \"lDDT\":\n", + " plot_plddt_legend().show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "11l8k--10q0C", + "cellView": "form" + }, + "source": [ + "#@title Plots {run: \"auto\"}\n", + "from IPython.display import display, HTML\n", + "import base64\n", + "from html import escape\n", + "\n", + "# see: https://stackoverflow.com/a/53688522\n", + "def image_to_data_url(filename):\n", + " ext = filename.split('.')[-1]\n", + " prefix = f'data:image/{ext};base64,'\n", + " with open(filename, 'rb') as f:\n", + " img = f.read()\n", + " return prefix + base64.b64encode(img).decode('utf-8')\n", + "\n", + "pae = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_pae.png\"))\n", + "cov = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_coverage.png\"))\n", + "plddt = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_plddt.png\"))\n", + "display(HTML(f\"\"\"\n", + "\n", + "
\n", + "

Plots for {escape(jobname)}

\n", + " \n", + " \n", + " \n", + "
\n", + "\"\"\"))\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "33g5IIegij5R", + "cellView": "form" + }, + "source": [ + "#@title Package and download results\n", + "#@markdown If you are having issues downloading the result archive, try disabling your adblocker and run this cell again. If that fails click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \\\"Download\\\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).\n", + "\n", + "if msa_mode == \"custom\":\n", + " print(\"Don't forget to cite your custom MSA generation method.\")\n", + "\n", + "files.download(f\"{jobname}.result.zip\")\n", + "\n", + "if save_to_google_drive == True and drive:\n", + " uploaded = drive.CreateFile({'title': f\"{jobname}.result.zip\"})\n", + " uploaded.SetContentFile(f\"{jobname}.result.zip\")\n", + " uploaded.Upload()\n", + " print(f\"Uploaded {jobname}.result.zip to Google Drive with ID {uploaded.get('id')}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UGUBLzB3C6WN", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Instructions \n", + "**Quick start**\n", + "1. Paste your protein sequence(s) in the input field.\n", + "2. Press \"Runtime\" -> \"Run all\".\n", + "3. The pipeline consists of 5 steps. The currently running step is indicated by a circle with a stop sign next to it.\n", + "\n", + "**Result zip file contents**\n", + "\n", + "1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if `use_amber` is enabled).\n", + "2. Plots of the model quality.\n", + "3. Plots of the MSA coverage.\n", + "4. Parameter log file.\n", + "5. A3M formatted input MSA.\n", + "6. A `predicted_aligned_error_v1.json` using [AlphaFold-DB's format](https://alphafold.ebi.ac.uk/faq#faq-7) and a `scores.json` for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.\n", + "7. BibTeX file with citations for all used tools and databases.\n", + "\n", + "At the end of the job a download modal box will pop up with a `jobname.result.zip` file. Additionally, if the `save_to_google_drive` option was selected, the `jobname.result.zip` will be uploaded to your Google Drive.\n", + "\n", + "**MSA generation for complexes**\n", + "\n", + "For the complex prediction we use unpaired and paired MSAs. Unpaired MSA is generated the same way as for the protein structures prediction by searching the UniRef100 and environmental sequences three iterations each.\n", + "\n", + "The paired MSA is generated by searching the UniRef100 database and pairing the best hits sharing the same NCBI taxonomic identifier (=species or sub-species). We only pair sequences if all of the query sequences are present for the respective taxonomic identifier.\n", + "\n", + "**Using a custom MSA as input**\n", + "\n", + "To predict the structure with a custom MSA (A3M formatted): (1) Change the `msa_mode`: to \"custom\", (2) Wait for an upload box to appear at the end of the \"MSA options ...\" box. Upload your A3M. The first fasta entry of the A3M must be the query sequence without gaps.\n", + "\n", + "It is also possilbe to proide custom MSAs for complex predictions. Read more about the format [here](https://github.com/sokrypton/ColabFold/issues/76).\n", + "\n", + "As an alternative for MSA generation the [HHblits Toolkit server](https://toolkit.tuebingen.mpg.de/tools/hhblits) can be used. After submitting your query, click \"Query Template MSA\" -> \"Download Full A3M\". Download the A3M file and upload it in this notebook.\n", + "\n", + "**PDB100** \n", + "\n", + "As of 23/06/08, we have transitioned from using the PDB70 to a 100% clustered PDB, the PDB100. The construction methodology of PDB100 differs from that of PDB70.\n", + "\n", + "The PDB70 was constructed by running each PDB70 representative sequence through [HHblits](https://github.com/soedinglab/hh-suite) against the [Uniclust30](https://uniclust.mmseqs.com/). On the other hand, the PDB100 is built by searching each PDB100 representative structure with [Foldseek](https://github.com/steineggerlab/foldseek) against the [AlphaFold Database](https://alphafold.ebi.ac.uk).\n", + "\n", + "To maintain compatibility with older Notebook versions and local installations, the generated files and API responses will continue to be named \"PDB70\", even though we're now using the PDB100.\n", + "\n", + "**Using custom templates** \n", + "\n", + "To predict the structure with a custom template (PDB or mmCIF formatted): (1) change the `template_mode` to \"custom\" in the execute cell and (2) wait for an upload box to appear at the end of the \"Input Protein\" box. Select and upload your templates (multiple choices are possible).\n", + "\n", + "* Templates must follow the four letter PDB naming with lower case letters.\n", + "\n", + "* Templates in mmCIF format must contain `_entity_poly_seq`. An error is thrown if this field is not present. The field `_pdbx_audit_revision_history.revision_date` is automatically generated if it is not present.\n", + "\n", + "* Templates in PDB format are automatically converted to the mmCIF format. `_entity_poly_seq` and `_pdbx_audit_revision_history.revision_date` are automatically generated.\n", + "\n", + "If you encounter problems, please report them to this [issue](https://github.com/sokrypton/ColabFold/issues/177).\n", + "\n", + "**Comparison to the full AlphaFold2 and AlphaFold2 Colab**\n", + "\n", + "This notebook replaces the homology detection and MSA pairing of AlphaFold2 with MMseqs2. For a comparison against the [AlphaFold2 Colab](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb) and the full [AlphaFold2](https://github.com/deepmind/alphafold) system read our [paper](https://www.nature.com/articles/s41592-022-01488-1).\n", + "\n", + "**Troubleshooting**\n", + "* Check that the runtime type is set to GPU at \"Runtime\" -> \"Change runtime type\".\n", + "* Try to restart the session \"Runtime\" -> \"Factory reset runtime\".\n", + "* Check your input sequence.\n", + "\n", + "**Known issues**\n", + "* Google Colab assigns different types of GPUs with varying amount of memory. Some might not have enough memory to predict the structure for a long sequence.\n", + "* Your browser can block the pop-up for downloading the result file. You can choose the `save_to_google_drive` option to upload to Google Drive instead or manually download the result file: Click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \\\"Download\\\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).\n", + "\n", + "**Limitations**\n", + "* Computing resources: Our MMseqs2 API can handle ~20-50k requests per day.\n", + "* MSAs: MMseqs2 is very precise and sensitive but might find less hits compared to HHblits/HMMer searched against BFD or MGnify.\n", + "* We recommend to additionally use the full [AlphaFold2 pipeline](https://github.com/deepmind/alphafold).\n", + "\n", + "**Description of the plots**\n", + "* **Number of sequences per position** - We want to see at least 30 sequences per position, for best performance, ideally 100 sequences.\n", + "* **Predicted lDDT per position** - model confidence (out of 100) at each position. The higher the better.\n", + "* **Predicted Alignment Error** - For homooligomers, this could be a useful metric to assess how confident the model is about the interface. The lower the better.\n", + "\n", + "**Bugs**\n", + "- If you encounter any bugs, please report the issue to https://github.com/sokrypton/ColabFold/issues\n", + "\n", + "**License**\n", + "\n", + "The source code of ColabFold is licensed under [MIT](https://raw.githubusercontent.com/sokrypton/ColabFold/main/LICENSE). Additionally, this notebook uses the AlphaFold2 source code and its parameters licensed under [Apache 2.0](https://raw.githubusercontent.com/deepmind/alphafold/main/LICENSE) and [CC BY 4.0](https://creativecommons.org/licenses/by-sa/4.0/) respectively. Read more about the AlphaFold license [here](https://github.com/deepmind/alphafold).\n", + "\n", + "**Acknowledgments**\n", + "- We thank the AlphaFold team for developing an excellent model and open sourcing the software.\n", + "\n", + "- [KOBIC](https://kobic.re.kr) and [Söding Lab](https://www.mpinat.mpg.de/soeding) for providing the computational resources for the MMseqs2 MSA server.\n", + "\n", + "- Richard Evans for helping to benchmark the ColabFold's Alphafold-multimer support.\n", + "\n", + "- [David Koes](https://github.com/dkoes) for his awesome [py3Dmol](https://3dmol.csb.pitt.edu/) plugin, without whom these notebooks would be quite boring!\n", + "\n", + "- Do-Yoon Kim for creating the ColabFold logo.\n", + "\n", + "- A colab by Sergey Ovchinnikov ([@sokrypton](https://twitter.com/sokrypton)), Milot Mirdita ([@milot_mirdita](https://twitter.com/milot_mirdita)) and Martin Steinegger ([@thesteinegger](https://twitter.com/thesteinegger)).\n" + ] + } + ] +} \ No newline at end of file diff --git a/_sources/ColabFold/Tutorial_Monomer.ipynb b/_sources/ColabFold/Tutorial_Monomer.ipynb new file mode 100644 index 0000000..fb770d9 --- /dev/null +++ b/_sources/ColabFold/Tutorial_Monomer.ipynb @@ -0,0 +1,1132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "\n", + "# @title **Install Python Packages** { run: \"auto\", display-mode: \"form\" }\n", + "\n", + "import os\n", + "from sys import version_info\n", + "PYTHON_VERSION = f\"{version_info.major}.{version_info.minor}\"\n", + "\n", + "if not os.path.isfile(\"COLABFOLD_READY\"):\n", + " print(\"installing colabfold...\")\n", + " os.system(\"pip install -q --no-warn-conflicts 'colabfold[alphafold-minus-jax] @ git+https://github.com/sokrypton/ColabFold'\")\n", + " os.system(\"pip install --upgrade dm-haiku\")\n", + " os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/colabfold colabfold\")\n", + " os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/alphafold alphafold\")\n", + " # patch for jax > 0.3.25\n", + " os.system(\"sed -i 's/weights = jax.nn.softmax(logits)/logits=jnp.clip(logits,-1e8,1e8);weights=jax.nn.softmax(logits)/g' alphafold/model/modules.py\")\n", + " os.system(\"touch COLABFOLD_READY\")\n", + "\n", + "\n", + "if not os.path.isfile(\"CONDA_READY\"):\n", + " print(\"installing conda...\")\n", + " os.system(\"wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh\")\n", + " os.system(\"bash Miniforge3-Linux-x86_64.sh -bfp /usr/local\")\n", + " os.system(\"conda config --set auto_update_conda false\")\n", + " os.system(\"touch CONDA_READY\")\n", + "\n", + "\n", + "if not os.path.isfile(\"CONDA_READY\"):\n", + " print(\"installing conda...\")\n", + " os.system(\"wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh\")\n", + " os.system(\"bash Mambaforge-Linux-x86_64.sh -bfp /usr/local\")\n", + " os.system(\"mamba config --set auto_update_conda false\")\n", + " os.system(\"touch CONDA_READY\")\n", + "\n", + "\n", + "if not os.path.isfile(\"HH_READY\") and not os.path.isfile(\"AMBER_READY\"):\n", + " print(\"installing hhsuite and amber...\")\n", + " os.system(f\"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer\")\n", + " os.system(\"touch HH_READY\")\n", + " os.system(\"touch AMBER_READY\")\n", + "else:\n", + " if not os.path.isfile(\"HH_READY\"):\n", + " print(\"installing hhsuite...\")\n", + " os.system(f\"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 python='{PYTHON_VERSION}'\")\n", + " os.system(\"touch HH_READY\")\n", + " if not os.path.isfile(\"AMBER_READY\"):\n", + " print(\"installing amber...\")\n", + " os.system(f\"mamba install -y -c conda-forge openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer\")\n", + " os.system(\"touch AMBER_READY\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "CX768sUrS7lV", + "outputId": "1d72ced4-56b6-473c-843c-97e232c1ab6f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "installing colabfold...\n", + "installing conda...\n", + "installing hhsuite and amber...\n", + "CPU times: user 269 ms, sys: 52.3 ms, total: 321 ms\n", + "Wall time: 1min 24s\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uTVzPTKjTjXX" + }, + "source": [ + "# 1. **ColabFold Tutorial:** Predicting the structure of Bax protein\n", + "\n", + "Let's get familar with the workflow for predicting a monomer structure. This example looks at Bax, an apoptosis regulating protein.\n", + "\n", + "We will only use the default options set by Steinegger *et al.* which excludes Amber minimization and structure templates.\n", + "\n", + "**a. Background**\n", + " - Encoded by the BAX gene in the Bcl-2 family\n", + " - Highly conserved DNA sequence and structural motifs\n", + " - Different regulatory function in anti-apoptosis or pro-apoptosis\n", + " - Permeation of the mitochondrial outer membrane\n", + "\n", + "\n", + "**b. Pro-death signals triggers conformational shift**\n", + " - Bax adopts a globular state when inactive\n", + " - Apoptotic signals displaces N-terminal $\\alpha$-helicies\n", + " - Becomes membrane-associated\n", + " - Oligomers form, resulting in a pore and cell death\n", + "\n", + "Dysregulation of Bax is implicated in cancer progression, and suggests some contributions towards in Alzheimer's and Parkinson's disease.\n", + "\n", + "Experiments have shown point-mutations near opposite of the binding site region, has been shown to affect activity. This is also near the loop region between $\\alpha$-helix1 and $\\alpha$-helicies2, which has been difficult to structurally resolve. Does this loop play a role in membrane insertion mechanism?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kOblAo-xetgx" + }, + "outputs": [], + "source": [ + "#@title # **2. Notebook Setup** { display-mode: \"form\" }\n", + "\n", + "from google.colab import files\n", + "import os\n", + "import re\n", + "import hashlib\n", + "import random\n", + "\n", + "from sys import version_info\n", + "python_version = f\"{version_info.major}.{version_info.minor}\"\n", + "\n", + "def add_hash(x,y):\n", + " return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n", + "\n", + "#@markdown \\\n", + "#@markdown ##a. Protein Sequence for Structure Prediction\n", + "#@markdown For bax (human), the amino acid sequence was obtained from\n", + "#@markdown UniProt ([Q07812](https://www.uniprot.org/uniprotkb/Q07812/entry))\n", + "query_sequence = 'MDGSGEQPRGGGPTSSEQIMKTGALLLQGFIQDRAGRMGGEAPELALDPVPQDASTKKLSECLKRIGDELDSNMELQRMIAAVDTDSPREVFFRVAADMFSDGNFNWGRVVALFYFASKLVLKALCTKVPELIRTIMGWTLDFLRERLLGWIQDQGGWDGLLSYFGTPTWQTVTIFVAGVLTASLTIWKKMG' #@param {type:\"string\"}\n", + "#@markdown **Note:** For modeling complexes, use `:` to specify inter-protein chainbreaks\n", + "#@markdown (supports homo- and hetro-oligomers).\n", + "#@markdown For example, **PI...SK:PI...SK** for a homodimer.\n", + "\n", + "#@markdown \\\n", + "#@markdown ## b. Job Name (also name of saving directory)\n", + "jobname = 'colabfold-bax-1f16' #@param {type:\"string\"}\n", + "\n", + "#@markdown \\\n", + "#@markdown ## c. Number of predicted models to relax using Amber\n", + "num_relax = 0 #@param [0, 1, 5] {type:\"raw\"}\n", + "\n", + "#@markdown \\\n", + "#@markdown ## **d. Option for Structure Template**\n", + "#@markdown Search for PDB structure (xyz-coordinates)\n", + "template_mode = \"none\" #@param [\"none\", \"pdb100\",\"custom\"]\n", + "#@markdown `none` = no template information is used. \\\n", + "#@markdown `pdb100` = detect templates in pdb100 (see [notes](#pdb100)). \\\n", + "#@markdown `custom` - upload and search own templates (PDB or mmCIF format, see [notes](#custom_templates))\n", + "\n", + "use_amber = num_relax > 0\n", + "\n", + "query_sequence = \"\".join(query_sequence.split()) # remove whitespaces\n", + "\n", + "basejobname = \"\".join(jobname.split())\n", + "basejobname = re.sub(r'\\W+', '', basejobname)\n", + "jobname = add_hash(basejobname, query_sequence)\n", + "\n", + "# check if directory with jobname exists\n", + "def check(folder):\n", + " if os.path.exists(folder):\n", + " return False\n", + " else:\n", + " return True\n", + "if not check(jobname):\n", + " n = 0\n", + " while not check(f\"{jobname}_{n}\"): n += 1\n", + " jobname = f\"{jobname}_{n}\"\n", + "\n", + "# make directory to save results\n", + "os.makedirs(jobname, exist_ok=True)\n", + "\n", + "# save queries\n", + "queries_path = os.path.join(jobname, f\"{jobname}.csv\")\n", + "with open(queries_path, \"w\") as text_file:\n", + " text_file.write(f\"id,sequence\\n{jobname},{query_sequence}\")\n", + "\n", + "if template_mode == \"pdb100\":\n", + " use_templates = True\n", + " custom_template_path = None\n", + "elif template_mode == \"custom\":\n", + " custom_template_path = os.path.join(jobname,f\"template\")\n", + " os.makedirs(custom_template_path, exist_ok=True)\n", + " uploaded = files.upload()\n", + " use_templates = True\n", + " for fn in uploaded.keys():\n", + " os.rename(fn,os.path.join(custom_template_path,fn))\n", + "else:\n", + " custom_template_path = None\n", + " use_templates = False\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "C2_sh2uAonJH", + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title # **3. Multiple Sequence Alignment (MSA)**\n", + "\n", + "#@markdown ## a. MSA options\n", + "msa_mode = \"mmseqs2_uniref\" #@param [\"mmseqs2_uniref_env\", \"mmseqs2_uniref\",\"single_sequence\",\"custom\"]\n", + "#@markdown - pairing mode\n", + "#@markdown - single sequence\n", + "#@markdown - custom MSA upload\n", + "\n", + "#@markdown \\\n", + "pair_mode = \"unpaired_paired\" #@param [\"unpaired_paired\",\"unpaired\",\"paired\"] {type:\"string\"}\n", + "#@markdown - \"unpaired_paired\" - pair sequences from same species + unpaired MSA\n", + "#@markdown - \"unpaired\" - seperate MSA for each chain\n", + "#@markdown - \"paired\" - only use paired sequences\n", + "\n", + "# decide which a3m to use\n", + "if \"mmseqs2\" in msa_mode:\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.a3m\")\n", + "\n", + "elif msa_mode == \"custom\":\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.custom.a3m\")\n", + " if not os.path.isfile(a3m_file):\n", + " custom_msa_dict = files.upload()\n", + " custom_msa = list(custom_msa_dict.keys())[0]\n", + " header = 0\n", + " import fileinput\n", + " for line in fileinput.FileInput(custom_msa,inplace=1):\n", + " if line.startswith(\">\"):\n", + " header = header + 1\n", + " if not line.rstrip():\n", + " continue\n", + " if line.startswith(\">\") == False and header == 1:\n", + " query_sequence = line.rstrip()\n", + " print(line, end='')\n", + "\n", + " os.rename(custom_msa, a3m_file)\n", + " queries_path=a3m_file\n", + " print(f\"moving {custom_msa} to {a3m_file}\")\n", + "\n", + "else:\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.single_sequence.a3m\")\n", + " with open(a3m_file, \"w\") as text_file:\n", + " text_file.write(\">1\\n%s\" % query_sequence)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ADDuaolKmjGW" + }, + "outputs": [], + "source": [ + "# @title # **4. Advanced Settings** { run: \"auto\", display-mode: \"form\" }\n", + "\n", + "#@markdown ## a. ML Model\n", + "model_type = \"auto\" #@param [\"auto\", \"alphafold2_ptm\", \"alphafold2_multimer_v1\", \"alphafold2_multimer_v2\", \"alphafold2_multimer_v3\"]\n", + "#@markdown **Note:** If `auto` selected, will use `alphafold2_ptm` for monomer prediction\n", + "#@markdown and `alphafold2_multimer_v3` for complex prediction.\n", + "#@markdown Any of the `mode_types` can be used (regardless if input is monomer or complex).\n", + "\n", + "#@markdown \\\n", + "#@markdown ## b. Re-Train with Predicted Structures\n", + "num_recycles = \"3\" #@param [\"auto\", \"0\", \"1\", \"3\", \"6\", \"12\", \"24\", \"48\"]\n", + "#@markdown **Note:**\n", + "#@markdown - If `auto` selected, will use `num_recycles=20`,\n", + "#@markdown - If `model_type=alphafold2_multimer_v3`, else `num_recycles=3`\n", + "\n", + "#@markdown \\\n", + "recycle_early_stop_tolerance = \"auto\" #@param [\"auto\", \"0.0\", \"0.5\", \"1.0\"]\n", + "#@markdown - if `auto` selected, will use `tol=0.5`\n", + "#@markdown - If `model_type=alphafold2_multimer_v3`, else `tol=0.0`.\n", + "\n", + "#@markdown \\\n", + "#@markdown ## c. Amber Relaxation\n", + "relax_max_iterations = 200 #@param [0, 200, 2000] {type:\"raw\"}\n", + "#@markdown - max amber relax iterations\n", + "#@markdown - `0` = unlimited (AlphaFold2 default, can take very long)\n", + "\n", + "#@markdown \\\n", + "#@markdown ## d. MSA Pairing\n", + "pairing_strategy = \"greedy\" #@param [\"greedy\", \"complete\"] {type:\"string\"}\n", + "#@markdown - `greedy` = pair any taxonomically matching subsets,\n", + "#@markdown - `complete` = all sequences have to match in one line.\n", + "\n", + "#@markdown \\\n", + "#@markdown ## e. Sample settings\n", + "#@markdown - enable dropouts and increase number of seeds to sample predictions from uncertainty of the model.\n", + "#@markdown - decrease `max_msa` to increase uncertainity\n", + "max_msa = \"auto\" #@param [\"auto\", \"512:1024\", \"256:512\", \"64:128\", \"32:64\", \"16:32\"]\n", + "num_seeds = 1 #@param [1,2,4,8,16] {type:\"raw\"}\n", + "use_dropout = False #@param {type:\"boolean\"}\n", + "\n", + "num_recycles = None if num_recycles == \"auto\" else int(num_recycles)\n", + "recycle_early_stop_tolerance = None if recycle_early_stop_tolerance == \"auto\" else float(recycle_early_stop_tolerance)\n", + "if max_msa == \"auto\": max_msa = None\n", + "\n", + "#@markdown \\\n", + "#@markdown # **5. Save settings**\n", + "#@markdown This will ask for your credentials, to save the outputs.\n", + "save_all = False #@param {type:\"boolean\"}\n", + "save_recycles = False #@param {type:\"boolean\"}\n", + "save_to_google_drive = False #@param {type:\"boolean\"}\n", + "#@markdown - if the save_to_google_drive option was selected, the result zip will be uploaded to your Google Drive\n", + "dpi = 200 #@param {type:\"integer\"}\n", + "#@markdown - set dpi for image resolution\n", + "\n", + "if save_to_google_drive:\n", + " from pydrive.drive import GoogleDrive\n", + " from pydrive.auth import GoogleAuth\n", + " from google.colab import auth\n", + " from oauth2client.client import GoogleCredentials\n", + " auth.authenticate_user()\n", + " gauth = GoogleAuth()\n", + " gauth.credentials = GoogleCredentials.get_application_default()\n", + " drive = GoogleDrive(gauth)\n", + " print(\"You are logged into Google Drive and are good to go!\")\n", + "\n", + "#@markdown Don't forget to hit `Runtime` -> `Run all` after updating the form." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mbaIO9pWjaN0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "collapsed": true, + "outputId": "78730d97-e9e1-424a-e392-2ef0241e13e3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Downloading alphafold2 weights to .: 100%|██████████| 3.47G/3.47G [01:33<00:00, 39.9MB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-12-08 01:03:26,095 Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: \"rocm\". Available platform names are: CUDA\n", + "2023-12-08 01:03:26,097 Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n", + "2023-12-08 01:03:28,068 Running on GPU\n", + "2023-12-08 01:03:28,251 Found 3 citations for tools or databases\n", + "2023-12-08 01:03:28,252 Query 1/1: colabfoldbax1f16_f85cf (length 192)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "COMPLETE: 100%|██████████| 150/150 [elapsed: 00:02 remaining: 00:00]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-12-08 01:03:32,044 Setting max_seq=512, max_extra_seq=1703\n", + "2023-12-08 01:04:07,724 alphafold2_ptm_model_1_seed_000 recycle=0 pLDDT=70.2 pTM=0.694\n", + "2023-12-08 01:04:13,490 alphafold2_ptm_model_1_seed_000 recycle=1 pLDDT=80.9 pTM=0.793 tol=1.07\n", + "2023-12-08 01:04:19,257 alphafold2_ptm_model_1_seed_000 recycle=2 pLDDT=78.4 pTM=0.775 tol=0.281\n", + "2023-12-08 01:04:25,019 alphafold2_ptm_model_1_seed_000 recycle=3 pLDDT=81.1 pTM=0.798 tol=0.165\n", + "2023-12-08 01:04:25,021 alphafold2_ptm_model_1_seed_000 took 39.0s (3 recycles)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-12-08 01:04:31,023 alphafold2_ptm_model_2_seed_000 recycle=0 pLDDT=68.3 pTM=0.67\n", + "2023-12-08 01:04:36,783 alphafold2_ptm_model_2_seed_000 recycle=1 pLDDT=79.3 pTM=0.783 tol=1.21\n", + "2023-12-08 01:04:42,548 alphafold2_ptm_model_2_seed_000 recycle=2 pLDDT=78.2 pTM=0.779 tol=0.353\n", + "2023-12-08 01:04:48,310 alphafold2_ptm_model_2_seed_000 recycle=3 pLDDT=79.4 pTM=0.789 tol=0.121\n", + "2023-12-08 01:04:48,311 alphafold2_ptm_model_2_seed_000 took 23.1s (3 recycles)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-12-08 01:04:54,270 alphafold2_ptm_model_3_seed_000 recycle=0 pLDDT=74.4 pTM=0.721\n", + "2023-12-08 01:05:00,044 alphafold2_ptm_model_3_seed_000 recycle=1 pLDDT=83.6 pTM=0.822 tol=0.98\n", + "2023-12-08 01:05:05,823 alphafold2_ptm_model_3_seed_000 recycle=2 pLDDT=84.2 pTM=0.828 tol=0.401\n", + "2023-12-08 01:05:11,599 alphafold2_ptm_model_3_seed_000 recycle=3 pLDDT=84 pTM=0.821 tol=0.224\n", + "2023-12-08 01:05:11,600 alphafold2_ptm_model_3_seed_000 took 23.1s (3 recycles)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-12-08 01:05:17,555 alphafold2_ptm_model_4_seed_000 recycle=0 pLDDT=71 pTM=0.702\n", + "2023-12-08 01:05:23,318 alphafold2_ptm_model_4_seed_000 recycle=1 pLDDT=78.8 pTM=0.778 tol=0.867\n", + "2023-12-08 01:05:29,082 alphafold2_ptm_model_4_seed_000 recycle=2 pLDDT=78 pTM=0.763 tol=0.439\n", + "2023-12-08 01:05:34,846 alphafold2_ptm_model_4_seed_000 recycle=3 pLDDT=76.6 pTM=0.736 tol=0.257\n", + "2023-12-08 01:05:34,847 alphafold2_ptm_model_4_seed_000 took 23.1s (3 recycles)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-12-08 01:05:40,809 alphafold2_ptm_model_5_seed_000 recycle=0 pLDDT=75.6 pTM=0.776\n", + "2023-12-08 01:05:46,584 alphafold2_ptm_model_5_seed_000 recycle=1 pLDDT=81.9 pTM=0.828 tol=1.01\n", + "2023-12-08 01:05:52,360 alphafold2_ptm_model_5_seed_000 recycle=2 pLDDT=81.6 pTM=0.823 tol=0.38\n", + "2023-12-08 01:05:58,134 alphafold2_ptm_model_5_seed_000 recycle=3 pLDDT=81.8 pTM=0.822 tol=0.216\n", + "2023-12-08 01:05:58,135 alphafold2_ptm_model_5_seed_000 took 23.1s (3 recycles)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-12-08 01:05:58,341 reranking models by 'plddt' metric\n", + "2023-12-08 01:05:58,341 rank_001_alphafold2_ptm_model_3_seed_000 pLDDT=84 pTM=0.821\n", + "2023-12-08 01:05:58,342 rank_002_alphafold2_ptm_model_5_seed_000 pLDDT=81.8 pTM=0.822\n", + "2023-12-08 01:05:58,343 rank_003_alphafold2_ptm_model_1_seed_000 pLDDT=81.1 pTM=0.798\n", + "2023-12-08 01:05:58,343 rank_004_alphafold2_ptm_model_2_seed_000 pLDDT=79.4 pTM=0.789\n", + "2023-12-08 01:05:58,344 rank_005_alphafold2_ptm_model_4_seed_000 pLDDT=76.6 pTM=0.736\n", + "2023-12-08 01:06:00,018 Done\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "#@title # **6. Run Prediction** { display-mode: \"form\" }\n", + "\n", + "display_images = True #@param {type:\"boolean\"}\n", + "\n", + "\n", + "import sys\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)\n", + "from Bio import BiopythonDeprecationWarning\n", + "warnings.simplefilter(action='ignore', category=BiopythonDeprecationWarning)\n", + "from pathlib import Path\n", + "from colabfold.download import download_alphafold_params, default_data_dir\n", + "from colabfold.utils import setup_logging\n", + "from colabfold.batch import get_queries, run, set_model_type\n", + "from colabfold.plot import plot_msa_v2\n", + "\n", + "import os\n", + "import numpy as np\n", + "try:\n", + " K80_chk = os.popen('nvidia-smi | grep \"Tesla K80\" | wc -l').read()\n", + "except:\n", + " K80_chk = \"0\"\n", + " pass\n", + "if \"1\" in K80_chk:\n", + " print(\"WARNING: found GPU Tesla K80: limited to total length < 1000\")\n", + " if \"TF_FORCE_UNIFIED_MEMORY\" in os.environ:\n", + " del os.environ[\"TF_FORCE_UNIFIED_MEMORY\"]\n", + " if \"XLA_PYTHON_CLIENT_MEM_FRACTION\" in os.environ:\n", + " del os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"]\n", + "\n", + "from colabfold.colabfold import plot_protein\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# For some reason we need that to get pdbfixer to import\n", + "if use_amber and f\"/usr/local/lib/python{python_version}/site-packages/\" not in sys.path:\n", + " sys.path.insert(0, f\"/usr/local/lib/python{python_version}/site-packages/\")\n", + "\n", + "def input_features_callback(input_features):\n", + " if display_images:\n", + " plot_msa_v2(input_features)\n", + " plt.show()\n", + " plt.close()\n", + "\n", + "def prediction_callback(protein_obj, length,\n", + " prediction_result, input_features, mode):\n", + " model_name, relaxed = mode\n", + " if not relaxed:\n", + " if display_images:\n", + " fig = plot_protein(protein_obj, Ls=length, dpi=150)\n", + " plt.show()\n", + " plt.close()\n", + "\n", + "result_dir = jobname\n", + "log_filename = os.path.join(jobname,\"log.txt\")\n", + "setup_logging(Path(log_filename))\n", + "\n", + "queries, is_complex = get_queries(queries_path)\n", + "model_type = set_model_type(is_complex, model_type)\n", + "\n", + "if \"multimer\" in model_type and max_msa is not None:\n", + " use_cluster_profile = False\n", + "else:\n", + " use_cluster_profile = True\n", + "\n", + "download_alphafold_params(model_type, Path(\".\"))\n", + "results = run(\n", + " queries=queries,\n", + " result_dir=result_dir,\n", + " use_templates=use_templates,\n", + " custom_template_path=custom_template_path,\n", + " num_relax=num_relax,\n", + " msa_mode=msa_mode,\n", + " model_type=model_type,\n", + " num_models=5,\n", + " num_recycles=num_recycles,\n", + " relax_max_iterations=relax_max_iterations,\n", + " recycle_early_stop_tolerance=recycle_early_stop_tolerance,\n", + " num_seeds=num_seeds,\n", + " use_dropout=use_dropout,\n", + " model_order=[1,2,3,4,5],\n", + " is_complex=is_complex,\n", + " data_dir=Path(\".\"),\n", + " keep_existing_results=False,\n", + " rank_by=\"auto\",\n", + " pair_mode=pair_mode,\n", + " pairing_strategy=pairing_strategy,\n", + " stop_at_score=float(100),\n", + " prediction_callback=prediction_callback,\n", + " dpi=dpi,\n", + " zip_results=False,\n", + " save_all=save_all,\n", + " max_msa=max_msa,\n", + " use_cluster_profile=use_cluster_profile,\n", + " input_features_callback=input_features_callback,\n", + " save_recycles=save_recycles,\n", + " user_agent=\"colabfold/google-colab-main\",\n", + ")\n", + "results_zip = f\"{jobname}.result.zip\"\n", + "os.system(f\"zip -r {results_zip} {jobname}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KK7X9T44pWb7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 534 + }, + "collapsed": true, + "outputId": "5b6a79ea-fa87-4104-9730-f29b67de9d45" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Prediction: Rank 1\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", + "text/html": [ + "
\n", + "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", + " jupyter labextension install jupyterlab_3dmol

\n", + "
\n", + "" + ] + }, + "metadata": {} + } + ], + "source": [ + "# @title # **7. Visualize 3D Structure of ColabFold Prediction** { run: \"auto\", display-mode: \"form\" }\n", + "\n", + "#@markdown Rank number/score for the **monomer model is the average pLDDT**,\n", + "#@markdown where a ranking of 1 correlates better local structure confidence.\n", + "#@markdown An acceptable model prediction should average $\\ge 85$.\n", + "#@markdown The number of ranked models was set in step `6. Run Prediction` (step 6, line 75.).\n", + "\n", + "#@markdown \\\n", + "#@markdown Here, you can change the value for `rank_num` to visualize the 5 predictions.\n", + "\n", + "import py3Dmol\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "from colabfold.colabfold import plot_plddt_legend\n", + "from colabfold.colabfold import pymol_color_list, alphabet_list\n", + "\n", + "rank_num = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n", + "color = \"rainbow\" #@param [\"chain\", \"lDDT\", \"rainbow\"]\n", + "show_sidechains = False #@param {type:\"boolean\"}\n", + "show_mainchains = False #@param {type:\"boolean\"}\n", + "\n", + "tag = results[\"rank\"][0][rank_num - 1]\n", + "jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n", + "pdb_filename = f\"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb\"\n", + "pdb_file = glob.glob(pdb_filename)\n", + "\n", + "def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color=\"lDDT\"):\n", + " model_name = f\"rank_{rank_num}\"\n", + " view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)\n", + " view.addModel(open(pdb_file[0],'r').read(),'pdb')\n", + "\n", + "\n", + " if color == \"lDDT\":\n", + " view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})\n", + " elif color == \"rainbow\":\n", + " view.setStyle({'cartoon': {'color':'spectrum'}})\n", + " elif color == \"chain\":\n", + " chains = len(queries[0][1]) + 1 if is_complex else 1\n", + " for n,chain,color in zip(range(chains),alphabet_list,pymol_color_list):\n", + " view.setStyle({'chain':chain},{'cartoon': {'color':color}})\n", + "\n", + " if show_sidechains:\n", + " BB = ['C','O','N']\n", + " view.addStyle({'and':[{'resn':[\"GLY\",\"PRO\"],'invert':True},{'atom':BB,'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"GLY\"},{'atom':'CA'}]},\n", + " {'sphere':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"PRO\"},{'atom':['C','O'],'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " if show_mainchains:\n", + " BB = ['C','O','N','CA']\n", + " view.addStyle({'atom':BB},{'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}}, viewer=(0,0))\n", + "\n", + " view.zoomTo()\n", + " return view\n", + "\n", + "print('Prediction: Rank %d' % rank_num)\n", + "show_pdb(rank_num, show_sidechains, show_mainchains, color).show()\n", + "if color == \"lDDT\":\n", + " plot_plddt_legend().show()\n", + "\n", + "\n", + "#@markdown \\" + ] + }, + { + "cell_type": "code", + "source": [ + "# @title # **8. Align PDBs** { run: \"auto\", display-mode: \"form\" }\n", + "#@markdown We can compare the predictions with experiment.\n", + "#@markdown Example: PDB 1F16 was obtained from NMR, so there is an ensemble of structures.\n", + "\n", + "import Bio.PDB\n", + "\n", + "pdb_code = \"1f16\" #@param{type:\"string\"}\n", + "#@markdown - Reference PDB (Experiment)\n", + "cpred = \"\"#@param{type:\"string\"}\n", + "#@markdown - Color for Reference PDB (Experiment)\n", + "os.system(\"wget -qnc https://files.rcsb.org/download/%s.pdb.gz; gunzip %s.pdb.gz\" % (pdb_code,pdb_code))\n", + "\n", + "start_id = 1\n", + "end_id = len(query_sequence)\n", + "atoms_to_be_aligned = range(start_id, end_id + 1)\n", + "\n", + "# Start the parser\n", + "pdb_parser = Bio.PDB.PDBParser(QUIET = True)\n", + "\n", + "# Get the structures\n", + "ref_structure = pdb_parser.get_structure(\"reference\", \"%s.pdb\" % pdb_code)\n", + "sample_structure = pdb_parser.get_structure(\"sample\", pdb_file[0])\n", + "\n", + "# Use the first model in the pdb-files for alignment\n", + "# Change the number 0 if you want to align to another structure\n", + "ref_model = ref_structure[0]\n", + "sample_model = sample_structure[0]\n", + "\n", + "# Make a list of the atoms (in the structures) you wish to align.\n", + "# In this case we use CA atoms whose index is in the specified range\n", + "ref_atoms = []\n", + "sample_atoms = []\n", + "\n", + "# Iterate of all chains in the model in order to find all residues\n", + "for ref_chain in ref_model:\n", + " # Iterate of all residues in each model in order to find proper atoms\n", + " for ref_res in ref_chain:\n", + " # Check if residue number ( .get_id() ) is in the list\n", + " if ref_res.get_id()[1] in atoms_to_be_aligned:\n", + " # Append CA atom to list\n", + " ref_atoms.append(ref_res['CA'])\n", + "\n", + "# Do the same for the sample structure\n", + "for sample_chain in sample_model:\n", + " for sample_res in sample_chain:\n", + " if sample_res.get_id()[1] in atoms_to_be_aligned:\n", + " sample_atoms.append(sample_res['CA'])\n", + "\n", + "# Now we initiate the superimposer:\n", + "super_imposer = Bio.PDB.Superimposer()\n", + "super_imposer.set_atoms(ref_atoms, sample_atoms)\n", + "super_imposer.apply(sample_model.get_atoms())\n", + "\n", + "# Print RMSD:\n", + "print('The calculated RMSD is: %.3f Å' % super_imposer.rms)\n", + "\n", + "# Save the aligned version of one of the chains of 6ANE\n", + "io = Bio.PDB.PDBIO()\n", + "io.set_structure(sample_structure)\n", + "io.save(\"%s-aligned.pdb\" % jobname)\n", + "print('Saved Aligned PDB.')\n", + "\n", + "# rank_num = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n", + "# #@markdown - Predicted PDB (AF/CF)\n", + "# color = \"lDDT\" #@param [\"chain\", \"lDDT\", \"rainbow\"]\n", + "# #@markdown - Color Predicted PDB (Default: lDDT)\n", + "# tag = results[\"rank\"][0][rank_num - 1]\n", + "# jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n", + "# pdb_filename = f\"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb\"\n", + "# pdb_file = glob.glob(pdb_filename)\n", + "\n", + "# how_sidechains = False #@param {type:\"boolean\"}\n", + "# show_mainchains = False #@param {type:\"boolean\"}\n", + "\n", + "view=py3Dmol.view()\n", + "view.addModel(open(\"%s-aligned.pdb\" % jobname).read(),'pdb')\n", + "# view.addModel(open(\"%s.pdb\" % pdb_code).read())\n", + "view.zoomTo()\n", + "view.setBackgroundColor('white')\n", + "view.setStyle({'chain':'A'},{'cartoon': {'color': 'red'}})\n", + "view.setStyle({'chain':'B'},{'cartoon': {'color': 'blue'}})\n", + "view.show()\n", + "\n", + "# show_pdb(rank_num, show_sidechains, show_mainchains, color).show()\n", + "# if color == \"lDDT\":\n", + "# plot_plddt_legend().show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 551 + }, + "collapsed": true, + "id": "aA_ldVyPrSWt", + "outputId": "e474d900-1469-4198-fa88-f547c5c702c3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The calculated RMSD is: 5.711 Å\n", + "Saved Aligned PDB.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", + "text/html": [ + "
\n", + "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", + " jupyter labextension install jupyterlab_3dmol

\n", + "
\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "id": "33g5IIegij5R", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 37 + }, + "outputId": "0f0e3801-536c-4492-d1b5-4db1b5939e69" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_d2010ba7-b6ad-46a1-a55a-49808bff52f7\", \"colabfoldbax1f16_f85cf.result.zip\", 1846420)" + ] + }, + "metadata": {} + } + ], + "source": [ + "#@title **9. Package and Download**\n", + "\n", + "#@markdown If you are having issues downloading the result archive, try disabling your adblocker and run this cell again. If that fails click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \\\"Download\\\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).\n", + "\n", + "if msa_mode == \"custom\":\n", + " print(\"Don't forget to cite your custom MSA generation method.\")\n", + "\n", + "files.download(f\"{jobname}.result.zip\")\n", + "\n", + "if save_to_google_drive == True and drive:\n", + " uploaded = drive.CreateFile({'title': f\"{jobname}.result.zip\"})\n", + " uploaded.SetContentFile(f\"{jobname}.result.zip\")\n", + " uploaded.Upload()\n", + " print(f\"Uploaded {jobname}.result.zip to Google Drive with ID {uploaded.get('id')}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P_yNIT-iBd65" + }, + "source": [ + "Colab notebooks are adapted from:\n", + " - [AlphaFold2 Colab](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb)\n", + " - [Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all.\n", + "*Nature Methods*, 2022](https://www.nature.com/articles/s41592-022-01488-1)\n", + "\n", + "*From the original notebook:*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UGUBLzB3C6WN" + }, + "source": [ + "> \n", + ">\n", + "> ## ColabFold v1.5.3: AlphaFold2 using MMseqs2\n", + ">\n", + "> Easy to use protein structure and complex prediction using [AlphaFold2](https://www.nature.com/articles/s41586-021-03819-2) and [Alphafold2-multimer](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1). Sequence alignments/templates are generated through [MMseqs2](mmseqs.com) and [HHsearch](https://github.com/soedinglab/hh-suite). For more details, see bottom of the notebook, checkout the [ColabFold GitHub](https://github.com/sokrypton/ColabFold) and read our manuscript.\n", + ">\n", + "> Old versions: [v1.4](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.4.0/AlphaFold2.ipynb), [v1.5.1](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.1/AlphaFold2.ipynb), [v1.5.2](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.2/AlphaFold2.ipynb)\n", + ">\n", + "> [Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all.\n", + "> *Nature Methods*, 2022](https://www.nature.com/articles/s41592-022-01488-1)\n", + ">\n", + "> -----------\n", + ">\n", + "> ### News\n", + "> - 2023/07/31: The ColabFold MSA server is back to normal. It was using older DB (UniRef30 2202/PDB70 220313) from 27th ~8:30 AM CEST to 31st ~11:10 AM CEST.\n", + "> - 2023/06/12: New databases! UniRef30 updated to 2023_02 and PDB to 230517. We now use PDB100 instead of PDB70 (see [notes](#pdb100)).\n", + "> - 2023/06/12: We introduced a new default pairing strategy: Previously, for multimer predictions with more than 2 chains, we only pair if all sequences taxonomically match (\"complete\" pairing). The new default \"greedy\" strategy pairs any taxonomically matching subsets.\n", + ">\n", + "># Instructions \n", + ">\n", + "> **Quick start**\n", + "> 1. Paste your protein sequence(s) in the input field.\n", + "> 2. Press \"Runtime\" -> \"Run all\".\n", + "> 3. The pipeline consists of 5 steps. The currently running step is indicated by a circle with a stop sign next to it.\n", + ">\n", + "> **Result zip file contents**\n", + ">\n", + "> 1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if `use_amber` is enabled).\n", + "> 2. Plots of the model quality.\n", + "> 3. Plots of the MSA coverage.\n", + "> 4. Parameter log file.\n", + "> 5. A3M formatted input MSA.\n", + "> 6. A `predicted_aligned_error_v1.json` using [AlphaFold-DB's format](https://alphafold.ebi.ac.uk/faq#faq-7) and a `scores.json` for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.\n", + "> 7. BibTeX file with citations for all used tools and databases.\n", + ">\n", + "> At the end of the job a download modal box will pop up with a `jobname.result.zip` file. Additionally, if the `save_to_google_drive` option was selected, the `jobname.result.zip` will be uploaded to your Google Drive.\n", + "> \n", + ">\n", + "> **License**\n", + "> githubusercontent.com/sokrypton/ColabFold/main/LICENSE). Additionally, this > notebook uses the AlphaFold2 source code and its parameters licensed under [Apache 2.0](https://raw.githubusercontent.com/deepmind/alphafold/main/LICENSE) > and [CC BY 4.0](https://creativecommons.org/licenses/by-sa/4.0/) respectively. Read more about the AlphaFold license [here](https://github.com/deepmind/> alphafold).\n", + ">\n", + "> **Acknowledgments**\n", + "> - We thank the AlphaFold team for developing an excellent model and open > sourcing the software.\n", + ">\n", + "> - [KOBIC](https://kobic.re.kr) and [Söding Lab](https://www.mpinat.mpg.de/soeding) for providing the computational resources for the MMseqs2 MSA server.\n", + ">\n", + "> - Richard Evans for helping to benchmark the ColabFold's Alphafold-multimer support.\n", + ">\n", + "> - [David Koes](https://github.com/dkoes) for his awesome [py3Dmol](https://3dmol.csb.pitt.edu/) plugin, without whom these notebooks would be quite boring!\n", + ">\n", + "> - Do-Yoon Kim for creating the ColabFold logo.\n", + ">\n", + "> - A colab by Sergey Ovchinnikov ([@sokrypton](https://twitter.com/sokrypton)), Milot Mirdita ([@milot_mirdita](https://twitter.com/milot_mirdita)) and Martin Steinegger ([@thesteinegger](https://twitter.com/thesteinegger)).\n", + ">\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [], + "gpuType": "T4", + "cell_execution_strategy": "setup", + "machine_shape": "hm", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/_sources/ColabFold/Tutorial_Multimer.ipynb b/_sources/ColabFold/Tutorial_Multimer.ipynb new file mode 100644 index 0000000..b19448d --- /dev/null +++ b/_sources/ColabFold/Tutorial_Multimer.ipynb @@ -0,0 +1,662 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "\n", + "# @title **Install Python Packages** { run: \"auto\", display-mode: \"form\" }\n", + "\n", + "import os\n", + "from sys import version_info\n", + "PYTHON_VERSION = f\"{version_info.major}.{version_info.minor}\"\n", + "\n", + "if not os.path.isfile(\"COLABFOLD_READY\"):\n", + " print(\"installing colabfold...\")\n", + " os.system(\"pip install -q --no-warn-conflicts 'colabfold[alphafold-minus-jax] @ git+https://github.com/sokrypton/ColabFold'\")\n", + " os.system(\"pip install --upgrade dm-haiku\")\n", + " os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/colabfold colabfold\")\n", + " os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/alphafold alphafold\")\n", + " # patch for jax > 0.3.25\n", + " os.system(\"sed -i 's/weights = jax.nn.softmax(logits)/logits=jnp.clip(logits,-1e8,1e8);weights=jax.nn.softmax(logits)/g' alphafold/model/modules.py\")\n", + " os.system(\"touch COLABFOLD_READY\")\n", + "\n", + "\n", + "if not os.path.isfile(\"CONDA_READY\"):\n", + " print(\"installing conda...\")\n", + " os.system(\"wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh\")\n", + " os.system(\"bash Miniforge3-Linux-x86_64.sh -bfp /usr/local\")\n", + " os.system(\"conda config --set auto_update_conda false\")\n", + " os.system(\"touch CONDA_READY\")\n", + "\n", + "\n", + "if not os.path.isfile(\"CONDA_READY\"):\n", + " print(\"installing conda...\")\n", + " os.system(\"wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh\")\n", + " os.system(\"bash Mambaforge-Linux-x86_64.sh -bfp /usr/local\")\n", + " os.system(\"mamba config --set auto_update_conda false\")\n", + " os.system(\"touch CONDA_READY\")\n", + "\n", + "\n", + "if not os.path.isfile(\"HH_READY\") and not os.path.isfile(\"AMBER_READY\"):\n", + " print(\"installing hhsuite and amber...\")\n", + " os.system(f\"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer\")\n", + " os.system(\"touch HH_READY\")\n", + " os.system(\"touch AMBER_READY\")\n", + "else:\n", + " if not os.path.isfile(\"HH_READY\"):\n", + " print(\"installing hhsuite...\")\n", + " os.system(f\"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 python='{PYTHON_VERSION}'\")\n", + " os.system(\"touch HH_READY\")\n", + " if not os.path.isfile(\"AMBER_READY\"):\n", + " print(\"installing amber...\")\n", + " os.system(f\"mamba install -y -c conda-forge openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer\")\n", + " os.system(\"touch AMBER_READY\")\n" + ], + "metadata": { + "collapsed": true, + "id": "CX768sUrS7lV" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uTVzPTKjTjXX" + }, + "source": [ + "# 1. **ColabFold Tutorial:** Predicting Protein-Protein Complexes of TA Systems\n", + "\n", + "Like the monomer example, we will use the default options set by Steinegger *et al.* which excludes Amber minimization and structure templates.\n", + "\n", + "Predicting the structure of protein complexes can be investigated using the AlphaFold oligomers\n", + "\n", + "There are subtle differences when running the AF-Multimer externsion:\n", + "\n", + "- Model confidence metric is iPTM\n", + " - Interference predicted template modelling score\n", + "- Better for homomeric interfaces than for heteromeric interfaces (high conservation)\n", + "- COM between chains are added to the loss function\n", + "\n", + "This example focuses on a toxin-antitoxin (TA) protein system, prevalent in bacterial systems. Toxins, or over-expressed proteins, become accumulated in the cell causing disturbing other cellular processes. Yet, their natural role aids in infections and apoptosis. Antitoxin, protein or RNA, can neutralize the toxin by forming stable complexes. How does the protein-protein interactions maintianing a balance between the toxin and antitoxin. \n", + "\n", + "We will predict the TA complex in *Vibrio cholerae*, specifically, VCParD1/2 (antitoxin) and VcParE (toxin)1/2.\n", + "\n", + "**a. Background**\n", + " - TA systems are essential in maintaining cellular homeostasis\n", + " - Low sequence similariy to maintain specificity towards cognate receptor\n", + " - ParDE was the first TA system to be identified as a stabilizing element upon plasmisd loss\n", + " - Potential in biotechnological applications\n", + "\n", + "**b. ParE attenuates Toxicity Protecting Cells**\n", + " - ParE can inhibit DNA gyrase supercoiling\n", + " - Protects from anti-gyrase antibiotitcs\n", + " - At the same time ParD is degraded\n", + " - C-terminal domain is key to formation of PaParDE complexes\n", + " \n", + "The C-terminal residues of ParD was found to be crucial in formation and stabilizatino of toxin-antitoxin complexes. This resulted in protection from proteases and attenuates toxicitiy from antibiotics in bacterial systems.\n", + "\n", + "Interestingly, structure-based studies have resolved several oligomeric TA complexes. Despite low sequence similarity, their structure is highly conserved. Let's investivate VcParDE and later compare it with PaParDE.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kOblAo-xetgx" + }, + "outputs": [], + "source": [ + "#@title # **2. Notebook Setup** { display-mode: \"form\" }\n", + "\n", + "from google.colab import files\n", + "import os\n", + "import re\n", + "import hashlib\n", + "import random\n", + "\n", + "from sys import version_info\n", + "python_version = f\"{version_info.major}.{version_info.minor}\"\n", + "\n", + "def add_hash(x,y):\n", + " return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n", + "\n", + "#@markdown \\\n", + "#@markdown ##a. Protein Sequence for Structure Prediction\n", + "#@markdown For bax (human), the amino acid sequence was obtained from\n", + "#@markdown UniProt ([Q07812](https://www.uniprot.org/uniprotkb/Q07812/entry))\n", + "query_sequence = 'MHTLTANDAKRNFGELLLSAQREPVIISKNSKNTVVVMSIKDFEELEAMKLDYLKHCFESAQKDLDSGKTVDGATFLNTL:MQNKQYKLSQLAQEHLLKIKHYTIENFAEAQWQKYKSTLLSGFQTLADNPGLGKSCEDIYQNGFYFPVGKHMAYYTKEANFILIVAVLGQSQLPQKHLKQSRFVS:MAKNTSITLGEHFDGFITSQIQSGRYGSASEVIRSALRLLENQETKLQSLRQLLIEGEQSGDADYDLDSFINELDSENI:MKPFNLTVAAKADLRDIALFTQRRWGKEQRNVYLKQFDDSFWLLAENPDIGKSCDEIREGYRKFPQGSHVIFYQQTGSQQIRVIRILHKSMDVNPIFGA ' #@param {type:\"string\"}\n", + "#@markdown **Note:** For modeling complexes, use `:` to specify inter-protein chainbreaks\n", + "#@markdown (supports homo- and hetro-oligomers).\n", + "#@markdown For example, **PI...SK:PI...SK** for a homodimer.\n", + "\n", + "#@markdown \\\n", + "#@markdown ## b. Job Name (also name of saving directory)\n", + "jobname = '' #@param {type:\"string\"}\n", + "\n", + "#@markdown \\\n", + "#@markdown ## c. Number of predicted models to relax using Amber\n", + "num_relax = 5 #@param [0, 1, 5] {type:\"raw\"}\n", + "\n", + "#@markdown \\\n", + "#@markdown ## **d. Option for Structure Template**\n", + "#@markdown Search for PDB structure (xyz-coordinates)\n", + "template_mode = \"none\" #@param [\"none\", \"pdb100\",\"custom\"]\n", + "#@markdown `none` = no template information is used. \\\n", + "#@markdown `pdb100` = detect templates in pdb100 (see [notes](#pdb100)). \\\n", + "#@markdown `custom` - upload and search own templates (PDB or mmCIF format, see [notes](#custom_templates))\n", + "\n", + "use_amber = num_relax > 0\n", + "\n", + "query_sequence = \"\".join(query_sequence.split()) # remove whitespaces\n", + "\n", + "basejobname = \"\".join(jobname.split())\n", + "basejobname = re.sub(r'\\W+', '', basejobname)\n", + "jobname = add_hash(basejobname, query_sequence)\n", + "\n", + "# check if directory with jobname exists\n", + "def check(folder):\n", + " if os.path.exists(folder):\n", + " return False\n", + " else:\n", + " return True\n", + "if not check(jobname):\n", + " n = 0\n", + " while not check(f\"{jobname}_{n}\"): n += 1\n", + " jobname = f\"{jobname}_{n}\"\n", + "\n", + "# make directory to save results\n", + "os.makedirs(jobname, exist_ok=True)\n", + "\n", + "# save queries\n", + "queries_path = os.path.join(jobname, f\"{jobname}.csv\")\n", + "with open(queries_path, \"w\") as text_file:\n", + " text_file.write(f\"id,sequence\\n{jobname},{query_sequence}\")\n", + "\n", + "if template_mode == \"pdb100\":\n", + " use_templates = True\n", + " custom_template_path = None\n", + "elif template_mode == \"custom\":\n", + " custom_template_path = os.path.join(jobname,f\"template\")\n", + " os.makedirs(custom_template_path, exist_ok=True)\n", + " uploaded = files.upload()\n", + " use_templates = True\n", + " for fn in uploaded.keys():\n", + " os.rename(fn,os.path.join(custom_template_path,fn))\n", + "else:\n", + " custom_template_path = None\n", + " use_templates = False\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "C2_sh2uAonJH", + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title # **3. Multiple Sequence Alignment (MSA)**\n", + "\n", + "#@markdown ## a. MSA options\n", + "msa_mode = \"mmseqs2_uniref_env\" #@param [\"mmseqs2_uniref_env\", \"mmseqs2_uniref\",\"single_sequence\",\"custom\"]\n", + "#@markdown - pairing mode\n", + "#@markdown - single sequence\n", + "#@markdown - custom MSA upload\n", + "\n", + "#@markdown \\\n", + "pair_mode = \"unpaired_paired\" #@param [\"unpaired_paired\",\"unpaired\",\"paired\"] {type:\"string\"}\n", + "#@markdown - \"unpaired_paired\" - pair sequences from same species + unpaired MSA\n", + "#@markdown - \"unpaired\" - seperate MSA for each chain\n", + "#@markdown - \"paired\" - only use paired sequences\n", + "\n", + "# decide which a3m to use\n", + "if \"mmseqs2\" in msa_mode:\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.a3m\")\n", + "\n", + "elif msa_mode == \"custom\":\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.custom.a3m\")\n", + " if not os.path.isfile(a3m_file):\n", + " custom_msa_dict = files.upload()\n", + " custom_msa = list(custom_msa_dict.keys())[0]\n", + " header = 0\n", + " import fileinput\n", + " for line in fileinput.FileInput(custom_msa,inplace=1):\n", + " if line.startswith(\">\"):\n", + " header = header + 1\n", + " if not line.rstrip():\n", + " continue\n", + " if line.startswith(\">\") == False and header == 1:\n", + " query_sequence = line.rstrip()\n", + " print(line, end='')\n", + "\n", + " os.rename(custom_msa, a3m_file)\n", + " queries_path=a3m_file\n", + " print(f\"moving {custom_msa} to {a3m_file}\")\n", + "\n", + "else:\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.single_sequence.a3m\")\n", + " with open(a3m_file, \"w\") as text_file:\n", + " text_file.write(\">1\\n%s\" % query_sequence)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ADDuaolKmjGW" + }, + "outputs": [], + "source": [ + "# @title # **4. Advanced Settings** { run: \"auto\", display-mode: \"form\" }\n", + "\n", + "#@markdown ## a. ML Model\n", + "model_type = \"auto\" #@param [\"auto\", \"alphafold2_ptm\", \"alphafold2_multimer_v1\", \"alphafold2_multimer_v2\", \"alphafold2_multimer_v3\"]\n", + "#@markdown **Note:** If `auto` selected, will use `alphafold2_ptm` for monomer prediction\n", + "#@markdown and `alphafold2_multimer_v3` for complex prediction.\n", + "#@markdown Any of the `mode_types` can be used (regardless if input is monomer or complex).\n", + "\n", + "#@markdown \\\n", + "#@markdown ## b. Re-Train with Predicted Structures\n", + "num_recycles = \"0\" #@param [\"auto\", \"0\", \"1\", \"3\", \"6\", \"12\", \"24\", \"48\"]\n", + "#@markdown **Note:**\n", + "#@markdown - If `auto` selected, will use `num_recycles=20`,\n", + "#@markdown - If `model_type=alphafold2_multimer_v3`, else `num_recycles=3`\n", + "\n", + "#@markdown \\\n", + "recycle_early_stop_tolerance = \"auto\" #@param [\"auto\", \"0.0\", \"0.5\", \"1.0\"]\n", + "#@markdown - if `auto` selected, will use `tol=0.5`\n", + "#@markdown - If `model_type=alphafold2_multimer_v3`, else `tol=0.0`.\n", + "\n", + "#@markdown \\\n", + "#@markdown ## c. Amber Relaxation\n", + "relax_max_iterations = 200 #@param [0, 200, 2000] {type:\"raw\"}\n", + "#@markdown - max amber relax iterations\n", + "#@markdown - `0` = unlimited (AlphaFold2 default, can take very long)\n", + "\n", + "#@markdown \\\n", + "#@markdown ## d. MSA Pairing\n", + "pairing_strategy = \"greedy\" #@param [\"greedy\", \"complete\"] {type:\"string\"}\n", + "#@markdown - `greedy` = pair any taxonomically matching subsets,\n", + "#@markdown - `complete` = all sequences have to match in one line.\n", + "\n", + "#@markdown \\\n", + "#@markdown ## e. Sample settings\n", + "#@markdown - enable dropouts and increase number of seeds to sample predictions from uncertainty of the model.\n", + "#@markdown - decrease `max_msa` to increase uncertainity\n", + "max_msa = \"auto\" #@param [\"auto\", \"512:1024\", \"256:512\", \"64:128\", \"32:64\", \"16:32\"]\n", + "num_seeds = 1 #@param [1,2,4,8,16] {type:\"raw\"}\n", + "use_dropout = False #@param {type:\"boolean\"}\n", + "\n", + "num_recycles = None if num_recycles == \"auto\" else int(num_recycles)\n", + "recycle_early_stop_tolerance = None if recycle_early_stop_tolerance == \"auto\" else float(recycle_early_stop_tolerance)\n", + "if max_msa == \"auto\": max_msa = None\n", + "\n", + "#@markdown \\\n", + "#@markdown # **5. Save settings**\n", + "#@markdown This will ask for your credentials, to save the outputs.\n", + "save_all = False #@param {type:\"boolean\"}\n", + "save_recycles = False #@param {type:\"boolean\"}\n", + "save_to_google_drive = False #@param {type:\"boolean\"}\n", + "#@markdown - if the save_to_google_drive option was selected, the result zip will be uploaded to your Google Drive\n", + "dpi = 200 #@param {type:\"integer\"}\n", + "#@markdown - set dpi for image resolution\n", + "\n", + "if save_to_google_drive:\n", + " from pydrive.drive import GoogleDrive\n", + " from pydrive.auth import GoogleAuth\n", + " from google.colab import auth\n", + " from oauth2client.client import GoogleCredentials\n", + " auth.authenticate_user()\n", + " gauth = GoogleAuth()\n", + " gauth.credentials = GoogleCredentials.get_application_default()\n", + " drive = GoogleDrive(gauth)\n", + " print(\"You are logged into Google Drive and are good to go!\")\n", + "\n", + "#@markdown Don't forget to hit `Runtime` -> `Run all` after updating the form." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mbaIO9pWjaN0", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "outputId": "b0f5d314-ac48-46bb-906c-88ee9506ad5e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-12-08 03:34:55,968 alphafold2_multimer_v3_model_2_seed_000 recycle=18 pLDDT=85.8 pTM=0.479 ipTM=0.372 tol=0.629\n", + "2023-12-08 03:35:09,602 alphafold2_multimer_v3_model_2_seed_000 recycle=19 pLDDT=85.5 pTM=0.478 ipTM=0.374 tol=1.08\n" + ] + } + ], + "source": [ + "#@title # **6. Run Prediction** { display-mode: \"form\" }\n", + "\n", + "display_images = True #@param {type:\"boolean\"}\n", + "\n", + "\n", + "import sys\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)\n", + "from Bio import BiopythonDeprecationWarning\n", + "warnings.simplefilter(action='ignore', category=BiopythonDeprecationWarning)\n", + "from pathlib import Path\n", + "from colabfold.download import download_alphafold_params, default_data_dir\n", + "from colabfold.utils import setup_logging\n", + "from colabfold.batch import get_queries, run, set_model_type\n", + "from colabfold.plot import plot_msa_v2\n", + "\n", + "import os\n", + "import numpy as np\n", + "try:\n", + " K80_chk = os.popen('nvidia-smi | grep \"Tesla K80\" | wc -l').read()\n", + "except:\n", + " K80_chk = \"0\"\n", + " pass\n", + "if \"1\" in K80_chk:\n", + " print(\"WARNING: found GPU Tesla K80: limited to total length < 1000\")\n", + " if \"TF_FORCE_UNIFIED_MEMORY\" in os.environ:\n", + " del os.environ[\"TF_FORCE_UNIFIED_MEMORY\"]\n", + " if \"XLA_PYTHON_CLIENT_MEM_FRACTION\" in os.environ:\n", + " del os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"]\n", + "\n", + "from colabfold.colabfold import plot_protein\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# For some reason we need that to get pdbfixer to import\n", + "if use_amber and f\"/usr/local/lib/python{python_version}/site-packages/\" not in sys.path:\n", + " sys.path.insert(0, f\"/usr/local/lib/python{python_version}/site-packages/\")\n", + "\n", + "def input_features_callback(input_features):\n", + " if display_images:\n", + " plot_msa_v2(input_features)\n", + " plt.show()\n", + " plt.close()\n", + "\n", + "def prediction_callback(protein_obj, length,\n", + " prediction_result, input_features, mode):\n", + " model_name, relaxed = mode\n", + " if not relaxed:\n", + " if display_images:\n", + " fig = plot_protein(protein_obj, Ls=length, dpi=150)\n", + " plt.show()\n", + " plt.close()\n", + "\n", + "result_dir = jobname\n", + "log_filename = os.path.join(jobname,\"log.txt\")\n", + "setup_logging(Path(log_filename))\n", + "\n", + "queries, is_complex = get_queries(queries_path)\n", + "model_type = set_model_type(is_complex, model_type)\n", + "\n", + "if \"multimer\" in model_type and max_msa is not None:\n", + " use_cluster_profile = False\n", + "else:\n", + " use_cluster_profile = True\n", + "\n", + "download_alphafold_params(model_type, Path(\".\"))\n", + "results = run(\n", + " queries=queries,\n", + " result_dir=result_dir,\n", + " use_templates=use_templates,\n", + " custom_template_path=custom_template_path,\n", + " num_relax=num_relax,\n", + " msa_mode=msa_mode,\n", + " model_type=model_type,\n", + " num_models=5,\n", + " num_recycles=num_recycles,\n", + " relax_max_iterations=relax_max_iterations,\n", + " recycle_early_stop_tolerance=recycle_early_stop_tolerance,\n", + " num_seeds=num_seeds,\n", + " use_dropout=use_dropout,\n", + " model_order=[1,2,3,4,5],\n", + " is_complex=is_complex,\n", + " data_dir=Path(\".\"),\n", + " keep_existing_results=False,\n", + " rank_by=\"auto\",\n", + " pair_mode=pair_mode,\n", + " pairing_strategy=pairing_strategy,\n", + " stop_at_score=float(100),\n", + " prediction_callback=prediction_callback,\n", + " dpi=dpi,\n", + " zip_results=False,\n", + " save_all=save_all,\n", + " max_msa=max_msa,\n", + " use_cluster_profile=use_cluster_profile,\n", + " input_features_callback=input_features_callback,\n", + " save_recycles=save_recycles,\n", + " user_agent=\"colabfold/google-colab-main\",\n", + ")\n", + "results_zip = f\"{jobname}.result.zip\"\n", + "os.system(f\"zip -r {results_zip} {jobname}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KK7X9T44pWb7", + "collapsed": true + }, + "outputs": [], + "source": [ + "# @title # **7. Visualize 3D Structure of ColabFold Prediction** { run: \"auto\", display-mode: \"form\" }\n", + "\n", + "#@markdown Rank number/score for the **monomer model is the average pLDDT**,\n", + "#@markdown where a ranking of 1 correlates better local structure confidence.\n", + "#@markdown An acceptable model prediction should average $\\ge 85$.\n", + "#@markdown The number of ranked models was set in step `6. Run Prediction` (step 6, line 75.).\n", + "\n", + "#@markdown \\\n", + "#@markdown Here, you can change the value for `rank_num` to visualize the 5 predictions.\n", + "\n", + "import py3Dmol\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "from colabfold.colabfold import plot_plddt_legend\n", + "from colabfold.colabfold import pymol_color_list, alphabet_list\n", + "\n", + "rank_num = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n", + "color = \"rainbow\" #@param [\"chain\", \"lDDT\", \"rainbow\"]\n", + "show_sidechains = False #@param {type:\"boolean\"}\n", + "show_mainchains = False #@param {type:\"boolean\"}\n", + "\n", + "tag = results[\"rank\"][0][rank_num - 1]\n", + "jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n", + "pdb_filename = f\"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb\"\n", + "pdb_file = glob.glob(pdb_filename)\n", + "\n", + "def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color=\"lDDT\"):\n", + " model_name = f\"rank_{rank_num}\"\n", + " view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)\n", + " view.addModel(open(pdb_file[0],'r').read(),'pdb')\n", + "\n", + "\n", + " if color == \"lDDT\":\n", + " view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})\n", + " elif color == \"rainbow\":\n", + " view.setStyle({'cartoon': {'color':'spectrum'}})\n", + " elif color == \"chain\":\n", + " chains = len(queries[0][1]) + 1 if is_complex else 1\n", + " for n,chain,color in zip(range(chains),alphabet_list,pymol_color_list):\n", + " view.setStyle({'chain':chain},{'cartoon': {'color':color}})\n", + "\n", + " if show_sidechains:\n", + " BB = ['C','O','N']\n", + " view.addStyle({'and':[{'resn':[\"GLY\",\"PRO\"],'invert':True},{'atom':BB,'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"GLY\"},{'atom':'CA'}]},\n", + " {'sphere':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"PRO\"},{'atom':['C','O'],'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " if show_mainchains:\n", + " BB = ['C','O','N','CA']\n", + " view.addStyle({'atom':BB},{'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}}, viewer=(0,0))\n", + "\n", + " view.zoomTo()\n", + " return view\n", + "\n", + "print('Prediction: Rank %d' % rank_num)\n", + "show_pdb(rank_num, show_sidechains, show_mainchains, color).show()\n", + "if color == \"lDDT\":\n", + " plot_plddt_legend().show()\n", + "\n", + "\n", + "#@markdown \\" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "33g5IIegij5R" + }, + "outputs": [], + "source": [ + "#@title **8. Package and Download**\n", + "\n", + "#@markdown If you are having issues downloading the result archive, try disabling your adblocker and run this cell again. If that fails click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \\\"Download\\\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).\n", + "\n", + "if msa_mode == \"custom\":\n", + " print(\"Don't forget to cite your custom MSA generation method.\")\n", + "\n", + "files.download(f\"{jobname}.result.zip\")\n", + "\n", + "if save_to_google_drive == True and drive:\n", + " uploaded = drive.CreateFile({'title': f\"{jobname}.result.zip\"})\n", + " uploaded.SetContentFile(f\"{jobname}.result.zip\")\n", + " uploaded.Upload()\n", + " print(f\"Uploaded {jobname}.result.zip to Google Drive with ID {uploaded.get('id')}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P_yNIT-iBd65" + }, + "source": [ + "Colab notebooks are adapted from:\n", + " - [AlphaFold2 Colab](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb)\n", + " - [Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all.\n", + "*Nature Methods*, 2022](https://www.nature.com/articles/s41592-022-01488-1)\n", + "\n", + "*From the original notebook:*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UGUBLzB3C6WN" + }, + "source": [ + "> \n", + ">\n", + "> ## ColabFold v1.5.3: AlphaFold2 using MMseqs2\n", + ">\n", + "> Easy to use protein structure and complex prediction using [AlphaFold2](https://www.nature.com/articles/s41586-021-03819-2) and [Alphafold2-multimer](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1). Sequence alignments/templates are generated through [MMseqs2](mmseqs.com) and [HHsearch](https://github.com/soedinglab/hh-suite). For more details, see bottom of the notebook, checkout the [ColabFold GitHub](https://github.com/sokrypton/ColabFold) and read our manuscript.\n", + ">\n", + "> Old versions: [v1.4](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.4.0/AlphaFold2.ipynb), [v1.5.1](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.1/AlphaFold2.ipynb), [v1.5.2](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.2/AlphaFold2.ipynb)\n", + ">\n", + "> [Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all.\n", + "> *Nature Methods*, 2022](https://www.nature.com/articles/s41592-022-01488-1)\n", + ">\n", + "> -----------\n", + ">\n", + "> ### News\n", + "> - 2023/07/31: The ColabFold MSA server is back to normal. It was using older DB (UniRef30 2202/PDB70 220313) from 27th ~8:30 AM CEST to 31st ~11:10 AM CEST.\n", + "> - 2023/06/12: New databases! UniRef30 updated to 2023_02 and PDB to 230517. We now use PDB100 instead of PDB70 (see [notes](#pdb100)).\n", + "> - 2023/06/12: We introduced a new default pairing strategy: Previously, for multimer predictions with more than 2 chains, we only pair if all sequences taxonomically match (\"complete\" pairing). The new default \"greedy\" strategy pairs any taxonomically matching subsets.\n", + ">\n", + "># Instructions \n", + ">\n", + "> **Quick start**\n", + "> 1. Paste your protein sequence(s) in the input field.\n", + "> 2. Press \"Runtime\" -> \"Run all\".\n", + "> 3. The pipeline consists of 5 steps. The currently running step is indicated by a circle with a stop sign next to it.\n", + ">\n", + "> **Result zip file contents**\n", + ">\n", + "> 1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if `use_amber` is enabled).\n", + "> 2. Plots of the model quality.\n", + "> 3. Plots of the MSA coverage.\n", + "> 4. Parameter log file.\n", + "> 5. A3M formatted input MSA.\n", + "> 6. A `predicted_aligned_error_v1.json` using [AlphaFold-DB's format](https://alphafold.ebi.ac.uk/faq#faq-7) and a `scores.json` for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.\n", + "> 7. BibTeX file with citations for all used tools and databases.\n", + ">\n", + "> At the end of the job a download modal box will pop up with a `jobname.result.zip` file. Additionally, if the `save_to_google_drive` option was selected, the `jobname.result.zip` will be uploaded to your Google Drive.\n", + "> \n", + ">\n", + "> **License**\n", + "> githubusercontent.com/sokrypton/ColabFold/main/LICENSE). Additionally, this > notebook uses the AlphaFold2 source code and its parameters licensed under [Apache 2.0](https://raw.githubusercontent.com/deepmind/alphafold/main/LICENSE) > and [CC BY 4.0](https://creativecommons.org/licenses/by-sa/4.0/) respectively. Read more about the AlphaFold license [here](https://github.com/deepmind/> alphafold).\n", + ">\n", + "> **Acknowledgments**\n", + "> - We thank the AlphaFold team for developing an excellent model and open > sourcing the software.\n", + ">\n", + "> - [KOBIC](https://kobic.re.kr) and [Söding Lab](https://www.mpinat.mpg.de/soeding) for providing the computational resources for the MMseqs2 MSA server.\n", + ">\n", + "> - Richard Evans for helping to benchmark the ColabFold's Alphafold-multimer support.\n", + ">\n", + "> - [David Koes](https://github.com/dkoes) for his awesome [py3Dmol](https://3dmol.csb.pitt.edu/) plugin, without whom these notebooks would be quite boring!\n", + ">\n", + "> - Do-Yoon Kim for creating the ColabFold logo.\n", + ">\n", + "> - A colab by Sergey Ovchinnikov ([@sokrypton](https://twitter.com/sokrypton)), Milot Mirdita ([@milot_mirdita](https://twitter.com/milot_mirdita)) and Martin Steinegger ([@thesteinegger](https://twitter.com/thesteinegger)).\n", + ">\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [], + "gpuType": "V100", + "cell_execution_strategy": "setup", + "machine_shape": "hm", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/_sources/ColabFold/old/ColabFold_AF2_mmseqs2.ipynb b/_sources/ColabFold/old/ColabFold_AF2_mmseqs2.ipynb new file mode 100644 index 0000000..d8e69bf --- /dev/null +++ b/_sources/ColabFold/old/ColabFold_AF2_mmseqs2.ipynb @@ -0,0 +1,1012 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G4yBrceuFbf3" + }, + "source": [ + "\n", + "\n", + "##ColabFold: AlphaFold2 using MMseqs2\n", + "\n", + "Easy to use protein structure and complex prediction using [AlphaFold2](https://www.nature.com/articles/s41586-021-03819-2) and [Alphafold2-multimer](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1). Sequence alignments/templates are generated through [MMseqs2](mmseqs.com) and [HHsearch](https://github.com/soedinglab/hh-suite). For more details, see bottom of the notebook, checkout the [ColabFold GitHub](https://github.com/sokrypton/ColabFold) and read our manuscript. Old versions: [v1.0](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.0-alpha/AlphaFold2.ipynb), [v1.1](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.1-premultimer/AlphaFold2.ipynb), [v1.2](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.2.0/AlphaFold2.ipynb), [v1.3](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.3.0/AlphaFold2.ipynb)\n", + "\n", + "[Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all.\n", + "*Nature Methods*, 2022](https://www.nature.com/articles/s41592-022-01488-1) " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kOblAo-xetgx", + "cellView": "form" + }, + "source": [ + "#@title Input protein sequence(s), then hit `Runtime` -> `Run all`\n", + "from google.colab import files\n", + "import os.path\n", + "import re\n", + "import hashlib\n", + "import random\n", + "\n", + "def add_hash(x,y):\n", + " return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n", + "\n", + "#@markdown Example query sequence is for bax (obtained from 00_monomer_pred/bax.fasta).\n", + "#@markdown - Use `:` to specify inter-protein chainbreaks for **modeling complexes** (supports homo- and hetro-oligomers). \n", + "#@markdown - For example **MDG...KMG:MDF...KMG** for a bax homodimer.\n", + "query_sequence = 'MDGSGEQPRGGGPTSSEQIMKTGALLLQGFIQDRAGRMGGEAPELALDPVPQDASTKKLSECLKRIGDELDSNMELQRMIAAVDTDSPREVFFRVAADMFSDGNFNWGRVVALFYFASKLVLKALCTKVPELIRTIMGWTLDFLRERLLGWIQDQGGWDGLLSYFGTPTWQTVTIFVAGVLTASLTIWKKMG' #@param {type:\"string\"}\n", + "\n", + "# remove whitespaces\n", + "query_sequence = \"\".join(query_sequence.split())\n", + "\n", + "jobname = 'bax' #@param {type:\"string\"}\n", + "# remove whitespaces\n", + "basejobname = \"\".join(jobname.split())\n", + "basejobname = re.sub(r'\\W+', '', basejobname)\n", + "jobname = add_hash(basejobname, query_sequence)\n", + "while os.path.isfile(f\"{jobname}.csv\"):\n", + " jobname = add_hash(basejobname, ''.join(random.sample(query_sequence,len(query_sequence))))\n", + "\n", + "with open(f\"{jobname}.csv\", \"w\") as text_file:\n", + " text_file.write(f\"id,sequence\\n{jobname},{query_sequence}\")\n", + "\n", + "queries_path=f\"{jobname}.csv\"\n", + "\n", + "#@markdown Amber minimization option.\n", + "# number of models to use\n", + "use_amber = True #@param {type:\"boolean\"}\n", + "#@markdown Template mode refers to usage of PDB templates from RCSB.\n", + "\n", + "#@markdown 3 options:\n", + "#@markdown - \"none\" = no template information is used\n", + "#@markdown - \"pdb70\" = detect templates in pdb70\n", + "#@markdown - \"custom\" - upload and search own templates (PDB or mmCIF format, see [notes below](#custom_templates)).\n", + "template_mode = \"none\" #@param [\"none\", \"pdb70\",\"custom\"]\n", + "\n", + "\n", + "if template_mode == \"pdb70\":\n", + " use_templates = True\n", + " custom_template_path = None\n", + "elif template_mode == \"custom\":\n", + " custom_template_path = f\"{jobname}_template\"\n", + " os.mkdir(custom_template_path)\n", + " uploaded = files.upload()\n", + " use_templates = True\n", + " for fn in uploaded.keys():\n", + " os.rename(fn, f\"{jobname}_template/{fn}\")\n", + "else:\n", + " custom_template_path = None\n", + " use_templates = False\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#@markdown ### MSA options (custom MSA upload, single sequence, pairing mode)\n", + "#@markdown 4 options:\n", + "#@markdown - \"MMseqs2 (UniRef+Environmental)\"\n", + "#@markdown - \"MMseqs2 (Uniref only)\"\n", + "#@markdown - \"single sequence\"\n", + "#@markdown - \"custom\"\n", + "msa_mode = \"MMseqs2 (UniRef+Environmental)\" #@param [\"MMseqs2 (UniRef+Environmental)\", \"MMseqs2 (UniRef only)\",\"single_sequence\",\"custom\"]\n", + "\n", + "#@markdown 3 options:\n", + "#@markdown - \"unpaired+paired\" = pair sequences from same species + unpaired MSA\n", + "#@markdown - \"unpaired\" = seperate MSA for each chain\n", + "#@markdown - \"paired\" = only use paired sequences\n", + "pair_mode = \"unpaired+paired\" #@param [\"unpaired+paired\",\"paired\",\"unpaired\"] {type:\"string\"}\n", + "\n", + "# decide which a3m to use\n", + "if msa_mode.startswith(\"MMseqs2\"):\n", + " a3m_file = f\"{jobname}.a3m\"\n", + "elif msa_mode == \"custom\":\n", + " a3m_file = f\"{jobname}.custom.a3m\"\n", + " if not os.path.isfile(a3m_file):\n", + " custom_msa_dict = files.upload()\n", + " custom_msa = list(custom_msa_dict.keys())[0]\n", + " header = 0\n", + " import fileinput\n", + " for line in fileinput.FileInput(custom_msa,inplace=1):\n", + " if line.startswith(\">\"):\n", + " header = header + 1\n", + " if not line.rstrip():\n", + " continue\n", + " if line.startswith(\">\") == False and header == 1:\n", + " query_sequence = line.rstrip()\n", + " print(line, end='')\n", + "\n", + " os.rename(custom_msa, a3m_file)\n", + " queries_path=a3m_file\n", + " print(f\"moving {custom_msa} to {a3m_file}\")\n", + "else:\n", + " a3m_file = f\"{jobname}.single_sequence.a3m\"\n", + " with open(a3m_file, \"w\") as text_file:\n", + " text_file.write(\">1\\n%s\" % query_sequence)" + ], + "metadata": { + "cellView": "form", + "id": "C2_sh2uAonJH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#@markdown ### Advanced settings\n", + "#@markdown - \"auto\" = protein structure prediction using \"AlphaFold2-ptm\" and complex prediction \"AlphaFold-multimer-v2\". \n", + "#@markdown - For complexes \"AlphaFold-multimer-v[1,2]\" and \"AlphaFold-ptm\" can be used.\n", + "model_type = \"auto\" #@param [\"auto\", \"AlphaFold2-ptm\", \"AlphaFold2-multimer-v1\", \"AlphaFold2-multimer-v2\"]\n", + "\n", + "num_recycles = 3 #@param [1,3,6,12,24,48] {type:\"raw\"}\n", + "\n", + "#@markdown - The save_to_google_drive option will upload zip file to your Google Drive\n", + "save_to_google_drive = False #@param {type:\"boolean\"}\n", + "\n", + "dpi = 300 #@param {type:\"integer\"}\n", + "#@markdown - set dpi for image resolution\n", + "\n", + "#@markdown Don't forget to hit `Runtime` -> `Run all` after updating the form.\n", + "\n", + "\n", + "if save_to_google_drive:\n", + " from pydrive.drive import GoogleDrive\n", + " from pydrive.auth import GoogleAuth\n", + " from google.colab import auth\n", + " from oauth2client.client import GoogleCredentials\n", + " auth.authenticate_user()\n", + " gauth = GoogleAuth()\n", + " gauth.credentials = GoogleCredentials.get_application_default()\n", + " drive = GoogleDrive(gauth)\n", + " print(\"You are logged into Google Drive and are good to go!\")" + ], + "metadata": { + "cellView": "form", + "id": "ADDuaolKmjGW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "iccGdbe_Pmt9", + "pycharm": { + "name": "#%%\n" + }, + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5e951ead-06d9-499e-ecf8-860d2bbbf8cb" + }, + "source": [ + "#@title Install dependencies\n", + "%%bash -s $use_amber $use_templates\n", + "\n", + "set -e\n", + "\n", + "USE_AMBER=$1\n", + "USE_TEMPLATES=$2\n", + "\n", + "if [ ! -f COLABFOLD_READY ]; then\n", + " # install dependencies\n", + " # We have to use \"--no-warn-conflicts\" because colab already has a lot preinstalled with requirements different to ours\n", + " pip install -q --no-warn-conflicts \"colabfold[alphafold-minus-jax] @ git+https://github.com/sokrypton/ColabFold\"\n", + " # high risk high gain\n", + " pip install -q \"jax[cuda11_cudnn805]>=0.3.8,<0.4\" -f https://storage.googleapis.com/jax-releases/jax_releases.html\n", + " touch COLABFOLD_READY\n", + "fi\n", + "\n", + "# setup conda\n", + "if [ ${USE_AMBER} == \"True\" ] || [ ${USE_TEMPLATES} == \"True\" ]; then\n", + " if [ ! -f CONDA_READY ]; then\n", + " wget -qnc https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n", + " bash Miniconda3-latest-Linux-x86_64.sh -bfp /usr/local 2>&1 1>/dev/null\n", + " rm Miniconda3-latest-Linux-x86_64.sh\n", + " touch CONDA_READY\n", + " fi\n", + "fi\n", + "# setup template search\n", + "if [ ${USE_TEMPLATES} == \"True\" ] && [ ! -f HH_READY ]; then\n", + " conda install -y -q -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 python=3.7 2>&1 1>/dev/null\n", + " touch HH_READY\n", + "fi\n", + "# setup openmm for amber refinement\n", + "if [ ${USE_AMBER} == \"True\" ] && [ ! -f AMBER_READY ]; then\n", + " conda install -y -q -c conda-forge openmm=7.5.1 python=3.7 pdbfixer 2>&1 1>/dev/null\n", + " touch AMBER_READY\n", + "fi" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\r 0%| | 0/40 [00:00" + ], + "image/png": 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iIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiogALJCIiIiIiooC7rztAvWfKG6s/LkZ+KsBBAFpU9V4A19eWlrbs674RERH1tsdWT/m4iPxURDrk4GmxWuYgEWUkqrqv+0C9oKSm5jlj5CQR6bDcWrvNWp1eV1q6YR91jYiIqNc9XlPynDEmQw7a6XNL65iDRJQWh9j1QyWrV//DcTqHAgAYY4YbkRX7oFtERER7xeM1Jf9wHCdzDhrDHCSijFgg9TMTX68uNcZckK2NcczoktWrP7e3+kRERLS3PFo9MXcOGjP68ZoS5iARpcUCqR+Z+Hq1IyJ/F9P5jFka5/d2f4iIiPamR6sn+jmY5spRGsxBIkqLBVL/cpUIjgrVUmRwL/eFiIhob7tKRMLlIMAcJKK0OItdH3Hw00tnGdfcKCLTAKiqNkD16rc/evwLADDx9epiADeEPGsGAdb2YneJiIh61INLD57lGj8HBVCr2mAVV3/i+LdfAIBHqyfml4MizEEiSosF0n5uVOWSw43r3OYWuBUpH/rHquqSQ5596TcDhg38JoBbAQxA2GAwUtkL3d0rSuteFmu9MwBc7xeM4qpqM2AXeV7ikvqpH23c130kIqKeMX/JqMOjjrmt0O2Yg06Qgw+/dMhvzMABe3IwJJG+m4N1Z5WKbbJnQHG9uDINLlyNazPiWORt9y6Z+ko9c5CoG1gg7adGVS4ZBeD7EHzVLXCj6c6IiQiM61yTaE0YN+qe4i8Lt38RWdeT/d0bSuteHg7gc6r6NWOckuR1IlIEmI8DZv2U1Uum1JXOensfdZOIiHrA/CWjRgH4vgBfLXQz52DEMdfEEwlj3LYcDH0Fqc/lYN1ZpXtysMh0zMECKUIBPo4I1q+eNWVK6ZI65iBRF/E5SPuZUZVLhgD4FoBvAhjkRF240Sx1rADRoqgVEQMAkagL44S6tWz8G5OndDkcYrUrBJAjAewAdGNNybFeV/eVTWndywLgJABfgn9DbWGubaxNrE0kWieumTabP9xERH3M/CWjOuRgoeugwM2cgwrAi0Yt2nIwEoEx4XKwfPIbXc7B2nti0jwGR4rFjoLN2FhyUU2v5GDdWaX55+BOuzbxTmLitJo1zEGiLuAVpP3EqMolhQC+CuD7AEa2Lc9V7LhRF23FEZB65iz5c7HDGTULoEtnlmK1rwiAuwF8CtBo24FitStWA7gfwAsAXqopObZbl/dL614eCeASAFcAiOWzrYhzBCCXAfjf7vSBiIj2nvlLRqXNQSdLsaMArOsCGXIwYwp2IwdfezompgV3OwX4VOE7iAKAGujq+2OrpRX3S5CDJRfVdCsH684q7XoODpQjEMFlYA4SdQmvIO1joyqXuAAuA/AjAIelro8URTMWScYxiBRGOiyLFkYgAhijHYbbqQLWCoKI2PTG5Clj8+1rrPZVB7CvAzgyR1MFsAp+SLS96mtKjs36wxZcLToZfhicByCarX028Xhzo6qdvGba7Pe6ug8iIup985eMypqDA6MRuBmKJGsMbCQlB6NRBEGINEHYVihtKp/8Rt45uGJ5zIluxuvRxq7lYMlFNVlzMLha1DM5uCHeqM06eVrNGuYgUZ54BWkfGVW5xMD/8LseQEmmdmotkKFAcgs6f/vSFUfJy60FANnQtV7rfOQujgC/CpsWvK4Ilr0Xq12RHBTLa0qObQKA0rqXRwO4FP7wgcld61snwwD8BgAfBEhEtB+av2RUqBy0VtM+lKT96lGqdMVR0nL1i6Qu5aBpwfwQxRGQIQdr74l1yMGSi2qaAKDurNKez0FlDhJ11QF1BenptdOnA7gOIuOgugnAzXOOeP2lvdmHUZVLBMCpAG4AcEyu9sYxiBR1PoHkFrhwXKfDMhFB4QAn60QNqoCqLFk1qeSkfPodq115LeDdnM822WkCQD1gHQBHiPRcsa6qiMeb2v551pppsx/tqX0TEfVl09f+azqA60TMOFW7CcDNrx9x9l7NwflLRuWVg64xGBiNdFruuS7U6ZyDkcLC7DMW+UG4pGLSqrxy8KXa2LUD63Fz5IN8tspCkcAG1OMNOHgfRwik53LQKuIN8bZ/njWtZg1zkCgPB0yB9PTa6Q87xpzVYWyyKjxrF0HktDkTXrO93YcRC549Txz5lTFmfD7bRQcUQMyefqcbWgcAjiMoKHI6LU+lCk8Ep1UVx54Mc/xY7crzAcwHeur+UwVgQ8+4l69EohXWJtr+uQHA1DXTZm/vnaMREfUN09c+9LAxhZ1y0NrmRSI47bUJn+j1HLzj2RHnRUV+Fc0zBwcXRGGS+p1uaB0AGMeBW1CQe4eqHkROqyiuCpWDL9XGzgcwf9AbgNuUs3lu2wG8BMiW3gnCxHsJ2Mb2b+cGAFOn1axhDhKFFGqal77uqbXT73Yd56zUqT9FBK7jlAvwwnPrju6lX9f9q0YjFjx7v3HN/fkWRwDgJToWJplmtRMn7NSmcAA8XNZQ89FcbWO1Kz8C4K+hdhxK7xZHnpdILo4Afzz7jb1zNCKivmH62gfvdpyitDnoOEXlgHnh6HWVvZaD85eMktufHXH/QGPuz7c4AoBWb08OZhxah/BTfEPEAfBwZUNZzhx8qTa2Jwd74iu0G8ATvVccedu95OIIYA4S5a3fF0hP1E+d4Rrz2WxtHGOOV9Xre6sPidb4rU7EOS/0B3cKL55A25U+N+p2uJqUTG1eVwOLADxa1lAzM1ODWO3KUgD/AhCcjuuJD/PeKY6s9RCPN8PzWtOtvnJi9eKcIUhE1B9Nrf/bDGOKsuagMQXHq9pey8Gt8cStg52u52BrwmvPwWDWurTt8hwVUwTg0cqGsow5+FJtrEMOxofls/sMlgPS2vNBaJss4pvi8DanHe1xZXVsInOQKKR+XSA9Xj/VFZF7wnwgi8h/Prfu6C/1dB8G3V95knGdy7u1EwXUs60AYNzM3zIvochzxOQgAI+VNdQcm7oiVrtyDICFAIbvWdrdD/SeHc6pqp7nxdHa2oREogWqmUeHqOrtE6sXF/VoB4iI9nNT6+e5Is49SbNgZyRi/vPodZU9noPfqRx00iDHdCsHFUDCaqsC0CzTflvPQ55BOAjAY5UNZZ1y8KXaWKccbD0IsJ1H9oXX5cnF01NPPa/RQ+u/W5HYmIDuzvzeFXp7dWwic5AohP4+i921ApTm0f7Pz607evNHDn/1X/kcZNmm4woAjAdwhBE50RFzkoiMVNWNJ4787JxXE0fn1ekUOwH8UoypM67JWewl4haRaF5171AAT5Q11MypKo69DgCx2pWDADwK/z0lEfg1ddeHqffQ1aPFAG6Nx5vXAvo8gJw3XonIJFX9E4DP90gPiIj6hmsBySsHj15XufnVwyvyysHjNv2+PQdFzIlGnPYcnDHkyDnRxNq8Op1iJ4BfOkbq1Jh7cgWJl0jASXN/UhZDATxR2VA2p6K46nUAeKk2ljYHNQLsnAIMehMwaQcs5LAbEK9HgnAxgFvjG+JrEUe4HIRMUpc5SBRGv52k4fH6qVMAvCYihUX5fVA2Ayj/yOGvLmlbsGzTcQJgNIDipNcRSX8/DIC4xsA1DlJvgH1j12H4Rv1X4eVXj7YA+BOAn2+pmLXlsBdXRJyI874bdQfn2jBaaNR1M4zDy2wLgJPj8eY3ATwE4PTMTRV7rgbl8/PTrfuP3oP/wLvbVk85rq5t4cTqxT8HcF2oo6sCsJc2lJ0yr0s9ICLqQ6bWzwty0C2MREbls2kzgPJXD69oz8HjNv0+VA4a48IxbqccHN20DudvuBVufifY2nPwgllbtjy+4rCIdZz3rZs7B91oVI3rdikHh8TjuXNQgcg2INIIuDsBE8/YsiMLYD4gtktB2J6DUx5Z3Z6D1bGJ4XMQCgzDpWVLG5iDRFn0ywLp8fqpBsAzAE4CgALXzfok7jYC/wZPgex2jXlARIZhTwgMyLatYwyiTuYCaG3TKFxRdw0SmrNIsvA/AH+ypWLW+uQV41esXOBG3CyFi09VfzVgUGScCC7I1TZlu7c9r/U5Vc1rO0BvAuwKACcGr6OR8WyWl2+BtAjArQD+tXrKcZ3O1wXD5lYCmBKqp2qbAW9yQ1lFF58FRUS0/5taP69DDrruSBgT5pmjrRBpgaB1tzHbHhBJhM5BYxy4TuZjDGt5B5/99x/g5J4RtT0HL5i1pUMOLlg5foG6uXMQqr+KDhgwDiL55Znq2wM97zknzxx0duGmwTUIl4OLAdmUVxC25+CUR1Z3ysFg2Fz4HByozRiNyWULG5iDRBn01wLpKgC/b/u3iKDQdTPObmNE4BqnwxSi+Sp0I1n3DwAvb5+I7zdchrhmvKJ1P4AfbKmYVZNu5eHLXi11o+4b2YbZqdXdYmTkgEFuAsDdAD4d/l34Z/oSiVbkcWXoLwC+XFMyo32DWO2KgQA+hD1BcSKAEcEREOIq0rsA7gBw++opx63J1YGJ1Ys/AuDZMJ31f94TKwB8tKGsYneYbYiI+pqp9fNSctCF6x6UMadEPoBjtkKkpcvHjLiFGfZfCJGhACzG7lyGT2y6Ha4m0rQDEOTgBbO2pM3Bha8eXmpd940czznaDZGR0QEDupSDooqBiUQ+N2n/BcCXjy+pac/B2ntimXNwJ4BHcw61a8/BKY+szpmD1bGJ4XNQFBiPFRB8tGxhA3OQKI1+VyA9Xj/1CABVAAYmL3dEEE1TJDliEHFyPzsoGyOCArdz0RNx/KKr7ZiqivXNw3DpqqvRrHue02CtrTbGXLalYtaKXMeasOK1e5yIc2G6daoKm7BnvnXsUQsAoKyhJgJgPoBP5vN+VG1QJOW0AMDZNSUzMiYdAMRqVwj8M1tBSOgcwE5M/lYEP4ZrAHxbBI+snnJc2AELAICJ1Yv/COCruU1pFFsAACAASURBVNoFBRIA3Avgooayiv71H4CIDnhT6+elzUGRQrjusE45aOR9OM6Wbh1TxCDipj5/KArHOQYihyTlYByDWpbjMw1XIKp7ciZubXXEmMsumLUlZw4ueG3CPeqkz0GoQqw984yj3loAAJUNZV3KwTyKpAUAzj6+pCZrDtbeE+uYg+9hDl7GRGlMGooIBYZiDT7AtwXyyJRHVueVg9WxieFyEOpfDwxysGxhA3OQKEW/KpAer58qAJ4AUJ5uvWsMoknPThAAUSfzlaWwHOm4XwCIum7GK1Jv7R6Oz1RfC+spEi3xRihGbD/nlNDfiAmvvPZHY8xXxOyZlsh6dodaveytY4/6Z3LbsoaaAgD/BHBGPu8pRJG0AsDsmpIZO/PZb5tY7fKDAL0AwHQAWwHcUVPy4fqu7AsAJlYvHqKqG0Qk69j0pAIJAL7fUFbx864ek4hofzO1fl7WHDRmAFx3aNKSVrhOQ7cn0DHiwHWTh9dF4bofhciQtO0HNy/HhfUXoclabIgnGqPAiOtO2R46Bxe8NuGPasxXkDw9n7U7RPWyM456q0MOVjaUdSkHjSoGZC+SVgCYfXxJTZdysPYPsYPwDi7ANkxHEbaiCXeU/LGmyzlYHZs4RI1uEJsjBx0FDm//5/fLFjYwB4lS9LcC6YsAbsvWJuI47VeM2iZV6C4BUBjZEwyuMXBzXJW6qe4U/G3d8bBx7xe7L5z73XyPefiyVwuMMRep6qEisizRmli04YRj0979WtZQUwjgYWQIzEyyFElvATihpmTGO/n2uzcVVz31JRFza7Y2qh6SZuFTAJ9sKKt4qLf7RkS0N0ytn5czBx1nMBxnEADAmHfhmG09cGRBNFLY/i9jSuE42SfPm7Lumxjy7v1Y5dlfLJi7O+8cXPjq4QVqzEVQPRQiy5xEYtFpx25Im4OVDWVdykETXElKUz++BeCE40tq9qscrJpR/CVpluw5OEyTH+ChAD5ZtrCBOUiUpN8USI/XTx0L4A0A6U9XJYk6DlzHQcQ4oSZvCKPAcWGCfRVkud+pTd32kfjUkssB4NTdF86t7JFOZFHWUDMQ/lCAvB4U53kJWNth5MBWACfWlMyo7cHu9ZjiqqeWiJgT063zn5PU6ebgnQBmNpRVrOrtvhER9aap9fNC56DjDIXjDIBjNsKYHT1yfNcpaM9B1z0DIoXZ2+96EVJ1PhQ4tXLu7l7PwcqGsi7lYNTzUGg71F1bAZx4fEnNfpmDVR8qXiI7JX0OFigwBqlPwdwJYGbZwgbmIFGgXzwoNhha92eECAUAaPW87ar6ZE/2odVLQFXbZ8LLZUR0F+D/tv5iT/Yjk6ri2C4AZwFYms92puMVthYAn9hfiyOfN0fVq0su/FU1U3EE+A8JfKi4qnLkXuogEVGPC4bWhc5Bz/tgu6p9svsPAN8j4bUGw5gH5yyOACAeGQHdizlYUVzVpRxsNSZ52qIWAJ/YX4sjAMAozNHhWqduUg6KQgenLY6AIAerTi9mDhIF+kWBBOAi+B96YX1LRM721L7ZUx1Q+EVS2PuZdiQKAWD57gvndmnscldUFcd2wH+uwytht5E9k0wogM/WlMxYkmOTfaqhrKIVsCcBibdU41CNw7/nKOu0ssUA7iuuquzO89GJiPalLuSgOdvqwB7LQUCR8FohMiJc88ROAFheOXf3XsvBiuKqvHMQIvCScvD4kpr9OgfLFja0YhhOwmF4Sw9T6NjgnqODkO23vmIA91WdXswcJEI/KJAer596MIDf5bHJkwBu/8jhr+5SxclWbVMPdGMjgOes6l1GJNRZpde2jQVCTsnZk6qKY40AToU/w1E+vllTMuOBXuhSj2soq9gC4GwAu/LYbA6Am3unR0REvWdq/bwu5eCrh1fsgkZOtlrYYzmoau8SOTRUDsqOV4F9kIMVxVV552Bw6vObx5fU9IkcLFvYsAWCsxHBLkQR9rc95iBRoM/fg/TYm0c+KCJnh2y+G8C00yatWtu24IX1x05xjFlqRIZn2siqxj1rm+PWRhLWRhNqJWFtS8LaZREx550xeVX7/Kj1jXPeAJD9zlQAly69CK9uG3fW7gvnPhqy750Mnrfo0wK5WkRKoNqowAsK/dKOS8pzPsiirKFmtKouFZEjsrXzn4vU8ruakhnf6Go/95Xiqspz4M9clI8vN5RVZL3BlYhof3Lkm/MeFEFeObhq0iXtOXjs+r9PMWbHUpGWjDmoKnFrC5ptfGDEtg6M2tYBYm1Bi7XRZaZgx3mrpla05+Ccxq2hctCsOgdmx7KzKufu7noOLrru0yKFV4tES6Beo6L1BdWWL+0o/0XOHKxsKBsN1aXIkYNQxeBE4nczS2r6XA5WnV7cpRwsW9jAHKQDWp8ukBbUlX7dMSb0WbPWePytuOfVKgCrutMCrfBH5B4yuKBgwoBItFBEkLAWzfFWxK1F3PNgVeFEIpA0Ezqotc2qWnZ+aV19feOcEQDez9WPuDU4sfIabbGR4bsvnPtBHm8ZADB43iIRyCNGzBmpQ/qs2mar9pQdl5S/kGs/09asPhRAnYgMzNTG8xLV1iaOqimZkXZmoP1dcVXlDwD8NPwWqoD9CaA3NJSdntczKIiI9rbSur993RgvdA7G44Vved7AWiAK1YKdwMBWQMYA9pCCgm0TopEPCkU8WBtBa3wgrC2E5xVCPReR3QNhbOcctMZrVseW1R03tX5O49ZQOQjbCmfZkSraPLxy7u78c3DRd0Sk6BFjhp+RPNM3AFjb1GztB6fsKL8hZw5Wrpl2qAB1miUHI55XXWTtUceX1PTJHKw6vTi/HFQoLH4CxQ1llQ3MQTog9dkC6eGaksERx9kmIqHm6fasRUu84/9zVYWnilyfeMZ1YbJM222tXX9+rPbw+sY5p8OfISer1xsPwedevOTV3RfOPSZM35MNv3uxeNZ7xjHORzL2R+0Oz3oH77y0Iuewian1q6aJyFIR0ykcrPUarPWmr54yPZ+havuV4qpKAfAPAOeH30oBeJvgD1n5S0PZ6Vt7pXNERN1QUnPfYMdp3iaioXLQWgfx+CAkT8ygaqA6DMCAzBsq4DYVwkm4GZtYJ7G+duaRh89p3BoqB7HjFbirPvFq5dzd+efg4t+J5217xnGGZ85Bu2uH520+eGfFr3Pm4NP1U6dZkaU2TZHkWNsQtXb6SVNW99kcrDq9OP8cVAAe2nOwrLKBOUgHlD57D5IR+WHY4khV0Zro/JBrEYEjkvWLICJZiyMAMMaMu2/1lA/Df0J2TsH9R8+EaZvKWu+BbMURABgxg43I98Psb9WkqdXW2hHWendb621Qtbus9dZ4XuJnqyZNndiXiyMAaCirUACfB7Ay/FYCwBwK4EYA64urFv6puGphrFc6SETURSLeD8MWR6pAIjEAqbPWiViIbEW2WzbFc7IWRwBgPHfclJdXhc5B2bkC6GoOeo0PZCuOAMCYgYONDAyVg3Mmrap2rB0RsfZux9oNRnWXY+2aiOf97GOTVk3sy8URAJQtbMg/BwWAQXsOVlUU/6mqopg5SAeM7J94+7fQD3uLex4yXSkTETgAbKb1bsgvkeq5AI4L03RlY9cmaBj616duMcY5J1xrmQvgB2FavjF5WiuAi/PtT1/RUFaxq7iq8mwAywGMCrdV+y8RAwBcCeDK4qqFCwH8BkBlQ9npffPSKxH1IzZ0DnpeITRDLeWP1P4Aqp0LKABwW8JNbKYSPgdlx3KgKzn41H/dYpxh4XJQ3NA5OHvyG/06B8sWNuyqOr04vxzc86PQnoNVFcXtOVhW2cAcpH6rz15Bgj93c06etUh4Wad49qeyTr+i/aF3uYhIi2q4YHi98VAAeC7UjgND//rUz4yYK/LYpC8Xvz2uoaxiHYBzAYQcT532J+J0AI8DqC6uWnhFcdXCLGNSiIh6XagctNaB5xVkbSNiATR3Xu4ZGC9cnAikBarhCqSdrwD55uBTN/zMmCF55KAwB5OULWzILwfTP7WkPQerKoqvqKooZg5Sv9RnCyQFct58mWloXTrpPgdyDa1LdujgIVUiGJSr3dtNg/FO0+Dq3RfOfS/svof+9alrjZhQQwX2UD4RO0VDWcXz8M+ChZD1xNiRAG5R1Y1HvP7o7Ue8/ujh3e8dEVG+TIgcTD+0LkPrTkuckFePAGDI6MFVEMmZg2jZCLS8XV05d3f4HHzqxmuNGZRfDmqCOZiibGFD+BzMfn3Iz0HoxtfPPuL21z9+BHOQ+pU+WyBB9btWNWv1k21oXafdpVmWbta6tNtau270oMEHh2m7cttYQDG+aN6Cm4rmLTi+aN6CrKk17K9Pf14geT2XQFWtAt/JZ5sDRUNZxe2AhniORe6fGxEZJmK+AMi/J7z2yIvjVz7EM2lEtNeoyndVJWsOZhta11lK5lmByXHvUXtT460bPHRQqByUHcvxLgrHFy0Yf1PRgvHHFy0Ynz0Hn77p8yJFeeZgq1V4zME0yhY23A5F7hwM8euTQIbJbvkCWvDv1z474cWVl49nDlK/0GcLpLNitc2e552jqmknoYt7Xs6hdW1UtdPngHFdpE6hnZHIVwCcEKbpa41jAauDAfwHgKUA/l00b8HNRfMWnFA0b0GH78ewvz59DoDbQ/cDbTPzeTfsuKR8U+iNDjjyGUCzfH0UyDm3YdLeRGCMM1PEWT9+5UPhLzsSEXVDbezTzZ4XPUdV0n5geV4059C6NqoGQMe2bksEGQagdyIaPgdl5wo0aqRjDi4Yf3PRgvEnFC0Y3zEHn775HCCafw56W2/YUX4DczATwWegyPz1yS8GISowm81M2SbrV355PHOQ+rw+O813m0dqSqaJyK+NyCwARVYVieDZRWGoKhJpCiQnGg1VIKnqMhE5/qjRY+oBFOdqf+ELl6D6vYwn2TYCuB/A/QUoGiGQByW4fVbF72HEJDC6cDsOLmzE1KFv4+gRG7HbK8Dr2w7F/HXHwrP279svKb8oZ8cPcMVVjw0G5HnATO+4xp/btKusTdz51lEf/3z3ekdEFF5JzT+midhfi3izABSpGnheAVRdhBlapwqojgRQtGehBaI7B4YqkBS6TCDHj5l6UKgcdKrOxMs7NiCe/hztnhws+s4IwfAHRQYI4ECxC8A2iHpwUYSIHYAB8ZEYtHs0PJPArugWbC18E5595+/by29gDuZQdWrxYAieh0HHHOxeDMIeYu88at5bzEHq0/p8gZTs/1ZNcgFMADA59aWqEyTlaXIpxdEHAN4G8LZx3YOM45SFPOynjho95nkA7+RqGLcG/119MpriUTR5ETR7EbR4LpptBE1eBHFrMMRtRpFjcVhhC8YUfYDRhR8EBZH/95EF/myjBW4Ubso9UjtanUShaZ576PDFT4bs+wGvuOqx0wD5EaDHAXBCjSnIwlrb+NZRZ2Z8Gj0RUW+atOqhLDmICalPtlB1oDoUwXOQ2nPQbSo4yIlHQufgmKkHhcpBtXHEX/sx3rICq82waIZqi/93bYZFHI4dAteUYYBzHiIogqsRRBCBqy4i6sJFxC/gtrpwmjrmYDzanECBO3fxp4czB0OqOrX4NAh+BH+iKaebMQg7xDYe9cBbzEHq0/pVgZTN/62aFFXVSRaYC2CUqu6y/jMYNgB459NlDU0A8EBd6UAA/wYwMsRu6wHEjho95uMA/i9XY7UWyDDsz4F/ni/MKIKiSEHG2fWs2njC8yaPHb7435m2P/iOpXONmJ9DMBmAheoahV71zudnLsl99P6puGrhIQimMQVwUFf3o6p27fQzOLyAiPY7k1Y9FFU1k4CCuYAZpersAgY9A5gNAN5pKDumCQBKl9TlnYNjph4UKgftboW3Mc3YLQtgN/z51TRHEFqg4N0ITCJDDjoa9wZ5kxefPzxzDt6+dK6x5ufQIAdF16joVe9cfuDmYFVFcc/kYETt9AVrmYPUpx0wBVJYD9SVXgXg9yGbf+W8KatveXPb7FtEJOfUo+p5gO0cDC7CFUYAUBiJwjHZP3cSnrf0kGFPpR0LPuZ/X/qdEfP11OGDqgqr9sZ3Pn/898L1pH8qrlpYBOBCANcACHv2tJ2q3b12+pmdnsZORNRXlC6pyysHV8+acsvs996/RdzcOehttbDvp/zekQCwA7kLIwBQILolAqcl+y3UXpG39KnPDkufg3956XfGmq+nDh9UKKxjb3zn8gM7B6sqiruXg4N09/T/W8scpD6tz07S0BseqCt1FfhuyOabAdwV/D3cQ+vSFKMOwhdHUTeSszgCACNydLrlY/536ZnpiiMgmGhAzHUH37H0Y+F60z81lJ3e1FB2+h0AjgJwCoCHNY+zCKpa3WudIyLqZaVL6lxoF3LQC5eD2pzycarwrxyFLI4iW92cxREASDxDDt629Mx0xREACATGM9cdfPuBnYNllQ1NZZUNHXMw3S8wGegA5iD1fSyQkljgGwIcFrL5b8+bsrr5zS0nCcINQ+hUIAkAE7I4ijguIk7Ih/WJRNMvN7dkm3giKJJ+Ga5H/VtD2enaUHb6U/F409mJREuN5yVyThmvamFtIp+H+RIR7V9a8A1I+BxcPWtK80nLtgjccDmoqc+i9QAkwgWhu92BuzvcyC2xGXLQmluyTTwhEBjLHASAssoGLatseCp+avzsxAmJGm+cB3Vy5GChwh5qmYPU5/Ep04F760rFBX4YsvlOAP8T/F0QotDszlBG1ziIuuEf1qfaKYIw9q7lMSNmbM6NBbH8etfvnQloqbVxWBuHMS6McZAy3westbA2/tC6oz/52j7qJ/VzsZm1xfDP5kYBVAN4tmZpCcdIU48pnV8nGN3FHJQQOdiqnWdHCzlbmrPTILI9/K8satLk4C3LYwYhclCZgynOxGCU2lILO8nCbDQw6wykuWOhaYdY2BL70NE3rGMOUq+ojZV3ysGSmkW9koO8ghQQ4GQAQ0I2v/W8Kau3AcDkUc9bAE05t0hz71EYRkxexREAWLUL0iy+OuTmRaNuX8LCGcC4V/4pAH6QvMzaBBKJFiQSLfC8ODwvHvy9RVVt2GEpRKHFZtaOic2svRfAmwBuBfAHAIsBrI7NrD1yX/aN+pkCnAw3fA6unjVlGwA8/+FRFvHcOWh3d+33GNMsiGzLL5ZsVLuXg3cxBwHglZvGdczBCGAnWCROSiDx4QS8yR68yR4SxyTgHe+pDlfmIPW42lj5mNpYedocrI2V90oOskDa4ythLvKrP9fObzou1AdzbpimQFKkvS2pnYigMBLueUzth1Hb5Fl7afKysXctHwHg0gybdOyT36FxoQ/Yv1UAOC7dimA4HaxNIHhW8X3rjzm3Zq/2jvq92MzaswFUAfg0On9elwB4JjazduJe7xj1TwZfCfVcWO2cg9qSOwdtY5rAi/g7zERaBdH3wj+wFgCsq002mpKDt+SRg4Y5mCR9DhpAhyvsERb2CAs9SAHBfcd8ez1zkHpUbaw8Zw7Wxsp7PAdZIAG4t640okB5yOYvnDdl9fqUZZeqte9m2kAzTO0NZH5QtQB5F0eq2qyKYw8b8cyulFWXI3jIRS5WPSDEg/76u3RXj3L4r97qCx14YjNrB8Vm1t4K4EFkn273IADf2Tu9ov6sdH5dBC0hczCBF1bPmtIxB0Uutbs1Yw5671l/Cu9UBkBBpo3gF0dhJnAIqGgzjB77zKdGdD0HDXMQSHP1KDfmIPWY2lj5oNpY+T7LQRZIvtMAjMx18V8BWOAXqcsnj3gmDuAItbZSVdurIbUWmkhkHV5nAdg0By6IFMBI+G+PqjaJyEfHDH1ydfLysXctjwD4eph9WLVtV5B4RtofcnlSyLYPrj/m3Kre7AwdOGIza48D8CqAL4Xc5KJe7A4dOE6Dxci0RUwyBdDSOQefGTcyDsURdoet1MSeHLTNisTbFnZbloQdACCast4CBVsiMF4exRG0SVQ++uQlQzvm4C155KDYtitIzME8c/CYb69nDlKPqI2V7/Mc5Bhb38WAf69o2wNb07HAxgumrH443brJI55pAnBqfeMcVxOJGIDRUG2F/9FflPJnh2UeMMAqxhn/AzlaGCkY4RiTzzMEVEQuHDW4clmadecg5Mx81rZn2gF/5gw8a0Z7WWxmrQvgewB+BP+jKKxBsZm1wgkbqJsuBgA0wj8fmykId2Lj6rlT0ubgM2NHNgE4dU7jVjfxro3B09G6C7lzUFCEQRiAVh2HVkyEh2h0izvCxPPMQciFlZcP7l4OOl7be2cOMgdpL6uNlXc5B2tj5dKTEzYc8AXSvXWlwwCcDfgnxtIVScGVI1gg49Sfd1QVR1SkDMAwAOu+OG3NU/n2ZcuOCoHqPyByfj7bVW075KaPHT7vXxlWXxNmH8GDYtv+Pjmf4/c3417554kAwj4HY8H6Y85d0Zv9of4vmKHubgBpH2yZQ8ZhTURhlM6va89BtAJ4H8BwdPz1RAFsB7Azcw4W31IVkV1Shrifg2u+Oy3vHKy4bYdoXP8hkfxycFfBrpteuHhM93LQf1Cs//cDPAdfuWlcXjl4zLfXMwepW4IZ6vabHDzgCyQA5yNpBLTCf6i3BC9F++2jXsLz/nVHVfEM+Fd6ioM/JypQCmMObb9hSBW3r5q0BarXfXHamjtC90T1b/kWR3fWH48bq04tev+SPcuG37ZYAJQ54l5V4BaG+kGz2uE+qZn59KEf+n95tOVZM+qy2MxagX/j+O8BDOribu7i1SPqpg45iBb4v25E4f+W4MEvnCw8r9X7V/EtVZ1yEK0oNdvNodLs56BCMen6VVvU1evWfG9a6BzU7fo3GZJfDr47dDM2jtpYBIxpXzb8ziAHPfeqAoTMwT1XjwDmIHOQ9oraWHmP5GBPT/ct3Xk+T39wb13pcwgxxtaztrUlHu/04DkFAGOATJMpqC6EyOVfPPLNTdn2/9728goFnsi4nzSe2FSCa176FCzMNrU6VhM6A8C58IcTTIw6BXBN7inCVRVxrzX53/a9y09Ke2mz+LYq44hzmKq2WNjNDZeX9asfoHGv/PNDANIN0UjnyfXHnBt2cg+iDmIza0cCuAXAed3YTRWA2TVLS7b2TK/oQFQ6vy5UDtq4bY3v7JyDiANms8k4mYIaXShWLn/z+iOz5mD5z7ZXYBSekLBPUAfQOLARDWPWAoJt2qJj9e2UHIwXwLUhchCKeEFre4Gkqva9yzLk4A+qjPOec5gWaYsdaDc3XN+/cvCVm8bllYPHfHs9c5C6pDZW3mM5WFKzqEdz8IAukO6tKy0GsCZM25Z4HF7KZAs5i6P2hroTIj8F8NsvHvlma7omW7aXr4DIMWH6AgArt47FZc9fjGbP/+C3CbsNFsPb1gsEhe6AULPgedaDZxNJ3VV4SJhtl89u/+GYdNuqMUbMfQKZJSKiqlBowlOvWqErAKxW1Tct7FpHnOq+WjgdtuL+VSIm7Jz6s9cfc+4zvdoh6pdiM2srANwF4JBu7OYFAOfWLC3hEDvqstL5daFzML4zDhtPmXRIAfOugSSyZ41Cdwr8HHzz+iPT5mD5b7avkEHhc3BXwS7UjX2zbVIF2C12G3Yl5aAKClsHhJoi3DMevGiaHLwsKQevWTXGNJn7pEVmiRVRKLRAE94wr1oLk3JQ7VrHONV9tXB6/E+HrRq1S0Ln4DHfXs8cpLzVxsp7LAdLahb1eA4e0LPYqeoVIdt1Ko7ahbniIzII/ux3r93+xuRTMrQKPWPOup3D8dUXP91eHAGAGBme3MY1kdBThCdNztCmw4JJt60a6Yizxog5SYKdigiMGNcVd4ZAvgjglyLyL0ec11W1efytK+8b/5eVfern67Dl983Oozh6jsUR5Ss2s7YwNrP21wCeQNdDIQF/+MvJLI6ou9SGzEGrnYsjAIgjZ3EEAII9OTj5B2+kz0ETPgdb3BasOaShvTgCABmUkoNe+OcnWTdHDl69aqSz3VljmsxJYoMchMC0GNd9150hu5Jy0AQ5+P2V943/z76Vg7f+/bDZeRRHz7E4onzVxsoLa2PlPZaDvVEcAQdwgXRvXakocFWYthmLo5DEcSCRCCQSiUkksuiO2tgbd9TGSlKaFYbZV2NrEb784mewtTVlcp+UDHBNZM8NVEk3UqWyaqEpKxW6KvnqkRFTKSJpnx8hInDFTV0WdYzzKYGsmfCX1/rSfW6hfh4AwNpEdW92hPqf0pPqPmYKzTvOAOcad5ALZ4ADE837I/hNACfWLC35Wc3SkkTO1kRZlM6vE4T83EtbHAH+ryk5NwacXQ4iWyOIvBeJRbZGFsWuqX0jdm1tl3IwYRKoP3QNEm7KwQuxZ2IJ9QskKIAmAM2Z+5o0tXc7ha5KvnpkWkylJDLkIATuVrdDSSUiUccJcvA/+04OFibC5+AzxZY5SHmpK634mDHmHcdxrnFdF47jwJiu5WBJzaKfldQs6rUcPGALpHgi8QkjEmoK0USWB73mIpGIXyCJ7Hk5TimMqbq9puTY9nbZHiUeUAW+tvQCrN3Z+XlZItL+3XTE9ceCe4B4AvH8v8NDp6N4KVePVNUCenHbv4tvq4oKZEbW9ygCk+ZHyYiZoKq/zfW+9hsiU8I0879E9sqxy+7977HL7j1g/w9ReEfOfvNKU2CeNK4Z2nZ/hRiBiRo4RaFnMr0FwNE1S0vC3htAlFWiOfEJMeFy0GvJkIO5PgE9INIYgdPkQKxAIBArcFqcUtNqqkq+VdOeg9gVJgcVaw5pQEu0pdM6EWm/xdvxXEijAOsAeUcgbwuwHsBmdHpgrefmyMHrqqLSkiMHrcDsTpODpm/lYFErQuXgpsGKZeNw5UXLxv73RcvGMgcppzePPPVKY8yTxpihbSOcRATGGDhOfjlYUrOo13PwgP2hFpE5YdpZVdhs92llWSeum3GYm4hExJjHAGDr9nIjQEG2fQHAe00DseL9wzMfz/GPFZWC9iBqXwdpL5pg27quHgtklgAAIABJREFU/i/77W9FmyzsWe9f/tHX25Y54nxYQozVy/I+L067Yn+kuj53I0D3FJXfAXDn2GX35r4DmA5YU0+pP08c+VPG/yOOwBRk/Sh+D8DZNUtLvlKztGRXb/SRDkxhc1A9hXoZ8imKTldf9mwIuNtdiM3ws28lYlr9HCz/5naDbSjQRPYcjL8fx66izP8NZJB/ujG6uQCyTTocWyCQXQJsBLAz6KIo1KTJwcuScnC78+G2YXX/n70zj5OiOvf+7zlV1d2zg+wM27Cvg6gIQtAIRGXGBWIwSBINUYP6er25MTHR3EST+EaNyU2uiVclGnxzozG4IQFBFk1EEQMGGcBBlmkWh20YYPburqrzvH9Uz0xPT3fX6WGYQakfn/4A3adOne6uPt96znmWVCLzs8/BsA4lDm4cyI2eK/cCeG7+pnyPg56Sas+YK68nSsHBqKGUQscBXDdi59rbR+xc2yEcPGcNJCimErRT7B45uUwTT+akaSCXbUMSovuzO0dcASCbABLJ+mMGMeNoQ27q/oigkQ7Byc/buHoHGzDtCGy26yTbm2y2v8uQWSduvXRlXHsll4cU58vOf3qzekqiztVit6QljkHZos03APwtf9OLbU1N6elzrLEz984DsMRtjYH0pK+/AWDczo0jlrXz0Dx5AlQ5GEnhRSEAzkk8b2p1GoSdmoPCEt1H3LPzCgDZsED4FEhkJLFk8KeMSCRhfocmkUHQQjpEdQoOMoEqCDgOmIjARhwHvxnHwYgiB5OGKlN2/g8+Gxys82OxdNnIO5LNKOvW4qlvAPjb/E35Hgc9tdLesVepcTD5628AGDdi59oO5eBnxi+2vcXMfwJwi0sbd/c6ZkBKJ5tdoxw3OqVxENFlAHYCjsEloudtnJ4o5lFjuc/RBtQWcYgJPvbvZ/A1h26duC1pO9Dp+ndy+cKLlDL5jHpylyDQKCIaycyrP75jWM1pnjstfXrR3Jf7ffjyR0jiUsjMYJnw47gSwNv5m14sLp8479gZHaSnz4zGzty7AMCzyjuwArE3WCEA9wB40qtx5OlMSZWDKQ0kAJztFBqPNUooQtBCihy06TIIh4OoB7AH4G7sxBSJ6HNVACKA3cPd5d2oUuRgDcFX59/PxNcc+r8pOBhR5GDyXzqXP6rIwRuiHASNZPDqj5d0LAcXzvv05Vee7fdRwQkk5GBEMN4YxYne65UA3p6/Kb/4hYnlHgc9AQD2jr0qPQ62VBMH27vGkYrO2R0kQ9fX21IeSdXGkm7rKFHjhRmw7ZNgrgMc17o0xADyYvsTcOJMtei/Gy+ZGtPf6uAWHTGD0vhKiWigILGx37MfLuj37IcJL16i09tBkpBlKu3GPLXnQV3oDYZmbNeF/rIu9OqxT+3dO+apPf1P5/zpillewtIujd9JSmEcNeoiAO/lb3px8Jkcn6fPhsbO3HsngD8i1S1TvJovuX8BuGDnxhH/4xlHns6k9IC+XpqpOSjDMunOSJMI4FyG3cM+yTrXQQJ6bRocpJYchARQASdmaH/039GNI1tPPRiWDLLS4KCkgcIWG/v98MMF/X6YhIOqnhRJfu2SFTl4w54HdegNBoztOvSXdejVY2/Yu3fMDR3LwW29+ZKSPlwan8CpMpPxaiHjRPKotYsAvDd/U77HQU/YO/aq9DnYrH8BuGDEzrX/0xnGEXAOG0jzhpeylHKylDKU6HVbSphWixtiG0AZgLVwgsTuhVPYagKAvFvGlZ0HIfLIMLaopteO3oT/AbFgSCE3A4mkRDd/FcZ22Y85A9/HQxf8BU9c8izuHfc6BmUnXdDJhHMB/2+/Zz/MSfB6hsrYEomZAcb/SdVm/KJ9NO7pshW60B8QJJoKEBIRNKEN1kjbO/rJ3cqpX09X5RfdEGK2x7E0F0tpQUoL0jbB0oRCHo2hADbkb3pRuY6Hp8+fxs7cew+AJ9I5JvpbYQCPALhk58YRpWdkcJ48xaj0huEsbTlZ2ok5KE0Jq0Gdg2V3jztP1Is8o8rYkizuKF7Rm3BlDtrxmeviVQsIKeA3fcipz0avEz3Q91hvdD/ZDUYk6c5SMwd/eBocTPCWlTh4wz4ad0PZCh36AwIxHARBgzZYg7Z39A0dx8EHi8tDq0fwuGcv5sXLR0msGSbxUqHE/7uI8WkX18OHAtgwf1O+x8FzWHvHXtU2DqKZgyN2ru1UDp7ThWIB4E87hmYJIRYT0ZeIKJOZw1LKvaZtr4VTPK8s+veBb40rM1P1tXjPmG8CWKx6bpZy67eGl55/onpmMYDlbu3fPjx062M7Lh/fJ6MafTOq0DujCn0Dp6J/V6FXRjV8woaAgEZai+1KZmBXVS/8946r8I+jI8GJbePdAG749JYLP2p8Yvizpd8UJFzfkyUtSLQIdIXN9gMHvj3hZ8mOGb9on87MS4hoTqq+bWlv3X77kJQZhNpb+ZteJAD3A3ioDYfXAphTPnHe2vYdlaezWWNn7iU4dRmSXvPJZIftE2zynJ0bR7zT/iPz5Cm1hj63I0voYjGJKAclh6Ul99qh1hwsWzguJQfH3LHnm0iDg9KQW0sfH37+zO9WK3Gw6ryqrZ+OKR/vq/fBqDNg1Bst/tbrdQgpICICWr1okayIwQj5wzh+XiXqMuuTrWs7HHwkhoPfLP2mkAoczLMgu8RxUNoPHPhFCg7esE9n8BKCCwdhb92+pGM5OH9T/mlz8IWJ5R4HzyHtHXtV2zlo2yeYec6InWvPCg6eVQbSjKqvjBVEUyRzhWT+x9tdXjnR2WNS1eI9YwYD2ArFoFdmrgfQ71vDPj55onrmfADPux0jbcuJd0ohAQFd6HHPaS0MpjpLw+8+vhzP7Lo0URcROD6fT7DFeYYwNge0gOvKVcRJ+OC8N7AtWX7j04UX/iVZ+/GL9mUA+CuAa9z6ZmaY0tRK7xh+egWp2qD8TS9+C8AiNFfXUJUJ4ObyifOSfgaePj+KGke/BeHupicVp1ZpyTIZkhfs3Dii6syMztNnSTOqvj1WUPcpkqsqJFf+4+0uL35mODjmjj3pcZC4HgL9Pv79sJMzv1utxEErbENGXDgYJuj1LTmoaU4q4UYOWmzjaN4xHM9L+PE2c5A5z6gzNgeqFTiYF4GdE+Ugsy2l/Manj6bg4A1pcBAME6ZWuqTjOTh/U/5pcfCFieUeB88BRY2j3wIxHFSUlLJMSnnBiJ1rzxoOnhUG0oyqr0zRNFoqiHo0Tl6SGaZpH7ElrwewpfHxj/NePesqxy/eM0YH8A8AU1TaMzMT0WULhu5YDwAnqmfeAeB/3I5TMZAMMlrsHOmkQ1DrOY2Z8dr+Qty7eW6yMa6AjX6GMMYHNHf365AdgilNAAgD+HL5woveSNZ2/KJ9XQAsAzDNteOoLGmdv+P2oVtV27en8je9eDWAJWiDu6GU9uOHJ33t39t/VJ46W+NmlekALmHmWQBuJaIesa8zc8LaYy3aSH6JBM3bvnZIh9/0eDq7NKPq/0zRtEFLBXWP4WANTPOjI7bcE8fBf5x1HBxzx570OAhmAl2248mh6wFg5nerlThohezkRWudjmFURWsBRuUUpGztNcHMqMw4ifIehxN3xbwCQD+jzhgfqFbgYG4IZlYMBx9NwcEb2sBBWOfvWNI5HJy/Kb/NHDwl8x5/Y9LHHgc/hyobV+TOQZfVQmZ+iYjmDdm+6qziYKdnsZtZ/ZWphi7eIaIWs5cggt+n9zYte65pyaa7+Esr55xgxjFm/pQZT77Xc+mrHT/qVvohFKEAAET0SKNxFJWS77XbijSBWhhHGmkJjaPoGDBnYAmWBC/C5sqCRK8Xs8bKq+BRQ7sBwLXlCy9KuqU+ftG+3gBWARiv1nNjvSauUG3f3iqfOG95/qYXpwNYAeC8dI4VQru7zwfP9zs86WvXn5nReepIjZtVlg/gKgCzAMwEkJcs5pCIwFpKI+lxEvSd7WuHdP4qladO1czq70w19HHvEAXiOJgDv29ab9PqMde0Ns5tvJAurbw0joPrP3scBD3SaBxFpcjB1D8XsqiFcaRpWkLjCHB+o90auuJEwyk0ZDQker04GkOkNjSK4eCjKTh4Qxs46PzpNA6+MLF8+fxN+W3iYBdRdfeMDyb0Wzdpi8fBz4HKxhWlx8Gm0KKEepyIvjNk+6qzjoOdvoN0Rc3c/Zomklc/BWDbEmEzcWpPKblCSln4Xs/XU2biOVNavGfMxQA2QH3r+V8ALlkwdEdTMYfKqhlPEdFCtwOlZaaEQ6x7HYGgx+0mJdKGowNw0/pbk75usAG/SJ0cAgAarIaQxdaV5QsvSuo7On7RviEA1hFoYHx2nFSSLKu2LRzsHhp6hpW/6cWRAN4EkPJ6jRczQ0r79iOTv/70mRmZpzOlcbPKDDg3fbOij8J0+2AZNZJa6lEA93nGkScAuKLm4f2a1seFgwcRNv8OoHWSgmYOvtspHBxzx542cXDHk0ObODjjO1VPkXDnoNlgJayT1KhY9zoigmEkL9jeqGqjBsHeB5K+btQY8NcocDCvIWRlWleWP5qCg3P3DYHEOpI0kImdT0whn4WErNq2pPM5OH9Tfps4KJlwUPa//b3J73sc/IypbFzR6XMw8U7SowDuOxuNI6CTs9hNP3V9bzfjCHB8hwO+xJtdQlAPIcTOqceu8yVscAa1eM+YLAB/hjoUGgB8LdY4AgAwt0vgZazRoZM7FABgQPbJlK+rZuTThf5gKuPo/EX7ZwmIHTr0gRo06NChQWsRQJtI0SDXtgSItrvKJ87bCeASAEnrZSQSEYGIvn9mRuWpvTVuVlm/cbPKbhs3q+xVAJUA/g7gB2gDFACARKtr/CfwjCNPUU0/dVNvN+MIADStPwK+WUjk4dTMwS90OAfH3LGnTRyMNY4AgKUiB11+NRwzCl1X46DPSv2xxe5IpZIe1h9MZRydf83+WaJG7NCr9IFajQa9Wod2SgOFXTgIho2zg4MvTCxvEwcFMbKp1uPgZ0Rl44r6lY0ruq1sXFH7cLD17/AnOIuNI6CTXeyEoDFptEXAryMcsVptoghBecz0IwAPtPMQ3fRrAMPSaP+9BUN37EzwvFqNBTcwgMHM0IWOOI/FpArb7XMJGMIoSfbahEUHRgmI5YSWg4qmMIUNG4l2lJgZlrTKbLZ/rTKGS548OomALwPozk7l5aXv39HLvapgGiqfOO9Q/qYXLwWwFMBlqscRUVqrbZ46VuOuLusFJ/7hSmhwqnxIKLvWuIqa+vr+9rVDftVOvXr6HEiIvmlwsDsC/qsRjqwGc1Xca58dDu54cuhpcNAlnkFjsGAn/rb14kTiY8gl9EFxHjBCKTh49YFRIiyWE8dxkAlanQZb2uCMBBwEw4JVZkORg18/Ook4ykGKcvDP7cvBFyaWH5q/KT9tDmZSvcfBs1hl44qaOQgkr3Z1+vr+kO2rznoOduoOEjMOpePiJ4gQ8CWe9IjwjfYcm5sW7xlzDQBXd4AYrQTwZOKX+KDbwSqBbgCcauZpJJpZf3SoclsXJayjEdWqeOMoViLBZRjNXAeb7ds+uXNkyjd+yZNHe0x98tgWnbSNutDv1YX+LUPoL2skTlzy5NEvKr8DRZVPnHcKjv/ty2kc1q6A8tR+GldcNhWEchL0ZRKURYJAgkA6td8M6VzBd3nGkad4MdekycFsBHzFEKJ3q9c6moNj7tjTbhxkhhIHWaForcyQSeOOEqk6UOtyYuWuknPQwqp44yhWokG0okQ0cx1s2Ld9ssSFg18/2mPq/GNb9Ii2UTf1e3VT/5YR0V/WLHHikq+3PwdfmFieNgdtaB4Hz1KVjSuaCqCciL5MRFlRzxdlL6I0dNdnwTgCOtlAWpf3cikzqtM5hojgNzRorYwk6jDXgsV7xvRi5v9N45DjAL61YOiOxBMc43FXQCoC1Kf5lS/okC3wy5KrUrZxc4GLUesIVwATFh3wE1LvnhCohZHEzIjYEUiWqz+5c+RbbicWoA81oZ0f/74FiVydtHXnP7d9bNd//HpAzw2/b7dfevnEeSEA86SUb6u0Z+bj7XVuT+2ncVeX9YTAW0SJs5mQRm2rAR4jlgwAt2xfOyStonmezg2ty3uilPlUmhz0w29cAU0Mjn+lwzg45o49beLgjieHJuEgu3KQbQUOMuC3fcoctCFxpGvSQuoA2oGDVx/wk6XAwVAMB8GIIAIJufqTJQoctOlDzdLOjx+rkCJXN7V1598RHNv1V/8Y0POvG9qNgy9MLA8BmHdK5ilxsIEzPA6ehSobV9QTSMHBdjCSor/tW4ZsX/WZ4WCnGkgAYEv53XQTRZCT4Q661uKmukNqRSzeM4Yk8wYiUsu44+iWBUN3JA2e7db17TVgTuq3zMxg26V6OACf8NVqSbLWJdJr+89/MsJGnfIBKSQtWTT8qdIrhj9VOmL4U6WxTvKTVODS2KbROIq63P3Q7bgpTx59SBNa/6T9EgmDA9sMw7+fWYa7rf/tsm7v/KZd/ArLJ86zLSt8wLJS1k104qhs64X2OKendhbheSKXm8p0K3/EiJkBifu2rx3yx7b34unzLlvu/y5zeovrRBr8vsuga82J0DqKg2Pu2ENsps/BHU8OTcrBt3/fdQ1bKTgoGVbI/TPymUatxuo/2pMZJ5+EQGoOqtY0q5BFwy8tvWL4paUjhl/aBg5GCOBm40iZg187+pBmp+AgkzBsY5vR39jPEQ53e3L9sm5PvNMuHHxhYrm9yZp4YLeV2hvFZoH99kCPg2en3Dl4Gop6QN03ZPuqzxQHO91AWpv78rO25J+3JZuez9Dg052JkBk/b++xJZIl5X8Kovhlu1RatGDojmWurZi/yNJ+Pf5zYCnBlgVmrgOwA05q0EUAfgzgZgDTAQzN1LIu8Am/e5qdptPxHxZM+PmdAC6EU9gvoZRXziR+DCezzU4A9cOfKj06/KnSD+pk3b+rfLcEijeOXvzkzpFb3I4TJG5xa+OP5DrnIGFomn4NkQh2X//b9poMviClBcuKINH7dIwjE8zS/Rrw1KEqvDZ4DRHNdGvX1tUzZg5B4o7ta4Y80qYOPJ0zWpv722dtuf/n6RpJAOAzLoBPnwqAOoyDsl7+JxnpcXDHk0Nd50CW/EXblK04KC0Jq8ECy9QczGrIuMBv+dLi4AfP5LtzUDFJA8JxHLy09OjwS0s/qKuu+3eVzK3EBDZbGEcvfrJEgYOWAgernY+FfGRo52nXkJ+C3f+wvp04SF/YJwuw3RoDK4FxarGGHfYYnOTzPA6eZQoWFp95DgJ3DNm+6jPHwU6vgwQAa3Je+slVdTf8nZmXEVFagWG6LgBCXWbAePNMjS9WAviualtm3ktErdr/ZtvgAIDBAIZEH0Mb/x0Qwhqcla1nGgZqbRvHzAjqpUREyixIWcPMRd8ZvbvFTBuqKtbgpFg1FIdW3phV7dNbLvyk37MfTmbm/yKiO1Tfm4J6Rh8XM1jJ0CKmRihYcMCXUpc+XTlJUAJH/DgJaCDLAOvOTo/QtH7Stl+BQvXyVOrx3uI+cL43SGlDShua1pxanVnCtm0AHAaw+XTO5al9VXhtsDuAP7R3v8y8D4wdAF6HxHPb1wxJvb3oyVNUa3J+9pOr6hb9nRnLiLQ0OTgcoNy6zEDvDuEgjNPn4OBLtyXloNCEld0lSzc0A3bYRqTGhDQlpJRZkmQNExftfm90Cw4Wfy3Udg4+cuEn/X6YgoOqdmtrG6iZg4JBUoGDYQL70uDgDZWTBBQ4KAXIJLDhDFLkaP1kTXtw8JtNHDws+6JSdsMgbR+yqRYabJzkrjhk90U9sjwOnmUKFhafOQ4iykHwc0O2r/pMcvCsMJAAYFXWkrdm1X91IjOvSjfjl66JLGbecFXdV+YD8hgcpxgNzvvTkjx0gDMA9AD4BIB1q7KWui7xEFGuypiYGRHLCoRt++bfbBuch5YQyEeSyIYQM3aGQ6BIOP68gKZNhm2/C2Bq3GHfAXCxyriiWhjIW9GUAunTWy4MAbgz/w+bd4LwW4pZKiA+/RgMk034yX1RL8Y9cNEnd47co9D1v6mcX7IEqOVXS0Jc3XPD76cem3LXeyp9JNEX4p+w7YTzwD8rpi4IJ3rBU8er8NogwQkU76XS3mUHtA7AW3CCz1dtXzUkeNoD9HTOalXWt9+aVf/cRGa5isi9BEasdK13FjNtuKpuzXxgtDoHbZGBsN4Dpn4CQq5b1bubOwc1dQ5alVbAPmHfPPjSbcocZJMROhJGGC0rYhAIGmuTbbQPB1c8H2jm4CNRDv5g804gjoMRRQ6m+ORMy1SqKaiZmpNBU2DRJ0vakYOidXYLyhZX9/zrhqnHvjql3TgYgR+77BGJ2v2zYupzHgfPEgULi88YB4dsX/m54GCnF4qN15Qj172alembo+vpe/8578U9Vqd5FmM0ToHMkADWAJi/KmtpUj/uP+4eLUlhr9G0bdhSRt3GbOUkOMIwQCL5e3fikewJ3xm9+yMACFUVDwNQAtUUqcCfA3krkmY6yv/D5mEAngdhPAAjC1kkUoynUTKSPLUQMSFLZClt0YbsUMRia+And45MWfDw0qcr+wDYD4XVQkta2Dv2NbDW8tpg5jIiOv/YlLtqXAeWQN3fffYNIjFLoenDFVMX3N+Wc3hqfxVeG5wP4HnV9myzc8PSrI/hgGAlgHe3rRzsQd9Tu2rKkatezcqcMUfXe6R9LLMfwAQAOS4NAdQFgLpAkwsZCymh22tgafNXFWQn5eDohbslCfcJ3a6xIeskWDLsalttJ4YBwzYgOAUHwbCFPWH3e6M/AoDir4XS5uCK5wPJOfiDKAcR5eDBLBIK9yRyv0xqJJEgZOlZSt4UocxQxApYAz9Z4sLBG9LgoGFhb1EZWI9z42/k4FentJGDd71BVKvIwec8Dp4lChYWp8fB1rZCCw4O3vbG546DZ80OEgBMPnztVwHMqakLIyvTB5+RXnQ0EYFZR2ojyUmXHT+1E0EAuJIZh2bVz/4JgN+tzFzaKiONZD6ouexwSSlhS9k0Jp+mqRlJRCmNo8b+mOhHAOaGqooFgGegDoVjcFbZkqr8tot2I2YVbsQzOw8ASBr8CbiuLDgwYxs6uV9uPuE7umNh8kDeGC2EoiuFJRNTmZxYsv8CcJtKP7Hqtv5pg0ifodh8fbr9ezozKrw2mA9AOYtONMlCLYC1iK6ObVs5+MCZGp8nT5MPT41ycDmyMqfDZwxM63iiMJg3w5nGk3jqMYBTWaBwyxAUkkIgIq5k4kOz9tY7HByS2YqDbPJB8rtwMCIh66IcFAQtT4Nd5W4kESilcdTYhpyaT3OLvxZqfw4+GsfBuTsPQHfhoM0pd5A4wLDJhm4qcDDkO7pjWTtz0HcmOPiMQcQeBz9jChYWp89BtOTg4G1vfO452OlJGho1+fC1fRFTH6GuPoJQWGU3qKWcXYrUE1CqdS8i+AE8CmD3rPrZt86qn92iM2b+aSqDgJlh2i0nIiKCobkbe2kEwWVH/74dwKWqBwH4P4G8FZVptAdYoVq2m+XHjpudigSJ/hcu+nRIqjaXPl3ph/PeXWWzjMY1xQ+y6bO+teeG37fFB/sbKil1o9fK+23o31M7K+pa9wyALirtmZkhcTuAbttWDp6zbeXgRZ5x5OlMavLhqTEctFBXvwah8I60+yGy4GyoJNnZb/C1Mo5aHM/UzMG99bfO2lvfkoOWCwclO8ZQizERtBwFDirE6UTVZg6ueD6QHgdtBQ66rZ/7ANOvyEEp+l8404WDN6TBQU2CtbjvixgwJKBLAHxrz79uaCMHLQUOAvA4eFYo6lqXHged66zb4G1vzBm87Y1F54JxBJwlBtLkw9cSgD8C6Br7fEPIRH2D6bpDES/H0Eg2ySr3lQ8neG37rPrZ18+qn00AcNuInX+0pXw6WcayiGUlPIMggqtHgoIrW1QVoarigcz8qOoBAF4J5K1Ip7Bpo25hTl6az3H5c/9MbbbT+R7dih3OhaLfrCVbGtlEGogMCGFACB+IDADihZ4bnshXHVy39U/rAH6k2Ly2YuqCU6p9ezqj+jacwoZKIqKfbVsx+OltKwdH3Ft78nR6mnx4agIOMhpCG1DfsLENHKwFkMRrKqScvKyZg3vrr5+1t54AYOefRvxR1ifhoGRYJ6yEO0WkEchIzUG33aMYVRR/LZQ2B1c8H0ifgzZuYcuFg6fcahkCtmGDqRM46ItykJyBUNcIqF8DRN8QRH4IlB8Ccs0Xer62Pg0OPpMGBwO1FVOf8zh4dihtDg7e9sbTg7e9cc5x8KwwkOBsE1+Z6IVwxEJdfeIUyqnVbrXQRsCpFL1xVv3sywHgthE7bzdt+y7Ltm076k5n2jbCSYyjRrkZSG7udTH9PMPMrxFRtntrAMBJAHcptm2hT24beQQSCzhB/tkm48jtq4m+bYvVdgQZfPOFiz5N+GFN+P3+kZLlYyr9SGYnQQMAEINIjxpIzV0TEYTQs4n0Hb03LhqlNEBgPoDBSVdnW47Ccys4C1R4bXAIgF+ncciHAP7vGRqOJ0+JlIKD21BXvw6sOIc2K8k9qZl2ca9mDu6tvxwAdv5pxO32Kfsuu862ZVhChiXsahvWcSullzvpKTjI6gYScQdy8PWRR9CABWwl4KBkcCUDbrePFgCKMVZcxOCbL5yZhINX7x8ppSIHiSH1GA72DINyLVDMx0w6Q3Q1s6l7ZEfvjWvT5GBX14ZAjsfBs0DBwmKPg2mo0w2kSYeuGcrMv0nVxrQkauoikDIdI6l9k08Q4WJB9NbVoTklxQ2zv7hw5CdPWFJOM227ojEhg+uIXIw8FRc7Zg59q0fBdUQ0QXnwwL8H8lao+DMn1Ce3jfwT2zyKbV7Bkk+w5DDbDDZbBa8nFYNhSjX3AgINquj17qLG/0/4/f7MCb/ff9OE3+9/R5AoVUntDQB2zO4RQ4Ao+eVORHnMvKX3xkV39964KGnDbuv6RGTUAAAgAElEQVSf1gD8JwCwy64YM4OZv68yVk9nToXXBjU4N3eqqZPDAG4qWVbwmUxN6umzp0mHpg1lJhcOBlFTtwJShtLoOUl4Sry7lYoYoBBdLGrprau3hEqKtzd88ZMXRz4ha+Q0+6RdYZ+0IeulKxNcOaiwuMngUMHA/mlzcMXzgbZz8LWRf+JqHsV1vIJDfIIbOMxVDD7ETnSGm+qdOCXTlwYH/ceaOXj1/swJV++/acLV+98RQpQKochBn9W0SMlZFigjRUIlg/OYsaX3xrV39964NgUHn4nhYF9wqoQarIO5i8fBTlawsLhNHCwoWXHOcrBTs9hdfOganYADRNRHpb0QZOZm+48QUcpgSQDgpDEvMmUMUryIAF0TLXZ/mBmSsZGZv1Ac3DGQmd8gooR5LWMVsW3IJJ83CQFhuMdaCmDNLT0KLiOhXPV4JYDiQN6Kdv2ihz9V6oeTvGFg3GNQ9O9+iA0Gi4YBZWlZECkMlUbVdNmFuox9L/TcP+MUgK8ByAOAgOaHIdw/J2ZGyG52Cj8wfnnilUsJ5B+6AN1ODIEufWBIRLRQnbTxgm4FNjLzHsm856278w4BQLf1T8dlfqHozlTLvh3jyFpSOe3bX3UdrKczpsI5QQMSq8CYnsZh3ytZVpDOKpsnT23WxYdm64TIAaIqRQ7mmrnZXzlCpClw8BI0h+rEqDoDVK+a08CpzaNXahBWDAfBkBm8kQ3+wq6HgsoctKtssJUYR0IKGLZSzoE1BQX9LyNKj4Mrng+0LwcvTZODBoA8IKs2C0IqcFCrRl2g9oWedq+WHPT5YegKHAQjlB1uWgo/MH8nKDPBRyCB/KMD0e1UL+i2DiZGRI/USfALujQ2suQ9Msx73rqzkYPPxHHwFIhKQdRyg43ZB+YRSyqn3eNxsBMVLCw24BRXTouDBSUrzmkOdmoWO2Z+RgihBAUAkJJ/RESLmXkTEQ1K3m+qJSwCc+ssdolbAoYmWt38EhE0wmRb4qM7Ru0a92Tp8CnMvJGIhiXry5YyqXEEoFX8ESOxk+C4QO5RVeOImaW05c+zuq1qExSqDs7IItBlmtC+ThBjAZgAvyOZ79t1+5shAHuij1Ya/lSpBqAvGoFBGAhgtMnmLD/5z3M7d/apocg6NmR+7HMEUsqEBzgxT41iSKfiR7ykwLiP5yBgtbyB0G1flmS+zYR5G4igEWHG76pqJMunMCG+qB6D2QRz83XiLDpIC8APlAbr6YyocE5wIBhLwGnVRlkP4LdnakyePMWLufszQpSmwcHqHxFpi5n9m4jCg5L32wcJjSMAyA6BG3wgBXc2igDGMa0pFXjT8yBoDTTZZvnRrtdGjRs+p3QK27yRtOQclBGZ1DgCoGQ0AEBubvZRVeOImaWU8uerXsxqEwdnXF+VRUSXaZr2dSFoLACTGe8wy/t2vTMqNQcvjeOgiYE4hdGmYc7yQ4GDdg6yrKz5sTcDRARdU+SgYTcZRywZ8Cc2jsbtmoiAGWMwM6BH9CwJvs3UzNsgCFoGYcbTVTXSlE9hfDwHu4B5IpiPgKgeDhe7AOhmAbrHwU5UsLB4IIAlSK9GmMdBdOIO0uTD15JkrhdEqstY6wFcvrHPMvuquhsyAf6QSIyMb+QYR27FFhKn+o6XoYuUcUPMDFty0fLAayuf+HiYj5k/EETnxxtUMloLKUaHAOyFM6nuBbBX+Hy/BFH/eNOO0NIPsjin1+/yM7OUCsPZpg1pyyMAinJ6rd6Sqm3VwRkCwHAAkwFMAjCZQON8mk+Ld02TLCOS5dVZ+avWqIwjVhcu+nQwnPfsKmlJIMat0id88Gu+pt2oJmgk+IpCVjiavQ6I6DU4UviPVobusF1fQpf6vsnPzxIRu6W/eFXmYewd9rbK8J+tnLbwVpWGntpfhXOCswEshkSXNLxt6wAUliwrKDtjA/PkKUaTD08lyV3qBZ1Kk4Pv2VfVvZwJ+D4kOpGAg90BjEXKDNCWAE5mg+wU8UgMGIf1FjtHrZsw7FxZtPzCwMph137sY5s/IKM1B9lk2DV2rPd7Kw76TN8vCdS/1Zwe9xvu1av777KyMtU4aNuQ0uHg6iU5KTk44/qq1hwkGufz+TQh4rguZURKefWqF7PS5+DMNDhoyBYukT7DB7+hyMGscFP2ughHcGRBGSiuDtKwsrHoUpc8jkhCIqJZLfqvosPYO+QdleE/WzntVo+DnaRgYbHDQcWMdVHVASgsKFlxznOw0wykCw9e3cvQhZI/MDOblScbnjBNOQdAdzhf4Lphg7ts1DVtAZxtbAuQxwE+CsdCin1YrZ9jADwawHgitFqJ0gRB19xXs2zJ6/7mf3Vm4/9/t2PoPAJ+TETdmdmWzB/bzG8C2AVnQiz7j3Fl9bF9PL5rVG8GDksgYQ5yYkY0L595Y9d+RTk+n+uELG0J22wyymoBXJ/Ta/XqxieqDs44D1EARP+ehJgfEYHg0/xJ46IkS0uyXZCV/+anbmOJ1/nP7N6myYyxbu1YMthqNhmztMzEK4wCaPyAACe1d8R2ImYZjIMDVoO7hSBEzKqbLXDRtq+7+rtHbLPVzl9Zvw041W1fqsNsACMqpy1UAqCn9lPhnKAfwGNorC4vkU444sKSZQWL3Jt58tQ+uvDgT3oZ+jpFDmpm5fK7nzArhzZz0Ma6Ybef2qhn1C4ArH6A3wK6HQf6HgXInYOSgJqM0WjwjacEZQu0KgG9yj2hg50h1/1tsr+Jg0Ov2jEPWjMH2eKPuaElB8veGdeCg6Mu2dUbwGFQ4nhc5qaEQGa/fn2KfD7DnYNSwrZbcnD1kpwmDs64vio1B4ng9/uSc1BKy7ZlwZt/zUqfg1/at01j3Z2DgsG+GA4GFDmoSUSyohyUjIN5B8AzwxDdYhZrJeGiHVPdOShMSBHHwa7v41S3lNmeoxy81eNgBytYWNySg+lpYUHJCo+D6EQXOyFcqwY0ybLZIMJ3YuaoLAA37i47dR0zTT8wYd0HbR3HrPrZ3QD8EM6F5Aec+UUTqSeMGHWL/c+/jdnzIoAX0xkDA3OTGUcAwESAYyRt6tFjzdr6k7NqhRBJM/cwM2yrxY5VNjOvqD408wW2meBAYHiy492MIwAQJHRm+QSA61K+uQSq7Lq5X8/Kae4NY05vkJHc/UKieauNmlN7MyQqemxBKKsCIqLB589pek9dqvspBQM7bVqCoc+RsW4G0p8946jjVTgnOAzOb++CNhy+Bk46Y0+eOkxCHEiDg5cZlN3rO1TVNG9lQceNu//Q5TqO5E0/8MCEtnEwB5i1t741B03HQFJUCw7uWTUmbQ4CmJvMOAIaC8EzAGxa81LO2lk31rtz0G7NwZlfqX6BWYGDLsYRAAghdCm5bRwMHO/Xs0Ehz0LjIg8BhpYGB6PZ8thmVGQcQygQgtgO+KYCjZ7qXWrOazsHj491M5D+7BlHHa9gYbHHwXZSp2Wx04Soksyu6XhsyZDMyMz0IX6LmwiZRPzOgC0zx7R1HCszl1auzFz6fQDD4NSgkHqCuKPk4vK2nrtRErjLzd8vOjWtjx7wzWQ7f8zs7BzF14Qj0kkTN0HQN5ACCgBgaKmh0Nyn+IJrozhlr7lvaE3Oni6S3FOdEhEQ/c79rTf5WooB2M6KYQQNqM7ej7Ihr+FU91IAgJQ2IuGapgxKmq0W25voczDslN4wEsAvlDr31G4qnBO8EcC/EA8FtZ+xBWBBybKCzstY4+mclCZ2V0nuqsDBQZA8GpkXH4bIaplPmnTKJIPeGXD/lrZzcEhm5cohmc0cZEj9hKZ08+wMAqfNQRDucuWO83JjyujUHIwr2A44HBSCbiKCKwd9PkOJg0JQ+hy8fs3QGn9NF6mQBpZAgB3loKbIQZaIWBFUczXKuu3FqTwn3bs8BUQ2CjRmi9dstTXy+PgzADDY4+DZpmBhcWIOqskCsKCgpH0Ten2W1WkG0sY+y5iZ/zdVG2aGZTsTiBCErKzWk4PjHsfvDdgy88LTGc/KzKUHV2YuvcXQxE/iDbFU42PGw6dz3t/sHl0IopQTddP5omDI7LbyFWnJ26WULQjA0jGOOEU6dKELkJb8/RnCp5RlLqq27EDOZd1EXdZ+pcaNfFIGNctI+aA1ONJ/PWyfEyza+JDSRqihCrZtQop064m0OEmqmr5/qZy2cNdpdO4pDRXOCWYWzgn+AcALSBqR7iLCL0uWFZz+DZ4nT2lqY5/3mLmXCwczYNnTARCETyJrSjkgWt5Yk0E+6HhvwP1bTo+DQzIPrhySeYtxVPuJCKtxgMFgg0+Lg6On7i4kRQ4iysGVf8l8RcoEHIwaR6nCB4RIvQjq8xkQ6oXb28ZBjVHnq1NqTI1ljFQ5KBEp7/kpjvQ8DFu3nRpNJgAJyEogtJpgHyNIoVinI+GgACHykr36l8ppt3oc7CAFC4szg4XFp8dB4JcFJSs8DsaoU+sgSYnbbSmDiV5jZphWyx+vrgtkZCQMOs0D8NaALTPTXsmJ1Wzz+l5C0D2q7ZnxwfLAa++dzjkB/DSNthsb/5HVfdXTdsS+0Aybx6ywBTNswopYKY2jJglKuLKuCwMa6SAWTnYjppTxG8xckcbYo8fIAQBQk6M4dxJBI/WihoJ137D91yKnNh8kGEJzHiQYRBKARCRci0BDznK18bb+AOozT0DTCFrrYTHO4aJqHanCucHswuuD3wawH0DyIOD4LCetX3+u5PUCxWrwnjy1v6QcdLstRyfhYCZMaw5iS5fo54WQMf5Y68ZalIP3bzk9Dr5t9hIRoc5BH3+w/IJAp3Bw1YtZT9u2faFpmscsy4JpmrAsS6mwfDL7yDB06LoGIQhCkGsypzZxULLDQX+14gEETaTDQeEbdnAYcg7ngoICIqhBlGmgvQJ0nIAwIfI+EDieqcZBSsBBoxaa1hWa1gtxNxQeBztIwcKi7GBh0bcBTs1Bdz1XULLC42CcOtVA+rD/cmnZPCli2ccbXemkdHaNIpZMeG8eCOgwjITDzgWwesCWmV86jSH9BmplocHMNUSYdRrnwm92j74QwGzF5hX/Pry0MvaJnF6rt4IxkZlL06mLS0SguAQUBvlgkB8CGggCBNH072R9v1iVt0H9rFExFzBL1Gd+CnbNNgiAGTJl2vbW0qQPBQevRPfKsU1jJwJIACSc3aTckwMqU3aSRBIShwf+C4CzqxlnJC2pnLawtC39ekqtwrnBQOHc4OWFc4M/K5wbfBeMU5B4Gk6wemo1GkmxQcyEWgjcWPJ6wYIzOnBPnlz0Yf8fS8u+dFLEuu64LYdAcg9I2ROWPQkRax64ZXgPACAw/CSMfglvrh0O3r+lYzhIXEMmnRYHR0/dnRYHS98f3mLuXr0kZysQ5WAaIqJWbvs+nwG/34CmiSYDqfHfyXQkdKyNHGTUG3VN2VZTD5YhFYrRx0qTGgqqB6G72TxFkiRQpQAdchZAc493bRsHbYnD/Z34IyEyoGk9Y19eUjntVo+DZ0DBwqJAsLDo8mBh0c+ChUXvAjgF4GmA3DmYWLUAbiwoWeFxMIE61UACgC0DVlRIyV82LVualoRpS9guuyBZWa3jkaLKALB8wJaZqpNtk2ab188CcKNqeyL69uu+V0+me544/SyNtr9P9GROr9UHAExFs1+2mmI+PgENuvAl3L5vNJbi5/At9Qb+40j+NXlv3dNL9ZRZb95LAC6QtgUQo95/yPUYJ3ERq0Ekbtx9j0xB/qFpoJigViIguyYfekP2/BSHJxkL4+CADxDJbL4xiVthfCjdPj0lVuHcoFE4N3hJ4dzgjwrnBtfBAcFbAH4MxlTYCStbJVejYeQYSmsgMLRkaUG6QeSePJ0RbRnwjQppD/iyaRVJ05oH0/4qbHkxUhW9z5oYF4/U7DXscPD+Lelz8G0zPQ4yffv1L/o6nYOrl+S0jYMx0jQBn6910W8ATcZSvKoi1SgLlV2TN+stdQ7OftPhoGUDBNQruNmx1lYOEvqiD/LRtwXfqY6Q3ZAD3TLS56DNONizDJHM5tA5ITIQU7HF42A7KVhYZAQLiy4JFhb9KFhY1JKDzvWuqQbaJtAaAEMLSlZ4HEyiTjeQBm2dnnnqVOiWhgZLeSxEzfFICXbSfQBeHrBl5tdU+5ttXp8F4EnV9nCqcv81jfat9Jvdoy8BUKTUmPkAiJJOOjm9Vp8EcAWAl9oyFp8IpPRtpqald0e7Qgau3j8IcNKh/lcap+oLoAezhG2bONxnNSSSxwKxZEiEUdN9F472+mfacACA806OwqD9RdAsPwAg9+RgDNp3BQikVK5dsoQlbYTRgD3D16C6575WbaI8faVy2sLtaQ/QEwCgcG5QK5wbvKBwbvB7hXODKwCcALABDmynI5pZCwAU4pqTyQZwH4CrSl4rOHpaA/bkqR016LGtmadeGnVLQ0kPdQ76JLKmOvFIbHF8+T+Hg/dvUefg22aHc3D01N3KHGTmA5SCg6uX5JwWBwMBt4x1Ld3t6sw6bKvZDrSVg9GY4cOZhyAp+aTGgiGFjRrU4Kh1tG0cxHkYhIHQoutKuV1yMei8gSBS5GBYwqq1Ea4KY0/+DlT3bL3xRJQJAK9UTrvV42AbFSws0oKFRRcEC4u+FywsSs1BAG00jpo4WFCywuNgCnWqgTRo6/QRAD4AcHMkYsE0FVyuotJ1gcxMA062zlYXiQbgfwdsmblQsbsH4VS6VlE9gDuXGq+cbqaPnyu3JLr7P4Z9nPK2MKfX6hCAeVCtfhzdpSMIpaQMjQbU7rCB4v0DYTZfOvPz3rpH1Z2jKbMKSxsW1yHY7wWYoqZFI2aGKWpR2ft9HBr7Gk7124wTvbeiovfmNsEhu64vhpTNRo8jE9B/33QI1lz9yhtlSRshrQb7Rq9DKC+xN0K0L2/VrA0q/Grw5sJ5wd3QEYaGDyHwGJwbpuSBpm375R0EcFnJawWPlLxWcBqRyZ48ta8GPba1mYNl3WEeU4+x1ruGkDnxGLg24Y/C4eD9W84cBy83OoyDRHT3x+8NS/nbXb0kJy0ONi6wOjtEChyMTvb1dj1KarbF8mh+3qy32sBBCcsyEczeC1NrmZ2QwTA1E5W+4zgkDuGUOIUTOIkKWdE2DiIbQzAYPbr0QP+B/SBIqHOw1kaovh77xu5EqGviHa/oZ+NxsA0KFhbdHCws2g0gDOBDQIGDbdNBAJcVlKx4pKBkhcdBF3Vaodgh22fcS0QPCUEGxdQ+8Pv1lP6+8aqpiSASSfkevn9gwtpfJXtxtnn9BQA2Qd1YvGep8Uo6q0Wt9Jvdoy8D8HfF5psBXPwfwz5uepP+5fNvJkH/SSQGAczMfISlfCB89V8WA0D1kS89QEQPJuuQmcGm89swyAdD+JM1bT4GEsdsQtG+AdhvtsomuBfAuKrpv25I1UfWm/c+AAfCreQLd0Nu7XDAkAh3OQYr41TCPjJqe6H/viuhp061nfg9cHSVlYGA7kdAd0/1fQonEBy5DlagPmkb2+ZPKqYubFXN3lNqFc4Lvg3CF+NXbZkZKTYWU7+WWMsALCh5reBE2kd68nQGNeQP2+8lHz0ksoRBBjkc1Cz4R+yE8JnK/dS83R+RTzJTNfn+gV9MSM7Bt830OXi5cVocHD11d9oc/Pi9Yc0cnLT8ZoL4TwINAsAMPsKQD4Q/uHoxAHxpbrUrB2V0obAx9shNUjJCVhhbq0oQkq1KWDkcXDk9NQdnv5mcg7YfuXYuQECYIrCSZFvNQAb6a/2gU/oJ9FgwUAAgBwjk+BHIVuBgXRWC43fCyogkbWPbNZ9UTJ3jcTBNBQuL3gaScNBVae0gLYOTxtvjoKI6ZQdp6I6ZrxiG9qiuC8PZtm7+kk0zdXrOeGVmuk4Qjw3YMvOnA7bMbHUlzTav1wEsgvrn8C8AjysPLoF+s3s0IZ3dI+DHscZRYMX8J4SmPSeENpSIdCJhCKH113Tjj4E3vv73zFU3Zeb2XvNTlvyTRJ8jM4NtBpyVig06+ZTS6JgM3PxpfiLjCACGALhfoZukufkj/koc7/Y+jud+gIi/8t1k7Rqyj6Js+CtoyDiucLqWcm4+AAj1aeXYwI9SGkfRz/jutAdzjqvwq8HHSFArKADR7ynVz1qdCSaA7wCY7RlHns42Df3fHa8YvYxH9a66IXwxaadtHeb+QYncx5Mqc4Lr5f3YgPu3/HTA/Vtac/Bts8M5OHrq7rQ5GGscBSateEJAe06QiHKQDEGiv0b6HwOT3vh75uRVmWteyv0pcwoOcjMHfT5diYOSJT6u2ZnIOALag4NaGMd9FTiuVyAiIsk5iAaU2UE0uJeSbCWS5JhyFckz+cXr2IhPUxpHzmcZ8TiYpoKFRY8RpeBg+6iJg55xlJ463EAaumPmg7ouvpzsdSkZlqW+86dpSm/hJwB+ncBI+jcAqnUjJIDblhqvnE4BHQCYCWCaYtsNAN5s/I9/+Y3XkNDuJAIEJHSy4SMLAWEiU0SQq1uX5YmG0svfmzHrtyeCf91SVzU7ZFlbbClrpORaacsdbMmHIHkigNxMLef7RJSrMpBfVHTD5oaMVE1+kPfWPaNculEqXmaG62+Fs82cUJavDvuGLEN1XsLMuCnlZPBLnOY8kfxm9oPMnNT3kxmvVExduDrtgZzDGj9/nwaBf0vVhijFd6Q2a5UDmFLyWsF/l7zmFYD1dHZp6P/b8aCepyfnYF02rMN9lfvTutQBPlduOhxsbSSlz8HLjc7j4KTl1xDEnQSnDAUxRR8CggV06JcJKUp7XPzerGDw4F+rqqpnW5a9RUpZw8y1UsodUvJDzJgIIDcnJ1OZg8G6/aixalI1+UHerLdOn4MEmBROzUFY2GfvQ7VUTBXeonsClZPjKKkgv+V/kDlB5d2omOteqZj6VY+DaWjf+GINUODgaYnLAUwpKFnx314B2PTV4S52w0u/dFII6uLWzufTVI0fnDgRVl1tWwzgtgMT1trXRb48EMAnROTuX+bo10uNV76n2BYAEFh+3XQiuowBk4DFvxi+6xCcGg4XqxwfNs3XbSlDALox0DssxWhdsFD+zQgB0rSmHxkz25DyFWa+8Sejdknz0OwnANzp1s1RS2DMnkFQsCo2A5hUNf3XrUid9ea9PQGoBASeANA9I6trfrS/5NmBGOhx5CL0OJZ+0egM8sMnlOJTx39Q+FwtgGVEGBPzWZrMeMK28d3KaQu9iScNFd4Y/AoRuQZSs82JEzIwnOeTf+ofwYk3Sv/OwZOnDtDwJaUnRUC4cJDhKyiDlqd2GZ/48xBwrYLLFWMxCLcd+MUE+7q3Iulz8HIjPQ5estzhIMMkwuICGp4WB03TfF1GOQigN0OOjlYoUjq/0AS0OA5KKV9hyTfu+ucoOfubphIHw3YEG45/oHJKh4Mrp7fm4Ow30+OgzHLnIIAeogd6iPQzPWeM98PXV5GDY96McjAwhqJxy8ymyRx+wraPf7dy2q0eB9NQsLBIjYOuN7fJfgf8EUCXFZSs8DjYRnWogTRo63Sf36+FVa1ilXgkZsaJE8m3fhPoXz5DTBk9PKtEkFCt3L0fwJilxistohOvODXdD2CgJsQUABUANq/MXXs0sPy6iSS0FUKIHrHjLMw+tfOrvQ8r+ejaUiJsOj7ojfeDyvvhgGMYJahkCgAs5c5bswoKuwv/ISjUkflFRVf8V+V5imcW5YD4I4BXAWytmv4oA0DWm/deCWCVQgfr6q785UwA6P7uw5fA8VFP6SSde3Io+h68FILV/bEzRQCGmv/28L/d7t8NAD03PN2VGZOJUMGMDyumeoZRW1R4Y3AFEblmrkpqIDWq0Uhq/haqQfgVGA95u0aezlYNemyrzz/cr8ZBxXgkNnWc+ONgp7i3ihj/8vmNKaOnDC8RBqXHwcuNFhyc/sApP2wMFA3NHFz769yjgUuWTyQSrTiYydk7e1JvJQ5KKWGaLd+7qmEEAJquQUvCQWnLnQUF/Qr9fp8SB8tq92F/3UG1ExPKIdDMweXTHQ7OflOdg0uvdDh47btqHKRc9BV9lJIuNSrzggCMXoocvK2Rg3/rytwwmchXwWx9WDH1K95c2wYFC4vUOJj+PXo1gF8B9JC3a3R66lADaXjpl3QhSDnyVAiCz6el3GYMh23U1qa329+np1Hep2dmvmp7MxJ6xDQbKgH0jz4GAOivCdFTI4pdmYJp8ab3N2efb9t63LIM43sDdqGLoZapLxSJQDK3yTiCEBB66knvYi3vT5cHet2k0t2EXX1wUKYMAI6ThuiqRhkcQLximTwdatW1H6u78pf3Nv6n+7sP3wzgObeDMup6ov++K6BbauNMw0Dq97fb/eWJXih+PERwao50AZAnpezNkoexZBtEf3vznqwE5e7PbY2fv28AgCAUHOVcDSTHk/4tMN4C4e8lLxUcaZ9RevJ05jR8SakuAkKdg5l18A3blRIB4b3dULsmvR2EPv17lvcZ2VOZg+EDkUfCZWZLDjL6izrRkxocNzcAYGJYmrXpw62bz7el3ZKDDPTlATDUsksjEom0uEFMxzgSQkA3Us/xebk5f+rVq5sSB987/j4idhr3Gs3VMZo5qJnTQYocXHplMwevfVeNg2kmb8icGIDRXZGDtyXh4Jo4Dvore3PmsWGcccxGJO9vb06Y7HEwTvvGF6tz0P0e3eGg8/h7QckbHgfbSZ3hYtcgBCmnINM0gmEkNpKklDVVVebTUiKtLf9xI7NguBgQjbKsCCLh1mktdSGgJUkL2hAC3t+cjdhrf0puBYp6VCids9XuUZp+qGQYrr6r1/l6N4w0clMGFQHAP+sMzNzTDYYvA6S8MiUQ/7tn5hBLBJgT1q6K1Y11V/6yReGy7u8+/CsA97idVQ9no/++K5ERal15Pl5ZIgM6udcalRF+CkAA0ck/5tH4/4QXEjND2rIEwJdWfdczlBo1fv6+n8EpcpdSSTLZlaMZBG+XvFSwv90H6MlTB2j4S6UNwtJNxGYAACAASURBVC/UOdjlBIyB+xMaSbI+UFP1av+nZa1Ij4OXjISRrcZB86iFhtK4xAQMiGoBEU7MhRCH8OH2zS2ey5G5OI96JGwfr/jdo3SMIwAwfO4c7N27e0NuTrYrB09FTuGDk5thIHFB9YRqLEwdIwaHmGSAicHEqbzWb6xbemVLDl77rhoHSUd/rR8y4Pq2kDUpA/p5Chzsx09BbwMHyYbssqsEJL+06sILPQ5GtW98sToHW6sFBwtK3vA4eIaUfo7I0xQzPw/QLartbZvBbEPXBRoz3jEz2za/D+D6fePXHhmwZeYRAElTmMaqax5BV4xtYpaIhFtHMQqipMYRAGQEgFHDG1C6q7kK+pQkNXQSybSa7wzTNV9jY46SyQBhiO4OBQB46ZTDcMuKwDBUed561EQUaLRHnAxCAMuExtK/EnT4A2YUESFl8GvEqEVZwbLKoXvnlPsjXQoVB+um29tyEBFB07VCacvdVzxW22v197PTTzf0OdP4+fsMALcqNXaui+NoBsFbAPaUvOS5znn67ItD/Dz8UOfgqfPAlgG91xGI7FoQAWwLtqtz34cU1++7/8IjA+7fos7BLnnQ/e43xgDAJiO0p3XWNopQUuMIAAIUwLCC4dgd3NX0XDbnKSfIsay254HQdHcOEhGyszKVOHg45IQNWbBgQG33KxG8CRQg1gB26hw5hpJMZCwl5iC4iECpOUgRlCFYORRDyv3wp+ag2iUAiDZykDVoJ0cVyqxDu6/YuazX6pHXnvMc3De+WJ2DjlpxsKDkDY+DHaAON5BsmxcCcpKmibEKzcMAdknJpZGIXUqE/US004zwhycq7XwAE7t9OmMMgLG6gYM+P/VPNScSMfr0MsBQm6MjkQYkmuVUsiT06m5jdxnDspy2OYatdFbLtiHbuqtHBCgUuxuqZcNQ2A2yGHg1aiCxtCFtC0I7/UuGKFqRXAAsGTG5cWoB7Ilvf/wL99nd1j98OTP2EiEr/nXAKYLBjMdZM3/qj3SpZuaHiej7ScfQtgrUaUtoIpeZ/wzgKx1ywrNb1wDoo9j2DgCLSl7yirp6+vzJrrIXQsMkLVtT52BtTmmkNqeU/A37Sbd3modDH554NspBvONwsI9x0DfQ3z+VcUBE6NOzlzIHQ3sj4AQOgdTgfnT37O4IamWwoq5pqq51tp1euY8W4yK1oq/Z2ZlK7SRLHAk7BhJDQsKGULYsUowTUbdEFmBI2Jrd+IUk5uCyL9jdrlt/OTPvJVBiDhIzEz/O4J/64a9m8MOEFBzU0tgNOw2Jur657D/pcdBR2hwsKHnD42AnqMMNpLJx6+xhH88cb1nyMSFoARHy4GwqnCTCO0T0AYDS6CNYeVwCwE0ALmXJE5jRF8AooOWNspQE51ecfFLt09PZhWKWgIuBIKUF20qc/EEluFYIYFD/MPYEo7suijQy4zJppn5HceNS2D0CgDF6jlJ/f6/xocJqBoFlRWAIPa1wqD6aiasy62CAsbYhC2VWyzhTEgSNGFF+bqm78pcJJ4LKafcdPe+dh8cA+AeAgY1jiDJ0v5S44eRl9/0TAHAZAODeab87Xg/CAwlrDCiMvb3cT4moeMbDVbTuvrxzfdVnoWK7fSRo0dYXB3lQ8PS5VNld4+xhz3883jppPSYyxALSyeGg5JMk6B3S4zj47HGgkYNhOYHDnJiDtdIpht0UBtpafbr2hNAFOMyAkXomtGslzCOJd3JIKnAQAv169se+w05JBgYrLU7ZyTNKu0pl9wgAcnIS2hitdCJyAhHZfC/g7CIJ9/cR87KfDHTzd4cAoTJyAg0yFNdUQLMJtmYBhC11S69MzMHXpx09b/Y7YyCjHIyehMEAYb8kecPJ1y77Z8wh90676Xg96vAAJUjgoZK70MUVUFkU7lI8o+p+Wpf3C4+DatpHRIsGbV3hcbCT1OEGEgDsHr1WwvGlTelP2+O9mT8SGn5K1OicRc6Og8UtXLOIgEBGY6G9prQGrZSb4xhFtrQgKPkkyswIJ3CtS1f9+kRQftiHhpBAjaWhi0udCivZqhmze5IGIpDCalgGNBRoamD4/+ydeXxU1d3/P99z7izZSQibKIkBJIAgIBYfsD6gQLS41yLV1l+ttdZabdXHPo+2VrGt1qW2tS6tVts+ti5YrTyKLEJxV5QSQ1gSdgQUCBAgM5nl3nu+vz/uJJnJLPdMSBRwPrzyCpk5994zd2bu+37O+Z7vd87+ztEHDKUsyAyzSAYY431hnOkP4BtFrRjg6TCUzM3YEBU4f+dA7Ihbu0tEEJKh7JRhBe3ad/otWwFUlr159ynMuADOm/3CvtNvqU3V/q3ryu/48oN7ljHxUySot9MHBhh7SVDauOnuFjlr7kYBWPlZHO9w1EmXbhkMYLpm8z/WPZ0zRzkd3Vp/2Qg9Dl7wzk9EgZxNsmPRJJsK9n7LMTkxkY/gH5Ln3LgrxCcJSFBRkTNAZgcURD6B0mSKZcUINaYsiBo7YKZed2hA2QDs3PspwtEwbLYgKGMytrSzRzrmioTe7JGUAprRdfgknLzmXUFBZphFIhCKjCKUenrhuMJjkO/NS0jm1BINYsW+jxDhaMI2QkkoaWfm4EunOxy84M1TmLmDg3NPT83B/y2/48tX7VnGYX6KgjEOgoF87CUvlYBdONhNwRYULvvCc3DLSTOy4mDOHH2++lwMko76vDv1biHxP51NDAmC9AC22WGS/HnxZqdtZWTy56qjrhLDsqMwpDfJJDEzomYrWKUfwVJKQaRJHRovIYCqijBWN+bjX819cFG/9OUPnAx4ySN17a/GxSSRZtKJaqNQK0QwooBdEWCox8QeW2C/EmAQbDO8V8qivQC3p4Y9TpqY7A/iDH8QX/a3okgoeKQXUiT2iYgw1MdYduw2jNxWgQNKxj0HwKlf46p9p9/yIYAPddq+dX35fADlX35wT18Ap4Cw/K3ryned82i460OUWYoVA8B5OArAUD2rsQyEUjC2NDw7LJtz+F3NdhacemU55fSFV58L371bFMpkDnoEZLkHdpMJjjIgAf/QPJDRNrWOtCbJ8MeuuzZg7bVh9JZJJokVI7LVhGpJf3+mfArS1OGgQEW/CjRua8R+akafTKXtmDOuPXIzSYYmBwsLC7RmmRQrhK0I8kUeomzCYqdvNpl7Jcu9ANo56BM+lHp6odTTC708JTDIgNfngWEkniMiQrGvEBP7TMBbu9+DFZeNxgm50+TgS6frc/DxGAe/t6cvCKcgiuVvPVG+65wnwrZriEo3GST2HgCOEg42Vk8rA7gUoC3DGl7LcfAo1WFpkPq+O7VACNyc7gJG5JgkK8rw5QmIpDja1CaJFbfH3DIUTDsMQQYECWfBJDNsZcI2M9dVspkhmbUusP36WNj2iYUVLb0xJC+AUUWBlKYsYllpr1Ntr8ZWgA0CA4gqgVZlIGwbKPIplJHed3SkdK3RCwDwKBv/OLYj6YzNwD5bIF9wSx7xznVRXyFAA/oJm3rLxPNsCE+SOYpXkSQ8Wr4Ll+7uqBQfm/2r1+pcF/TW9eW7Acxrf4ARBiFzTvBuCgRQtgKA8wH8onv2+NmrelbjDQBuJ1BJbIExV89q3Abg7wR6C8B7a589YX+qbU+6dIsPwBWah3qx7ulKnUKKOeV0VKvvV98tEH6RmYO9PbB2RuEbnAeR18msMFKG2ylLtdcG4ihg7rIh8gnCS07yHJNhtyhEt7vUXvIzOMhIFbrVWeW9+uCTvZ+gpbUFLXYeCqkoJQd1EjOoOK4zFOzYuiCv9GkxGQCKi/WiKFgBJxV25DlgZphsQpJsERA7QypUSEQDvMJLnQuPezxGkjmKl9fwYGRJNeoOrGp/LGb+eo6Df+jEQYkwlAsHNXNSuEmVbAKOcA42Vk9zOEhUAjgJwxqrpzkcJIeDJ6xdlJKDW06akRUHK+vm5Tj4OeuwNEjMuEZQ5hzMRASfn2AY6abT43NsOne64YhCfn5ie8UWVNyNMGvGPttKwdCYRQKAIcdHsGKlxJymCqwNNmNK2W70MmwAjJBNaI7KvX090dfgZCvZG/tp///vto58MWgbg6KcPBxYZJj4wZCk9ZwpVU4eDDT0CqbHburbJQnoYyiAUUksKkd4UoNMkIQh3a+oU/ISY7DZian4LEeWVsGlkjt3w+Q2K4Zl2gAw/sy7DwxccktJyloSh7OqZzU+BODa+JHbWJqNQQBuif3w8Fnr6gG8A+BtAG+vffaEj2PNLwSgl9sX+EN39TunnI5ksc3XCJdaBCQJvuP9MEoyoLxTfqBwIIICX9w9sQJUgKHiRoTsZtvZLpMEoPIVZFCPg5X9j0f9ppXYJ5sQUkGUqDJ4yAAzoNhG1I7uNdiTloOf0NYXbdiDYuttEiRhoFIO0eqHx+NBnl8vI6tSiRAgInidEMFKQVRZJAtTbielgMfrfnvV218GHOj4m8EM+gw5aGAVTBcOaiejz7APTwusY94CgPFnHrh14JKSu444DjZWT3M4GGfCiZI5uG749AQOnrB2UY6DR6gOS4NEhOPd2giBDOaofU8JvzdvsTBieIa1R0pBaRaCs5lNwSzcjBwA9Cq20ae3haa9HtS3lqK+tbRzk94Ang2e9Y+5nZ/wzP1aHzhfwJQ6s+8ueIXenfwoSh/aEC9mBqsU+2SAOP3iVALBIzPHl7fJ17k+BOPfwZp7tYsnHqpY4RIQb6A07x+za5FSVylbwYwkvKRzcYRd+Kq/3vgldDJHaUQARsd+rgGA4bPWbQfwtoc8J2uO7K6DUzE+p5y+8CJB7hzMEzB6aWA8bhBwy7+3YMSZw9NmMFMRBWuf3kAh57PJIRak3DlYXFCM3sW9sffgXoREK0LotM7XiHHw7bOSOfiluRk5WC77QmjW6SvprTl7xAylUocRiLgC8Z1FRPD69KZdOpcLYeJ/B1+q+ew46McliPCGdO8fexgoPrRjqIIdMIf8AzDa17MdcRxsrJ76JYCu1eBYEgfXDZ/ucNBj5Dh4hEm38udnKmZ8nOl5IqeAbDYyowqBILB+QzRpVAhwzFFcaJ2CU4zrPQBzAPwawI8AfBXOrMOAunpRuGqNyJjTX4AgYjeWgyvDIMoYs/X7ggUXpxqOmpBug2PzWnFSrwPpnk6QzYR+pLcotfPsUYcoY/y3x9APcbDiZ+2YLWZ8Q2vDbtK8H/i3QGEmMye9h6w4ZVrbOFlwRjU3AVihlNpuWzYs04ZlWjCjFiKhKKJhs3Odp/O78SV8NmI8dggp0Y8FMIuIhmq2/2Pd05Vf9AxHOeUEAGDFmTnoJciS7NJNmzujCGwLYt2iDbAjydd5FVKIfmK2GSpXDq76oL5w7YY1GTlI0vkBARX9K90Y8fuC0xZkxUE/5aFY6IWOMxg+n94gXqr7BMC5A870Gnx+9wK1HcfouNwx2GLiz5aDF/u3oAgz2UjBQS+D+yLTXWIiB/1N2+3SBljlH8Hq9z7MYxcjMuIJREc8CfYl3KcceRwEPab7nqZQ1hysrJuX4+BhoMNyBgnA72N1bFJ+Ig0j/ehNKlmWQjSiABCa9xOWr7AxcICNokJn7VE4zAiHed7A/rgbwDYAnyzq9a+MU0mefdN/C4iCiuMUSooTP8sGCRidRoY8+QqDBkawdXva+erjANwOoHPNglNTN2ec1f/TTF1M0IqD/fGNMr1zli7MMFOsuUd6tUfwAODfYWeEjRU3KcaZgWn3Nmpv3E165Vr/i+c8Ei5nxbeCcBoYXmZ8DIUVAJrhBD8cALC/0/9D8673t7/pZ959YAqcAm5uOuPMuw8ULbmlpKX7X033q/rSxlEATjqUfUj3geU2RQD89VCOlVNOR5l+z4rvplRZdSRglBrZcXC/heiOCGAA+5v249//XIFjhg5AUd9CsA2EDoQRbg7PGzCwfzsH/zW7V2YOjtv3WxgoODZ4HIoLEqcahJcgPIlMKPDk45jygdjRtD3dLrPkINBH9M/UxQQFfAdBmu1tO/U9aqZz7vV5tLLotWl/xDEODNWkBJ8Z+L9pnz0HZ/pfPOfv4XLO51th4jRIeNmDj1GMFSAXDk6L4+CBR/U5eODWoiUldx0RHGysnjoKoEPjoNT+TOQ4eBjpsDRITZMWt/Z9b+rLRDiv83PZmiNm/iQSomPiXyozsP2Ttr/a6iehfHX14nd09umZO30aQNcTCTRuAL40rsNQeIVMmSVOCoHq4218slPBtNJ+WW4oWHDxU8Gz/hEfg9wBhriiSGNK9mNgnl5R6lbbwDsHBqGx4CAG+TOHTrDitqxr2pLCyJiUobMUM8/eW/Yb2+ZnAtPuXZ7VwbpZr3zfHwTwk0PczdtwDFVS7GQneQHUAPjHIR7vs9LsQ92B0J+kfr7u6cq9h3q8nHI6WtT00qTWvjPfe5l8lMhBipmjNOm5U4kt/iTyaegYxAURMBg71n8CrE9oWr72T9V6HBw3dxoI1xMRNu7YgLEnjGt/TvpFyhA+4RGoqjweu/btbC8em0I3FJy24Kng22clc9CZvgGcTDEoRgn8Qi8ywiYbB/IOIGgFkW9k3oZjlcezkeGRGZMydJZi5nWBjb+xhf1M4P+mfb4cvCzHwQw6dA7qm+bnK+vm5Th4mOiwDLEDACHo253DnwyDILKBAvPBA/t4oWaeypN7v1mTOZsLAM/c6f0APIXYTpv3C+xqcvafzhy1SQrCqGEZ6koAkhkv++dd/LB/3tde9r3ytTohcaY0AGkAUjq//R4b0/sn12ZIpzebKxBRBmbvLnLShWeQnTGLUPK2ggQMoZ/mxgGPunzhlx+66bMwR6vuHNxNSUrTa8ktJSaAV3XasuIf93B3ukXVlzaOA3DhoaR4JWQ1mHFExaTnlNNnIeETyRzsZSTNzGQSMx88sG3fQs2snCf3PudNdw6Om5vAwQOB/dizvwlAenPUJiEFhh0/LNPuJYNf9k+e97B/yryXfZNfqRP58kxZYEDmG5B5EjLPgPR7UG7ozx4dyGsGC4UNoU2u5sey0g8kptpSSAGPR3+QkJnBtrp8+0un3PRZmKPB563qeQ6W3KXNwSorcERwsLF6qsPBQ0hpSxnWq6VQjoOHkQ5bg7Rzwmt7iejXbX8LgWzNEQ42c7Ft4Yp0Cy07yQBwSqYGnrnTBRwo9AM6Rpg2b5Wu5qhNZSWZV/4TYRARvg/gHCKMTrXLC/pvR57UyyCw1/QHVwac5Axroh78dndeWjgoywanXX8Uq6gd31cQPFmkVo3pZln1wt+y2SBbNd5TXdV4b/U7G3490vQX+tX6X4+INt5b/U7D3cPG9uBhkxYWpxThlCmzm/+jB/vRXbqz7T/cRTjohlwy82oA73bpIDnldBRr51MTEjlYICD8WZkjHNzVXGyTdYUiLWa4c3Dc3A4OMtp5snXXVldz1KaS4pKMzxNoEDHFOEijUzGmn90/Y7HWeJnCDAZ9AQBAq2rF5uDWtBy0bZU2OQOApO2ICD6f/rqjmG5+4UnZoxysrmmsqq5pfGfktA2mv9WvRkxbH62uaXxn2NkNnzsHdwn/KZ9sPfPI4mCWM4pt0r1vzXHw8NNha5BiegDAQSD7pAzBFoYVW2ifZq1lKk1yef6/AUzr+FPB62GMGwEtcwQAOnXsnF2l/jKWe0I4oVA/dFex+BqD2gst/GJPES7dWoSPw4ClHJOnbAUrasI23TP4cVxqt2ySMsT0fDzse0INvxo2Whii0fAYE0mQQUQQQngMjzHR8BkrGn417F+r7hx8XA8ceiEzu6Z+ip2vOVNmN5f1QB+6RdWXNv4HgBkA2iNQu2qSNPWnXHKGnHJKK4eDBMjC7JIyBPe2wAo7IFSa2U6RLQeVkzp75LARWuYI0CvoSjalHbj3KA/yVeoU26nExF8DoZ2DmyNb8NHBerRaISilYhnrFEzTyjh71Kb4SIxskjLE1OMcHHZWw2ihRKNhGxOJySAQBAuPYRsTDdNYMeyshn8NPndVj3CQNDgYFAZayZjTvHXKYcvBxuqpHRwEED8onp20Pxt/yiVnOLx0WBuknRNe2wfgt0SZF0Z2VqiVEQl1/K3SLLZModPSPeGZO30SgJ/HP+bzKkwazygu0u4aNK69cF5v6ucmlu3J5lw897uRy+cDOBvOolsAwMJgHsZs7IO+a/ugcm1vnLyhN87+uDe+sbM3fri7FLObirkxYvwdwF8AvAJgGYCNIBxkYjAUjCyTMgBYDeDbqJzToxcAacg3hUi9IIqI4PF5pnjzvFvX/HLoU6vuHHxMqnZd0ZJbSg7CyebjLsKxAP48ZXZzj4c9dFF3JvzVZpKI0fZPRzowibXJVQzPKac02vnUBIeDXspq3VHoYCsigQ4QKqmXuhuZODhubhIHvdKLk0eNR2GBPghtjXqDBAKp1K+3xC7LioMb3hyZxMF91j68d2AZlja/iTf2vY339n+A2sBHWB1ag3Xh9dhkbuIgB5M5CBxkZihmeL1GVkkZEOPgnMd6dsRJ2vJNwWk4CILH8kzxRrxbh85Y89Tg81Z1HwdL7jp4jAppcbCFjGMB/Ll565Qjg4MAHJOkslqjluPgkavDMklDJ/2GCDcD0FqJGY0wWlsSP5C6BT+ZebrvlWlXAXg+cs5r7dWQPXOnlwF4GuiYz/d5gUnjJYoLs/tu79uvdzElavvSJO6/v18vMQMzh4joxwDQMu3x7UWvXVXDzMuIKI5ihBaWaLGArVbHR8Gy+Ml7Tn3uO6n227rxbJ9P5P9OCHG1VkccHQRwISrnBLLYJms13lNdY3iNzLEbAKQhSUjxDStqzVp15+BHAdx14s826i/qSieFNyGRMZUnM7eNip4H4AY4o8OHjaovbTwdwNSkJxJLirVd0DeBMY9AkwCMQacBFwUHJJluZBj8Rv0zVXq56nPK6Yur35AkfQ6GImjdlxhpwJozSMw83Tf5FYeDr5/TwcFxc5M46PV6cfLY8Sgs0J/NAYD9B/e7NwJAisAiuTCsT7N6aQIHX562vejc12oYvIxACW7Ohg2bbYTjbh0ssp5sfPbElBw8++utvvx8X5c4OOcx9CgHq89qrDFYg4NKkoiIb1iGNWvwuaseBeGujf934iFz8Hi79c0dMj8jB/1sY6AKAYcpBxurp6bmYLucD0o7B0HziNJwUCkwi8wcZH6jqn5+joOHmQ7rGSQA2Dnhtf0AntFpa9tsBg4ku3VmPRdPRFIIPAZgp++Vac/7Xpl2nvflaV4ATyCuSJ3PC5zWBXOkmCP163wv67RN910y9OLIAeCPPx26pr2ORsu0x9cCOJuZWcJGH+Mghudtwzm9V+Bb/d/GVce8gW8PeAtnl9Wp8/p89Mt0O82nolmCsoICAFyGyjnr3Zsdsi7UbRibTTJ8Bb7rhBRbVt05+L5Vdw7ue4jHv9E1zC7x7btnyuzmtOlrP2tVX9pIAH6h1diZVbqu4dlh16999oSTAfSCE3YzG8ASAEEAsDOcDmYOKFZfPcRu55TTUa+dT03Q56Bpm4Gm5HutthlgNzkcFA4HJ7/yvG/yK+d5T385iYNdNUesOLJu8zo9DqYrSp6h5EQn/XHNO0M7OPjyNIeDYAYDRtSAP5CHXnt6o++u/ui36xj03TUAvfaVqbJdfdJysKgwv0scnPMYDi8OOrNJhi/su07YYsvg81bdN/i8VYfEwTIVubFMRTJycFJ0Dzwdn8V7mrdOOWw42Fg9VZ+Djq4b1vDa9SesXZSegxlmTJk5oFSOg4ejqKsLzz5LDfhgWplhUBNR+pguZlb797JQaT6HHi9BGu4X1XBIta9dcvaLIBgFbafJ5yWcdopEUUF25oiZQwo8ceGbhXuYsYkIrqnfnO9U4nGuqWhAb59Lqm5mKKDs9hPWNv9XfVUhnFo24wCMa7W9NX5hDhDEgBCgFOEBzGyC+Uv3j9zwUdKTm2fuBSGbuOHZqJxzRxbtu6zGe6t/ZXiM/+7KtpZpIRqORqDwAoCbRt2xqUsjaWf8Yv8vIXBrqtEiVgy2kr5vHwMYu/T20n1dOV53qvrSxmkAFmk2fw/ApIanh6W8gAyftc6A87mbRKALJMnTCOQhIjCzYvCHilXN6meH5EbNcspJQwOu/KDM6GU0kczAQcVq/yd7hUoTy+2JeCFt98CRcDgEKw6EzByEyQVsMmA75mj82FNQUFCQ1Wtg5hDbmPjW8jf3MHgTgdw5aNhJM0jHhivgFT63YwGMsrXvndBcNa4+gYO2sGuEEgMIBCFEyjA5ZjaZ+Usblo9M4uDMq5A1B+c8hjuyaN9lVdc0/sqwu8hBaSHqjUbgwwsg3LTp+VFd4uDDuy7/5au+AbeGU9TBq7CD+EXLKuQljhZ+DGBsacXSz52DjdVTs+bgsIbFKTm4bvj0Dg4SXSClPI0IHRxk/lAprhmyekGOg4ehjgiDBAADl0//oRD4bcobT2YEW3hbJIS0iw6lAXi87hNmZpQRCac+Jz4PMPFkoyvmaDsRnf583qubASB//sUPEeHazNsAKkX89Q+rVqNActqkEMwMU6lwq2m+CAcGw5BqlWAacxS3nwCA0vtHrO/I3LBx5mmQeCtTvzvpVQDnonKOfpqMQ1DDr4adaniN97pa8ZoVw4yYsEwLrPje0bM3dwkyU+5sfo4kzURs7RwzAwrg9GvhXgFw3tLbSz+3L2Ns9ug9ZKhY30lTG54etkR3/8NnrSMB0YeI+ilWDWueHWq6b5VTTjnFa+C1y38o8kVqDipGsKllWyQUSs9B04DH9LoexzSjiERSh3N7hAcnV5+MgvyszdF2Ijr91fvzHA5+ef5DxJSZg8RQRjI+BoWqIIVMG7YUS7oQNqOZOZjOHMXtJwCgdP2HI9o5OPMqnAbKnoNzHsNnwsFhZzecapjGe+lm39zEYJheE5bHAhfzvZufGt0lDr614/zn5vkGzFxplCBCEkXKRE10F84Lf4J8pDTwrwA4r7Ri6efGwdjsUVYcHNawWJuD64ZPv0zAjgAAIABJREFUJyFEHyL0U0o1DF2zKMfBw1hHwhokAMCO8Yt+N3D5dCLiu4Sg9jhspTgQbuXfRUJuRc70LhYyzRnx+4CJ4wwU5md90dlKRJOfz3t1S9sDzLgRwCwi9E61gWOOkh/3kg2fULDYeeM6myRmhu0sHvUDuDRjr1xMBBEVslK3A7gt7uGzMm6UqI0AvtEVcxRcdlZ/Av0AwBQAewAsyp8w/2G37ar/p/H9dfcNb5CGrM7qgAQIKUECMPwGbEshEgj/eOXtx5eMnr35e9n2H4xvscXDAJykmdDgHAA3Abg/20NNua3ZADDctuxLAExioBCMtcpWd7xzX1+9pBGOvgJ9KLwBvYrp7Vr77AkMYHfsJ6eccuqCdjw8/ncDr11O5KW7hKejQqoyVSC8v/V3kVAoMwclAI1bMmkYQIqSfT6PD+OGjUO+37VUUmdtJaLJr96ft6XtASZ2OMiUmoPglJn3iAkCwiloLpITOLUtoGflzkG3wTQiKlTdwMGumKOzZgX7EyVycP4z+a4cbJxf/f7w6esapMqSg3mA7C0AH8EgAypgI9wS+fHx31hZsvlvo7Pm4InWwW+daB0cFgWdxCD43E9BlznYfO4Uh4MRzyUAJoGpEMBaFfXc0feNVw4bDp6wdlGOg0eQjpgZpDYNXD5dwqmqPRrAe/v3cB2AFXAWxyWICBCx4rJtmeF0JheCLQrxp+UQzNEWAJOfz3t1a+cn8l69+BgirCJKrDrdYY7aj9UK4GNmNPX1hvKvqNhwcvvrQ4dJioXVAQBspRDJWPAVII00q8y8j4gG3Dd8XRQAsGHmSzBwvvuGiIIwHpVz6l3bximwrKYvge4TEN+kTu8Ug3cy+Iz8CfPXZtrH2rtPKJeGrJOG1MrMQ1JAepLDAJgZoYOtymw1jx195+ZPs3kdADBldvNQOJ9L3QB9G8DpS28vTVsHYcptzX4AJyIWJgJgLDOPJqKkFcvMDMu0H3j7nj43uR04Nnv0bwC69TFOb3h6WDYjqDnllFM3auA1yyUonoN70nMQBEEGBAknM1yA0q7tiVewtcUxITEdgjnaAmDyq/fnJXPw9FePIaZVxJTIQTCUVPGrpGMc5Cav5c0faFWcHN++zegw2pPgQCkFy4WDOunG2zi47oPhUQCYeRVeAulzcM5jyIqDNZcE+hLRfUKIb1InB8fMO5n5jPnP5Gfk4AlfWVsuLVknlR4HRW+CHJiCgzaj9ZOQMgvMYzf/ZXTWHGzeOqVLHCytWJqWg83nTknBQYwmQjIHFWCHfQ/0WbLAlYOx2aOsODisYXGOg0exjjiD1FkFC6adD+Cl+MeEJOenU0pUItYySPHrkPw+xumneOD3ZW2ONsMxRx+na1Cw4GJSCtcR4RIARczYyYzFADXAicndBmBfeMbzDAA/a6z+TwCvux3Ysm1EXdKo6hikmL533/B1f8SGmQTgE0j0d2Wrwm2omqO9yDGwrOY4AP8F4LsS0p8O3gzep6D6FUxYkJF66+8fYbDip4QhLukMmHiRIEhv+vPAzGjdH3x4xK3rf6D3ShI1ZXbzJQCezWKTbXDWI+2dcltzW8z8WHSAYCSymPVlZtiWfd1bv+rzUKZ21Zc1ziJBzwghOrLUKYayUo74LWp4eliNbh9yyimnnlfBzAXJHCQZ+0kMIaNWcmoMuSgcDcGKOiD0eXw4Zfgp8Hkzr/tJoc1wzFF6Dp62gJRQ1xGTw0HinSx4MQiJHFw6gwGg+tRGLQ7atu2aTlzHIMX0vXUfDP/jzKvgcBAaHGTcNudx/cX+NZcEOjgopT9D+OA+pVS/Bc8WZOTgiGnrDRb8lLDFJZ0HHONFhQSjKn19LbYZwf2tD6//64gucbB565QucbC0Yune5nOndAMHATvsu67P4gUZOdhYPXUWkXjGuW+MG3hOvbB90bCGxTkOHuU6og1SwYJp7Y6fqMMYpbuw6BqkSIRhRhhFBYwvjzdgGNkl+2PmXVYkNJtt+yCcL7In7qf9bwIKDSFHxkY+NirmB18oe2NNuv3e1jDsW0TkmivftG2YbnUmZPr47U7aAWDIfZ4xgwA0OtsifcSiggmFAgyZ4xrIEVhWMxRO0cHLAXgEnH+ZpKD+kD9h/jU6HV//6xFDALwghBid6nnhkRDS5Xi2CgopelVcU+teRTeFpsxufgRA5v4yOgoiMnaA0YJ0a8eylG2r1kgoWrjsNwMSvuijrtzkATCVmb8O4LJUCVCYGXbUThhFBjCh4elhHxxqv3LKKafuUcHMBR0cBLUbo7QcjBAo6n5pidgRmKEICvwFGD98PIx08edpxIp3hfZGZtuRzBwEoVAaYiScmfCNrPjBN/5WlpaDwyY0aHFQxyDJLDk45qThHRwE0l+hGSaAgjmPuwc01lwSSOSgy7ooAFBK/WH+M/laHBwxbb3DQU7NQVkpIIpdjhdRQeETvWofqOgSB5u3TnHnYAiONdoGoAE7sLb7OKhMozXaXFI4YNk/EmC2adRX9DhoW50zIU8Y1rA4x8GjXEfMGqQ0OpcIY6VHJM0WpRKzXoidU1iW8R9jZPbmSCmY4dZ+YH4kUztDCHgSF5lOZearv9Y8+aWobV80t/ytJOdqM08zNF6AlunVPRnAQABXw7l8xTqCZJPEcFJYM5a6maPAsprRAG4BMBNxQRQ6YR8EmhVeNuNa/4R5CdMb71/bj2xLnQVgOoD+YLwEYE7/4f3GMPNMAH8kooTaEDpFF4UUBQB+DOAu18apdSOckNDkafuOcxavgV08TkpJKfKFoAsA/HPUlZsEgIlw4vK/BqA8080BEUF6JaxwOxNfyZmjnHI67HQugcZK4dEq3s2Sta61bZnwxpwwJmtzpCyF1qZwP7Yzc1AYAtIjkjg4+fLml+yofdFbz5YnwYyZp+mYGt0CnZoGKZmDgHPt7sxBR0vdzFHNJYHUHNToDxHNmnFZ+Np5f/cncLDfj98n5bc7OEh4CZMwp9/b/ccwYhxEJw5qJJ0Svh7kYBOcNBa1ADrsV7dyUHisfPKYFwD456ZRX8meg9KIz+r4Ss4cfTHUYwYpUHNlEZxFd6MBtAB4F8AbhQuf6JYpq4IF00hIekQa6WeMUsnNFzADtgVUHUvw+9NPO6fc1jFHgMuFWZKANwVwiAgGyQvg1LuYlaJzWnG8WgZJKTClOHdCJC7WUgpQ6jaL1WIjHr5tA3Ntm3cc8o10hwwsqzkVwK0Azk31vKZB6gVnIeUr71/bTwCYwMyXALhCGqI4ruksVvznnWt3nT3psabnNv7mxAXMfA+A72YKu0uj2VsfHbug4praFVluh6W3l4anzG6eCScOu6M4IQOpE/l0v5TNN466ctOpcD5Tg9zax4uIIAzRFm73s57oX045Ha2aG6hJycHzCxd2DwdnLiBB8hFJhj4HJcCCQSmypLaJiWGbFo7tcyz8Xr2irG1SpkJrU7jzzHOShCQY3mTGEhGkQWk5yN3IQaUUKAUHhRAJjyuloJS6TSlenDQYm/owaTlYc0kgMwf1DFI7B/v9+H2Hg+BLQLhChGUiByX/eddpO89uumPScydO3biAEeOg5ghpnGaPvXHrgtoHKrLmYGnF0nDz1inJHNwJ4EE4JXR7WGzRjZtGfaXrHBQCSuU4+EVSj4TYBWquPBPAn4GktNsvA/hO4cInDjmDR9Fr02+QBj2Q/b0up03WwAxEQsq2LMgJown9+uj7R1YKZiSUOv1cJ+UZHreqyjCVPfCFsjc+iX/8ljVDH/dImbKyd7xC0ahe7rTOqb6lTHtiZtPwYD4ZOnldT8OQOe+0/RFYVkMAzoADhDMybSghtUySFTY/WjWn7h04BfEyLkJlZlYWj5r0WNNqAFh955CHPXme70tDaoXYxWkNgJMrrqlNnfvWRTGT9FzHi+jKXrIXM6M1EEY4ENEzzimkLAXbtF9oeHrYxd3cvZxyOmo1N1CTkYPnFy48dA5e8toNkozsOWgBFEqdrIHBiERDthW15Ogho9GnVx/t3SqlEGqOQIXcOejJy2zqmBm2qQa+8beyBA4OPWXN41KDg9FoVKfLSam+04XdMTOGVw8OGobU4uCcx9HOwZpLAvoc1Az7M4X1Ud2AVXocJGbOU6Oa7pi0GgCGfHv1w57dnu/LsISsEhCF2XGw9oGKLnEwZpI6OHg/nBQePSxWhHBTL0TCh8BBZcO27ReGNSzOcfALouzixzQUqLlyCID/QzIUAGfE5NlAzZWHHFMqJP2wa/VuCMzJkzzMCAD4hmVhAIDr/T6nArKOWCnYZlTr5l6kmrXp3EMiCKIrknuOe9y+3EopHXNkAlgBpZ5mZudWPZ05AtCbfNA0RyEAHwJAYFmNCCyrOQ9OTYHFcIECAK0q7wBg+D1j8sryr4ULFACAiIgE5gBA3U8qi6yINSu0P4RIMIJ0xRTTaAS6Hl6ApbeXzgHghJt8hsv+LNN2ZiZTjNRqy/lY3N5NXcopp6NecwM1rhycG6g5dA6S7BoHDYDzGSwTL0YsOAAPvmFFrQEArvcZPm0OKqUQjZoapV+d8GYdDpJI5iA0OaghE8AKFcfBTObE5/NC0xy1c7DmkoCouSSQHQc1b+A9yhiTH83T4yATwaI5AFD5X3VFVpk1KzQshMiACOw9WWUgPyQOllYs7eDgdnwm5ggA7LAPRMJJX99lEZDj4BdK3W6Q4IwJZMoDOgXAZYd6EDqkGFUCM0Ep2EphiVK4jJmK9/3nwr9HznmtKXLOa78vKsBfdPbUZo6c3eoZJK39Mio6P3bXiPUbbKVeTb8Np8peFwLwPpyL0nfgZIEpvH/UppPvH7XpMiL6fXtYXRpVQa8goFJqaygQvKu19ivvCI8MCq+cKzxyAhkd2dEybg+lbZL6jx4Aaejd9JOgEW9+u3chgOsAp/q5GTIR3BuEbWZlkm7Y+uhYV8Bl0E0AVnxWBknZCmbU8b+Gp+tgYOa6hqeHre6ufuWU0xdAhz8HpWOSVIGyVZ5aovzqMi7g4n3//M+/R14/pyny+jm/z/cX/EVnV23mCADI0AgR01j/CQBgTuLg+g9HbFAuHEyRnCElBzetGHXyphWjLiOi37eF1aVTQX5e2ufipZTaGmwN3fWVS1vfkVIEpRRzpRQTdNZJx7bXNkkDAv0hoclBi0b0vv/NDg4SYPYzERwYhN2aHQfH3rj10Dm49xD2kIWUKWEFnfcui6yFSWLmumENi3Mc/AKpW9cgBWquFABO12h6HYC/fTrhKwRgEoBKgFcSYVX/9+frDmccSlXqnQAeA+jx5skLtqdswbiHmb9HRGmvPgnmSFO61a2JKGVxM0PKcyzbfl4K8dX4i7lihmlZrYr5Qzhxvm0/6+4ftSl9QJeUrqn9BmfkfIds06oGUE1EHYaInNfMgpx1LC5x6Qy9BcTFx/aCvyQPZjAKy7LSpaRul5B0MZwUqh3HUoxAUwuK+he7Zg2K01+2Pjp2dMU1tft1N2hT3Hqklch883RIitVAajdHQCyluSFhZzdrBmZmMDJWu88pp5w6NDdQkxUHJyz/lGBjEgxUIoKVFKVV70/u/9lxUODx5ucmp+Yg2JWD8eZIt0e6k17pOCilPMe27edFJw4yMyzLauUUHNy0YlRaDkop2W1GK79AzyBZlt3BwcTXAiKOGaDM+9BNHtErVIw8y4+oYcKCBZXp5CuAoiKZgz5GiyeAYi7SSvIR01/G3rh1dO0DFVlzsH09EvUwBxXBDnthBfMBdl6Xk3BBumY3TNqX41hzHPyCqbuTNPiBxMKnafSlptMuehDg7xKhvbACM6xPJ5z9ARHegZO29N8ANvZ/f36KTDb4lJA8y5JKkz8K4b9eaEGfAzYLBpNTU3XH4FV1aaAAvFC0aNtXA9NvhZD3pIxHTmWO2J0MujNIgmhJqsfvHNbAAC6+Zc3QfgD+i4BeDOy0bPtpBhrvH7VJG5g3rx9xEYhudGs3WHcGKcNFp32xv2lnDDFTUK6pvgHnhr9saG/sXrkTHq8HbDAs04KyU7x8BrwF3h8hxWeTFSO0rxUF5bp17HAcnGWll+tuEK+lt5dunHJH89UAnurK9p3FzIqZbWWzRykFZSuoNCZUekRWBomZoWx155r/HfqOe+uccsopJm0OXr7m5QcRHv9dsqiDg8TWhNc//YBACRx8f3L/ZA6CtTno9/jRq6AUUhoMcmqxgrFj1cOD03Jw0W+Ltk3/UeBWKUVKDiaZIwDKdJ/90J1BIpGagw3vD2MAFw89ZU0/ODf8vQDstG37aQCNm1aM0ubgiInrLyINDurOINkZwvvaFvvbqTgVJ6WU1qAdgdA7WIadJbvhgQcMTm+UBOANpeGgYLRyCIWsx3ocIgdLK5ZubN4z5Wr48RS6tJopUcxQbAubLcOjTAPKMsCmgVShKyJLg+TUQlJ3Dl2zMMfBL5i6PUlDoObKjQCq3NpFI0FEwwd0drkfzgjQcnTAYtMJv7BnCEkvu42y3PVEszn9o7Cnc6vYy15EhLOq6uvSnoSvtkyfSkI8DaI+bRsqZYM7fcGYGWy7r7z3Gx5dk1T8TMmSFp2GXdHN60eMhBNykNEZlMKDW2mY6/6YGeFgq2s7ZSmwCxwkZIRArhUJlaWwb2MTQIAdtmGFbTA7JoFjqdqZnbTt3jyPMzXFHTHe0pAQhkAkEIUA4C3IqgjixRXX1L6QzQbxmvKz5nfB+I8sNzMBrELiyOjK1kD4BwDucdtYKYVwIOJ6EGZmVryZFV+eM0c55ZS95gZqtDi4fPeZeO1jrXvMlBxc8+iqGYKkKwdLC3qb+Xn5ns7tmBlgLCJBZ9U/WJWWg9N/1DJVCPE0xTjohLGppBtNVgyrOfMgGAB4/IauSSpe8mRJj3FwxMT1Whz0eAwMO+F41/0xM1pD7nf8TlY8l2y3UkaINDgIhSa1D2DANmzY0oIS7ISsUyxonRhsKHgsb3ticiYGGJAsIVgg6o0AeQI+4XXtf5wurn2gosscbP75lHfxQfdwMLy7tzYHI2H394gdbWbmy3Pm6IupnjBIf4BTLyCjmBWCB3ehiyvWmwGseGWU6v3aKIxZM5CwvQwJgwXMvPnx3zRvHLcpMtUlrfeTg1fVXel2wAv3nzGcmX8CYFbncAPHHNlar8Utg11Mu54pWdLfdWdd1M3rR5QC+ADAELe201Q5pkv3rtiWjajORUexM4uUWg0A7paQYQI9l65RvCLBMMxQbCZPQS/ghAHixAxOzIxwSxhWq2tdvzbtBTCq4praT3U3iNeUnzUTgPfAmJCmSQhAHZyLf23s9+qlPy9NcjgTbvi0nJl3u6UvV7ZCOJjWICk4i4ifBvBS/RNVWqMXOeWUU7LmBmq0OBi28vH7lb+FpbIanGlTM4AVB9Yf6B3Y3DImsicCsyXx+sXMm8uL+m70+/1TM2aMU/zkqocGu3LwjB/sH842/wQiBQdtht1igzUydLplsItp15InS3qMgyMmrtfmYHl5Kfr3K3fdp23bCEfcw+7bDGYaORyUMkykx8FwOIKoGXvvdW+pBEBeSjCqzIxwQRhmqXaa1b0ARtU+UNElDjY/MoWwHe+hPjsOlr68NAlkn064WI+DmQ1SAger6l/NcfALrJ4wSCfDGeVyVbi1GZYZcm+YVh2VNm0CPulF6pHJ2PriePrh8ut3jxGk7tSMaLu1qr7ubp2GFzRPLgLzTwH6DwBlzLwbrDbA+SKbsR8r7v/tf0uiMp/huVPjMO88U7LkNK2eZ6mb14+QcNLMnq3T/nq7EscZ7uFnZjQKK+puLlgpKDMJDLUAfgngn4UTFqrwshkSwDpojMAqW6G1ORB3AGSuL5TCHMUrHAjDDGivK5sPYEbFNbVd/hJN+VnzNAA/gRMiEgTjn7H9Ni79uT6lxl+3fbU05IhMbayohWg46T16Dw4Mnq9/ompXVp3PKaecUmpuoEabgy9vvgqr9h7C5T4KZ34pEisRETXVno27tx7YfOCHA0sGjRGGuJOkFghvrX+wSouDk69uLmKLf0oCDgct3q3CvAHszkGSVObxGVocXPJkSY9wcMTE9VlxsLJyIAoL3JfLRE0Tpul+2VaKU2Xaa+fgwucK1YzLwvocVAqB+AgONyIJgHzpMwmG88OIlmoPFs4HMKP2gYouc7D5oSnTsAs/gY1eYARxAP/ENoeDpS8v1ebg9vEXrpbShYOWBTM5BXw7B6vqX81xMCcAPVcHaQVSVUzuJNuKIBTsaiqT9NMFlmWuCIcOjCNS2ZRCu6Gqvu63XewM6oZX+iFwNRHNAOFYMNYBeGj0qs2L29p8/cCZMwC8orG7vz5TsuRbXe1LJt28fsQv4dRi0GtvV6m+Rr5rMHQkFEq9/qeTOoXYvQ0HCAsLJyQWTgwvm/EDAL/X6WO4pRVWJO4amm4yz8UcOU0YgV0t2Uxsfq/imto/arfuIY37/sejDK9Rl270jJnjayGtggODZ+ufqNr8WfYzp5y+KJobqNHi4LaWE/C3xp907SAHAexJ/ZRlWitCteFxZJB+1jjghvoHq7rMwcrz6/ywcTUxzQDjWJDDwc3zRrdz8MxvH9Dm4JInS77V1b5k0oiJ67PiYFXVcSo/z+/KwVA4opVivFOIXTsHFz5XmECeGZeFtTnYGgrDsuI4mIFh5M/8mWBmtAwIQDNBHgB8r/aBis+dgx+PO3+UYWTmYCQcTuJgVf2rOQ7mlKTuTtLQpsfRlus+g6ThAwkDrLKtmtk2c5Rmv9IzTkovlMpq9d9vNo06KVxVX/cH3Q3qhleWApgB4EKSdB5Jij+fw5n5/JUnHj8PwLmjV21maEzlx7RBv9v6unn9iIuRBRQAsA9yLYCRGRs5i/ndd8bcZo4WALircMLCtzI0/zOAO6Gx2NmT50s0SIS0cHDLkEcgePK9O81gNH1oR/wuGA9sfXTskopranvkPdPVikcG1Y+7dtv/GIb8FXVa5MbMiIbM3cz8BIBn6p+oqv+cuplTTl8kaXHwuKJ16O3fgb3hLDN2R5HWHAGA9Mhxsq+E2pdVorvfjLp+U7j+wSptDlaeX+dwkHEh2XQeqTgOMoYz+PzjZ6ycB+DczfNGf+4cHDFxfdYclELocVDDHDntGIhxcOFzhd3CQZ/Xk2iQ0onck2QQEbwtnp3RXmZqDhIAD5xCMQqAiQfG3rh1Se0DFZ8rBwetmFu/bdz5/yMN41edTZIzuxpt52BV/as5DuaUUT1RBwlwXLn7in0AHm9XsjxmvggRAT6fdjaWeD26adRJ38rUoG545aC64ZXX1Q2vXAKgCcBTJOmiTuYo1g8CSZoB4E+xh9rBIOAMzqR5A7r9InPz+hEnAnq1neJ0a76iW9wa2RohBcwMZak3AIwvnLDwbBdzBP+EeUEAj+p0UhoS0hM31HWI5Re9Bd5GOCEhiSIAEqC4H0jkgzB/66Nje2qwQVsrHj7uXjNqjbZM+zXbsnfbltplmdY70ZB5nm3Z/eufqLo1Z45yyukzkzYHx5S/kf3eXVZHEAi+Cl9Xlvk+Our6Td/K1KDy/LpBlefXXVd5fl0HB226KMEcxfWDVGoOuoCw2zk4YuL6LnGQhDsHLY0MoTET9QaA8QufKzzbxRxh3t/9+hyUElJqTPlo8tHbkoaDfjgB4UUEFJDzuxT58GP+2Bu3fu4cPG7F3Hst0xxtW9Zrtm3vVra9y7Ksd8xo9DzbtvtX1b96a84c5aSjHgmxA4BAzZV/BvAtt3asbARbsg351LshDwb2gMi1zE9nKQCXVdXXPQsAdcMrCcBoABcAOB8pQibIk7kqODOzUuqYR14YND/Q3zvGCyS0Z2bYia/qS8+ULPkwq15nUCwpw4cABmex2fMALrlv6Bq2Gy5aKg1jcqpGSilEQuH2tIDxYmYFRisz/xvMPy8YvyBlytZ0Ci+bMQBOrW3XtDpW1EL4YOxeJF30JQOC3ccEGPzPwM6W5XDCHhwJIFOJCFZ4oeLq2otdd55TTjl9YTQ3UKPFwVazEA+t/C1s9ujvfDucWaQMYskIvBd0ZVQKKQCX1T9Y9SwAVJ5fl5mDDJDpGr7MSqpjBvUdON9L3jHwJnOwEwi/tOTJkm7jYCwpQ5c4uObdoXzRFfZSw5CTUzVSSiEcjqSO7GZWgMNBZvx8wbMFWXFwxmVhfQ5aVkcWvXS3dgIQ7tGCYMX/bDkukMjBQgC+DJ+jML9Q+/OKHAdzOirUk27/MWiAgYSE4fHDMrshGX7y3mP37VmZJMHMf2scOXxoWIV6w4FBZfrWcAUPEZFF1g4YJDqbo9jzzhvB3MaGbhs5iyVleAbZQaEewLfvG7qGAUApdQab1otSyvMoVknOCauzEY1E94F5A4CNsX63/yaiXXlj56V14K9/vVRapj0JjC8rWwWU4hCcwnEFbb9Hf330lrKqshPcOmx4DZAUTgjfoXt+E8C9cMInJ0JqFDYkXLT1j2PLKq6u3XfIR88pp5yOFmlxMN8TwAm9/o21zafq71mnlEtsoIhtdma/9UEomPlv1Vc2Dg3vCbtzUGmFL5NVYO0gSQJGag7CcNaBxkDYbRyMJWXoEgfXvDvU4SCrMyyLHQ5SBwdtpRCNRPdxJ/4hjoPz/p6Xlkqlr8+XtrVhEmB+WammAKvdiRz8JgpGv/aDLWU7h7tz0DAgBLmmENcSxXFQYCKKABgunx8vLhp729ay2p9X5DiY0xGvnjRI7wNYDZe4XQAwPAUwo+FsZ3o05ZgkIv0LBhFJA547JUzYGrNVOto1tkhEyzOn+DaIgLAdvmbq6pHvou8HE1fu1k6nFq9FowuJBDDtowAr5ocEUU0WmzcDuPC+oWvaU8N5RrzEAC40V19QRkTnAuht23a9su0P88bMy7qSNgAsvrj4WiHFrw2PjOW3lVC2ghm1wXEX942LN6Lsu2Va+/Tl+51ZpENkA4FKKq6ptbY+NvavACbqfC6JQMz4PoBfHNrRc8opp6NI2hwc0+91rDk4AWSSA5OuAAAgAElEQVR3IwhVx28Gg9xucONERNLIN+6Ufgk7rF9YM5OKrELhcSl1QQbBtu3wat/akX2/jw92PzKxSxwsXLCIIPYiMP3rzOCHCNlzcM27Q9s5+NJfPAzgwgv+n9nBQWXX27b6cN7f87rEweLFD18rRPmvpTHEBwASw6HUftjmh2DuiKHcOP5FlL2il8jD7/OhtTXDgLMmH4mopPaBCmvsHVv/ComJ0En0IYjAnONgTkeFeizEDgACNVf+EIBWRpzAwT2xZA3qWiL0AjAewMkABiW31g2xi8+QxxAiu9fKzAjZQdiZhuoIEJ7M09V7jvfg2UcGIlroPq3t/zSCb36tEXBi198G8C8ASwGsmLhyd9oX/trYQgPAwyBcDidKmNd+vfjgtgt6lbgetEMKwFfuG7pmYaZG31l5fDGA4+GkH3V+E40kIU4iIdoWk5qs1AIA33l85Ibd8dsv/mrxzdIj7k0FSWaGGbGg7I73aty3x6F4QLFr51kpBJsC6QGgG2LHvCK4t2UegNtcGyccHw9WXF37w2y2ySmnnI5uzQ3UaHPw0V33oDnaDziAaylMmTn4MVxRyIoRWB7seEAAwshu6TErRmh3CHYkAwcVIKzM+/V4PBh4TH8I4X78iBVBo7EeSMHB3Y9MTPuqCxc9FeNg6HLA9gPg4kUnHOz1zqCsObjm3aEZOXj81JVJHCTQSEHiJIEODio4HNzw2sgEDhYv/u3NQg69l1LEbjNbsMz3wKpj+cG4l/8bxftT3A517jwzAi3BjG2Ee2JaMHhFy+hg1hxEmB+s/XlFjoM5HfHq6QV1TzHzPTrVoD3ePETDgbcHLJufkPVn56ln94EDiJPRAYvjsu8KQSlkZZKICHmyILNJYuemPt2I2MG+Ei/eP0DLHAGAJ9Lev3wA02M/AHDw3dF934ADincBLJ+4crcCgEWjC8tJYg0Jp8o5AESKBH18bklJNmORlm0vjljWlu+sPN6Ac46rEG+COn56J2xIBGEkFf7zkJTnslIbrlo9pOrxkRv2AMCiCwsLPT7j7nTni4jg8RmIhqy2VJwQejU8ACLsbWgCK4Akoa32h3MoAggoG9TbNcyEmYcDGKd30AQ1dGGbnHLK6ejWU8y4hwiuHDwp/w28bs98e9mpAxI4eOrrO7uHg8optZCNSSJByOubh9CuEOxoGg5SbIYqTZidlBID+vfVMkcAwJSeg32//24CB3c/MlEBQOGCV8shrDUkWts5KAIGlbx7bDbmCDbZiy3D2nL81JVZcZBAMCiZgxLyXMVqw5Bpq6s2vDZyDwAULrqt0PCMuzuVOQIAIgOG51RY0dfbZ5KoJU+r/wSgacBeQDBIxdaFcayHDICB3oEy15BINrrIQc5xMKejQz1qkAoXPrHvwNT/t1xKY5JbW4/Xj2gkmJQStf/785vgpMNcAAAbRo4V+QVlzUII9ymFRDFA3wV4IIA7dDdqM0mtdhAqnUlSSFkvIFQs8OKvByDQV/80996QtnBuMYBzYz8AYL17Ut+FrRy6FQKL480RAKy6ogSaxQGdndk2IpY1HUCDQQTF7JIrsEMpzFG7SIgiKPUmgBEAIKS4vHMF9qRtiOD1G4iETRT0zYe/yA9W7JqaVFkKu9ftYX+hjwyPTLkwudeAXpDezO8HEemRKE6x5WOPZbtdTjnldHTr/MKF+/7UfMXyPp5PXDl4Uv7beD8wI4mD70/un8DBkd/bIArG5jcLTxc4qPBdANlxUBDy+uWhdVcrVDQFGQgdKZ87SQiBAf37wTD0ORhC2hCxJA72/cG7C0Ontt6KPgcXx5sjACh5tRqkETXQJptsWNLhIJkEFuy8Lg2UpjBH7RIkihTHcVD0uZzI78JBDwzvJJiRJcjf2Q/+SClYph+MbZMiC3uGfMy+A71IRo2Utf96tfaCoTJnvCM7ew5CsQXOcTCno0M9lea7XZFQi2v+fgAQQqKwuE/GQOcNI8cKEnI+wF0wR7hyyOraP8GpKXBPNhsTEfJlAUSa08V28qyU6Se8dE9/7Kt0TTzTsR9mjP1bk25zA4wZefB/RECfzk8Gj9GHka0UInH1EwQRDCGcNVFuIo3sSETDr1o9pDz215d0+kSC4PUZqJpcBRJCq87Sge0HEAmY0dCBMFr3h2FHbXQOIbUt9/1kme3JSQTCuKni6truCdTPKaecjiotPThTi4MF8iCu63dDxuvIyO9tEMJL82HDnYOJlzIGcOXqPwzpGgcFIb9fftqQcpbJHCQi9O/XF16vfnY+ZkaTyIKDCjP8y/M+gh1I4qCxR7/ch4KCJa32c0YgCCVAFqUP20ZHW9dkTaDhQ6atjnHQo8dByofhnYSq9y6CINKqs3SgfDPM3o3RcJ8Qwse0wvbaTuKLOCnpjipicquokihmIIKban9ZkeNgTkeFetQgfTrhnFNsKzpC2dqJDq7P9KSQnvel9EzP1KZNcRcrBvCtIatr/wwAVfV1DOAWAA/qdqptf/myML1JikssYEvgldl98emJ/mwOgcrXD6B8Q3bZ/AhEfk6O3GDN2SNmRsRMLnUAxIyS20VfI2Qi9l60LZBdp9UxAL4SH4oHxqIjFDKaJGUrrJ+/Dh6PYZAiqKhCa1MY4f1RKEs5RWqZoUy9a7du5XlmKDB+UnF1bVafp5xyyumLoQnrPj1lQ+SkEXut9LWn42WTyshBmS/el/lyus6sRmcOrv7DkD8DQP2DVV3joCDk989PHaJHCaFxAIC+fcvh97tGFibogDqIMEWy2oaiRL4PkiMOdRNeMBimYaacKSKQs58MJklkqgHRtp8EDtr6HDzYHyX7K9v/zmSSFCusO+UDGN6hBvUmqHKFcHUrov3DUIYCx/7ZQs/5kKU5WMisEMFPan9RkeNgTkeNenoG6ToAiEa1b/onHZz6/0anemLTqFNuEkKekuXxFYBvDlld+7/xD8ZM0o+Y+U+pN0stJ9wuqbDtXgB/AeNXgHMNXfzjPtg8MbtCtX3rAph228dZbdMmwSLp4l3+kfs5Z2aETTPj4JigzJHKWcy2tKX9/F/dxCCDTqtIiFlni9vNTvtjzFC2wp6GJggQhEgM3zODJgI7W9GyI4iWHUFEWvQSIum8LmZsBjCy4urau7R2mlNOOX0RdR1AqGvVS+HtgzXp1we/mZKDo3606SbhFQ4H9ZfTKgDfXP2HIQkcjJmk7DkoCHl9kqKvHA6Sw0EA6FPeGwX52RWCD6ggPpbbstqmTaKpMOmchIfuTd04TgyGKVObozYRu84O6XQRaOeg+l9mPRZVvD4kwYDFis0mc1ApNPXdA+JhIOR3cJAAs5+J1pEBBEe1IDiqBdEyPQOqZZCYNwMYWfvzihwHczqq1GMG6dMJ5/QDMEt6vCCipFCnVIrdlM5O/Zy4Ods+ENEVQ1bX/j3Vc1X1ddxqBn9q2qlnT9JJkISA/ATAAwD+E0D/k9ZuuYIk3QZg29tXl2L1V4qy62jUws7jCX/6vyr884GB2HO8flgegLhFmB0aMrcFSBUrHn9Y24bSeV8yPacxgxR7798HgKn/OLhd2eppt22kV6LXoOSoFLYZKqpgR23YURsqqqCiCjuWbXftB+AUlNWRxgzSS0QYU/Hd2tyC1JxyyimlJqz7tB/InOUp+DcafQdgu9xoA05SGU7HQYOy56BBV6z+w5CUHKx/sIpbt7b+1GzJkoNeAeEVCRzcMvekK0g5HCwt7YWiosKs9mlZFihIqGqpwsDAMfBaWXKQCWhJnK1q+fJmKCNz1IAtbGetkZsy4FRnBimegwenXr9dqV3uHAxJlH6aXOaizRDZtg3btqGUgmKF7SN3ZN6hcH4sryYH3WfgXgLRmNqfVuQ4mNNRpx4zSCTEf3t8/7+9O4+Sqj7zBv59fvdWb9D0AjQ2AiIDxCUooiKIDEgbl+PgmBiSMWqWcUk8SsJEEyfjvBPjO5w3xhjJGONEk3EZFwzuYojEVhDBFlGICoobOw100wv0Wvfe3/P+UdVNL7eqbrEopL+fczixq+5WDbnfeu5vy4+5bg5EAGsjdksVCW8lEhkU+noa/foPfDL9FloUD9rgZ1kk5Zv8R09+f+MNJ7+/8dWT39/oA8C4v37qP3/L4PVvXh6pq3knay086yHIMfAKHFSfVIA/3jsClT8uQ1thFjMN9ShjcpsUE26rSSyc2oOqIu778IMD7CocvfVoy30nflzfZccrrLWr0+0wbMpwGCfN50/OxgMADZvr4bdFu+GnnIWphzSFnwL4dwCXjLh69Z5IByOiPsm4u27KLVocc/PXo01i+MSWRdovB35oDorBvhyM2ILUf0K/9DloURSvicNvym7Nv/zS/Ec3PnvyDRufPfnVjc+e7APAp4vG+YMHDVxfktXqEoku0l6bDyMGjhgUoAAjWoejrKkMxmaTg91pPx8133gbNqRLmULhGx+BObAczKL1aMvHfzmxMwcFcoW1jWlzcPjSUZGKLwCoH9QAPy9iDuZE+8wmdQtSZw6uvnkEc5D+Jh2SAmnHpJk5biz3OmP2tfLa6EVIitkFdH/uYumnaQFKAKA9aINvsyiSBL3a7b9Vf/ZPP6ooPCebi1NrEcRDmrpFsP7cAXj0oWOw5mvF8GMRunuF9EsevDaOGVdvR8mqZqDFR2AtPN9Hm+fBy6I4SpXDUVqPkp7u+kPFgkZrjJmiqm+n2qFkZLTFYVUVW16P3iWjYFDEqVLDi78GABeOuHr13BFXr85m+CoR9TGTPv4wJ9Zv9XXG3bfg57t2WKR9c+GH56CmW5QvpWg5uKsdfnMWRVJIDp59Tf1PCwv7Z5WD1lq0t/bubiYiGGAH4Jj6Y1DcUpyxmxsEsE7vru3x0fXY/uMlaP7CTvi5cVix8I0Pz/EQOEGkGeo6jh8magGDHjnYWHG1NaZoimqQMgdLN0d7LqxQbBkXPQfz90Tr+piii10DgAtX3zxi7uqbRzAH6W/WISmQHDdntjFOt/ZxVQvVzP9fUtVPUry+aX8uJcP7nc09gfWjXh+geLzra9+smzZejLklmwtTVfjxdH2QBe2FDt64ZhAef2AEPjynECnzwQBWu08U0SGnRTHxl3U495+rcea/7sTIZ/Ygf0f0YtBqz/lvulxhxBYkEVne87Xpj9W3isiFSCx32I2T58DNjTYL357tjfBbon2eklElyC2MNmA4pIvduwBOH3H16kWRDkBEfZqb/8FsE9vdLQc3aykaNfNDmgYtCM9B2yUHo49Bip6DewOon/nAqgrY7jk47aq68cbJMgetIh5SHHUQAzjiYFDLIIyoH4HCtsLUn9sBYEqhtvfvVwt81F32V1T/2xLs/F4V9vz9BngDW6Jfp2jKAilqC1JYDtZPv6BVxAnPweYY3CBaDjYW74FXEK24LdlWgtw9EXOwdxe7dwGcvvrmEcxB+pt3qLrYTQt7MfDTD0pM9NHVm1K8/dPu20WSKRiKOzcUB1aDjEWSwn40eu17G7q+JmIezmZq6ERx1I6oCdc0JIZX/nUInrxnOLZO6HHzF3TeuDVI/bsRAMWbfXzxiWac++M6nP0fdRi7sBn5NalvqqqKoMfxJFAU7PQwcF0bTIT+9Em9ggEApj9WvwOJ9Syaup0jytThyevbsjz6U7OiEQMQ9Z9Oj/M/DmDyiKtXfxz5ZETUt4kfkoOC14IxaXfz1CAON3MORq+QoudgrgPbYjMWSerpR2vvHd0tB42RrHOwvS2e+Z6c/JYSszEMaRqC4Q3DkR/vkYMuEp/SEcCWQzXFtOIC+OVNaJ6xAXXXrUTdVW+h+czN8IuawrdH4vesTo8CyXhA0WZgWBUk4oxwSJGD9dMvCM/B9ixy8KToOTigugiIMuYKvVqQHgcwefXNI5iD1CccmoViRUIfT1gbIAg8OE7vm1di0KF/X9FLD70Wtq8xznxVvVVERmdxJZk+XwmQaCLvuBFZDRJr3oU0m9tEK1ivQbIicmzUC1JVBPE4In9T72L36Fy88IujMWxVCybdV4uBG+KJqVW7zmbjA3DVE5GUC08IgJJNPko2+ThxQTN2jhJ88OV+qB+bB81JjBB2G30M+LgN/at9FNQEKKjxkV/vo6AhgCjQcGwuXh8faTmqjb87bn3KkaPTH6t/Z8mlJV8H8DySUWg9CxvY9GOQADTvakJ8b7TZeEyOgZvjRv69J1uQLICbANwx4urV2f+FEVGfJeKH5uCHdghGBEdjnNP7tmhVsN6W3/d/iv4QnoMxM1+t3ipGRmfRghQtB3NM5+LitsVC8gQmJyQH4xbapgecg/HWeGivh0xyg1wcvedotOS0oLZ/LeK5ccAkW3kMADhAUA51tngiqSoldBZLHQWT2ViLwteGILZtAsTPBURhC+oQH7oGtnQjggHboEVb0b9wO6RwJ2AsZOcXIU/+fZTL3rh+8XEpc7B++gXvlCxZ1D0H4w4sbMqlRTo09W9Ge2G0GfFM3MBtdVP3RukhWSB15uDqm0cwB6nPOCQFklq7CA5C1ysK/DjUBjBODJIsQlTtXrXB7KLK/30w1TFHrnldN46f/G8A/pjFpUTqWuB0nxkaqgECtYmWDEjiCVKidWsTgIW9jpKiIOxJVRF48Uhd+dLZeloBnpgwHKOXNmHiH3aj/67OLmYK4AYAHwF4DhF6VwuAoz5VFN9RB0/Sd2/vWnLVj4m8xlPoU7Oupj9W/6cll5bMQXJNDjGC+g27MXB0r3X/Ollrsem1jVGvYYkxsgDA3VFbH8WID+CCEVevfinqSYiIOtigcJGDsBwUvBScgC1aipPNFgyRPYjDwW7tv3eblsz+f0X3pMzBNT8fqeP/deOhycG8HjnYpgi8AOIKxAjUaqJlKUBoDkoWORhv82CjFkcpNiuIF2B43XA0FTRhd+FueHldczD3BkAj5yAEsMcOQkP5Qjje9ek26zY3kew4JdpniJCD9dMv+FPJkkWJHAwA8RzsHlyPwTUDU+5j1WLjKZFHHywRaxI5KBFz0Evk4OqbRzAHqc85JF3sRPBba21zqvetDeB7bfDiLR978ZZLfa+tOF1x1MVTAD7N4lIydi0QCBwTtplC1XbpdqcAcO+Ytet6VRFq7fYoFxP4XuhkCuE09K5uRJDv5qBfbh52nDcYz8wfi7/8fDgCFy0AvnzuO013funtpoVILAIYWa7G4Gj0fw71ow9egQQA0x+rvwvA3R0/b1q2Ge1N4Ws5qSq2vbkV7Q1pW4/WIzHLzsipD9aePfl3O3+rNvpEH8YxrSyOiGh/ifi/tf6AFDkoWG/L8Ud/In7jVXx8rzf90if804vTFUddJHLw4I1BKhYjcHJCNgsAbVfYVgttVySniLh33QNjet1LbcQc9Nr9tIt+d6UKhE1LIbmCnCEu8obnYPCggRjrjMHw1uFAkMjBpvPPvbPpS9dlnYPIPQ/qfCH19fT42VQfvAIJAOqnX5DIQZtI/82nbkKbG55zqoqtY7ahvShaDtZeMfXsnVdP/q06yW4yEZg208riiPqqQ1IgDX7tqbgNvAtVw+f2VrWtqvpdACeULpk/v3TJ/Eh3y5FrXg+QWHchkiDwM825XRJeHIXyAPwh7A1VvTfjtXgeNPLMcQqD3o+9YsZBvpsDx+zrEmiMQc2kYjz351Pb51eO/3OXzX8BIHTtizAignzkpJ0pqKPxRQHUj+1SIImkm/I7UjAkzQHwYmIAsGLt4+9h98e18Nu9zgVh2/e24aNFH6LmvV1h++9Gosg6A8DxUx+snTv1wdrOx2tq7ZOJzxHhm4VItOnuiIhCvHb0zHjQfuyFavPCc9Dmtqqa7yrkhFdKb5y/pPSGSDm45ucjs8vB5iBzDuYfhBy0mXPQa/cQ+NFyUBVASM8xp9Agp8yFye2Sg66gOG8ATn3x1Pbxj5xyADlooPlXwsqQlNt0Ds9SQKon7NvXlc4uiiGyy0FXX0yMfQLe+9Ja1Jbthid+IgfVoi3Wjg9P/wi7vlATtn+3HKy9Yurc2iumduagzdMnE0snRloDkTlIfdahGYMEoGz500t3TfnK0SLyuBhzikAKVHW3ql1oHOf6gUsfD28eyOx+Vf0lgLRNGKoK1YxPtAb27F6XxlNj1q7bGfbG985a96un7x11a+1J/UPvjoHvwQaZZphJ3Kyky5+uYsZBrpu6O7XJcUrcAP8L4GsAcM5be/WlUwuvVtXxInJihpMnzy0oQC6atS28U4IF4AAtg13Ei1wYN/GnI6RUFdb3Yf3Oz7oHwNoo5waA6Y/V+0suLfm6Wt0gjpQAwKalG5MfEKEL9alqYv0Mx1zj5jgPTn2wNmVnbFVcqVbPUdXSTINfNYuZQIiIwiwffs7SKVv9o8XZ+7hx9pwCiReoLditQf+FTmzn9UsH/cv+52AQMQfjmj4HBQOd3Og5uO6BMaE5uG7vB78alT/y1v4F4TnoxX34XvriSDvWt7MAQiLTKTSIlaT+2uLkOCXovy8H955znRa+dPfVqt54kVikHITkQgv+BbZ5Lgz29nrbR/KL057hkNZBcAtduAVu58ynahV+k991yvSscrB++gV+yZJFX9cCf4O0xEpggI2nJ+ubAKHtgQoPfuwdGFt8jRP83YO1V0xNPSjJ0SvV1XPUaKnYDDkozEHqu+RI/B649bRpz7luzsx021hrvaErK9Muxb3uuDFrY07shIinnT5m7bqlYW+8cWL57Y5xb6z65xKsubQUfsfTuMAir7YdhdUe+tVb9Ku3KKgP0K/BdvsZnl34px/2q6kbm/8dr3/vu59rHOSlKY46WLXt95Qs6RaYz03ImZOHnDtNFrMLeQjQJiH3VwHEBTZPK8QH1wyDccJD1fo+As8DgBd/d9z68yOfOGnpZaWniCMrRSRlEvrxAPFWD16rB1VUAzh65uL2jP+Ytz888Vg3J/ahG3PTPhywgd066KvLhmd77UREn4XT7tj6nFvips/BuPVWfm9o2hwc89V1a2P9o+fgugfGhOZg+cw3bndb3RtLiotRWloKJznJjg0s2ts9ePE4rLWw1iJI/q+1FlaTPwfBwn4jCmryGwu+49je2eL0N4iVZn6maz1tX/Lrkm45mPPcP87JyTvrTjHR1v8BAPU+gtP2m16vxwAUCmDWzkLBe3PDuyYC8Ft8eI2JHFy/+Lisc7B04V9OkT05KyUwKT904HwML6cKXuxNwLRUAzi6feYHGXNw4jXbj421uh+68Qw56Nityx4dxBykPumQtSAdSqp6raq9QCT1jcPa4PeZjmPEHBXxfO+LyKtpjnOJAJj8P/WY/D/1sAJ4+YLcFiAn9SV2CjRYce6PNy73BsS+8+43BuH9i0pgk7MHucZEKo4AQCC519afnXtPySudnZItdHgb4sjXnMjrFrkwiad43TdXKHYEjpRvnHVUyuIIAIzrwgYB1NpsuhV0qt3csCa3X85X+5fmzzeuyetsobKKeKuH9uY4bNAtA1ZEKY4AYOjlKzfsevzMG1V1Xrrfh7V27v5cOxHRZ0EDvVY9vUBiqR8k2VabOQdjBykHfXMJANQ3NKC+oQFAco0gAdwIGRYEwYqNp25YHqvO+c6gXYNQUlsKkxwXa/pFK44AQFzknj2nPveVeSWdOai2dni87VXk5J+DNM/dunNHw6KwZyuS+sAO9XPK8z/9QcriCADcAhdBWwDbvn85eFL7eWvq8s7/6vbgyvnaPjivo/u7OnF4OSsRdxdBnW5dzVdEKY4AYOW9Qzec+a1dNyp0Xrp1nKzLHKS+64gskIa/9eq2radNu9ZxYvdKyLfcIPDXAnpdpuOISNTHSfeMWbsu5Y1HIEd3/dkokNuS2FxV0xYmya5cDwjQkLvHt6f/bqc57tk6rP52GTadU4LckCnR02nz4z37MIyzULTDQx7SPkjs+nngwpnvI/gLgI0ANgHYWvnkF/2Ba/bWxAflZurTDuM4CLIskJ6cagYBuBzAVe3N8RP9eADHNXBjBn5gQ7vYJa3I5jxlX1/x65oFZ51iHPOtnn83ybFOL5R9bfl/Z3NMIqLP0ls/Hr7ttDu2XusMcO4VJyQHm4K18JE5B53oObjugTGpczDonoNAcpyLRs9BqDT4+b7dOXSnqRtYh7IdZShpL0GsNHIXQMAC8dpeA53Gqa2D1/4GcvKmRDqMiADuKfPhv9otB0c+84nvlb1a42p5CRyETiLRwclzsi6Qpj+5LwdL9c8n7o6tQW3uQBiMhcU2QBpS7ZpVDq54sOzXZ11ec4rxzLd6FkkKhY3ZF5Y/VMYcpD7riCyQAGDYqqW/33ratGUiZr6IjBGRPFXdYW3wwLBVS/894mEakbkPtxWRh9JuA20VSGj14WsAF07KcLDQX41/Z1M1AFSeNOAFgcws3OFh9NK9qPlSGTSLrnFWdcf9g5f37Lk9DgB8WPiwidahCHIRe37mqvZHO36+ftN4EeO82HDawBLXdEzPrlAbhI6vSk7h/kam8zxxpjgQXAbBPwM4s+v6TSbZp9v3Mo5dzvoJ3eBZr3171x+nLDXG3Coi5RCIWrtLVW8bPGv5vGyPR0T0WVt1w7Dfn/bLrcskR+ZLTMaIkTz1dIeN2wdW/XBYtBzUg5SDkjoHg8CH47gpc1DV/mrT4vHVADDg7soXpEFmerke9pbvRVnzYKRr5eh1LF93LH94cGgOWn8TrD8Wxk29hES3Y+Ve8HzjzJc7c3D8tzaJM9C8mNN/RokpS+agpwjqLfyaoNc0dyYmQIQcnPmEOHHgMh+JHESXHNxr8gDxYDMPY8o6B197ePC3p3xz11Ljm1vFSjkAsUZ3qdHblj88mDlIfdoRWyABwLBVS9cDiDzPZk+qWgXBP6bbxqpdc9y69Y1pjwNdC+DMFO+FFkmqai3sL8a/s+kn+7bFpaq6eddpA0qrfjYK6ma3Krlvgx90fe2ZCbFBAI4CAJMMGAvt/O80n0cV+kTX14zjPm0c90tdXxMRiOMCIrC+1+0YTmtg7z5xfb70Ek0AABGYSURBVOgUtwsmiQCYAMH3xcE/iYSHapRMTAb36sxb9lb2teX3A7h/f/YlIjocrLpx2IHlYKBViGXIQc+uWf/ocelz0NG1CFLkoGpokaSq1lr7i02Lx++bjtvVSzWmmwe0DCgd1XxsdsWRKoJW2y0HY89M6cxBMYnOD2rbISb9sk2qVlVttxx0hzpPu6VO9xyMCdwyB5Iv8Db73Yok6zTZ9YtPCs3ByxYkcrAN+H6b4J+8FDkYj/A1TVSt7mcOLn+ojDlIFOKQTPN9pFDo5VZtmlnPNLBqv5LxOKrf0DSrvyoUnvrwrOf7Nljl2+AngQalXYsjADjnnT3Nq/5t5A9XzB0Fm5vdX02g9t7fD1rWc/HAcR3/4YqTWAwvsext+s8DLDvvrebO38v3t51+mXHclAFqjAPpMV36oDV7zTOT3ZFdX1swSY5bMEl+hsTaDKvEwTdTFkcRqaJu5uL2aMuIExFRN2r1cuunycFAA+tFyEFHv6GSJgdV4fsePM/zg8BfFQT+T4IgKO1WHAHY891zmkc2HPPDUU3HwmT5FcW26r3Lfj8oRQ4KHPe4xLXYvVDNMLOsxpc1n3dT5+/l9P/Ydplb6qTMQafQwBnY/Xr9o5aYY5/pnoOXLZDjLluQyEEPWFUn+Gaq4ghItSJid3kar2uf+QFzkOggOqJbkA7U8e9/2PT+8WOnAlhsxBR1fc+qbbFqLz3hg48yLlN9xtrqTW+cWH6FA+eBrl3EgM41d1aLyH8r8OSEdzftTnWc6xpmnIbzB/5Xtp9j1MLdb99w+V+/G/LWOABwYGAkeeMWIFCFA4Q+mbPQNgu9qOPnOdWTikTM3b027CEx5ijRGVvaAxx/XzUAnL1gkrwE4J8AfAPA+I7txUHGSSPUarp1JTq2eS3TtRERUbgP/3h809ivvz8VisUm1iMHfdti4/bSjxackDEHq58/Y1P5P7xxhRN3HhDbIwehgGC1qPw3oE9uWjwhZQ7OuLHhtIHIPgd327q3//rbUSlz0DjDYEz/fVcUNALOAPSI7MS7tq1NbVNnDk66vbrIFEjGHHRKHAS1iRrR2ja0n/7vAHD2ZQvCc7BBkLEbfZG2oFH6pd2mn7YzB4kOsj5dIAHA8e9/uHLtcaMHGzHfFciFAKDQFQK57fj3P4z8ROaMtdWPrjxx6GIo/lNEpihUoVgjInNPf2/b+kz7X9cwYxyAFwEMyOb6x92/E198cOcWXB7+NpBsPeoqWSQJFJIskxLLTyhUcO35q5q7dqWYJyJFyCh5k/ctTv2/GxBrV1jVnwMoC93aZH4qFvgWxkn9BFETfpByAyIiyujDx49fOforawebIJmDAqjVFeLIbR/+8fjIOVi98IxHh164cjGA/xSbzEHBGlGZu23R6RlzcMaNDfuVgzvtLux0d20BRoW9PQ6IwXH/rsfLFho0QCUPIjmJxc7VQrUNUO/a5vNv6Z6DTuYc7GgHUvXQ/PeXATktKLHhORgAiEcYY3xU0IBGk7pAcjTQAm1nDhIdZH2+QAKAEz/42APwm+Sf/TZx7fZaAN/Ldr/rGmZ80QArBOjfUaxY9Brv2cvx82sw7qEaWGiqvuHjXEkxQYTsK4q6eANA50DcOdWTLgbw7aifw7RbnPqzT1H0aVvHKUKLo6hdytUqfC+AG+s9g5GqqvXtNV9+xd8c9fqIiCjcx0+deFBycPsLE/crB2f8pOGLKMIK5KF/5+xwzQCa0u9XY2tRo7VQkzoHHXd0aEsRAEDbEkXRPt1ycNLt1dFzUAE1LWie+g3Y0ncBAM2Cst6rZoSugxuqVJtxjF+DTSETSzga6FG24ZqPvrybOUh0kLFA+pzNbphxigtUdR2LI0gMDrOqKWcQHfv0bpzyu50drT+P93z/mQkxI5CTnOz6cM8+f1WzBYA51ZPKANwbdUcNAoy/fVNncQQku9CFVXlZrE1sfQsvUBjXINlL0KrFWwCuvPgV/93oRyIiosPRjP9sOAWDUCU5XcbixADkAdpfgZ0IzY3dtg47NbkWkNFeORh7ZooRGXCScXrNQJ7O7Obzb7EAMOn26qxyMNjro3nydxGU7oumuAjaAOT32DabL19DbT0GeC2oNiVokxgUYgu0/S0Brvz04hrmINEhwALpcybAwlQTFRgRIKRIGrWoHqfdVQ0BYNXWuOIsCtl9ZEyc/KiLwwL4n/NXNb8JAHOqJwkSoRBpLlRVxd89vB2l7/WerMdAYEOSLdO6GD23DbxgOYA/AFhwyTKb4ZkiEREdMXKxsFtx1IXkCrRUgR6jluptA6p1BwDAurbGaXVDc9CJjc0qB5vPv+VNAJh0e3XWOdhcdhuCst7LETUaQb7tnoMGgKsKP+K19dd2jAl2dObgkkuYg0SHEgukz9HshhlfcUSGptvGiMDqvo5wx7zcgDN+uQ2igFVts9DJZ63e1asCMTDndU7MkNkeAF1nEvomkH7a164GVdXhmOd2hb63b4RTdxoAcDIWSbUAHgTwh0uW2fejXg8RER0ZZsxt+IoMSJ+D0l+gexVIjoZqsI3YptsBAGpsm7p28q7bz+qdg6b8PGMyrmve4YBysC3nT2gb8bvQ9xpFcFSPHBQAxQrUQhPjn1LrzMEllzAHiT4rLJA+X2dH2cgg0R27/I098clztwCBtgTQl6zaq6b9tT6037UjMiOL6/jp+auadwHAnOpJxwCIPINQ/rZWnDBvY8r3U972NbxISs76tx7AzSLy/CXLUk8/S0RERziJloMoBrAL2IO98S2yFepoixp9yebaq+pvnRaag+KUZpWDzeffsgsAJt1enVUO+rFPsGdE6nkS9qYIwlwAAxWoh8J2LZJU4QLrfeBmiDy/5BLmINFnjQXS50h6d0sOlWxFWll9xoBpU9+qacu8B6BAe6TtVLeIJKYvnVM9ySCxYFykGYTEtzjhzg1wvCwGFfW4SPUBFQUE2wC8JMC8r67QNft3QCIiOqJItByUfIHm6coBbYXTau6YEikHoRoxB70tIrG7AWDS7dVZ5aDCQ+NR1wMm9anSTciQB2CIAp4q4sA2BV5SwbyFX2UOEn2eWCB9jhR4FMCVUbZ1gKZfF78cLRQSlgO4LMI1XH/BqmYv+eNsRGzVAoBhT+9oLPy0JcIU4Cl9AuBRKB6b9bqy6wARUV/jR89BlKHp5e8XZ5GDQaQchHrXN18wd79y0Cv+baOf917aHDRIdDRP0ZD0iQEezQUee2IWc5DocCHJLk30ObiuYYa4QI2IDIy4y9R5RZWRFoRbeGqeGMgeI50r4/USqP3kwrdaRwPAnOpJxwN4G4kHWlGsnPK1N1+J+XJTuo0CtT0nadiOxKx7jwFYNauK/wCJiPqqGTc2CMpRIznRc7BydlGkHMxbeIWIKdtjTGHKHLRB3SetF/7XaACYdHt11jkoxx77yg7XT5uDRwUWo2zqHHxkFnOQ6HCT1RzQdHDdXfyyArgmi11+FnXDf3irTS30QlUNbd23avdYtRMBYE71pBgS6z5EDYVWAN80nv5ENeXaE1DVjuKoHsB9SDyVGzGrSn84q0rfZHFERNS3vfzL4kOWg23/8L+qtu5C1dbwHLR791hbNxEAJt1evV856Er8J/3S5GBMFUNt7xx8ZJb+8JFZ+iaLI6LDE1uQPmdzGitEVZeJyJSIu5w9r6hySdTjPz8h9wQj5h6BTBbAKBC3sM+p6rUz326vv6FuWm4Qj98G4AcikpgkIfO/idnzyqt+AwBPTjJHCeRNIzKs6waqqgHsIgXuAbB4VpVykCkREfVScVdj1jlYObtoSdTj5z5/xQnG9L9HpHAyxDFQL27tnudUm65tn/lo/bR1dbnx1+O3QfADyRGop9BmTZRAqc2u+lH5bwBg+pPmqDqRN/ea7jlYoKojA7uoWBM5+Mgs5iDRkYIF0mFgTmPFDACVETd/FcD0eUWVWf3F3dAwoxiq1wOoADAEqgbQYgBDem6rqlBroX7oQ7eXAJw3r7zKdrywYJKIgZwnwFVIlFdrLPTXs6qU6zQQEVFGFXc1Zp2DlbOLssrBGasaitXsy0F11aiDYkh4DtqtFv77PuD1OtRLAM6r+lF5Zw5etkBks5Hz2gRXCaCOYk251V8/NYs5SHQkYoF0GJjTWCEAXgEwLeIu58wrqswYJDc0VgwFcBFUvw5gWq9Fh1QB23MZ2i5vWwvrdUuGRgDj5pVXbYl4nURERBlV3NWYdQ5Wzi7KmIMVKxuHArhIkchB6bHokBpFkGdTDjiwuy28N7yuy/k1AhhX9aNy5iDR3zAWSIeJOY0V0wAsibj5CgBn9WxFuiFRaB0P4GIkFribmPFIGYok63lQ2/mQ7Ip55VUPR7xGIiKiyCruasw6B3u2IlWsbMw6B1UUQYFNOc2c954Hu2lfDlb9qJw5SPQ3jtN8HybmFVUundNY8TKAKAvbnQngSwAW39BY4QCYhEQYXAxgdFYnFgHEAGrD33acjgLpSQCPZHVsIiKiiCpnFy2tuKsx6xysWNl4QDkoKjBxgc0Nf2DslDsdBRJzkKiPYAvSYWROY8VZAJZF3Hy9AV4T4CIAgw/oxGlakZLd7HYi0bWu5oDOQ0RElEbFXY3Rc9DFekzAa5ADz0E1yVakELbJwluayMGqH5UzB4n6ABZIh5k5jRUvAjg3yrYGKXsEZCdzgXTRvPKq5w/GqYiIiNKpuKsxcg7iCwCKD/ycKoqgX4oCqc7Ce927qOpH5cxBoj6C6yAdfn4adUML4OCUt6mPotauYHFERESfocg5iK04KEGoab4N2Xq7gsURUd/CAukwM6+osgrAos/0pClaEdXaVqj+42d6LURE1KdVzi6KnoPNABoO/JzqhuegbbKt2sgcJOprWCAdnrJqRTog1oYWSGrtDrV27J1DVtQe6CmIiIiyFL0VqfrATmRdG1og2Tq7w263Y1dcPoQ5SNTHcAzSYWpOY8VzAGZG2Xa/xiKpJgYYqW4BsElVd0MVqloD1fvvLFtele0hiYiIDpaKuxoj5yBORdbz8ioUNketxnQLBJvU0926V6GtWqPNev/yi8uYg0R9FKf5PnzdgqjBEJVqPYBXAF0Oq88BuuGO0qWpF0EiIiL6/NyCqDkYR6RvNIpEDqqD5Rqzz6mLDUtPKGUOElE3bEE6jM1prFgD4ORM22VoQdoM4FkAzwBYdkdRpXeQLo+IiOiQqrivcQ3aMudghhakbjlYObGIOUhEabEF6TAmwE0K/Hk/dv0rEkHwLIA1dxRVsgomIqIjTwFuQluGHCwE4PR6tVsOVk4sYg4SUWRsQTrM/UtjxV8UOCfV+8nWowCJhfWeAfDsHUWVGz+jyyMiIjqkKh5r/AtqU+SgADgRQL/uOVg5sWjjZ3aBRPQ3hy1Ih79zBXhKgYt7vK4GeFmAhwC8cEdR5e7P4+KIiIgOqVyci1I8hUZcjKDb64pj8TL6JXKwcmIRc5CIDgq2IB0h5jRWDBPgOwCGAlglwGO/Kqps+byvi4iI6LNQ8UjjMOTgO/AxFLlYhRI8Vnl2EXOQiA46FkhERERERERJXCiWiIiIiIgoiQUSERERERFREgskIiIiIiKiJBZIRERERERESSyQiIiIiIiIklggERERERERJbFAIiIiIiIiSmKBRERERERElMQCiYiIiIiIKIkFEhERERERURILJCIiIiIioiQWSEREREREREkskIiIiIiIiJJYIBERERERESWxQCIiIiIiIkpigURERERERJTEAomIiIiIiCiJBRIREREREVESCyQiIiIiIqIkFkhERERERERJLJCIiIiIiIiSWCARERERERElsUAiIiIiIiJKYoFERERERESUxAKJiIiIiIgoiQUSERERERFREgskIiIiIiKiJBZIRERERERESSyQiIiIiIiIklggERERERERJbFAIiIiIiIiSmKBRERERERElMQCiYiIiIiIKIkFEhERERERURILJCIiIiIioiQWSEREREREREkskIiIiIiIiJJYIBERERERESWxQCIiIiIiIkr6/+CfN4ZkybutAAAAAElFTkSuQmCC\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2022-09-14 19:53:42,884 Running model_2\n", + "2022-09-14 19:54:46,588 model_2 took 61.5s (3 recycles) with pLDDT 80.4 and ptmscore 0.803\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2022-09-14 19:56:06,938 Running model_3\n", + "2022-09-14 19:57:10,632 model_3 took 61.5s (3 recycles) with pLDDT 85.1 and ptmscore 0.831\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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JtOtZ19c23bv9amaMMcZsf43nNXfLQdJAMlIQntV0T63l4C7CGkjGmJKuaG5sAK4CPgOZ54zoVQOpFZh6fW3T69uhesYYY8x21Xhec94c7EUDqRWY2nRPreXgLsAaSMaYgq5obiwHvghcCVTnblcgiNZAAngRmH59bdOmbVU/Y4wxZntqPK+5aA7iAx2Rg/BFYHrTPbWWg/2cNZCMMT1kJmD4V+AaYGShckrYi0T0RtJtwAXX1zbZhccYY0y/lZmAoWQOokA7kYdTkMnBpntqLQf7MZukwRjTzRXrjzkG1WeB31MsFAjbRQ50TusdxXmEwxOMMcaYfumYc9cfoxFzEAHi0IsgtBzcBVgPkjEGgCvWHX0o4tyJyOje7tuLZ5EAksDh19c2LezteYwxxpjt5eiPrTvU8eROcWV0r3dOAanIQZgEDm+6p9ZysJ+yBpIxH3JXfHDUYODHOO75SPRWTrY+DLVbBky5vrbpgz6d0BhjjNlGjjrzg8E4/Ngtc84Xp285iAId9ObB3GXAlKZ7ai0H+yFrIBnzIXXFB0clgM8B/4k4NThbNeK2XeEXAZyFMCriPg8Dp1xf2xRszYmNMcaYvjjqzA+6ctApkxrH27ocJOAXtHMWSK9ysOmeWsvBfsaeQTLmQ+gLqw//iqquJVzLoaavPUcZtwH7/rSu6UsIZxLeQ4viROCbW3NiY4wxpi8OP231VzTYkoPibn0ONt1R9yUQy8HdgPUgGfMhcsUHR0ng+391XLex2wbX68vh5gFfuq6u6bnsNz+/ofHTwK+jHEAVFeGE62ubHu1LBYwxxpjeOOrMD8RP+X91Y1k5KOBVuEX2Kmge8KWm2+q65WDjWRsi5yCqisgJTffUWg72I9aDZMyHSOCnv9KjcdR7rwGnAY25jaOMm4HfRTmQCKLK/Z9rbpy0lXUyxhhjSkqn/K90axxBb2Zi7dSVg7mNo4zIOYiIoHp/45nNloP9iDWQjPkwEflG3vc10vDnNcBngUnX1TU9cF1d/rWMMmsc/QfwQrQqUSbwWJSyxhhjzNaQAjkYpCO1krpysOm2ugeabqvLu1NmjaPIOYhIGZaD/YoNsTPmQ+LyFdMOdOPx50UK3BdxXPI+i6QaANch8p3r6ppaop7v8xsaxwILgdoo5X3l0hvrmn4V9fjGGGNMb0ybteLAeEX8eSffTHUCbrmD5MlBzeSgiHyn6ba6yDnYeNaGXuUgqpc23VtnOdgP9OnBA2PMLukilMJTcQc+4Ux2EjaUVAF9FeS06wbMXdLbk11f2/TG5zc0XgD8OUp5gdMBCwZg/YyPHYPIF0WcKaq6DtUnFP38wHl/9Hd23YwxZhd2UZht+W4Ggt8W4MQdxM2MfAsUDXhVhNPm3jmg1znYdE/tG41nbYicg1gOdpmx+v5jRJJfFGfTFNXYOtWKJwgqPj9vyKwdkoPWg2TMh8DlK6bFgRWO6w1yvEj3RZ4ELr9u0N+2ehG7zzU3vuQIE0qVC5R5P69rOmZrz7ejrDzk2Cki8mURmarwPhrc73rxHw2e/3CfL6otM8/dW4Pgl4gcl3sXMwj8tUHgHzZw3h97HdLGGPNhN23Wijiwwou7g7x4hBwUnkS5/G/3DtrqHGw8s/klRErmIKrzmu6t22Vy8JDvrJwinnxZPJlKoO9rmvu9ge6P5v/74D7n4MyWH+4d+FW/FGfTcSLdDxOkB68N0kMPm9dw4nbPQetBMubD4VRgUOCnEdfNO4QAQFWbReQi4L7rBv1tq++efLa58WhgAgoR1t7b4TP4LJk4wQ00mCXIcYrOV/T+/V56LV1qv1XTZ37N9WLfly3fyLHA4YGfvmTFtGMOGf7M3HW9qUfrceePA74DcrY4Tt6plBzHrUf14cy5jDHG9M6pwKB00sf1XAotCKuBNosjF6Hc97d7B211Djae2ZzJQc0/jL27HZ6DE45d4gbpYJaIHKeq89XX+1+bv1/JHJx+zaqveQPc74vbPQf9jcEl0/5zxSHPfHd4r3Lw/Nb/HKfIdxqQs1e76ubr5XO8NfWqsR2Sg9aDZMyHwOUrpj0InNz5dycWI/v3cFUl8NOoHxx0w/Cnoz1UWsJnmxtjwGJgfwjvxhTKBlWSPlT8d13TDhtC9tr++53iOu7djjhlW+qhvh/4n9rnpVduLbTfykMaj3S9+N8KNTL9dGpZOtUxbuSiv6dK1WHzSZ/2NAhmO653eufxVAP8dIrA75lPqorvpxoHzvvj3Agf0RhjTMa0WSu25KBArCyGm7UwrAZKOuUTpP2Dnn5k+DbJwcYzm7vlYHjugkGYBCqa7q3bYTm439GvneLG3bsdNysHA/X9Dv9TrzyxT8EcPOTbK4/0BrsFczDd7C9LrUiPW/TzkSVz8K7N53jva+Xs/Zy1pyck/OgbtIyn/BE8E4zsUV5V8Dv2b5zXcNJ2zUGbxc6Y3dzlK6YNJ1yMrkuQSpHuaMdPJkknO/CTHajvP7OtGkcZnyMrFNJkHmvKoQo+XL6DG0czPcd7ILtxBCAiruu4t7y+//gLC+3rOO6NhUIBwHG9USC/Wz7lyNJ9ZqpPuV7s9OzjiTh4sTIcN9ajuIggcGjJ4xpjjOkybdaK7jmokGpP0b6pg2Rbko7N4ctP+c9sq8ZRRrccDM9dIAjh8h3aODrqtZlewnsgu3EEII64bsK9ZfyM1y8stK9T4RTNQbfGGYXH76Z8bnnJHGym7KmD3Pe7GkcAtdLB8d5SjnSW9SgfDrtLb/cctAaSMbu/8yjwb101yL5YR1uzIYLPNjcOB67KfT8NpBUCBV/DP6fhUd3BD6U64vxPoYu7iCAiPyi4s8hexY4tIjiu+6/At4qV23TixaeJ404ptN2LxREn748tXuy4xhhjesifgwqBr2iw7XOw8czmvDkYnle7v8KhdTs2B70SOegWzkFxKZ6DjuBWOCVz8IZNnzptX/mgYA7O8JYxWtbn27Tdc9AaSMbsxi5fMU2AiyIUbQfu2oan/hFQnW+DAj4QhH9OApf9osCaStvD0gMm1Tji7F2sjOu4Da9M2Gd8gc0l65pp2Hx7+ZQjLyxYSLms2B04AM/rdmMvM8Qu/UCp8xtjjAlNm7Wi3+VgjiRwWdO9+ddU2h4mnbC0xomVyMGY27DPYa/kz0EpnYNOuQB8e8rnll9YqIzCZaWeTz7RewOHLWs1ahDHT+613XPQGkjG7N6mA/tGKHfvDcOfad4WJ8xMzPDJiMV/9Iu6pq2ajWbhpBHHL5w04oFFB4xcunDSiCcWThrxmRK7HFmqYQIQaHBYgU3LS+3rSNfzXb9ePuXIWfnKiMiAUscRx+k21M73U0nV4PlS+xljjOkSOQefeXT4NsnBzMQMkXOw6d66rcrBERMXHj9i4sIHRk5atHTExIVPjJi4cNvkYLpADmrpHJTyribGr6d8bnneHKySZMkcrJc2pjvvdf3d75iQVH/Ids9Bm8XOmN3bxRHL9WpYwZwpCQEuF5EvCdQAHYHqHU9/f/yVTBvw31GOUbkqySkXvn7Z/I76xUcsXntPb84PsPjAPaeq6q9cx53c9aYwBjhq4QEjLwEOm/p/y/M9IHpsqWNnJq85HLg5d5uI8wfg6mL7Zw2N81T1/nc/csThezw3f1HOOV4Hppaqi+vF8P0UfqoD3089PXj+n20tJGOMiW675GDip3MEn8ulQ76ELzU4dGhZcMf418dfOYCBkXIwmUzy+jtvXFY/JVi8dtERvc7BPQ9cHOagm5WDhDk4cuLCS4DDlr84te85qAVyMCYlc9BJdDXAPFW9/yP/8e7hz/33Ht1ysFkTkXLwSHcZ//QHs67tCPyOiU/PHzV4u+eg9SAZs5u6fMW0SuDjEYq+AzT15tgi8pTnuD9zxdnDEafWEafBc9wvDn6+ZT25D6QWMPUXq4gnpdbBufvvBw26Iuq5Fx+457SmE/d6bPGZtc+++5HyyX6eq5grzlRUbytwiMZS5wg0AJiRb5uI/K3U/uFzTE7nn8uAJ5dNPjT3+/L1KLOIigiu6+H7KYBFpcobY4wJTZu1YvvlYIc85a73fuZsdvdwOpxap81pcJu9L7aUtUbOwVWrViMdUutsdu4eNP3vkXNwzwMXT9tryv89Vltb/Wx5efnkfGUc152q0PccTBXJQS9CDjqClIWNpM4cnPyZZd2+L4J+Pa2le7LiEnAcbfgdkwDZITloDSRjdl9nEm388y03DH8mKF0s9NCUxK9ccablvt82KMarF+5REeUYw59uZY/5rUBmUgNxfjzvoLrTi+3z3JTRH7/9qxPe+vV9ez19+//uPXPBFcO4/+ej+WXTPiw8p7ZHeUecjy46YGS3h3iWHjCpHjiwVP0yDaQxL40fOypfVYj+HFL4Z5GYIN3uDlb+5bfvqAb/W+o4AF6sDNeLA2z1goXGGPMhEjkHn3l0eOQcTPzgoV85rW6PHIwlY+yxIloOtrZupLU1k4OB4Gx2flw3a17RHBw9+YWPTzj89bf2GrPn03vvNXrmsKFDGD1qJPuM24va2poe5R3H+ejISYu65eCkE5ZGy8GwgTRm7MEv9TkHs3qREEdiON1z8IrK37yzQqsj5eD+Zf9kXPyfsINy0BpIxuy+og4ruCXqAR85uLLOEefCfNsWXLMPGi99SZG0cuDN73dbAk7ChSFm52sk/eK6A6p/+51JS2799ZC7Hv/msNEdg+LdF7r1HJ66bBjzLxnY/TwiTqCau5jcjJIVBPygq/f+6Nxtg+c/3Aq8WuoYueu9ijj7Lpt86IScMhepasmx3ACJ8mocx/1nlLLGGGOA7ZCDld98pM7ZmD8H93lnX5wIv1qrKu+/v7rbe9IhQprZ+RpJBxy9tHrSkW8uGdLQcNewoUNGx+Pdc9BxHIYNbWDgwO6P9IiIo0HQtxxMFc7B+f8+OFoOlnf/Xogn+07+zLJuObin03KRrxIpB0+tvp0Gd/kOyUFrIBmzG7r8nal7E+0iOO+G4c+8FfW4qvpNEemxQE/LyDI2jqmMdIyk+PzpupE88q2hLD28knRMwl4kHI+sRtJ5a46oO2/NEVc+f2rlB3P/rXbshjGVPRod2V74xCDS8dyueh2Y80bJYQWqim65MTajQLFnSx0nd4ruTJh1mxEo8eBNbSLyb6WOBeC4HhXVg86IUtYYYz7sph6/PHIOPvPo8Og5iH5Tgp45WNaeoDIZqfMI3w8YOXIEQ4cOobKyMrPGneAkwxzsbCQdccqauiNOWXNlZUXlB7W1NWMrKxO4buFf3QcNHNBjHVqF3udgoKi/9TmYmcmui4iAds/BoxMPtrmikXJwkLeazw36fzskB62BZMxuZvZ0qa37v5anIxb/bdTjPnJw5WjCRe96eO3jw4g2M5yS8n2CuPD24VU89q1h3PaH0cz7YgMrDqogcPA2NsRmf2HBlPuAd4Hvt9W5MXEcXK/4nDIad3njiC3hFGiA33PGt0YQHMfDcWKZlwdZ/VmZ4XWdetw5yyjZxd/5DFKOdO4biQdvmpNOJ9/LV7jnMeXK9lMuLbr+hDHGfNjJ7DtrW4Z/sM1zsPKbj4ymQA4OWzs0Ug6GyzX4iAhVVVUMGzaU0aNH0dAwmAqpAPBixGZPOemdrhx0XSfmOA5eiRx0XYeKiqwcDAIC3++Rg1IO7jAHd8/w5Qx2IGsgXpDeRjlYlvf70SMHj048OGdZcmykHHREr3yi/ZTtnoPWQDJmNzJ7uhwALNwwvmpQycKqrUBvZs35XuZBy56HckuHAkAq3eO6SLLK5bXja3j4h6O47b7x/O9t+3gf7FN+GlDVWcaL5T1tD+21W3qYfA1ap7+4qrXz70sPmDRCxNnXdWOIOJ0LwiLiEL7nZvbrNjnOXi+NH7tnnlOV7kHKmqgBQDVodx3v/rz13tySjjJhA5AAbmg/5dJo33BjjPmQkdl3HgAsrFpZVzIHlT7kIPlzUCJMNgCQzpODrutSU1PDqJo9GR/bj31i47xyp3sOlpX16LTKy3W35GDg+62rXp3elYOTTlg6QmplX7fBReLSlYNOheANdXEGCEi34XUAe409+KW+5aAjSNZzSOpru5Nw8+bg3S2XpJMaaf3XBHDDE+2nbNcctAaSMbuJ2dPlXOCpjgHe2CBReChapwXqBnkAACAASURBVMSqjmdvGP7M5ijHfuTgyqkUWdOh5q22ksfwgwBfez4Dq4SLxgYipBMu5Kwa58bike/KNbzWjqqSCtIEGnQbHy3iXCxS+PviOC6OE8v32Gm+u2cvkOcuWK7OYXaqSqDBL0Yu+nuPo6854vQBGvijku0bSx2u08nAaVELG2PMh4XMvvNc4ClvY2ys65deyaajZvOzzzw6PFIOVn7zkaI52FZWOgeDICAICt8MExFccXtkXjwei5yD7e0dqCrpVIogyMnBKrnYqS18HKfGwR3q5FsEqM852DnMTlXRVPCLRT8f2eMbcMSyNQPW+Q2j5m08tdThOm33HLQGkjG7uNnTpWz2dPkF4XSe5ev3i/Ys0F5/WBGp4CMHVwrwIxS6vbLsc+8qZHPh66Sqksxz14zOwxW48Ivj4rrRlmurXJNiyIttJINU5zC5nDHlcmqpgBEREvFKYm63u1gzcssNnv9wm2rwdqk6OY6HogTqL66oHHTV2iNPK1t75Gm5190pAMmOzQR+yazpdH37KZdGG+xujDG7OZl9Z5nMvrMrByvfizJxHayYtiRSDlZ+85GSObiqfhXpIu0FVc3be9SpUD65roPnlb7pCZBKpWhrayOVTBIEPXNQyiidg3GhfGyC+JBuPVYzcsvN//fBbZrWt3scwAFihI0sD5xqBzxF08HiypMqrjpyztqyI+eszZuD8zcfz5r00FIfs9P1T7Sfst1y0BaKNWYXNnu67AHMBg7pfK9lv0o08JFiDYukz5AFG6bdd2hszL88mSr+cKpyuoPMCB8hzdwJQgk0oHNEgePD1Gve4Nmr9gGv+3VPVelIp7InPsg6dPF5Qr1YpO52CJQTr1mDKy7pLUPklmYXEZHBUQ4lIpTFynGdGB2pzQQEx+WWeevAgyvKElUNsViicy+g510/LxbDi5UDTAZaOt9fe+RpaSAJpMi6Dre3baAiwuhIYBTwzczLGGM+tGT2nT1ysHJVLX4Q4LmFGxY+PhvGrZwWu+/mMal/+VTJHBR1Zojm5KATgGRSzIE3hr/BPiv26TGTnaqSSqWJNpK6u3g82tA6VWXNmnW4jou/paHWPQe9iDnoCGUjynBrXNrf7iBI9szBA897qyIxtqwh1pCpXyVQC+J1z8HYcI/YBA9ycvDIOWu35KDg4YIvMR4YcC4XT/pxlGpu1xy0HiRjdlGzp8tMwrUIDsl+/4MDqgjSaQo906Kq1L7QjBP+Vv/ZYud4ZGql5yC/c3C6QgFAEFxcHJWuFs6Qp9Yz4+ynqVm8Dt/3O/wgIOWnaU8lCQrVBegx5U6GF3FoHcBhv1nHqOc6iLtx3C3D6LoFnqqu7rlnYZ7rUVFWTcyJ7/Hi+L1z14G4yvdTmUUnHES8yHXtPDxQAdSKUCkSfhsCP0kqWXqYRsZX2k+5dN/enNQYY3YnMvvOvDlYtayWdIkcbB62GpzSOVj5jUc8Ued3jubJwcBFAqcrB9ePWMfTJz3JugFhDgZBQDrtk0ymCtYFCvceRR1aB7BuXTMdHUni8Xj2c0jdc9DvZQ5We1SOryA2MLbH3lNf7JmDLX6Yg3Ugg6RH46jU4enMQZVKSQuSEpaunsg/3z846jG+8kT7KdslB62BZMwuZvZ0cWZPl28AjwD12ds6qh3ah4S9Ln4qiQbdn/nRIMBPJRn8XNczm5+679BYwS5qB7nawem5CmvX9vA/VSUdpClrSXPol17a3OGnqzrSqVkp379VobXQ/gWP67g4EYfWDXuxnWl3buj6e8JNdIZY7h3Bn/S2HuGQuwrK41UPLZs8pRbgrQMPngJ8actwuG17GU22t6B5ntXKIwbcaBM2GGM+bGT2nY7MvjNvDjqbPeItYe9+MnwOp9u+QRCQTKVoHbO2861Pxe67uWAOijpXO1okB9XBCTI52JAmnUjz0jH/3JxO+1WpVHqW7/u30occ7M3Quvb2DjZs2HKKRCLR2bDqnoPahxz0hPIxCSr2KX9o8qXLagEOPO+tKcCX/E1BmETRRjRG9vCST9CRjjQ5Uwy4cXtM2GANJGN2IbMPkz1xeAGX7+Hh4JI9QzVrP1K9pUdGFT+VJJ3swE9mvqaSoMqA17qeSR0AfKLwGaXoqt4Q3oBztdulZPMtDQvStzQseOyWhgUXAkPK16cvGPXkRpxU9ztoha5obsShdV57wAnXrMHJyj9HhIRbBjnBMOaF5/8YBP7fIx049zyONwF44e2DpswAbgacIPAJe4+27XVZNaAXEzbMBKZu0woYY0w/JnfctScd7gts9r7HppjDZg9SW3pxqpfWbRkGp0oylaIjmSSZTIZfU2FvzuYRzZ2HLJqDopTMQcHBETf7t+rNC+Y0pBfMaXhswZyGC4Eh6XT6go0bNxXtScoWdWhdEASsWfNB9/qIQ1lZAnJy8IU7xvwx2Bj0LQerwxw86DNvz6AzBzcHUF64B6yvWjoG0vRWyW97p+2Sg9ZAMmYXMfsIOR2HJeLKRHEy03M6mS7tzE2mtvo8DQvVsEei86IcKNXvdGSXuOy+Q2N5r24CDVHq5ohDuSQyoxXoNkbsloYFbb/c9+nfn/DtVevP+8RbHHnDaob+M6tITli4sbLIF9sjfrWOActTPd73HI8qr/Jfst97cf+9T9iUbh2a9DsiB1SOUUCTSHBgZxJH7OnptVRyE77f83MV8NHtUgljjOln5HezTyflLpGUO1ECB1FBAgfp8KDDBYX4hp49D+FMotp17VcCOuq73Yi6LHbfzQWCR6LloO+QeLMcaXMgJwcXzGloe/qR4b9ftWrV+rfeepvVq9fQ1ralSG4mlZX1ZmjdBlKpnpM/eJ5HZWVVtxzce8qLJ2x8ddPQ5Krk1uVgTA4MKx4uKrs9PPnucby/cUTU4ts8B22SBmP6ubuPdAYCPxOR8wqVEUfQQHE7IvzCnvK7ninNOAg4HJifW1ThLYFIswY4IpRTRoemCt14eSexMRgw4aEWJjzUQmuDxxszqnh1Vg3rR8ZBBPG8bms4FDNycRuT72kpuF1Ernlp8tjHNakbgJ8C/wLQ4beRCpKUexXhHb9ekK4B6IoqBIEfTg2+HXS0tUSdsGHIdqmAMcb0E87v7g5zEDmv0Mw+knZRVwlifv4CWXz83C6CgjkI+hZIpIuxpB3KlpaTGtFRMAeDIBjQ0tJCS0sLnudRVVVFTU018Xj43K3Xixxsa2unpaXw6D0RuWbs1JceV+2eg+3LO0iuS1G+Vzluond9JSIixAAHNKkEmwKcgdu+vyVQjwdeO49PT7k2SvFtnoPWg2RMP3b3kc4ZwMtAwcZRFweql7b1GG+dK+37+fLlsvyl9cZ8s88VIiIknPiIx6fWfO3xqTXdbn+p6sotf4Hq99NMvquZT1z8Dqd/YTkjFm7Cc6M1NmKbA2Zduya3oZcrrqqPEX7/ut1FC9RnU6qVpN+Rf88SwkkVlI72lr7ehSspnLAh0vIcxWdfMsaYXZjzu7uj52DKoW3wppI56Cf9fFOo5s1BFXqXgyrElydG1Fz5+Ndqrny8ew46WTlIuGhsc3Mz77zzLsuXv8emTZuIxaI1jvINrcujcA5uDtj08iaSa5KRzpdL3HAR2PaX+jwqo6S3m/fj+ZWHRim6zXPQGkjG9EN3H+k03H2k87/AvUS9MyIQ2+wTpArPluOn06ij+PEeXfdn3XdobHjum8ct3Hiroo/0rvYAXAvc/PjUmjjAXw+qjvv4BwBdc3t3TpcqCCNe7mDE6370oXW/WEvdytJrBolIPS6JQts7/DY2pzZ2rpvUK2FVU6RSm7dbOHS0taqqFnu4twP4w3Y5uTHG7ETO7+5ucH53d+9yMBD88jSpIrPGpdNp1AfpOdjhrNh9N/fIwY3fP+5WlaDPOVhz5eNxgOov/TXuD8vkYIaIdL06Ojrw/XTkHFy79gPS6dK9ZSJSDwVyMID2ZR1sXrKZINWXHBRIQmpJ8Vn6tsbDSz+mgTo7PAdtiJ0x/cjdRzqCcKEIP1EY0IubVgDENgXhTDrJJK7nIU4YAKoaTv2duauWqnTwkt0urB7KZ++bFvsjsH/mNQHYH2FsXD3i9Hoo2UXAXo9PrTkL+F6gwXAIG0b5tAyPNGMNI55pZex976NURAoSccPhh4W+l76m2ZxqJeFWRO7BypbsaCbwU8TLqpA8K6D3lSptEMwSER94jHCViVxXJh686bVtckJjjOkHnN/dLaS5EOQniA7AofCMPrkEgjI/nJyhI4nneTjulhxMp9MEfpiDTruHH+/We+KBfjZ236965uAhjPWW1hNbXXAyu0IuAvaqufLxMAdrguG8V3hSg7KyaBMUtbZu5P3311BRURktB0WKNmDSG3w2vbSZxJgEsdreNw06XkniNweU7RtHqgRxt1EOlmnbxqq6WY4ERXPw6MSD2zwHrYFkTD/xp2O8wW7MedJxZO/OC14QKH46iPYQZAAS6I3AZeEMdoUf8k9VOJSv73HnKf+CawJJSeNrQCKIvjZRxtGq+jLQEFC4kQLgRXl+KlCO+sFyQGmnnXLKo9XCI1yStQBFafM3EdN4uswtR0QiXxtFIJXaTCrVBnBSdc2wJwinHo1HeHWVU6UCGEM4o9IDwOOD598fALSfcukRwOXAGYQP/y4BvpF48KYFUetpjDH9nXfDnwY76j4pSWfvrpno3ICg0kfjEXLQC9C0cyNwWbg4a6rgtd9p9/BregwvK5iD6b3XEtS1EV8ypOCNvgK6clDLi+dcECHrVZXly99DVWlvb6e8PGIOljpuWmlb0ka6IZZO7FHWyxwUUu+kSC1NgcNJNR+r7lsOOlpBgjGUMQCHB0jx+PxDBgfwIE+0n5I3B49OPLhdclC2V5eYMSa6e452y13PWeY4PVe5VlXSqeKNJFUN8Lnk0d+OuxUoOfbs0GtXMuj19p4bAsJZaQRWTU6w/Mgq2oZ6BJ5Q1hIQ26zhukcepAfFCCpdvDal9vUOJt2ygVieQ3Z+Bsd3KCfRbaG9bG8eUc3j3x9T9EZhCvj0rBeJt4UhU0YZcYm4yrivUHw0wgPAF6vjdWXAHYQP7UY7toKqc/uYF54tPUbeGGNMD+7/3FPutLnLJO30zEGUoCZdtJGkogGJ9CXjrjsoUg6uPO9F2vfMN9FPJggDSLwygKrnh+CtK0d8IahKoa6Ds9mDQIilY7hBODlER3UHG8atp9BgC1XFWeOQWFBe8EZjdXUVY8bkrsfa04svvtL1nFVZWRmxiEtjRPid/wHgizVTq3ufg2lFk3r7C7eN2S1y0HqQjIngvdsPrhbHOVMEN/CDOSPOffb9bXHc9Y8dU6FBcOrQA4dcsP6NdYPzjQEWEbyYQ6oj/2/3qrqOgNM++qQu+ChwySv7tAA1xc6bquj5+KEfg7X7Jlh1YIJVU8qhyut2EU8O2lKfRGzLFKR+HaweFufxQyuZdvVqBr7ZM5dEhECConfdBr/ZTprwopSvVJowtjaMiDP4jbAl1kEHnro4EuFxSodCDaQ3gc9PfOnNOZ1vLJs85RDgO8BXClQn12bgixHKGWPMLungWe9VOyJnIuIGQTDn2UdHbJMcPOaM9RVBoKcOGT/0gnW16wcHXp4cRHBaPfyBqbxXZBVdR8w/Tc/5xALOgX2mvVIyB532PJMhpITE0joSr9ZR/sogvGTOdNut4aM8IkIsKwfxIf5BnMoPKll9wCrStQVysDIoOgqjvT3axEHxeJz29kwOdnTguh6Os1XTCrwJfP7NRRO7cnDypct6l4O6e+WgNZCMKWLFnYeICH/2ymKnSuaq5njKyrumz/dT6ZNGnrew16tjA6x/7BgH1Ycdz50pnutMOmMCQRDw/j/f57U5S3qUDx/i7L5kkKomgR+IyNVn/yPIvhqvJ2IDKVkhvD8pwaoDy1mzfxl+Wfh+3Cm8AGpZrMD6DHGHZ78+mJmfWYmbZxSBuIKmtWAPUtXqFKQCUjEHjy0zyASZV2fbZsPIsq4GEkAb7VRo4TtyndIevHZUFe9MqSS2yeeAh1raG95Kfhf4ycSX3uzW9zVq8aIk8LVlk6f8RZXbRCi4GEPYeyQ/GPPCs2sB6uefLH5aLxbhCoRhwAYNuBv46voZD1mXvTFml3LI8SsEkT/HPK8rB9VxmH7Cyvlp3z9p4WMj+5SDx5yx3lHlYdd1ZrquOBPemESgAe8PfJ8lY1/rMY2YqIAv4G25jCqaRPiBqFwd/Os5vcpBpz38FVg2uySW1FL+6gDK3qjBSYUNJ6dIDsYK5KCDw+B/NrDysBV5p0GTSkE9RdL5j5tKpQiCoGRjp6xsSwMJoL29jfLyaM/lVlVVUllZju8HtLS0tieTqe8CP3lz0cRuObj4plFJ4GuTL132F1W9TUSK5KCiqj944bYxYQ5OnC9+R/piceUKkEwOBnejfHX9khm7RA5aA8mYIsSRua7nHt3tPRFczz0CWPburVM+sscFi97u9YFVn3Rj3rTstxzHYdiBw4hVxnj1vtdQVfzklpZGzkOWT4rIxWf/PXg1z9HXEy7mVtD7ByZYPq2MD8aVoTkPU3qZGXXyiXkeTpELsFa6vHNsBWMeyz89dcpJEQ/yDwVwfKhZmaJ5z7JijwuxYUT3/QMCOkiSoPskD4EDK/eK8cwZ1bz9kQo27lmJeFvuGD71qYaEBsGxIvKDuwqca9TiRXOXTZ4ySZUHRTisQLGnQb4DUD//5EFBoI+7XmYRvdAAHL4cBHp+3dwT92o+5uFNRT6eMcb0KyIy13Xz5KAb5uCUme9+ZNFf93i7t8dV5UnPc7vnoDgMWz+M2IsxXqt/FfWUYKDf9duq+GEDI+NJQS4OLjy7TzmYeK2OssUDKHu7usfoBimSg57nFW2IuOpRsaKCzSML5ODoFPE3Cg+JS6VSlJUVn7QoHs/JwSAgmeygrKznZHWxmEd1dRUVFQkqKyu6rbHU0DAoEWRysNC5Ft80au7kS5dN0kAfFEfy56DP06QJc3Di/EGBHzzulnndcxDny0E6OL9u77l7Nb95TL/PQZvm25gCVtw57VDHdY4utN313AHiyOJ3b50yozfHXf/YMRfkNo6yDdp7EA0T6qkaVEHNkEoS1d0uhG2EXdhHFmgcQRgMRa38SAVr90v0aBxB4dl1BPAidOGv27fIhd9Jk5IUhdaUqH2v9PCCDSN7BkeKFO0xZelBZcw9t5pbr63ne/cN56bfjOD5U+vYOLqqW+MIws/puO4xqvqXYucbtXjRehGOUOUbqnRd1FVJq3IbcPiYF57V+vknn6SqSxynW+Ooi+NIg+PI/SU/oDHG9BPTjl9xqOMUyUHXHSAii6fMfHdGb457zBnrL8htHGUbtHkQ9c82UDGvisr7a4j/MxEOJQh15WBwUd7GEUTIwYpXBpJ4qybv0O9iDaAoQ9niLYUbOOmJKVJ7FJ4Wu6Oj9LpE+Wa7S2WW+Cgri1NdXUV9/UCGDx/CiBFDqauroaqqsscCtJmGbskcXHzTqPXiyBGa1m+or1k5qGlN6W0Ih79w2xitnzg/zEHXyZ+DntPgeM4ukYM2SYMxBay8a/odrud+slgZP5Um1ZHygS+KyI17XLCo6D+olrkzRYNguThOj7UWsq15bQ3L//Fu19/T7Wla1m5+xU8Fp5799+DNYvte8so+9wBnFiujvg9+/gdyYo6Tt5co7nl4EVb3HvJEK5P/Z0PRMuJDTGO4uOsFeQfYIEi86asjxr186qCiK5YPX7yRMy9fyuZal/cmlrN8UjnLJ1bw/j4JAm9LvcUNpzl3PA/XLdxZrqpoEOz/v+VzXi754YC3D5oyRFUqHEffHrV4kdbPP7kK+AnwmVL7qip+SuPNjQ8X6yQzxph+YfoJK+9w3eI5mPZ9UqktObjor3sUzcGZZ7dIEOhyx5HiOdiyhnfXL99ynpFpNh/d8kpQ658aXHR20RzcZ9orJXPQ9338IH8OFhpe53lej0ZGPq31rWwYX6KNtk6ILYnhrnfXS1smB0XiI0YMHzdo0ICiObhx4yaWLn0b13UpL09QXl5ORUU5iUSiW709z8VxHDzPw/MK11tVCYJg/zl3lEfKwYM+/fYQTWuFU+a8vfimUVo/cX7vcrAtFW9e2tivc9CG2BlTiOrAUkXEdRARF7gh8IPzl/128vRRFy8uNlfayaUaRwBlVd3vDrllLrWDKxtEZCjhw5TFlLxzVkyg2qOBJCK4ER8A3bMp/7CCbudwlA4/uQBonPl8a9ftshtbZ10G/LzYvisOqOz42QPj1iYr3YLjoRFBHAcRp2jjKCwqBKrnAt8oWXFg9POLuh5Mrp9/8uHA74G9ouwrIojDZOCZKOWNMWZnUiiZg044HC3MwSA4f3LjsumLm0YVzcFSjSOAuNe9F8Z9z6VyXm2Ygxdtgxws8riOqvZoIIlI5IkQNg/bWLKMDghIfqRjAdDY+pOZXTk462OtJXOwsrKiY9y4vde6buEc7Kyv40jRxlFnWe1FDj7/m9FbcnDi/N7noOv0+xy0IXbGFCJSqOu+S/bF0nGdqag+Vahsy9yZDuGMMCV1tHYfahb+Yi2DgCfmnlP39bnn1BX8t6uqzSVPUGT4gB8+bNntvZgbbQHUwA9WDHwt+beSBZWNwFnZjaOMN0ru60pZ0cYRdH0+NxZtCnARGRCpYEb9/JPL6uef/H3gb0QMhU6qmn9Fc2OM6WcEepeDjjNVlYI5OPPslug5mMrJQRWkI8zBujPmfr3ujLlblYOFJgzK7N8jB92oOajBimRt9BzMbhxllMxBESkr1jjKlAHCCSWi6HUOTpxfVj9xft9yMOj/OWgNJGMKEJE7opTrXKUbwPHcqctuPujUAkXPIMKaAqpK64o8kwJlfucHrgEenntOXUOBQ2z1EtbJICDIhIMjEmloHYA4cqaIzFLVeYXKqKqifGzm8635pogt3UCieLB1cko8SJtTpwcjFQTq5588ifDO15X08hoaBErgs7g3+xhjzM4SOQezGkmu60w96JhlW5+D7XlyMJynrisH686Yu91yMAiCrkZS5lmdSPsJEXMw4GOtP5nZ9xyM8AlLTSiRU6foOThxft9z0A8IUn6/z0FrIBlTgOM6z6lq8YdpAOnZ5f7Z3Dda5s50gaujnNdPpWlvzrPiavebWbOA5+eeUzejZznN+3RomQofD4ZyjezLT939+a/YeA6TwqMnUkFA0vfxXDfawgzwpx/v88rTxy5s6RCRRg30V7l34FS1A+WjMxe3PlzgGMtUtfhyroBT4nofZWhdV52CYF3gpx8qVa5+/slu/fyTvwosBA6IdPDs86jip7WlZebDfZoS1xhjdjTHiZaDeZ5b7ZGDM89uiZyD6bRPezpPDnYffT4LeL7ujLkzcotpgRwUEYYOrWfffcew//7jGL/fWAYOqC1YjyAI8H0ftxc5+Mo/9nm65dpjS+Zg649nblUOlmoDRhla1ykIgnXptF86ByfOd+snzt+6HGxPt7S8M7Pf56A1kIwpoOHsBYGIlOwmz+5BAkB6tjpU9RyECeI6ODEXx3Py3v5RVdLJNMmN3XvcNdDcBhLAMODxuefU/dfcc+q2XAVVuw2JcBWOStfyIxnPsWVDqPcSVHlxRsaruLB8L77gjc09rg80AZ+vLCv7pOe6xecbzZwV+FbnX45d2KIzn2u9lIAhGuh/aaA3a6D/hk/1zOda7yl0kNurH00BJRcfLHVHLJZnqtO8lVZF0bPvrn606EPF9fNP2l9El4joD0Q0Hn7c6BPcqCrplOK6rBz1/EnXjHr+pCv2XHzi0MgHMMaYnWDBnIZoOZhzo1Ck57NLqnoOMMFxBNd1MhMh9DyWqpJOp0n6OTnoKfQcLTYMeLzujLn/VXfG3K4c1JwcBKitrWL8+L0YMqSeRKKMeDxGVVUFe+21J2P37jEjeFcOlpWVfdLtQw62XHustv5w5pYc9DM5mKa69YczC+bgo3+s3iY5mEhEqXLncELOfvSP1cVzcML8/SUtSyQtP5C0xAnoTQyGP9e2FG65u3LUsc9fM2rW81fsedTifpuDNkmDMcXNBwoNFQBAcrozNNCXsv/+1GXDhu/3L+Nv9hLdF5ZzPAjSPkF6y9ylfipNui2N+lkL4ami6YJXIQe4Cjhq7jl15x5zV/NK4NeqeoOLyCF+DcenB9BQVomXZxyyiLB/2QAag/pkU7D2QeDPwJzfHPj2uq8smSDAP4p99iy3/2jcyy/lvjlzcetqIt4x7KSwudSAgGJD7Lx4Il+vXiFX/29iztxiBQYvOOliEX4jWSftXLQ3vDNYvLZBoASBUjtA8DzZF/g6gCo/HvX8Sbf7vl6wfMrDNp2oMaa/Kp2DOb+sq3bPwWGHPjV8/PD9bo71GPLl4PsBQbAlB9Npn7Sf7vYMkDqK1muhy21XDtadMffc5j8d05WDIiI1NVUMGFBLZWU5sVjPX3tFhAEDaqmvH5hcu3ZdVw6+vfjAdRMOX9KrHHx5wbgeOdj6o5m9zkGg5GxHxdpHiUQ88oQSwNVz7kgUz8H9FlwsgfxGss4qgaCiYcO1RGgH6YDAD6jdewBelbclBxP641HHP3+7/4F/wfKFU/pVDloDyZgiUu3J92KJwuv6QOcECg4ajlcORLZcCJ84f+DpI6fvcUe8Kv+qb06m+ztIB3l7jzQo2jjK1qiqrz/+sZqLb/j6EX962Fn7+MFaO7New0aRmycUsn0svufGT+678Kyct08Bpkc4d5ownLYJVV2NSI9urW5lCty2EtfFjRf/eWV5QUS+V6xA/fyTxovwa8nTIuuMiUKNpMyQOgIfBtYLTs6aUyIirst5QMPIRSeeaI0kY0x/lEyl3ouXeNBfRHDCGUFR1QCkKwcHHvzE6XsMHHlHWTyeNwfdzCiMzmd+cnuPNK7oQI3yG2ujir5e8/HHLz5i30P/tPaD9Y/X1lTN7JykoNRwsz33GLZx4WN79JsclFI5WCAxXNftsZBsXg+uywAAIABJREFUEaVzcPz88RLIryVPk0xUIE3BRpKq4nekCdI+A8fX4yRy1mFyRdwK9zxSNIycuujE/tRIsgaSMQUs/eWkkcC1XlmsZFe24wppXwlS/tWjL3nh7SfOH1gN/Br4+KB964vv67moQmpzOMQ5uTEZ9hr5mr0wXkkiUjX8kBF/9DvSyRPL6ruujo6bfz2HnH2rs//+lSUTHKDoRTPLr3807uWl0WtaXKB6hwv5V+veUibv+7Gy8qinSQMX/iF+f1cKlz904n8inA00EE4R+1xFpUx1vMJDkTt7knrUL1D8lKIKVTU9G0fZHIfjfZ9rRy468UprJBlj+pNJRy0dCVzbs+enJ3EcNJ3G94OrX5g3+u2BU54Ic9Dh4/U1RZf1yTSSlI6OcGmcpJ9EUbRGoZrIUy6IStUIf/gf046frB80YEsOFljXqNu+OTk44fAlvcrBlxeM22Y5qKp3UCIHC61jWl4ebWgdmRy8//fxLTk47KH/BOmeg1WVUx3xCuegSvgYQE77M/AD/LYUGihVe9b0aBxlc6qd4/1W/9qRUxdd2V8aSdZAMiaPpb+cVAc8DIwMUj5uvPRaOn5H+odjLv2/bz9xwcCTgHuABECsvPQUm47ndF3sOlo62PRBG17MJR5hX4BYVYwJ/zqRRF055DzGGmUGm8waFtk+DkyKcOp24LuRKpnx1ZfGStx1TwDOAVYA9353v9ee7dzua3CTq/JdR5y8U476WTPsZawD5scSFYPEcQ6PWI1r/hC//3mA8odOrEJ4SRz2zNo+DJgQJZTDRlLYi6Qa9hj5ae3aVupxKBFBRL+qyiZ6PwzDGGO2i0lHLe3KQd8PIq2lk077P/y/J8Z8e+DEJ06ijHtwMjnoRshBJysHvQ7aGjfhtnrEVkbrDYkRY2J8AuVuAvp5Do6d+pK4rnsCkslB5d7Xnt6vKweDQG8S0e86Tv6pt7Nn2MtYB8yvqEgMcnqRg/f/Ph7m4LCHqkBeEnHy5GCE750vqBP2IqkqQdLH7winHBRHKBtYPAjFESQmX9Wk9psctEkajMmx9JeTyoA/ARMB/FTpyWQcz00lqsu/8cQFA6eiPEimcQREeohRRCivKUccoWXFRgJfSad8ku2pgneJOlXvUc0BFx/U2Tjqeewo45BV13T+8StLJsRQjXqB+vmPxr28IkrBr7401rny5XEXJDyvNea6D8Vc9/yY617pOc4z3/r/7L15fFx1vf//fJ9zZsvWZmmadAlt6Aa0pS1Ly5IUUREUyiKUFlARAS8KfvH6A+TKRUWu4nJdrixX5CougFAEKZZFBGxroUUolNLSssSSAt2SdEmaZGbOOe/fH2eyTGY7k6SgMq/HYx5tZs7ySeacz/O8P+9t87Tl122aGgK4v/xpx3bdmY7rtg08hu26xBy7GfgNXsfuQ4FRoZKyHxqW5RcK60leFfy9SJJxlJd6Q+1cxY5pr3EEEI74A3NC3xz3wilXD3YcBRVUUEHDpRmNTckcdHJz0DSMeCQc/o+KGcuPJMQfe4wjvxIRIpEIIkLHYfvQEhenwiZeE/MevrOoVEqZFZzZYxylyMhV+hRQpZeDhx73ekDz4ODGVZN9cXDSkRuMyUdt/IwVsNpNy3zENM1Pm6b5VcM0nps2b/PyqfM2hQCefrDccV13ppuGg67rYttOCgfLykp+aA2Jg5Keg36eYRBwQV0XuzPWaxwBhEdFkCxRFL3yHlW+Oe7IF/4hOFjwIBVUUD813TbDAH4FnNDznmPnBoOIBMygNQPlPgYEA0Tbo4RH5OaEGAbhsiLcuE0g5C1kqavEuuMEglaaankw5ugx1M4dgxnOfCv7ekAXWdf7f9XLc+UAJdQOfDfXRldvmCTAJ4Abg6Z5uJlS7UiwRBptx/kz0ABw38in3r6g/aSquON8SkQWg3apsj7uOrc/UPGXd/rvvzi2oBj4hY/xgleZqDe0LrzslLFicFLGjW3F9DGxi0AslkqRcJHf/hO9//3uuBdO6X77iEf/x9eOBRVUUEHDrBmNTakc9GEgiUjAsswZGNyHMYCD8SjhYG4OGoZQVBTGjrqYezyvkxYp8bExrJ0BjGjqgt8YYwxjpBbLyOzh8udBopeDqnp5rhyghHxxcNKRG3o5aFrm4alV/wQxpdHpx8GnHhj59kkL26ucBAdV6QJd7zju7X/5Q0USBxd8OpY3B3tC68I1y8aKGJk56NiYPsqFiy3EogN63gqEq4t8DUr7GPrdcUe+0P3280e8rxwsGEgFFZSs7+O51XvlxL1qOrkmWFU9OTwyPCFQFCBQHCBQ5L2i7V2EykK+JmjTMpnQMJEN9/crhKMQj9qYARMr4E1SVpFF/ckHUza+DCT75D+wyl7abUReA7hq87QSRHJO9gn94PuTN7Zm+jBhGH0Er2v6XNMwGGgc9ZdpGMdft2nqkTdO2/w8wG+90tu/TrwySlV/JCJ+u3h/557g0rX9fv5Mtq8lFoVAMHdDPhGwAmDH+94LRfBlXLluimH1k4SRdHvOnQsqqKCChl8pHLQdxz8Hw+EJgUCAgBEgYHr/dnVGCQV8ctA0mdg5gQ3hDX3LjQGwx8Qx20zMvd6jq4XFwUY9ZUYZkIOD/kLsXgOYdszmEsmDgxtXTc7IwYRh1MtBwzCyVpczDOP4qfM2Hbl59bTnARKlt4edg0t/HRzAwcx/n1h3lEBJMGeDdhHBsgLY/UAYqohgBnMbV27UHZhz/ZOEkfS+cbAQYldQQQk13Tbjy8C/p3ygXrJhTgnfmXDCBBl79FiqD6umfGI5JaNLMEyDWIffHnNQObmKgxpSejLgxB3i3XFKx5Vy2PnTPePIh0R83eZbE/9+AZHcweKqe4AfZfr46g2TGoG/AH8C5gJYuRNkAQZWEMqqhdHTzhORS3xu/gqpceKn59opFvWXL2payYZUxKf3yHFSj6+qPxvzt5Ov8XWAggoqqKBh0ozGpvQchKRS3Fn0nQmVE2Rs2ViqS6opj5RTEirBEIPoQO9CFlVFKzmofQAHBZxKh3hNnFKjlOnmYb3GUS75CbGjHwfFBwc1BwcnHbkhhYO5FixFBDQ/Dp72qeiB52DM3zOMaSYX84jU+PMeOe2pHkpV/dmY2X973zhYMJAKKghounXGF3D5IQ6e83lAAzQ/eUhAxljdWEeUWKd/OBx0/ASqBla/Exh73DimnDWVQFG/uVszV7MBfx4koNnbWPxNzKoRcZzZA9++esOkuVdvmPQnYDnQmDQOfyt4eTWNE/hfn5v2hNb1zvLhZaeMBY7KkeJFPAZuGiMmZSwiWImvJRgCy/IT865pq+B5hRu4qfa5k6/MeZCCCiqooGHQjMamLwA/zPS5nzC7bIpGY8Ri/jk4oeMgqroGcFBhXGwsU40pBAbYMFk56C8XtBlA8MdBdTXi2KkcnHTkhrmTjtzwnnGQPDm49NfBPg7WLEtwMDvj4rEYrusrzBIrAcLgyBBWlvD/Hmlc0a7U83stVOSm2pnPvS8c/Ic2kBpOaylqOK3Fd4ZzQQUNRk23zFiIyy2o4PnzBdRLOOxx+bpxO8sRPOVyP0c7unB8HKdHU0+dRnF1MQCB4gDTF0+n7vi6tBOs2plX9vIBA9CRc0uvQ2oIePzqTVNPAbh6w6TZV2+Y9DCwGvjoYMch8JqfwQKc033qGcaAsqxZdNM9waUvDHjvXEC8Xyf7ztEohMPK2FphwngoLk6/g2kJhgGR4sF7j3okIhgGN43528m+G1oU9K+nlobFRS0NiwscLOiAakZj00Lgln4UTCGa7SOSItc839UVxfaR19ujaXumUhxPcNAJMH3ndOr21aXlbZpwZd/jSsg3B73FLY+DU+duOgVg0pEbZk86csOQOYj45+CpF3SfMbA8eRbdtPTXwQwc1JwFoaLRKOFImDFjazlowniKS4rTbmeaFoZhEKlJ//lApfMe9UhEMCzjpjGz/vaec/AfLgep4fSWBrHkR5jMkoiYgDae07oT4V4ReRp4dvm9FTve73EW9K+hpltmmCi/Tt9kQTyPkKs4saGtnAGg0L2vi8jI4tSCC2lkBk0OO3s6bz75BvUfqydYnHl+UMfFBcQa0OtBJH0X01R5YFBdhciJ2X8P7TlgGNWlV2+c/Dw+Gun5GYRpGC/52KzneEf62c5V1bjr/jnNR4sBzwPXc8wBgzQMZVQVTKo3KSrqCx0YUwPdUYeXN9rYdvJOoQgEAv68R7kiVkQk5Lp6IVDIR/oAqaVhcYOI/AiYlSg9rK2N5+2EPg5WLL+rwMGChkUzGptM4NcZKAh4c+RQPUg96urqprg4kjUXp0emmkxvO4w3St6kfnc9QTczBz0DycUwJImDIuTFQUVXCdk5qH3GWFhVl04+aqMvDvqRkQcH8clBVVU3Gwfpi2YYaMQZhkFVVRUHT66npLSo9/Ox42vo7ory8rqN2AMWf0MjIgSKfUTrO4q7PzsIxZCQa7sX8h5z8B/CQGo4rcUCzgCulJAcN6AcoCCMBr6UeDH/3LY3gVXAM4l/Ny6/tyKPlpoFFdSrK0Cyd1VTQR1FXfUbrpbhON5Dcfe+TiIji32tJIVHhDn0zENxJfflrY6LOi4k4KCqr4RLi3+C17C2z18sDAwhdIBtif/fkKhil7b3AgPdLSIWPVDIsfqUBKzEz6o6MCpxFz6lfWPOvI0qtusKcP9ZXZ849oHIstcAwstOmUx/sCR+LcUzkkqKlTG1Ss1oNBQKiGGmTpXhkMmcGcJzL8bpz95QeOjeo/4SGDvz76eK6+ocVf034DCg3XX1F69OefReXwcp6B9eLQ2L+zgoctyA+UEgmYNt889P4WDF8rsKHCxoMLpCICsHhV6vST6tC9JKVens9IwkXxx0why6+1BfOVCuq7iuItLLmFeKi8N9HMysJA4mqtil5aC6yd4W6cfBXF6Ygb9vDwcHyDcH8clBN8HBT5zfdeyyuyIeB2uWJXMQ7Q37FhGKi4upqall9OjRGikOSU+RqP4KR0LMnjODv615Men90Ch/Fd6zeY/6S2DszK/9XdyYO0dj+m+4HIbQ7na7v3j19ikHhIOS68s8kGo4raUSuAT4IjBOAoL4WHlNo714Ls0eWKxZfm9F7lChgj7warplxm9QucDPtkVVRZ2GZWbNOFTR7OtUljfxmEGLSJm/5EUAFxclr2efm4DrS0aNvBjhVozUiVlVPSTAWzJpyYSe96/aPO1IRP6KDDAcVcF1M/96rpvRSBKgKBjEEMGU5NU91zNien6c8PXJG9/K9osVP77IFNHLg5Z76UeP7T40W/Kt7bo4fWNqAuY9EFm2K7zslOvwqgr1yjSVmmoYU6uUJfJ+DcMkGCz2Cl2IoK6D69r0N+veejvOO9s0sT0U+QivU1Wi3S5Glip3Ip4nKhyW1abJZBFJaUXvOO7rjuPO3jT1sf05T1rQP6RaGhYnc3DA/ZGHUjhYsfyuAgcLyqkZjU2/EfDHwaKiTtM0snPQx3Oll6tiUlSUvn9fOrmumzWMLo1uAq4fObLkYuDWnvNmGOtbS34uE3p+mDZv85FiyF9lAAe9ZuCZWZymgWuSgqEgPfd4/7EkjJieHydsXDU5OwfHPm6KyOWBgHXpcccde2g2b9yAMTUB85bdFdkVrlmWhoMm1dXV1NTUUpYAoRkwiJQEvQViES9ixXaTcP/W39/mnbc9W80IGRRNyh1ep44Sbe7GyFJCXEJCoDJA+KDwarPUTM/B/c7rTqsze9Nvpg4rB98XD1LDaS0zgSvwbsg+MzN3JcBMGgF8LPECcBvPbV0HvJp4/XLFvZXvZNq5oA+0dvrdMN5tF4VKBn+RAmCDmooTs4l1RgkWZXde9cjAwEVJ8bekqhX4dOkxTzwCoG+cMz2dcQSJlStTwemNuwbg+1M3PX/VpqmTgGcRGQf0Gj5ZH9tEMhtICcMoXZlvQ4SAYRD34NCS6fDFjy+qBj4nol8WYZTtGjS9YzJpfHpYOar9jSOAemDpx/d8/ET6hRWMKPOMotHVYPb7eg0jQDhcklwF0DQwDAvbiaLqnXdUhfQaSAGfUdLxuPZ6kHqMJBGwLM8oCgSkf4nwjGEbpmlMBp4iUSGpoH8etTQsTs/BwSuFg62N5yVxsHLF3QUOFpROvjlo2/Ei0/THrWxSVWzbIRqNEQr5mzgNw0A1uwGSUCvw6SeWlD4CcM4lOh2ycNA7XhIHN62e+vzUuZsmITwreBz0a/hl2q7HKEpnzPS87+bi4NjHExyUL4vIKMdxefvtd6irG592+zS5RfXA0o+cvacfB6GsrIyamlqqq6sx+4HQChgUjQgnR88YJoZlYEed3lDDyqqKXgMpUOXv+4zvjuN0ORAGI+GdEkuwyi0ClQEClQHMvmeuzBwsNiejw8/B98yD1HBaiwkswAsPOCHtYCKDXjnLqsRD5UvAh1fcW5nSlbigD66abp4xCXjdX3gyFI8uyXiNKpo+s3WAXNvFdVxc16VkVCmhUn/PRoriktUdvQpYVHrME2/37tN0zhtiyMFZj2vrMpm05NSB71+9aWo18Dgwy9cAodfLNFCWCCWh7D0wbNeN/+ekDUkza/HjiwQ4DvgCcDZowDCSc4VmTu1mXLXbe2xV9bxSGea2WEyfXL7G/XDNaM8wypBnSiQyEiND40FVJW53A8r+Tpt1G1xElKLi3HOYqtLV6aAKoZBQWhpIVL9LX9Eul1SVeNyZumnqY74Tewt6f9TSsDg3BwfvQcqqBOtfAj5cueLuAgcL6tWMxqZJwOt+r7qSkswh4n6fKT1vkPcqLS0hHPZndKkqTvZiEauARU8sKe3l4DmX6BsiOTioumzJzyWFg1PnbsqbgwO8Qb0SQwjl4KDruvENf52UzMGxjw/gIAHDSM43PuSQqVRXVydxMFvhhVgs9uSzz6758OjRo6mtraW4OD0ISysjGXOmVZV4tw0KHe2dvPzSBiQgFE3OnUKgrtL1RidqK6GDQpQ2lGKNDEAMtC1/EKoq8e3xqZt+MXXYOHjADaSG01rKgYvxwgdSm7v0H0xYhpbjkUOKtip60Mp7qwrhKAX1qumWGUtQOdvPtkbAIFJRlOqm72ccqavYURsnauNEHe//MRs76hDviKP9ck/ENJjwoXrCI/waSS5u+lC77wDXlx7zRF+m5JaFpaq6O5HknfmYrj4m9UtOSffZ1ZumjgQeAY7xN8D0BlLEspxwIE0Acz+5ql1fO/iVIoDIo4ssgR8hLBKht86riJK6+KbMnNpNdZXjxcn7GKbtujhZQhYtK0QoVJL9GE4M17Vp2x1n0xsuwZASDA4cQWo9qHjcJRZ1qakNEQ6ZSdeS47rEYo7vh4ze/Rz3qxsmPeK3sWFB77FaGhb75+ABMpB6pKqtqnpQ1cp7ChwsqFczGpuWiPfwnVOGYVBUlJo/1H/e8jxENrbt4DhOv//bxOPxpG0NQ6ivn0A47I+DXp5RZg4+saS0l4MLL8UfB1UfW/JzScvBqXM35cXBTAaSFbCcQA4OqmrXKysP9jhY+6glwo9AFolIPw6m90IdcshUqqqqUt7PJMdxsy7KBSMWkdLshqsdc3Btl9Zdu9m86Q2CNSGCZcG+dimCFx0WIGnxOL47TmxnlJov1xKeGUp69nd2u8ReiqF78+TgXuerG26eNGwcPGAhdg2ntRwC/D/gU4DPTlEc0MLjglQq+kTDopbjVv6uasiW4XHn7RQXNZ69e/TwlHYp6P2RykLQx0BOyrWpG3fZv6ODQEkQK2S2GwGzCeHl9m37Srv3dp/hRG3cePoHb1VFB1wp6ri8/cxbTDjxYKxQ7ttRMBjQbroF+FTpMU88lmbzhbmgkNDLmT743rTNe67eNPUk4A/Ah30ca6DiwB1By5pN7go/3QCRRxfNEmGVSOq8kf65UXjl9TBzgl2MLPOXp2UZhledMIM5ZVm5VzNFDFSVpi0OiGcceUUD+6tnBc8zklQhHnOpHh0kkqY/hGkYhENCPO74KqnbexbVf4iCOwUlq6Vhcf4cPMASkUpVfaKlYfFxVSvvGTIHdx53rqjrGqOfXVLg4D+xBBYqPCaQm4OuS0fHfoLBAKZptZum0QS8vG9fe2l3d/cZtu1kLKjgzYc64HjKW2+9zcEHT8Cyck9lhiED1+FagE89saT0gHBw85ppe6bO3TRkDlr5cLD20VkiskpE0nAw/QLK5s2vEwwGe3OHcsk0jaxGUsDPM4khqKs0vbkFTCFYHERi/cbnFRZE4+oZSQFvQTneGqP686OJzEo1is1yg3BjiPgrcey/+59W1B5eDg67OdJwWovRcFrLNXgX2+fJAwoa16TV9QMhQY4BvjbY/Y87b6cce96Oy489b8d2RW0B+9jzdrQcc96O/5p3/vZ/6L5SBaVX/eUvK8jJoDf43SfeEaOrtat0//YOY/+2jqva39lXEu+IZTSOAJy4S7QzhhNP9hDEO+O8s7q5f9lQv1oJzMpgHAF8zudx/i/bh9+btrkDOBXVh8B71K+TMPMCI5mcOVfXAX4BTCmPRL4oMNnHON4ML1sUEGF5OuMIMhfKc11h3asROrv8r7ybYmToXSUYho95VpV3ttnEbAiHJIPxRqKakkcJVRfLEoqLMpc/FRGCQYtg9oXGfsNQgN/62rig90QtDYuNlobFg+Ogj34kQ5XI0Di487hzZcexCy/fcezC7apqI2LvOHZhy45jzvmv7fPOLnDwn1Avr6hXgZMVfHMwFovT1dVV2tGx3+jo2H/Vvn3tJbFYPGu1Odd1iMWiOM4ADsbjNDe/M5hrfyUwK4NxBMPEwc1rpnUAp2qCgwDhcIiRI8uyFZro5WCkKPJF/HKwZllARJanM44gcxij67ps3PgqXV1dPk7jySuJnvq+iFecIadUeXvrNuJxm3B1CHHSg1AQJC7QBdqhWFUWxcdmnhbFFIKHBwkeFfDlylFVMIaXg8MeYtdwWsu1wLeHcgwJSG+1r+GWol6lMThr5e+qHvS733Hn7TSAMxX9viAT0x4XfUuFc1ffVbNmGIdc0Huopptn/n/A9/PZR1Vfb9+xr1YdzRqT1d0eJbY/7v0gEIoEMCwTwzQQQxg5sZzaOWOzn6svD+nbwNeTQur6a8vCQ4ENfsYuE5dMybZNV9PHg8CRjuoJ24heVRcuHWn2i3+OuQ4P73+X5+29PY0o7ga++b3D3ngd4DtNM6YD63ONxVX98o2vTQ8aBhld5OlD7PoUCbscNbOTYO72Cz3nJD7ArecnvA6gqzv6ypq1ndPBpaQ0s4E0UKGQSVmOsIXe8blKNGZnfWhwHHfdhkmP+M8TK+iAq6Vh8dA5KJL073CqnxF2VtXKe3xzcOdx53ocVP2+SBoOesd9C9Vza1bfX+DgP6lmNjYNioP79rXXqmbnYDTaTTwe6/05EAxhGgY9OTXl5SMZO7Y217l68pC+DXy9f0hdfy28FN8cXPJzycrBj5/XFQSOVNUTotHYVaWlRSP75wE5jsu77+5g79523H4cfOP5w14HmNHY5IuDqvrlN994LWgYRhYOpg+x61E4HGbWrJkEAv5A6P09kxkTCFsUleXmVFdH9ytrVq2dDlAyoTTDomOqQrNDlJ3tr7+t2+ES/Vv2kDtnr7Nuw82ThpWDw+qOajitpQL46lCPo3GFOO1q6mMSkC1iyFTgWMB/cGVu/TYRape1IVfCMDob+E9geqYvP/H+Qaq6et75238FXLv6rpqc9emHS3M/u01cdU8FvoLXJ8VReB64/Pk7x255r8bxz676y1/+QdPNM9uB2/BZuUFEJpdUlbK/pSNr+U8n7vY2q7MCXniWE7dx4oAILZt3UjqujJLqzJOGq24HwtmlxzzxeI5h+Vo1E5HrB77X1fTxYrxY6wagES8kIGyKMDE8IiVPMGiYnFUyDqND/v5crO207x32xkAgZW88m5Ahch/wq2zb9LRhyvTM2NVtsO7VCHOmd+GjFy+GCKKSVB3QT3idqrqWZX4IuFfE3+/XIzOPPEvDEMIhK23InRfnrlHbdk7O5/wFHVi1NCweHg56Bky7qj4mIltEDgwHE6F2WTmYMIz6OJjhBky8f5Cqrt4+7+xfAdfWrL7/PePgtrmfFNdN5iDqcXDs8w9uea/G8c+ul1fU/2BmY1PeHCwtLaGjY38uD1IvBw3T8nKVHAccL4d0564WyspKKS3NbGe5rtsBnP3EktIDxsGPn9eVloMiwogRqcWaTNNg3LgaRPh7W9ve0954/rBBcVB8cTB7P6ru7m42bHiVmTOn+2rG6+U9JhcJCqYJAU8zDtcKWgkO5mgwP0BmiX9Hs1GSCLlbH8fekrygqaq4+9yo3WYPOweH1YPUcFrLh/BKzg5FrwP/A/xq5cNV7T1vzj+3TfDck8fiVfM4Fjg0nwNrT5nkvmtqK3DUyt9VpXQkP+68nSZwDh4QfJ/H7fNQdQA3Aj9efVdNNJ9x5qO5n91WDVygql8UkfqBn6uq7eB+8oU7xy0d6rnmXNo8FuF2YCIQQPkdwnfW/qyuc6jH/kdT080zz8ebpHzX9XZd1zOSMoTZte/c/zN19fOGKVjBDIcVmHLqIURGprqeXXW3q7jHlB7zxJasA9myMAi8Q+4Hqd3AmC63owjvnmpMvOaQZvHEDFiYgcyT5n5bnXGbRm0FMVR5SFW/0XHS7W3faZrxIF4DzGzadG39+kMijy5aaxjMzr6p50UayIYiI06JYbPTjlBdaTNjarcvr07cdXATBpKIEImU+6lE94clcs+Z1c+cMhvctfl4kExTqCj33/tD8H7XuO0QjTm9CcqJ2PHzXzvk8bt9H6ygA66WhsXDysGqlff0crBt/vlD52BqCN9W4KiqlfekcHDncecOioPa13ell4M1q+8/YBzcNveTOTnoOu4nx73whyFzsHnOgrHQj4PwO+A7dWuX/stxcGZj06A4mM1I2r+//Weq+nkRAzNLvtEh06akDV1zXXf5CK34AAAgAElEQVS76+oxTywp3ZJtHAsvJS8OdnR0+eKgZZlYWfr2xOO285e/rNwqgqGqHgffPqltRmOTLw6uX1F/SKT20bWGYeTgIAysZNfznmEY2LZNVVUl06ZN9eWF7p+LJIZQWpm7ia+q/mHJzXJm9ZRnZgNr8/EgmaNMKq5M348+zZmQQBQJRIlvCRJ9Dtz9Lm6ni7PXQaN6/mv3HzLsHBxuA+lU4OFB7v4YHhAeX/lwla/s5PnntpXjWfg9sDiaLLHeLm66tZBngBNX/q4qCnDceTst4FzgOmBafr9CkoHUozfxVrOWrr6rZlj+2HM/u80CTgEuAk4lhydQVd049uEv3ln3ymDPOefS5qsx+I4kNYUBVY0CF6z937r7B3vswarxutYLgKuBKUAU72+9aMWNlcNS5rHp5plnAPcCPrvbJBqA7usmuj86MA91477tHfOAjaZljMsV21s9o4aKSZWYQQvQfUbAvBnhupJjHs99DW1Z+Ekg5/fhuPbGmBsdi9c/xVOWRreBSPbypACzXqug2fYuR1WNWeJ+5qoJq28DRuYYzq3X1q//YviRRY+aJj5WghTDUGqDnXykfCdTi7t7PTPdDqzeO5JNkQqmTIzlOE6ygWRZYUKh3A3ugIX3cfcSgOpnTl5dVMxc0/cjBIwcESJHMSOvUayVDEDHcWnb04ltuwDLgNNeO8THNVHQe6aWhsXDwsGqlff44mDb/PPz42D6B9dngBOrVt4TBdh53LlD4qCmNsvs5WDN6vuH5XrdNveTeXPQjtuH1724dNAcbJ6z4GrIzMG6tUvfcw62fu2EC9jP1bwrUwgTpVLfpJZFlVf/ZVg4OLOxaVAc7O6OEo2lcrCjY988YKNhmOOMHJNmzehqKisrsCwTVfaZpnEzcN3j95bkvIYWXoovDtq2szEajSVxMNtjcSgUyMnBVatW090dTRxLYyLymbqDJvji4PoV9V8M1zzyqGmavjwihmEQCAQZObKc4uLiXo+R4zjs27eX4uII9fUp0bAp6m8g+alel9DC+36Kx8Epz6wuGlc818xiPA7UyEtHEDgoexighNsJVG9BAn0sd/YV0/aTUdhbDejh4P2HDDsHh9tAGgO8hf/QvQ7gTuDmlQ9XbR7q+eef2xYAZir6JeDT/T8b4DkaqDtdcS9BOA8PCH4S6dIqjYHUoyeAK1ffVbNxsMee+9lt04DP4v1uNfnsG1e7RdEjX7yzLmt35nSac3Hz57C4I8ek8CBw5Qu3jW/OttFwaP51bbMU/ZkgRw/8TFFF+MqKb1X+aDjO1XTzzI/iVa7xn2Tteo1g411xYl2xHkD8YvZ3mj+3/LMVC8yA8ZBp+XIvu8AXj7l15//mNegtCx/Be3DIqqjdldJ41rtP0twrAsFI7hKspzWVsaq7b7vRwQ4+O2adn1GffW39+t+Hly16wDA4U7L8eYokrseO2PnKrJLWyhFBGRNMswqpqjy3t4w3qyqoqcpcBUdViSVykLqjEAmP0KIiK9cSWAcw+j7u7gSofuZjI0V4s7hEKvJJF6koD2NmiANMZxz1yHWVlraODsfRQ1875PGt/s9Y0HuhlobFg+Zg1cp7hszBtvnnexzUNBzMzvs7Xde9BIbOwTQGUo+eAK6sWX3/oDm4be4nB81BO263qOqRdS8uzZuDzXMWfA78cXD8Cw8dcA62zT9xlpbrz2R3Gg6OUOUk9yuVly8fFg7ObGzKn4OJ0Ll4LE4s3sfB5pdnf66ifvkCwzQfytRnboBc4Is7Nx+TFwcXXoovDnZ1RVOuVc/Lmn77cDi3nfjCCy+yZ8++3p8DwSC1tWNy7gecvX5F/e/DNcseMAzjTMkCQhHRsrIRrxQVlVSGQsEx6ar/qSr79u2lurqKUaMyO9L65yBFo1FKysNaVFLkj4M/xePglGdGSlDeLB5TUuHXi0QIKr5QjlmV/jqQcDuBMa+ljc5wu4O03Fjb4ew0D33t/kMOCAcPRJGG3wLn59isCfgp8MuVD1ftHdYB9IxjUcu3gWv9bu+K24pQOZRzegn02QwxdYFnMFiGsBeIpXlF+/+sEMDgI8AnEJ+9aNLIVgcX9zXg+BfvrNvld785FzdfgsHPxPT16NcJfBP40Qu3jY8PdqyZNP+6tkl4FXYWZ9suYQxfsOJblXcNx3mbbp55PN4qhb/amYATd8D1QlniXXHsqP3lw2/c8mOAlZdUPmcFzKOy7Z9YdTr3mFt3/iGvwW5ZOA7v4SyrBeaqQ8xJH/GiKBjJ84IYBgEfYJi8uZJWp2+yO7rsbU6s8PUsUnVt/frW8LJFjwCnGAb0Z4OBy+TIPg4vbmNK0V7XEAwRIRIIZm1Y+L23JnDITIeSNFhXVWKuy65W4d1tBl1dJh/+UO7iDMBd93H3Bf3fqH7mlIhp6v3hCKcYhl86QGlJkHCaWO+AZWBkyVWKxZ2mZ0c/kLXxYUHvn1oaFufFwaqV9xwQDrY0LM6Pg67bCkPkoCqaJQdFvQfeZ/DmVF8cRDUACQ767cmWRo5Xfvo14Pi6F5f65mDznAWXAD8TfxUzejk4/oWHhp2DbfNP9MfBcQr/5VxQOWHFsHBwZmNT/hx0vEYKqko8Fse27S9veenwHwNUTlr5nGlavji4c/MxeXFw4aX44qDjuESj6aMM0hlJhiEEfVQAWrFiFfF4X92I0rIyyssrcu4HVK1fUd8arlmW4KDBQCMpHI5QVFRMJFLkiojhVT3N7NVSVbZufYtZs2ZSVJQKwp6CFy0trWzfvo2u7i4+8vEP+RnrXff9lGQOTnkmYhaZ94dHRU4x/DY1DUDpmSWEDx+4AKsExr2KEcpckS/2dmXTszPnHTAOHggDqQS4B8/lPVBP4IUPPLry4SzLusMxjkUtBvAAcLqf7Xur2w2hYFAW71HK2TDI61y9R81zvx7ZaveEET0PnPjinXXtOXZhzsXN/w78Nyb5NvDdCFz2wm3jV+Q/0lTNv66tFi8G/hJ8rsomvssvrbih8qfDMYamm2fOAf6Ez4cH13aSStarapeI3Az8YPurO6rMgLF+YJhGv23jwIeOvW3XqrwHumXhdcC3cm0Wd2I4mr4AHoBKcjiqYZlYOcDQ4UDd5uqk986p3sDBRXtyDeela+vXzwYIL1v0NHBCzwe1oU5mlbYxo2Q3xWbyeIOWRcDMfjk8t6eUP7TWctiUbkZXOhiGJFY24Z3t8OZbJrFEz4bJBwe6pk0N+UkOOvU+7l6W7oODXzlZVPks8DURUnIh0ikUMikt6TP0RMhZ4ltVoysq7/fXVbGg91wtDYt9cbBq5T0HlIMtDYvz4+AwlBjP4j1K3m5QBx/a2Gzb7mmn8DxwYt2LS3NysHnOAo+D5F1RcCNw2fgXHhoWDrbNPzF/Dn7FgSP1S5VjVgwLB2c2NuXFQWfAtdCfgzt2bK8yDDMnB3e9dmzeHFx4Kb44GIvFse3Mt6A7oPWGaRoEsuThgneNLV+ePORR1dVEIjmdby+tX1HvcbBmWRIHg8EQxcWlFBeXYA4IS7QsK2tOFMDevXtoa2tl6tQpVFVVYhiJIlGOw7Zt23nrrbeIxTxDcfK0g7umTZ/qj4M/JT0HT3pFVPSzKF8TUnMC0yk0K0Tp6SVIMMHBcAfBsdkd6hoPRleM/ugB4+CwG0gADae1WHhg+CheXOc6YNnKh6sG7VYf1DgWtZQCq4AZfrYfrJHUU/xB89ovfyMpsVf+xpUqcZIeLp8EPvHinXVpXQhzLm4WvIn4mwCDMJB69GvgqhduG79zMDvPv65tJF6O0ZWA/6x26P893gB8Y8UNlUO+0Jtunnko8Gcgew1SwLVdNE1FuwQgbml7a/dLTtz+zcAVSXW103V1wfG3tzyZ9wC3LDRU9e8iUpdtM1Ul6mTvkzDQQDKDAXLFFj/VEeDs5r6kSwOXK+vWEDRyplL88Nr69V/5r01TK+969+DlZVbssMNK9jA+1EnEzAyvomDunKiN7RF+u/Og3p8jYYe4LcTjgmPHUO0b20dPLNoeDhu5QnZ2AzX3cXfW5KaDXznZwgsB+iYwLvshlYAllJSGsUwD0xSsHCX4VNVeUXm/z2LmBb0famlYnJaDVSvveU852NKwOD8ODtJI6t0vj30HPSkPcnx2PJWDdS8uTcvB5jkLkjnIoEuu/xq4avwLDw2Kg23zTxw8By9y4KMKCQ5WjlkxZA7ObGzyz0HXxU3zXfVwcHdb20u246RyULVT1V3Q8vrxeXNw4aX45mBXV/a6IQMNpEDATDFQBqq1tY2XXkqu5j1ufJ2fanI/XL+i/itT526q3LHj3eWWZR0WiZQQCoWynjMUyhxF0aOOjnZ27eq7/MLhMLZtE4/HcZzkNhIf/cSJ28ORsD8O/pTsHDzplTw4CIGDLErOKsWqMjEr3sYqT6kbkySNh+wVoz9ywDh4QAykfyQ1LGqZAPwNn6VR8zGSFI0qBBTNI6imn0QH1apXIY+aMuCog0PKQ+oSYPGLd9YlPYEmjKPvAf9f3zhBcqZlZNQevBCPn79w23hfq6Xzr2srAi7HK5Xrt8xJkgZ8h7cCV6y4odJX0nM2Nd08s15VV4pI1oBiJ2Znpb+qOq7trmrbunuH2u5RQLsqa1C+cPzPWwYVluH+/ezTDDFyVmlyXJu4m714wUADKRAOIjkm+J+0FPHNnX0hamND+/hUbc62D7i2vQHXHU3iHjXIfVuYhkE4kDvkb9XuMpa1pf+qvAembgBKS4zOExrTxB+k6uf3cfelPrYD4OBXTg4DX8BrypkhxkLp4V9xUZCSkhBGDuC5rratrLp/SKFQBX1w1NKweAL5cDAPI0lVo6gGVHVQDWLfKyPJcZx0bRiWAIvrXlyaxKaEcZTMQYbUk6qXg+NfeMgXB9vmnzh0Dn7OgY/0/p1uBa6oHLNiyByc2djki4O2k/1XVVXHdd1Vu3e37XBdPQpoB12D8oWWN44fFAfPvtg9zTByc9C2HWKx7KcYaCAFg4Gsoc8AW7Y08+abf+/bJxSipianLYkdtze4/TjoR4Zh+Ar527NnN7t3t6X9TFWxbe/vUFpW0nnCSY3+OPhT/HPwpFdyc9AEs9iEABR/tIiSj7+BEezOelw3GmlbWXviAePgv3zH65W/q9oCnAX4utl8JpftBv5TkNEqOh0hVy3+9BokGcTnvqqKnd44Aq90682zL2zu/YXnXNxs4PU9SIICmjO5N5tGJo75zBGXbZ2TbcP517UF5l/XdileidvvMlgopP5xvgDc1Xh9q+8qPJlUf/nLTSJytKruz3h+x835/YiIaQbMxqoJlZ+smFDx/eN+1jLz+NtbLhmscZQ4c8bGcv2VLbQO0v79Mjce6qcN3cmhBweFc6dVqCq47mHkYRwBWD6Se1WVNfsyx32LCKbpwWXSwYGczQQTusfndgC8Of2x7jenP/ZDoB6v3HGnaQpV5RHGjC6hfEQ46U/bHY3nNI4SGtycU9AHUlUr79lCPhz0dw16HBQZrarTea+vSZ/Giqp6uUfpe9SdA9zcPHtB78Ga5yxIz0GGh4Nbjzg9Kwfb5p8YaJt/4tA5GFCYmzTeLwB3tb7bOGQOvryiPicH03mOBkpETNM0Gysrqz5ZUV7x/ZbXj5vZ8vrxlwzWOAJQzdxovL+yhdZ5x0kdv59LrqMj+U8SDueOAPN62vVx0K8yFfkZeOz29n0ZP/c46PF00rSDDwwH/zS9+80/TU/mYLFJ1bxyxnxiNOVHjuhbhI9D99/25jSOAHCNAzrn/MsbSAArf1e1ErjM7/aSOVauFfgPYMKqu6tvXHV39d7Vd9W8ilcp5TTgjaGOdTikqjjqEMf2Sptn1r8B3wCYc3Gzhdfv4PNpt7Tpid0erI4G/nbEZVt/esRlW0f0/2D+dW3G/OvaFuJ1vP4Z4KvcS0YJ6TyAi4CHGq9v9VXDOZvqL3/5HVyd5jpuSvUBdVxc2/8CnRhimAHzlo3fnnzcUMeFj1hfV11czZZAnVTFrgt4Skzjdj8PTBuiyQbShEjO3KOUVWC/E5Lpo/nd9liIFjs7nAwvh8kdU2uN8nHabcCg8gmmT63omj614vnDplRuOHTSKMaMLqOqvJjxtSM4bEo1VeXeol045C9awDDkB4MZR0EfXFWtvCc/Dma+53s5WL3q3hurV927t2b1/f+YHHQc7LidtXEp/TjYPGdBdg4yJCMJEhzcesTpP916xOlJHGybf6LRNv/E4ePgGS6k9hxfBDzU+m7jkDn48or6dxSmuW4qB13vgd/3sUTEMC3zlslHbxwyB/3kfrqum3V8Awo0dAFPGYb44mCqgZQ7KnKw15SfJrCxWAzbzr4omqgm6I4ZV3tgOXjp1K7pl059/rB/n7Lh0OsnMebc0VR9pJzx59dy2DemUDXPWwsIH57R7k4ed2T/AeXgv3yIXX81LGr5IfDlXNspiiZX8WoBfgDcuuru6oxJnfPO3x4CvoQXt5w6NaU5Uz6hckl7puYhxYA/uOr+2cbJd9Xp38WiATgz55aJJf68Qw0k0dTMc09HRWQjwjumZY4yg+YhhiG+K+NkUm8p9+xDexY4dcUNlen9zXlo/Tfqw0bQ7AqEPLf7UAxIx3ZenHbN5qwri7mkfz9Hc30v2YozKOoi+izCUmAl8EKk/pGYs/2sC4FfZjtuzIUxGypxEo3rTHH5St2z5EpdU8dBE2EYgr/bwTJMQoHchsTvd9bwQnuuthMwojT212PmRo73ceqf3MfdV/rYDoDT4+cJMBf4FF5PmUrBynjvdHRG2d8Vy9kfCa/54fjlFUs+OJN3QcOmlobF/jiYGmbXy8HqVfdm5OD2eWfnxcEhXcSpzy8eB133z46dPwcRnxxMaDAhd/2ae3ochHdMMzbKNLsOEcMdOgcDCp9ye3KPMulZ4NTKMSuGzMH6OevDpml0BQIBJJH8P1g5jvPi5tXThsTBcy7RnBzMVpxBVV1VnoU+Dj5ydyR21kXOheTgoOu6PPnkXxDp+Y6F8XV1Oa8Tx3ZwcoQjDpRXMCI3B3ft2klHR85aJIwoL/3rMY1z/XHwp/jn4P3xZA6GqZQJme+djjc62T96HYEpmb1eCSU4+IkDxkG/fRr+VXS1osel65/TX/0q0e0Evg/cturu6pwm7eq7aqLA9+edv/03wLfxejVk1iD9dwr9jYB1wP8Bd6/5ZW0rwOwLm9fjJaD661lg8EPfJ3e9l5q6VQwZ72cXMQQzYPa/IULAbDNgzLaCQ78Ee0PCchtH4JWIXdF4fevHVtxQ+c5QzjvjG03dL3994m437pQbpoEVtDB8uLzTSQyZOpSxACjuHsHMaBH0eBaT99EOvOaUSxB9IHLwI+msp5m5zr05ahJzBBzFtGBKSUtO4wjIWg44kywfHVlViW3aX7IKyFqvVFU5fIavhnjEbefDZ+i5VwMP/iFw7+uZtjs9fl49cEHi1dtLRjCzgrKkKERROEhnNEYsnhWWDxeMo4KGoKtV9TiRHBzUVA5Wr7o3JwdrVt8fBb6/fd7Z/jg4WCU/iPdysHbN71sBmmcvyI+D5MHB3iHoVhGfHEyEMiVzUGcbZvdsy8peNMfXWEThBIVPun5qzB0DrGh9t/FjlWNWDImDTWtndE+c/fJux3HLDcPACli+PBvpJDJ0Drqu7jFNyc7BAcaIah8HVXngkbsjg+Lg/v37cV0HcDBNi6LiEl9GdD7eth7lKhYBXpn0zs79/jg4z1cNF+KV9ofPuEk9Dn41kJmD98fTc7Am+8JCyaQiiuRIOjtfJRbZlu2Z7uEDaRzBB8xAWvm7Kvv4RbtOUnSbIGn9nomH7e14CZo/W3V3dWe+51l9V8124KJ552+7HXjQuySSz+LzYT7N+ACDfXiVcX6x5pe1Lw7c5sU761bPvrD5k3jd3LN/x6bvUO7+uhWHKzD4BF652gkZtxRIZwQZlpH2/Xyl6O8RbIRz89jtMOCvjde3nrTihsqMN7iv87t6FcIdruMS64oxaEMpbeJPfnJVHzCEizJ97nmOFMDBK9X6G0EeCk/6Y65rPCcYNnT1fZeOrYwP7M453oEVrzTxynY5CuIrvE6Eh/e71oWq7qsiRsbqOarutkjEzPqg2DNWVabj5QR894z4ua/gNYV8EHhJPcN0Id4qWYYwkdw3mmEIJZEQMcthf3cs02rswzkPVFBBGVS18h571/GLTlLVbSIZOKjJHKxedW/eHKxZff924KJt886+HXhQBjR1HQbPUS8Ha9f8PoWDdS8uXd08e4E/DsJgeHwrcAX44CBeOeaBMowYw2Icob/nesdmWv4cbH238aTKMSuGxkHVq4A7XNclFo0xBENpyBxUdR8AIyMHbdvpwU4fB0Ue+uNvw0PmYHt7n6fGcWxfHp7BVI0U8RdeJyIPu657oaq+KiJZOKjbIuHI0dkzMkBNRUPax8Gb4skcnERuDvooym1ogJK9M4l117B/xEbUTFtt8IBz8AMVYtej4xftmgasEiQpg1vRmIp+E+FHq+6uHvqsBcw7f7uB6JUoi4E6hBjCXgQHCGZ5Jc2mieZ6OzD4BsKv1/yyNmcG2+wLm88DMjeJG5xx9D3gq2vvqFOAIy7bWoQXj341kDIbmAEzrbEQiAx+lSmhJ4Frl99Y8bfG61sFr4zpdVn3EBBT+kDosl9Vz1jxjco/D2Ug666fcL0Y8vX+/RzyNZQc21k/7ZrNOSfgbLKbzhTBWGsa5qyBnyUaw76I90BxT3jSH7PXz+wZ1/azBG8FOWvy6HXvFPE/u/oWaj9a1cTcihwlOl0XHRAbnSvMLmCaBC1feTpnXT5x3YOlfzpnNMLrIkZKqI+q64yt1TNnTg/mrvznuDgZQigVOkFCIGa2pyxD8qtG6qrS2Z3iTeoEKpdXLPGRwVpQQZm16/hFHgdlAAdVY6r6TeBH1avuHRYObp93tqFemWqPg14o3F7Ij4OJuvw78HKGfl275vc574Pm2QuycxAGYxx9D/hq3dqlCrD1iNOzc9A00/BOsQL7MIwhtcJ6Eri2YvlTf2t9t9EXB7dIBUsCc9gppdgYHOW8tX+87j7jjNG/HBIHJ8xad73IAA7maSg5jrN+8+ppQ+LgmZ+1xTBkrWmmcjDRGLaXg3/8bdgXB8+6yPHFwc2bX2PLlr6UrPLyKsorsjeIdV13YPn5nDJNM2c/poTOWrd84oOl4/80GuR1EUnDQXXGTq09c+Yh03NzsMTBGZHeilKTTkoIUYZJpqAMC4zJ+d1srsTpLNtMLPJO//s0wcFPHFAOfiANJIDjF+0K4E1mJ+KFfC1T0ZtX3ZM5x+i91LxPbzPwJtqgQmjNr2tbBnOc2Rc2fwn4ScoHgzOOrgO+3WMc9dcRl22dBtyC9/fslRW00vZQChUPupDO83iGUcpk3nh965XAj9LuZXilyge6dlVV1dEPrfh65fLBDgjgpa8dVC6mfFlEPoRQA9SLiOHHUFJV7Jh93KH/8fozQxkDgNN0lqHodw0xLsBbANjvqrPGduNXhSf98ZW8j7f9rBq8pMysOvPNMp5s7/tOx4b2cWHdK1ld6a5tQ5rQAiG11Zejwgv7Kpg5MkZlMGeBoz1AzeUT10UBih49o9IwrftFjEYRMbwVO3ezuu6ik08KqmkYL+U6YDzu5Fza1N7Rp3cPZ8s/yqaY7dDZFeupCvWH5RVLfOdIFFRQNu06flEqB1VvzpZj9F5q27yzezmIaqh2ze8HxcHm2QvScxAGYxxdB3y7xzjqr61HnJ6eg1a6e18JhnJ72jPoeTzDKIWDre82ZuTgn8xDuD1wPDpgLCO1U0+2N3zo36p/MiQOHnT4S+UiCQ7Sj4OGgWX54KBtH/f6c4cOmYNnXeQYqvpdwzAuEKEC2O847pp43L7qj78N583Bsy5yfHHw+efX0tra2vtzMBhi7LjsOUg+ioj0SlXp6NjHiBEjCQZzPkPtAWrWLZ/ocXDMo5WGYd4vIv04qJtV3UUnn3+Sml0+OFgdR3Os8ylAGZ4pmebrlnqQ0CA4GGqhc8QGXLMb4A/LKz5xwDn4gTWQPkiafWHzt+i/qiTeWneeunLtHXXpAZPQEZdtFbwVwh8Co2FYDaTNeDX0H1h+Y0XGi7bx+tZPA7+gvyNCQAKpxlGPVLULl6rl11fkHUaSSeu/WV8CHIGXnDjPMI1GK2hVDgSEuuo6tvOVQ6597cfDde5MGvn0NVVAWETagK7dJ9yU8+Z3tp/1UbwwhKya/EoVO2yhX4TEm9dPWrVvdrBs9ngrTEiErXaUl+IddOJ1V9d4ekMn5pq02RFsV+hwgrzRXc7Gzipqgp1cUfean1/19ssnrkupQhV5ZIFhBUJFIkb3vo/cawMs5DxRVTtTN3fwPDl2HpUJtdfEGygDYxA3Hnhg7OyOE43bFy2vWJI1UbigggpKVfPsBckchMEYR1fWrV2alYNbjzg9lYPDZyD1crBi+VMZ5+/WdxtTONgs5VwdOpO4pPc8jHb3dU1xd1R9a9Q3h42D9XPWJ3PQMBotKw0H1ePga88dcuA5OOHpZA7+/YScHDzrIscXB59+ejmxWFKPwTcnTJi0r6yseHY4HEIMg2h3lI79nbiuoq4Sz8BB13WxbRtVF8dx6O7upru7k0AgyPjxWXvg9uj2dcsnpnKw9hHDsgJFItK9r/kjHgevQNRUW5wsHLQUu9r2fc/oCCBdTbwKMEYPrp+Yik1n6WaiRW9ftLzyEwecgwUD6QOgRK+j2+gpXZqf90iBi9feUfcLvzskynh/C/iiYRmGaaU+FAbCvsPP3sYLp/jV8hsrfPmhG69vPQ24j0S0q5iSs9Gtuvrfy/+zIqXvhR99/OYuC2WsOrr10SuL0j5Jr/9mvQDjTMtcJKZ8WETK1NVXxJAbpl2z+e3BnNePyv58zSgxeFiEo/t3K1dVV0Q6gPYBr8R7aoPW/LBmx5hPj2zPmr3ZYgv1G6oBAXUoN22+Vdu2+pxRXbMM032FhHEAACAASURBVEiKOHZVWR9t59n9rey0A7TaRbTZRbTGixL/L6bDDaKugw4oKHH6qGbmjfC1gDz/8onrfJchPdtd/KBhyBmZPo/3xaz7lqb4wDwJBjJIIwlAVVeIyGcetH61ZdAHKaigD6ASvY76OAj5GEgKXFy3dqlvDibKeHscNAwjXVK9F2LnC2u9HKxY/pSvHVrfbUzi4G+to3gwMDvrPvXurv/+ReWXB8fBz3RZBBmrZbr10f9Oz8H6Oes9DprmIjESHFR9RURu2Lx62oHjYN2fR4kYD4tInhzEBmqmTp00ZsyY2qwcjMViPP10D3YUy7KYOHHi6pqa0bMMI5mDqkp7x35aW/YQj8exbRvbtnGcvv+7rouqm5KfVFk5ihEjkirEZ9L8dcsn+ufgVe6DRreRmYNVNhrKD4Q6HtKF28lokIpBN11GcVYI5mcePMzaMuiD+FDBQPqAaPaFzSbwO+DsPAwkG7hg7R119w7mnEdctvUIhP+zgtbhA1fPxBCCkcy+WlXdKyLfAm5dfmNF3nHwjde3NgB/BMrEEi/3KItUdb+IjP/L18p9Lemd8uPOiQiXiCmXIYwUEVTVxmWlqn780S8Vve85IiOevGakGLybKRE7vbTvJbByQjOHhLKHtD233+Ijb1QxJRTj4oo9nFPeTnFQkCwGcNTF+VVrqXnbrlK22wPTDBR1k1bhMFC+Uf8SASPnfNUMTLx84jrfLp+z7EWGYch6w5BDB47DcTWlm7ofZfYiARgJQ2mQq2iqXcDXROQnD1q/yr/8UUEFfUDVPHtBHwfBr4FkAxfUrV06KA5uPeL0I4D/s6w0HJQ4gWDmaEZV9orwLeDWiuVP5c3B1ncbezn47eDHeME8KOv2ljr7bTHH/6X8An8c/ErnRGJcIh1yGXFGigpqqE2QlRrSjz966z8ABw96cqSIkScH+0mEo46cTUlJ9tZRe/bsYc2a54lEIowZU8uoUaMIBLwWIJnkuq6zffsO85133iUWS+ZsIhQ8ZZ8JE+r95HQ1AxPXLZ/on4PX24bRZaw3uo1kDqI4ZS5uaf6o0dFkLvQ/EqSanM9mGY+NdiHO10Stnzx4mHVAOFgwkD5Amn1hcwhYhsmHcz2bqRIV4Zy1d9QNqVLIEZdtNcWUG0zL/OrAMCbDFKxQcuiB93CsvzFM44rlN1bsHcq5G69vnQU8JpaM9nkT/uAvXyu/KtOHp/y482A8sJ6DcES6nCYAdXWHulr36JeKYikfvoca8dQ1TxuGnJDfXq7XKDahtya/SXGOufi5/Sa74jE+VJrgt4Dps0Jh1IX7dpdw864ymmNWwnOUukB6dNkuzqze6ueQ37l84rr/8HXyflrIeeI47udF5CpFD0IxXXfwpQWzG0isAVkmmCeK5Pv99Ml1dafjukf8MfzbA7byWlBB/2pqnr3A4yB8OKeBpEQRzqlbu3RIHNx6xOmmiNxgmqkcFCOGZe1H+s27XoFP8zeG4VxRsfypIXGw9d3GWcBjtwTmj37K8lVF+wd/Kb8gMwe/0o+DcY6Q3YK4aTho6Q6NaN2j//s+c/Cgp542DOOEvHfsx/aG4+elrULYX21tu9m/v5Py8r7WW1aa6Jl0cl2XnTt38fbb7xKNRtN6jgBKS8sYNarazyG/s275xPw5eAXiFDufF0euUuEgTDXdIjdn3lEmaRWQuRXhGopYJuM5UfJ+TumTG+ja6YTbj/hj3Zhh52DBQPqAafaFzaUYbBIjc5duVWwRTl57R92Tw3Xeo7/07khVvUxETkDoFpG3RWQvQsQ0jTFiyAhgpevqHc98v9pXZRk/ary+dRIGq4yA4WdWiQFT//K18i09b5zy487J9MAAeuMTxJK0uVU9Ukd//sgVkUsHPfBh0Minr4mJ5FM6TfEMJO+nMVaclw7egpGjYVfMjWJr3+qXGIKRu9lpYmOv0pEKvLTf4tq3inlhf2p+2rnVTcwq2+PniNMvn7hug7+Tp9cZ8XOLgZPwGkaeRrYpPoPShNj9Hfgt8NuHAne/BnCm/RkBLlLVW0TEXzOmAXJd7XJVxz0c+s2QGz4WVNAHRc2zF5QCm5DMHESxEU6uW7t02Dj47tFn9XGQfhzEiRhmbIyIMwJkpbrmHdXP+Ks06ket7zZOWm5OWvU/wRP9c7D8gi09b5zylQwcbBMkloWDRfrzR37+PnNwwtN5cjChhIEUCgaZN+/InF6baDRGvF81Oq/vlf9KvT3Hb29v5/XX36C9vSNlm1Gjqikt9dVLePq65ROHxsGb4kPnYGqIXR8Hzw54HNxgexzEuUUwB8fBYGeXG+ge9/CE6mHlYMFA+gBq9mebx2OwUYSSgZ+p4qIsfPEXdb9/P8Z2INT49daDJCCviYifyhB3RYpD36IPBoen28gIZp/4VHXfI1+M+AoUPhAqfeIay7SI5xfGlew9OrtsH7fW7shqIClKt9OZ1MbJr4FkWAaGaaR4EG99J8R/bk2+NC8e8xoHF6UCI3kwuv/y+pdTrul0um3ztKNE5EoRmQF0K9wr8D+fn7wxKc7hjPi5AeAEPEicAdTmOrb3lzAA2YOXA/AbYNVDgbvTTrYfbV+0MxIMjMoHpv1lO+59D4d+k0//k4IK+sCrefaC8cBG0nAQxQUW1r249F+Gg3/bduZBNwU/9lqrUeKPgzeelZ2DNhgtOTho6r5Hfv0+cnD8E5ZpWnlykCTvUXV1FYdMm5LVQFJVOju7k7w+fg0kwzAwjFQONjdv5c03m5K2rakZQ1FR9r7Hqrr/5RX1vjg47ZjNHgfxOAjcC/zPxmcmJ3Pwpnj+HAwDYwEhmYNnB9JzsG3NzsieSaPMrsFdLnZx230PT6geVg4WDKQPqGZf1FwBPIJwlAiGemknbwJnvviLuvXv8/CGXfNvbPuMiNyZ6XMRwbQMTMvIHd8rYAR8PcxWLftCuBWg4actRSifQZiO8qSqPvzX/zcqZ73qwar0iWtM08LODwwK0hfK+6Oa7Zw3Yh8GBpIhFsXBIeoMCI0XMAJm1hwbMSRjo2BV5ZqmCHfs7APBKRVbaazYlXX08v+z9+bxVVXn/v/nWWufkzkhEwESwiTIPHsRBURArSPOVsVaJ+yt9tfxaq/a6frtYOtUb/W2Wm21YluLAzhRFUVEEcUwyhCBQAAZQ+bpnL3X8/tjnyRn3iuBIMh6++JVu8/ae68kuN959nrW87CqvW3guoRvuf6w6eR8AFcJov/0STkyTsn3JgDfJaK/3jxofUye38XBqwSA/wBwCQNXUpzGkAwwIN4A6CkAry3wPZc0B//shqvTADQREaQg+K3k37d4OErVLvT/rdNv9wyGE53KcRe5HgROAUGE3m5sBXBJ6aqFXzkPzj70+PW1lP7XRJ/TwSzIDcWQm4oh9nv8ohoERJWmB+eFPHjRwXQA1wMYCYQ8+Eo3erDvW1JKq5MeRESANGTwIPTq1TMmiAnHcRw0N8c2M5Uy+V5TIkqYusfM2Lz5c3zxxRftx3Jz85Cb69FXibl23dKBCX1w8qmb8gFcRUT/KUVyD65fNijWg78JenswBYzeeAMWXA9e7vPw4EdpgGgiEGRjHvyHikFKL02/DcffVLtwcPYR9aAJkE5wxt9cabGDQjAOrPpraee6lR1HTP9lNQH4CO5/2O0ISfD59Bu6tp/nsYIU4pLXvp368tRHDv4EAr+Iqp4TAHA/Ef1q6W35jZ26uSbZi+8MSNnJFLuwAOnjgRXo5w+6v/JHBUkc+sfmIGwV6zcKrQ4lInrvWTTVAW4e8EmPFiJKY+b603L2LZndc88VCauLMIPcnT9p3x64rv1h/IdNJ6cAuABuZ+/zAPhS4pbcjWAbgN8BePLmQevjyvvi4FXEwClwm1+OAxAE6F2AfrXA93ft1JgZtVf18Vlyd9v/JwA+S8LqxN9Hpbhpgf+Z5DuIDQZDQirHX2SBUQjgQOmqhV9dD1Y/G9+DW4vgWzwCYn8nfr/UWEEKcclr81JfnnrRwZ8ASTy4oJs8WLo4IKXsXIpdmB9OmTgOaWluEbp4Kz3MjGDQjkiva0MISvrCNX759w4CgUDz0qXL2j2YnZ2zpLCw5xVJWpa05XKkrVs6sN2DJ5+6KcaDltT34Pplg+J78DdB4hScAonvgTAOCkGk4l1k4lcLrvV1woPv9vFZmR0etC34qvrCatb/+6h8zU0LhmQdUQ+aAMlwwjD9l9VTAbSXvbT82t2oY0jWVymM39fZDY0QuCvJA62KiB4C8OjS2/K1NtnokvXWnfulRGGyvVKxuGl2JVYQn55UETZR938I1JFOR0DQCUBx/E7wiYKkZKtH7bdjZiKSvswFDACPbRs1hIHNDIotwdgRHAHAMBUIlAM4Ha4MrkBY7rQlBKw45XYBQBDBJ6Xb5pUIjlLNDPzk+v5rHkg62cNgRs2VmZYl66P/fnRmNclRavdC/99KumuOBoPhq8P06mcjPfjWKPhWDO78hRig/QRiDQ/WNzQCmh5ccIQ92Pet/VLKwiSt7uJDhJQUPyb9x4Q4H1FEOl0gEITjxC+klihISrZ61EabBxc87aaljZq2bQiAzW2OihobXlRoWCCJB4UQkCK+B91sGtn+70qpZjB+sua9/t3owcWZlpUV6UEGZGMu/IdKtFaTnNT63QsH5R5RD3Yt6d1gOA5Zcnfu+wAWAO5Dq6vBEQBA470CM8+CwJ0eS+z5AP4fgMppj1b9etqjVUWJxvb88D4qWPabXvnv/6a054f36UQ9dY4DKIfjVsRJMCOAgQuy6mIOgwAmbv93AFBxypC2wbaCE3TAUWWyNVfruC04CrGDACYwiFVbmScQq/DgCMq2fwY3RWYpgFsQtbE00c/CEgIplgVBHYGvFCJNEt3/14rRv9OZcFd4p8fzDUrxmujjjmI0B2zYCaTbBjNDMf+yu+ZnMBi+WizJndPhwe0FXQuOANcBGruZmHkW0AkPzq769bTZSTw45EMqOGlZr/xB75f2HPKhpgcdKOV0woMAmJGfHz+dLfo6SiV+TivFcJzYe2uU6gYAbguOQuxwZ+b2FGxbwVKRwRFsLw8mSJkXQsDyuStLbT8vIUQaCbp/9BkV3ejBmQ1KBSI9SICTWY3m4o2w05NXnWcwlK/liHvQBEiGE40fA3CkbpW1BET/0p+AEaTfFTQL7ty2T3u06g/THq1qb1gRCoweZOYWIWiPlLSDmRsKlv3mPo9AqQEAlAIcG7CD3P7Hsd0/yuGn4TZO/BGAXwD0ACD+cVVOrWdfAea2nklJUPymCjrnMfO5AN4HkLT6X8fFEdER9tsD17UC2NUWmwkwRCgwirra1xEnJzoZBDdAivsZESwhfvR0xWit2qpdgZmvUYrj5mgHbAetQTuh2B3FS15Nefb/umtuBoPhK4nrwVUDDusi7O9GD86u+sO02WEedAOjkAfFHiml68GTlt3nESiFPKjgODZsO9j+x3FsOI4NpZwoD+IBAP8o6lmo5UGvuIsZbzqOivSg3r6oCA+uWzqwFW7TYPe6SGjgTnsQlPjlJRFBCPGj0WdUdKMH7WuUsmM9KG0Eem5Ha2EFWMTfruak1y55tbT3EffgYbxCNxiOP5bcnbtp+i+rnxBE3zqsC2l4gYggWcJB/BS0BKQCuA3ArdMerZrHcO5TY/hRIejMqGunE+EOZp7W88P7ztx/2p3xfsFOWPat7YHOjE21M3/zePTn9XsunAaIxCVwgYSpdU2KcMCWL/Xz2/dkFb+5IeyjRa21F95HRHckuy4AMPiJOIe3Aeib7DwiSvqjUcyINrUlPQpKuJ/dDeC7ye7dVRbn/HPDjNqrBjLTIiFoOBFFPJdDq0m2JQVLQYJADoP3K8UPv5r6bLelPRgMhq8mS3LnbJpe/ewToib98DyosYLkVnOTcJwuenB21Txmvk8p9agQIo4HyfXgkA/P3F9+Wic9yG3/u6l2x8wYD55/bfM0IEkpeCBhap3jOAgGgy+lpqbe8+bzWREevPC6Vj0Pctc96HHdmGNSfNkenL5hRu07A5n9i4SwhhPJSA9m1KA5td62GvJYNuYJYnJYqP0qtf7hV0tKusWDJkAynIj8nJlvAWJ+V9ZlPxgvMfMcIkq6KVBCdDZAasMCcD0gri/ZPhNVvdagNb0qZhARncrM7/f88L4L+667dgoB/w1CLhjVOfkjRG2OZyuEuOVAfeDYkjxRRKfX7bF9+Gt1Lp45mIFqRz7deM5jG6LPEVKc6nVdZraFEA/G+WgbgDOSnuwhBkcpWFEbbaXGmzwiKvUcdBi8k/PPPQDGnHHoCvJZMldKoQAE4fYksf+dEb9EuMFgMHSRn3Nq8PA8aOElBs8heHhQis4GSG2EPIjrS/oWo6rqEFpbYtXU7sEhH17Yt7TU9SCQC6A6J6eHqK313NYU14NE5O3BqPS6QCCAvXv3Yc+ePbBt++nG3efEelB0rwe9AiTFCoIjPaiT2dH9HpwR8uBr5LNycqVMifRg9nhGTBuo7qsibwIkwwnHkrtz9836Xd1C6db012UfgBcA/AvA+298L905/7GWEgDnJzvJgkQAXaxiSu5DK7txILK3DoQtm7Cz/5toTY9YdYdwfBOLtp1bSYJ8YQ+8Ab1rZiG78WTs7PNisrvEFQOD/w/AbxOd5OY9u8Jb1ZKOP9cU4I2GHrBBcCgIIBCTQx5smN0PwLRkkwEAInrVl7kgXsO3ijjHOk3AtuEL7TeKDpYSoZiPyL29eC/vXwzANH01GAzdypLcOftmFaxZKLf26roHH0x3zr9Ww4PyMDwI9xf+7OxsZGdnw7Zt7KzchdbWyNhFCDGxqFevSqIoD/YpRnZ2Dnbu3JHsFvE9yN4ebFtBamhoxL59+1FdU922RRYAYjw4+/qgtgcXPO3rNg/ajo22SnbJSpiHw0fNg+cfEx40AZLhhIRA1zqO2i6lSJZTuwcdMvjgje+lR78Cew8eYhAQYMV6+24iTwTJyHN8KgMDtl6ML4qXoi6v3D3WmIfC7WfAR5m+ePsuM4J9UXRgFvYVvp3oTlnxDjL4fsXODwXJuJtlA04QC+t74MmaAqxqjXx5KKQFpZzRcU67JtEkong2wfFtCY53oPOQhxskyVBhBs/xzBBEj3gONBgMhuMIarWudYqrtsvd+foefKALHhQCzNzpHm9A7GqIz+fDgIH98cUXe1BXW9d+rLCwED6/P24574zMTBT16o19e/ckuk18DzLf7zjqh1KK+B4M2Kg6dAj79u1HY2NTxGdCSihWX4oHdb/PtmNDkFuYwYvQz++E8qAJkAwnJG/9V1bzzPvqhoF5sZBiTFtvBmYOMPNCIcTvAXz4xvfSk23SXOJ1HyKCVAK2ckBSqzS4e56MP46I0PuL01GX8zkyD56MnC/GQPp8MZUKwunROByHclcgaNXH+zjum7P03q9z057z+jFjmSAxsW3eDjNeqUvDzw/0xx47fgK6+0ZKjg8/FmyYTXDLjXpRC+C1eB8w8w6v75/XHqQQAQCv+qVcI4h+4TWYgY+/0X/Ndu/LGgwGw/HDW9eNaJ757+XDkBZYLHblj6EWv+vB9JYApwYXikNZrgcfOAIelAK27URUSNM5L9Hx3r17oa62DpmZmcjJyYH0eNnVo0cuDlUdRDAYdyUrrgdffy6dz7umqR/Ay4To8KBSCvv3H8T27ZUIxL9e28pMhAdnXx88ah7UIADgVWnJNaThQQAfr1nSf7vOhb8qmADJcMKy+M7sQwDGzbyvtocQYiIrrgBh29t3ZOvu91gFoB4J3j61YQkLtuOAbQYTewdKHrUlBSyUlF8ItIRWbjyehUSEoqoZ2NVzQbyxccUAAOm9X28FcErTnvNSwLj8wYOF+Q8cKvgdIDy35hLFNKgdC2CY13kA/uXLXBC/67ZSOzhOKgCho2aGRxnXZXDfyj1/+9DN1fMqx/1LYz4QRD/VGWcwGAzHG4vPmex68K0Pe4gDWRM5t7ECrb5tb1884ch6UFqwbae9NHVnAqV4CCFQ0rcY8O7DBCDkwV69sWtnZbyPE3rw9efSWwGcct41TSkALt+5a3f+rl1f/A4aJSoIXffggqd9cT2olNrhlRKn68HNHw2tHjejUsuDdAJ60ARIhhOexXfm1ABImIOWiNe+nWqf/1jLMgDnJhsnwyucMsA2P00+egnAXYjqaA5Aq/i+vzUXAQS055rZ0h9ZTSehPmNLzEde54YCpXn39AYeeO+/Kpj5Ja+yrcy8KerQHM2pJkorwG2DN+x8dMuIAxaJwpPS0jA8IwtplptZaCuFiuZGfFQb0y/hcwB/AzDv9qGb21MT5lWO6wXgYo35VAB4S3PuBoPBcFyy+KzTuubBean2+ddqeNCSQNi2IWZ+miiJBzXw+1MQaO2EBzOzkJWVjfr6upiPvM4NBUrzgMHIHfiFngdx5D244YPBO0dM2XJACCpMS01DVnYGfKHVM6UUGpuaUV1dG31auwc3fzS03YPjZlQaDybB9EEyGA6PJV4DZGQH70oA3116W/4CAKcCmAlg8WHNQK8nE4qqp8NCOiiyg7anGMKpPuN3rwC4PtkbKvcNoWp/2xRsmC2hl3e9E6EeEdHU1s60amtnnntej/wdl+cXYmJOHjJ8/vbGrj4pMSQzG1/LKwTc3hF/ADAJwMm3D918b3hwFOIG6L0geuLa0lWevTAMBoPhBGaJ1wAp4nhwwRH0oGYT2KKiXrAsCxTp5c55cNsZmh7kdg/Ovj542B6ceVmtNfOy2nPz83rsKCzIR15eDvw+X/tqnJQS2VmZKCzIA6I8uPmjofeGB0chtD246p3SE86DZgXJYDgMbOWstkTyKqlEBIskbHYYwPXLvldYCwBLb8tnAO/kvveb1WlNPff0PDjGn13XXyvtwIHd/u9KMaRGoVbLyUBBzWk4WPgBWDCUEwSzkzQtIpxhT5QLAH37yFuthux1Cxuz112kUiJbTDAzlGP/T/2sR8Kr3cwA0EvjFvN8mQvaH8K1tTMJwAS4b92uBtAzz+cDWb6E36OeaemYU9zvoR5Zb/4q4U0qxwm43cW9sAH8RWOcwWAwnLDYtrPasjQ8aEnYdsiDr4Q8uCDkwQHvrU5LT9vTs7DQn52TredBO8yDcXrcxcPy+VBQWISDB/aDWUApB8ys78HJIQ/2LrYaGuoXNjY1XhRd6tv1oPM/9ZWzuuTBBU/72i8487LaGA/6fD74fFbC71F6ehr6lRY/9ObzWQk9OG5GpfGgByZAMhi6yJSHDxCAW7N9mZ4P89A+pIeWfa9wSZyPX2jJ2O+vzHgLWXX90P+LczzvrcLfXCkGOwqUoAt2ODm1w1GfVY7W1AMQ0gfHVj2ixwx7ojwdwBAAQwGcHPrfoRAYLizhJyJkN41BVuNo2LI2sL/oZWJfE1ipnQz8oG7mwwuiLtmptILa2pn9AVwbOm9o5BACieRfpxB0PYCEYgBwFgCdFvIvX1u6aq/GOIPBYDghmXJhyINZGh509yE9tOyVBB5sbvFXVu5EVlYW+g/o53lvFZ49wQxWytMPAJCTk4P6ujq0trZACAnHsWM9ODmBB4HhQoQ8mJ2DrKxs2HYwsH//fmJwhwcrZx6WB2deVtsfCTxI5O7BSgaROGIeXPVO6QnpQRMgGQxdhEC3WEJeymCQR6UEn7DsFqf17gQfd/REEN5pAswMjqrVpmwFIbw3vRIIPfdPw86SFyFVKnythXnDnij/T3Q8/E8GEGMmEgThi3wgExF8qoe/z+7ra5m5YMMNg+3o84INszOY+VKNzt7rmlXjaaid+X8Apib+AjQ25JIo9hhxq/dFAAB/0hzXzj+3jpwqiOYSaDSASgY/ecXAdS939joGg8FwPEBEt1iWvFSnjLfPZ9ktrRoe1KCt2EM4Sqn2tGuPOaNnURF2Vu6AlBI+nz9v2GQND4Z6BkUf8/n8/j59il0PfhjrwdnXB7U92NjYfNrMy5DUgzqra0LQl+bBkVdunUo+mksSo6FQyQpPrntu4HHnQRMgGQxd4IzfV430S+uPgoTblZo83uaArAwrLRVARGWaHkvuE0QdewH9Qe+V/ujgqP24rUA+7ySDlEA++n5+HYJsAwQJwmOelfCsxANIUA7b/AiAb8fMifkKIvLM7w5yYDiAxz0nr1ewKOEPY17luD4ALtK4xlYA7+jcbF75MAvARZaQP/Nb1ugweY0GcMH8baPfUcyzrhy0TrcqlMFgMBzznHFx1Ui/z/qjEMINTjxXNcjKSI/jwQFLBKFDognaGUWQaP8PKwXSyDlPSUlB376lCAZtAJAAHvM6J1lgQkQ5zIfnwUAgqOdBPREm/GGMm1F5xD047NxyC8BFMkP8zMq1Rof1fhwN4ILRc7a9wzbPWvePQceNB02RBoOhCwhBfxWhJ0CigCUcIoKSdn6cjyKe5Fk13iveKoEYHGpF0BdTnScufpECAQIxAAUk/RLI+40VCYrbjZ3BP/eaS+hNoCSdx5HeozXZZG8AtFLVH/cqzjCvfFifeeXDfgZgBxG9kOLzjY73fbKknEFE8zXuaTAYDMcNQoi/CiHaegh6jiciKChvD2Z5vyhUCYoTOY6TqNdRDP6UlFDmhdZwnZWp+B5kfQ8KrabyWiI8Ih70Ks4w7NzyPsPOLXc96KMXfPm+8OCoHZkhZ5A8vjxIOn+pDQZDB1MePiBSpd8Jf1j6KHHhAMB9+K0f9cK2YErzwwD+umfSLe1dW3Pf+00NEXLSa/ui3+6z4VX0IaiCaHFaI44F0qsCVQOW+YWTgt4V50En2LCVjVblvshjwH1dEu9LEID0WJliZkVEqfVOfQaAO0EozrKaN5XN/vUvvaSiWMHmYPu/K0Q3ag+DCMLn2X4CAHxZaa9HpDrMqxwnmXkrEXkltwcBlFxbump/9AfzyocR3M22/wm3PKoEgBTLByvJW0vF7DhKZVwxcG1rwkEGg8FwnDDlwgMiNSXKgz4NDzZu2BaE7XpwiKiUSgAAIABJREFUwaQODw54r4aIctIz0tGvXz94FX0IBoJoaYnyYCAQqKqq8gsp0bt3b61UNNu20doS8qDHr8PSY2Wq3YMNIQ8CxYJo06hRw7w9qFTbahaUUnCcxHEJEWmtsgHwvf5cWoQHx82o7JQHV71TGuPBYeeWx/Wgr8CCTE/8PWKbHdWiMtbOG3hceNCsIBkMnSfmMaqQvAJmwNeIYErzQACPANjde8UTD/de8cQgAGCmNcxA4Rf/AaHxQI9aQWIAv6ou/Xig429aHkyrRn1edOuF+FjCau/RlPSuGu9QiEg0O81PQ6CaJP2YBF1XmlXtKQUAUNwREAkSnvu5NMmOPsDM52hIAQBejA6O5pUPy51XPux7ADbB7RVyGcLewEmvwhFEkplP15m4wWAwHAfEelB5eFAFEITd4cHZKx7uPXuF60HwGmZGYWEhdFZR4nqwunqg4zjLg4EA6uv0siksy2oPfA6jb23ofBLNzc1PA6gmoh8T0XUpKSlaHgwPiLwawXaCw/JgdHA07Nzy3GHnlsf3IAEizSPF0iLJzvHjQRMgGQydZNn3Cpkp0g0OO4lzosHYWfpx+KEsAN8F8HnvFU+8ImVKAysBy0nTCg4Utz9I9wI4q+y20rt3z7x1N4AzAfy1puca2Faj1tfiFynhE40Pa6ZPCLo6/KG+p7mH53nMHBNcJl390l/xzolz7GGdE9m2ez+7deSFz24d6ZtXPmzivPJhTwLYDeAhuFWNIhCkJzMCanTubzAYDMc6y14p5Oj0csdJ4kFm7GzdHX6ow4OzV7wic2UDg0M9ijQ8qKI8+E7p3bs/O6XDgzU1sO2Yeglx8aekeA9C1zwYCAb1PBgVXCYLEjuR+RXrQdbzoG3bvUdO2XrhyClbfcPOLZ847Nzy5B70exfHAADQ8eNBEyAZDJ2kYNnj/Woz98QcD3IQilX7w4uZYYsAPj/pTdTl7o4ZD3fh5gK/P+O8VH8uCHq/aIektAjAmLLbStub6+0/7c5WADeysH9Y1WuF1hNUkOgIkpLdWuNqMqqpeHUgAwdb05OekzSd7vCIEMOzFaN7ABjsdRIzA8zTACwEcxMTfcLAjQDSEp3jtXrUfl1Cufe0DQaD4din4OZl/WpFfczxYDAIpaI8qGx83rQFdSp2PNo82Nd/XuqQ1Pamp16wCvPgO2EeLD/N9SDzD6sOHtTzoBDaQZIX0Wl4bupc8j1RydLpDpMID44+o6IHSM+DrFwPMriJWugTqOQeFKmaHhR03HjQBEgGQ+eZurPfCgQpNo3WZhtBDqI2dS+2DlyCtWP/iYacmBTeGEiiM6llvwZwftltsbnB+0+7k/efdueDzVk7L2/M2qF1MUtYEB77NXXeWMk417hz5WUJi0owMxyOFyDFHV8J9+semWhAFNFvzmZrvd0Kf4tHZEEIQAgwUcKbagVIwIYrBq5r8BxoMBgMxwdTd2buQlDF/vJv2zaCwSBqW+uwtakCa5s+QwM3eV6QUjRXIVxcD8bZI7O//DTeX37ag83NzZc3NuplU1iWBeGx/1fLg3GusXPnF0lX1hwn1oMJhh+eB1nPgyosYCOQJYIColGAWinhXUWKRjjhYMO6eQOPGw+aAMlg6DxTHV8A60ctQE3GHtjkLqE7sNGYdhBbhv0bW0e8hbq8Xbplqd1x5P28Y2ZOt9J+XnZb8soyB07/7xcbM7dfqCjoeVECwS/98eZqw801fhmMhV7XEYjdP/T+/iG49cNrUBNIiRCEww6CHIh7nbCUuzoATwKYDmBATs7iu3JyFn8GoB5wn9MKBCf0RyEiiIkUA8WJZqNg5sgAqeNcQIiECeo6+8YIeN5zkMFgMBw/THWEg/U5G1GDOtjKTa9zlINGuxFbAtuwNViBOo67ahQfgpYzmZnTM9J+XuZRYe3A56e/2NjYcKFKVPIu/NZE8KfELQDU4UFoeDDO/qGGxiZsq6hEMGhHetBxEAjEX10KS7mL8ODiF3LuWvxCTrsHAYBE6CWrBKI6jkR4kDQ9GG8vGYEgAgIUjPMDIje49UQeXx40fZAMhs7jNnATChWD3wPgxjbCSp437InUygaofm9uXvzIIood51/96rjHdzwnBK71GitIAIynAGyGK4NNACo23jIkCAAjn9laCo2+CRISNiLzvpfsG4ZTXr0L04o24fySNZhWtBHZ/vi54QGlsHTfcLy9Z8zuVYcGXLfs0pnvxhlWy0C2cuuPtx9kuG/dBBgUHSAp9ToTMSUvsZT8iyMCM0f4W3v/EVG8r8NgMBiOV0IeBCpytgMMUAtBtAoIdRjv3vUUWv3ey5oeXD3u1XFn7nhOCOHtQTcbINaDy0MenKLpQSFhO5GOa2hoxIaN5cjISEdOdhYyMtITVsWzbRuNjU1obGzc3dzSet2WT0bE9SAI2STjlB8XDHZvH+FBpdTrJJJ7kD1iSWolsI8jfk66+4+ONw+aAMlg6AQFyx4vADAs+jgT4DiA43BbVlaQiLTqcLahGVpVdeaaraq1UAgJ4b1YzGki7eaym/omejruhLtBM2l37sT3ISzdNwxL9w2DRQ4uKCnDNwe/h9KMKkhiHAqk4809o/DH8rNRE8wEgGJmfqf/s2v+sH3OmO9ETBRoig6OOm5DUG6QVBB+eM7AdXXPbh/zIogui/vFM7s/wGTEuZ9Oeh3cpogfe44yGAyG44CCm5fFepAATmM4aQ4c24FoFhAtIkjopAf1XjJ2zoOtrYVCSs9GtgA4LT3t5rLFh+lBKZBoe60b+LjphtnZWcjL7REq2U2wbRv19Q04dKimbRWnmMHv9B+35g/bV0V6EEBT3OAIoWMWg21EeHDd+wPrxpyx/UWSiT0YL90v4togt3diWGyns/8Ix6EHTYBkMHSOKV4DlAKYMd2yMAXA7QD66lxYUOL83jYYXK1zLQAY/sfynoLELIb7j8cep5okwRHWf2MQj3xm64cArkh2kXj7kKKxWeLlnafg5Z2nuAdEfCkSERh8e/9n1zyzfc6YT9qOOyzyKNnz2F3pGRVz3HGuYuBtEE0Pl4pWcJSAtgApPDOEEfNjXH5xv1UtMBgMhq8GyT1oASpLgf083aqzOu9BD5g74cHTynsKIWaFGrF6rXTUJAmOsH7ZIB45RcODHnuZ2qirq0ddXUcKYjxHE0IeHLfmme2rOjwIwXmURIREBBaxHnQc5yoQ3qY4HnRsTQ9GfYdEinA37KQC8MMNoIIAmiOGLV/1p37HlQfNHiSDoXNM1RhTzYyP9ky65bcABgK4HMD7R+LmzNyZEpmXW8InQO2V7xJfF/yUxvU+9BogWIAVd6YMadI3hqEH+OMAUPjMB+nnvPaP+5hQqHHVmD4Pcwatd+b0X3MmHGcqO8577DiNbNuAbeuVD48ds0sSORKAJECE/kiKaVG+xPviBoPBcNyg58EU/mjPgklfvgd9PhE6z+u6R8aDQqAtINMl2QvM0GeuB0/5IH3AtNX3kSBPDxIhxoPrlw1y1izpf6bjOFMdx3nPsZ1G27ZhR+2PSgSDIwVH2EUZ5CAfQBYBKQSkEZBNQB7Cxy7xvPgxhgmQDIbOoSOGZQenzFUAsGfSLfaeSbe8sGfSLdMAjAfwV2aOuyvT9nk3l2bgU92JChLfakspYMT2G2o77sBpYOJ7NC75gdcAIoJUErABdthbZDrZFIRehc988C0AWy7u/9kdmtu8Pk/0wZyB65bNGbB2OpTKA/MlcDffejfMcPcfMdzSsrPT/Sm3SyKZINMv3CFLtGZsMBgMxwd6HvzzFNeDCybZexZMemHPgkneHtR7FOt7UIR5MEEBglBqWQPzEfSgkG4PQaXhQT16FZ7ierAgJ/8OzWp/CT24bunAZWvfGzBdKZXHSt+DbDFC9ZAWAZid0s9/O+WRRDwx+yi8Ve0SnQkfS5gAyWDQpGDZ45lwH+5exH1LtmfSLav2TLrlBiIqYVYvhb+tYWY0ZsX2VgqHmaHY+ZPOXIf/sbyvFFbH8jq1B0NQUf+AcG/ZTX11lr5XM3Oz16D2fkgKW+GgD4AZAJ6Fm4McidYzngsB/F+vtLrelw1Yo3MCntxy6havMXMGrQ/MGbT+5TmD1l8CoI+PxI9SSWzzxUtbYA6A+UEAg68dsvHca4dsXCiJ5iZzFBFAQADHWd61wWAwJKLg5mWH58EFk1btWTDJ9WALv8ROlAed5OXA3SBH04OnlfeVlhWRZta2z0YpFfEHwL1li4+gBzsKMGwFe3hQC9eDfsvXuzA3X+uMvc0HPD24ftmgwPplg15ev2zQJQD6iDT6kcihbSItVm4sOcCprgc3vjHk3I1vDFlIGTQ3bnDUhp8A//HpQbMHyWDQZzLaFgbafonmODtOPNII9ky6ZT+AS4uWP/ptZjzqRgmMg71WI6e6H3wqfi+2muztqOz9zgHgvzwnKkj+ICYPOvQMC0+3Y+ZGAv3R84IA1n9jUHDY0+XVEjJhszggomHsf+64cWwzgHcBvNv/mTW3A/g6gJsAnKJzTwBQrCQAzB26HH7h3VBvW30+frvh7Ft/u2HFH/ZeNSmpjCprZ54M0BVnFBXPhRB9KVSpLuDYTeU1hw5VtbbsA/MrYP7dnCEbI8xNbi8Kz+mY/UcGg+ErhOtBCaCn+xYIQQYOIlqFyT24YJLrwQnLv82SH4UFIAgcVAeR0zsbPit+bYea+lpUbq88APTynKiQ8geJKsVFvaBsJNL04LJBwWGTy6ul9PCgDPPgmjAPjuuiB+F6sHdBL7fqrAfNdgt2N++5tdf5e/6w97XkHpz5ZO3JZOGK4v8qmisyRF8SrgftQ07ToTdqDrVsbd3Hkl9hyb/buGhIZATr0/CghW2r/vf42n8EmADJYOgMU0ECRBLRmxtZ2QjZoRlAmc7F9k2+7bGi5Y9mAPxbAIBgbB+8CCXbzkRasEfH9aFQm1WJff0+grDFUJ3rSyG/qfk1PVp2U986nYH9nlx9il/4+yQSTvu93QDp7ztuGvtW+PHt3xhTC+BPAP7U/5k1owDcAMJ34PEccuCgKK0eVw3UWz26a9WFAHAS3Ejy3vDPKmtn+uFuML4g9GcwWRYorLoRESHF8qWPzO+Zyo5zRWnWmx8luFWe11wIqNCatMFgMBwfTEUxgQYRyArzYBODNyvALZ+g78FPJz9WNGF5Bhz8tu3Y9r07UFJQjLTUjhiEmVHbUIt9tfsgLE0PSvlNza/p0bLFmh4cs/oUv1/DgzLkwTVRHlwV5sFxIQ9Cw4PkwGf50LNHQbJhHfdp2Akk8ODMJ2tjPGgVWhHV6IgIvnwrvefV+alOnXPFm5dlxfeg9PYg5PHpQRMgGQy6kLhUiNj/ZIgIEBbY7Sj+0cEpc7X6MwDAvsm3/c4NkvAzAHD8Ldgx9A34mnKQ3lAIFoz6HpVgy03XFkKmel1z1ONbT7WEr4fXOGZ2iOhBnXn2e3K1BeBPDntXuSEipMrUpG/jtn9jzDoAP+g/b80cIHnRBcUKN5/8EfzS+97v7xuE1dXtxZLu7vXPFc99/LW76gCcC1cE5yAsK5qkjAiOor4OASFeBRBjpFcrx2URIdNrPgS86Dlpg8FgOF7oQ5eKk2OfmZROwEgB/kQBLfjo4J+n6Hvw08m/K5qwvMOD7GDHgUr4pA/pKelgMOqb69tXfYRfeHtw6tZTLd8R9uCYkAc1qp4SEVJTPTy4KuTBcRoeJIU++b10SpWjNlCHRrt9oefuXueveG7EpUMTelD2kAlLdZMkIdLie3DcTyuzIMjTg7COTw+aAMlg0KBg2eN+Er7hiT4nIoAkmJ2lXbj8LwBkAPhR24Fgei1q02sjBrlVcbzfmhHEJTobOBn8+qqbSvdpzvF2SXJcWPpcUiTJ0wEk/V4MeG5tBpF3JZ78lHp8feBqrfs+tnkq/CKIyQXl+FrvNSmjcivXMCOdEpUI8kpVIMqvrD+rsDTrrQNRnwzVmQ/R8bcx1WAwGOJRcPMyP50qEnvQR8BAAm/gI+NBJ4japigPukUPvD0oND3I/PqqdzrhQSnHea0etSGlhgfHrc0geHtQWhJFuRoFXAF80bQPRISMgnT06JuTkl6YtoaZ0xM1iBXpyb9PlEL5Z/2zvvCtq7K65EH4aYnWuGMMEyAZDBoQyQnJuk+7gwTATqfLmO6bfBsXLX/0DqWcEULIcxONU8pZWjX1e555vEQY7DWGmUGgH+jMb8BTa4f5hO/+trxnjV4SAHA1gF8n+rDomeUkIF9JtTxfBOKbQ5Yj1fKubLS7MRPfO3khhuZ8AaL2/PKMpCd5/UiJABJTEbsSlPCXhDaY0UrUtdSCRdtGWQDypBADhRC1Pim3TCteGbfqk8FgMBwN6CSaQOkeD80sAsCd9+Cnk7lowvI7lK1GCEsk9qCtllZ9NtXbg9D0IGl6cNzaYT6f7/7winiH7cFTlpOQ8pVUx9uDRQWFWqtHLbIVPUcUIq0oFZRBbXuPk3qQ/N4eFKnUJQ+CuRVEXfLgqD9vs0DIExlioMgQtTJXblk5pfioedAESAaDDkT9NUcm2q+SlFCQdIHj2G8LIc+MfvAq5exWyjlP51rMeAvAJcnHqPLVt/T3rHADAALiOUEdFR8UlE5D2IQP5MJnPiAB+bkgMcjrIj18DbjmJK1UduT5qpDdQ3n12o1Aq1SqELvjHB0W51jUtbHpgtJVcXMxFm4ZkS+AmwXRdAD5BDQQUSrcNIYCIUSuP6zzu2JW71SOe4GIrj6zb1nXutoaDAbD4ZBJ/T3HuL/rd82DbpB0gRNw3hY+EevBoNqtgkrPg9DwoFLlq5doelCI54QI86BS0FhJSuzBU0IeZG8PCinQM09v7xEVE9IL0uCdAB52joYHRXrXPAiiTav+pzSus0b875Z8SNxM0vUgRJQHM0SuLOxI/+Mgq3GvV75AFl1ddnbfbvegCZAMBi3Iu0kREDw4ZW5jV++wb/JtCsCM/PcfmiuE+A5AJQCqmdULROLHh6b9QOuBIEj8RbH6vSARtwwQM4PBN+lca9BT63MEibHR52uU5074hk9AfF9ADErWGK+NOYOXId3yfmHkKAcOdy440oWI4n0t3m/OgA1t/7JwywgJt2LR1wCcbxFNTNQxXgqBFF/kj04QCSHlFbbjDAYwTnfuBoPBcMTIhk6zvuDBP0/pugc/nex6cPj7c4UU34FACRjV7PALJOnHhzZN0/OgEH9RSv1eiCQeZE0PTlifI0QcD3pzRDyYX5iLmKq0cXDSFFQme+VNRKLXVxAkD9+DIx7ZEuFBSqGJJONPQGQJ+Iojf3TkIyF98gqn7uh40ARIBoMGobcaXhw6Eveqmvr9xxHqmt0VVt/Sr2X049suIkGvR6cFMjMc5Tyydu6AZTrXUlAjovcdKSjP9AIGJ0wrAOh7gPdbqxx/E64brDVNBGzvFLzDIN5mY40UO965cMuIG+DK4CwAuQBgESFRcEQA/Fbix7Il5dh3KsfNnVG6qst/PwwGg6ErkKXhQXGEPLhh6uF58N1+LaOnbbuIKIEHHeeRte9pelCpEdGrRUppeJA1PMjJPSilQEHPXJ1pwi6w3d/qNYMedxraI7vmQeKdIx7ZEuNBSiVQov5JFmD1SuxBmS3Hjnu1cu6qC0q71YOmUazBoEf7grVb3MyClD4QRZTFPCJiOBKsnTtwka3siY5ylilWLYpVk6OcCkfZ566dO+C7uteRJL+Id9xhJ+EbNMVq08brhzyb6JoUSsLwenP28/HPwy+9+x45yoHqptUjAKgNpEREX69WjksDMNDrPLbtOwA8BeBKtEkBSBgcAYAlpU66w81eAwwGg6EbCHmQQRlbIfJWQBYsBaXtBOA+q0kcQx5cOnCRbdsTHcdZppRqUUo1OY5T4dj2uWvf64QHZQIPOkk8qNSmjcsP34O9TyqC0PhV3UlTUOncbb/VB9Ea4cFxP63U8qCdGetBSCQOjgDIXIlEK0vtiO73oFlBMhj0yAQIluVHWBoypPSBWcG2A2BWDV/i/GJYN3dgGYCph3ONLTeM2H7SXz6rESQiyqW2rSJJSBAIoQarrKBeBHCFx2UDgPcK0uQirdRwBEOrR2673SPPZUuv37i39d0WEJoBVM4qvGTeN/u/pFMeKeZQsuAIcAMkL4iop+cgg8FgOPJkwqqH1WsRREZl+0GZ/wk40AP2/jPBjQOPLQ8uPQIeXDli+0kTP6sRIsqDoVUkGXqx1e5B1QkPeqwgZWZkABoJEnaB7UpQu7h6CM0VpDV4YWOPdze3AMFmgCuzBk+aV/D56Z5ns4z1YNLgR7hlx70gq/s9aFaQDAY9MqKDozaIBHy+VFhWyoCST5/vf/Sn1r0w87fjvSVjMGy2EVCBvUEV/GWQg/kbrx9y+cbrh3jEKfQmkPzNWWnGfqRorR4pOBx6c+k5ugPFwGe1vbXG2sKSJCmDBBWQoPGNKv0Br3M089Mj0Fw9AjNv7/TFDQaD4bDhjOjgqA3y18BX8hKsvs8PKPn08v5Hf27dS0IPMsO2bQQCgb3BYPCXwWAwf+PyIZdvXH74HvSlW5C296/pKjW0egS4C3k6O6bhOrxJNHkPBABaJYn2ZhBVFRAdGq98u709CEbcek5JvjMyR2P1CAAHu9+DJkAyGDQQwhodLziKHCMLAGws+fT5e0o+fV5nz9JxwdYbR/7dYecaxao5/LhiVadYfYeI+m3+5tB7yr85rFrnegR6CUi+glQXTNMKMoJOZAGHZA+0ZtuHt/aMwk/WXIoz3/4+7lp9sc50YXPkz71v2l7vkxLMPdnXpLN6FOIPugMNBoPhSCF6rBodLziKGJO+M+TBy+8p+fTyr44HPx35d8dxrlEqyoNK1SkV8uCKofeUf9wJD3LyAMkJKDfI8CBY4ES+IawDkKC2kbIVanfVoXLbLqz1rcN233ad6YIRWRvD31DkfY6MX9ApmQdlnqYHRfd70KTYGQwaEFEfzaGpAO4FcH3Jp89/Z9eEKxd147SOGttuHPX3fk+ufl6SnEig8Qrq7YobR3/exct94pV3XRPIQrNtId2XuGCRoxQcFbnKROgIkhwm1AbTsK2hJ+ZVTMGHB0+GwxIMRqvTjIJUvTdnQRUZdo3PWad1XjwU4vfPkEJ4pt8BgGKusKR8ocsTMBgMhi5CKVVd8ODl39k1Yf5Xw4Nlo/7eb8zq56WUE4lovFLq7YpV3edBZSs4loJlJw4aVIqCyojKtmAA1QBSAPYznKCD1oYADlZUoWF/I8DucXuoDT/iFvmLvU9Unl9azcneJyWatg2wxTH7kESWcJsNe8BBrpBZ3e9BEyAZDBoQaZX5DuckAG+UfPr8SwC+v2vClTu6YVpHlR03jXUArAj96TJ7vvEfe4v/tvIg3F4HCXlk/Vl859g34vbnZWa02pHJ1g5T5eK9I0rW1vQT62r6ory+NwIq8cPfJ/TaKARV5FM+36/xgjDJGzKbGRKR+5F8mqtHguiOacUru6sehcFgMCSErMYuevDykAfnH/8eXHOEPPjJf+wtnuDtwX37DnCf/CKKF0wxMQK97YhVGmaurKuqL2mqaxbN9c1oaWiNv2ITAKAQUWgqGdErSFZzjwQjw86Js/+o/bNWBlIiizXIfD0Pko/uWDmluNs9aAIkg0EDZn4VwJldOPUSAF8r+fT5XwK4f9eEKzsrmK8kBNoFDzH8Y9vUph+OfnWeJeQt4VGSYoWAHWxm5o/gNiRcAWDFKW/dG2SFLwSEX2cOkvQCpOgUO0nee6OYGamWD9mpqbCEhK0U6lqa0WIHwXCDJOGuJAUsIaqFEN75CkAFgJe0Jm0wGAxHGG4teBWZWw/Dg5eHPDjfeBB6HqzeXdtUNLBwnmwQt5AT5kGLESyymzk10oOf7dgY5AZ8IWo8POhDpzbZRAdIOiezpeDr7SB1CENmOFDNEs0bBYJ7hbuK1cJgySBBAZElqkXqseVBEyAZDBowq4eVUj8VQuR04fQ0AP8PHWl3/z7C0zvuIKKNAMYmG8Pg+sKMRbfubzjnHiL6BYAsMOqCjv1nBq8tzlkc9cRefhcR/MycNK+bQ0UdfNI7QLIVgaOu1WBnIE/WJTwnw7LQIz0zZlUow+9HUyCA/Q11qxh4QwGLwPxRlt//EoDzPScDPDiteGW3dw83GAyGeHBr4cOqNf+nIqXqMD14+Xd2TZhvPMh6Hlz0SMat5/yg4R6yQx4UXGfnOX9mH9Yu/u+cCCcU3Yq7KAN+bmJQIIkH810Pigzvnr4MB6DIBRvla4IMJojBJMMa0oLM05shsyJT8/zFQGBPFuqWiFVw6A04WMQOf+Tvr+/BlVOKj4oHTYBkMGiwd9J1qteKZycB1sdCiOwuXmYwgEUlnz7/Ity0u+S7Xb/a/BPA1ckGKFYrAKBn5r8PAPh2srFF/1qewsz/n7vQxOAEm18ZjKAKgMGwhPdKUJBjl/z/vW8qri59LeKYJEKWz48snx9SxH+zRkTISElBiZXX02E1b2LhBxuW7p44HHpSqAbwF41xBoPB0C3svfCbqtdrLZPQY/XHIvXgYXrw8pAH5xsPJkGR68F/P5jp7cFbl6cwQh7MY/ABgJw4HrQYwfEBsBUEpXvXEFcxq0dAbd/lKNh8bsQxynDgH90M/6hmiIz4GXBEQEqfeuRdktdTBbPmfdCvcMPEZbuPSQ+aKnYGgyZ7J83ZTISeSjlPMXOCGjGJaWswK4T/UiGsbX3LXpzft2z+Cfnf4M454xc4ytmd6HNmZgJpN/IDcBURucvzBIAYbf8AaP93Bbvt2NWzSz/7kddFbRX743n9wCxsrBsAAEiREoWpaSjJyEKPlNSEwVE4UohiACtXHjj9BgA/0Pz6HptWvNL7VZ/BYDB0I3vP/9ZmkoGeqqn4KWbRKQ8SGFNpH24V5fiZWHPpXFG+7btlp8y/oGzGienBsvELHEriQXTBgwh50AJQyOB0BlOYBzMZarANzmZA2lf3pP6eHoxNrwMRe6NmAAAgAElEQVQaij9AU8E2AAzZJ4C082qRdVMVUk9tShgchSP89cUArzx9x4FOeXDllOKj5sET8i+lwdBVdk/8eusXp1x9ExGdBGC+7nluYOQDUVtDOSmFkJcRyc/7lr2Y3o1TPmZh8HTFqirmOLNy2Lmucs44rQ29Rf9aTsz8k4iDBECwGyiRctMDBMNWDgA8XXvNjH/M7rvBsxOt4thHpF8Esap+KtJ9vZ3e6ZnI8Pm1+he1ESrOkAbgKb+0btA4JQBT2ttgMBwj7D71qdYvpv7+JiKl7UE/HNwiPscVohIjqBb5FMBIqpVniP2XnU77P//vsnEnrgcRx4Ng5ZBzXWWZpgdvXU4sozxoAchjoDeD+yigmIEeDLs05MErz/tHTwzw9GC8AIlIInjGcqTeWOlkXlUD/8mtIO1OFQDJIABOA/CUlSdv0GhkeNQ9aAIkg6EL7JpwZeWuCVdeAeAcAEnLfLpBUfwnB5EYCODpIz/DY5+dc8ZvCTrBXrayf+so51NHOZ/byn7aVvbgnXPGz9O9DjOfFQpYY6GOP4oVmNUhAD8KnedZzCHTasGwrD0AgJ4ptZg74G28cOpDuGvoAvRMbeqEDuJjSSlSfT6vAOvZacUrNZovGQwGw9Fj14T5lbsmzNfy4DTaj5FUG/ezk6l+oA1xYnqwbPyWoAz2ssn+rUPOpw45n9tkP20Le/DOsk54UPJZ5CTwoAj9IUClK3CPMA/C24OWlYr0dLexus+XiV69JmP48BtRWnoWUnLSDsODrvdkhhS+Ip9Xie9nV04pPqoepK50fDcYDB2UfPp8CoAfArgH7spABEIkX2Fg5oBSgdRdE640/zF2gcLnP9gkSHg2ZQjaQSh2bqq55synAGBH49fulkL8v2TnMDNqA4T1tYUYmlUJiw7/R6RYIeBEvpFjZgQdG7aKuy9q5LTilZ8d9o0NBoOhmyj59PKEHrSgcK9YjYwklUMPsT/wczUmddeE+caDXaDw+x9sEk0aHjwpCNXLuanmOteDX1tZfbeoS03uQb+N4KhdqKuvQFp6gXZp8GQoxw+nsXfkfZhhVztQjfE9uHJK8VH1oFlBMhgOk10TrmzdNeHKXwEYBuDFyE9JJ/3KD/PfYpco+OeyqTrBETNDsfMBwjZ4svt9T34egFRpY0LuF/AL/TS6ZMQLgogIfssHv4ypm/O6CY4MBsOxzq4J81t3TZgf14P90ZA0OAKATASNB7tIwT3LpuoER+xjqMJIDyLF8W6LIRlC+JCTPRhSpBzWXNtQLbF9lIgIvjwLVl7MotTrRzs4AsxfRoPhiLFrwpU7dk248jIA5wLYAmg3YeNdE6405Zu7xn/rDHKUowDMrbnmzPC3k696nadCK+xEBL+0IA/jzRkzI+DY7deMhyUlfJFB0v1dvqHBYDAcZXZNmL9j14T5ER48ieo9z1Mg3jVhvvFgV1CaHuzlKEjMrbkuzIM+x9ODnOm2rSIiWDIDgrRaDca/FhPspgKwE5Ns047MkLB6RARJX4oHTYBkMBxhdk24chGAUQDuIdKoJQ3e391z+qpCwHivMcwMpZxHaq45c0PURysV86Gk50UFM5aQSXssJbqOrRy0egRHbfikbFt1LAOwpFM3MxgMhmOAXRPmt3twCNV5erAGfuPBLkK2hgeJoQqdR2qui/UgpwcSexAMzmuOOCZFGqiT4QMrAac1G3ZDH7Cd4TleZsm2PUlfmgdNgGQwdAO7JlzZImXKAwB5NxkAaW/ENERDAa8RilU9g++KPt4/YxEr5hnc1jk2ClupmICGiGBplPIOsZaZb2917PkJ9hYlJLRSdf+04pUmH99gMByX7Jowv+UR+ckDg9Dg6cFM2MaDXYU1PFig6jk91oOLhhYx92iZwT4nrqRUSR04O/LyRAQhUnVnt5aVuN1uKJ6vWnMB1m+/KlIIAO5fOaX4S/GgCZAMhu7jVCLvtWgiev1oTOZYZHTFpkFjKjY/NKZi092jKzad3tnzGWpl0s+Zwax+WHPNmc3xPu+fsWhNUKmJtlLVKrRi5CiFVtuGk2C1x2NPmQO37O0ZAMaOynvvUQBXArgVQIvGlxS6B2rRiTLyBoPBcIxyKpH3fs9Msk9YD86sGD1oZsWYh2ZWjLl7ZsXoznswzcODYHCW+mHNdfE9uGho0Ro1oHqi6l1fzekBcGoQqkcz7KEHwL0a4l7TY/tAhAffyx//KCA67UFI+lI9qB/KGQyGznKmxpgAgOXdPZFjjdEVm1IEiVVSWMPaAg5mxpjtm8uY+WtrBww9oHcl+hazOp9IxBWwYrWy6uvTnkh2hYGZ/161pf7s4QBeBzCuU19IBwcBPA7gjyNzl+wM/2Bi4QcM4PGVB07/CMDzADSKSmD+tOKVnW5GbDAYDMcYxoMJmFkxOgUkVkHICA/O3D6mDMxfWzxgrZ4HHfoWp6rzqSWBBwvVyqo7k3vw3yV9V51dv2U4So6MB5fkTozw4Af9ChnA46fvOKDtQTDPXzml+EvzoFlBMhi6j+kaY1bsGDu7qbsnciwxYss6IUhsFmFSANyVGSms8ULIjeMrt8aWuInDwaum7HeUc75iFfE9ZGY4yvkMwGk61zkp6829AM5g5re9xkal3ZUBuAFA35G5S+6ODo7CmVj4wVoAE5n5n8mvr1oB/Fhn3gaDwXCMM11jzIpLx+44oTw4Y8sIARKbSYgYD5KQ4yHExlmV4/U8+Msp+52ezvkqNcqDYDi5zmdQeh58M+skbQ8yR2RNtntwSe7Eu6ODo3A+6FfoelB5eDDIX7oHTR8kg6Eb6Ld6QRqAGniXkr53x9jZPz0KUzpmGLVtw//4rJSfJBvDzJuI6PSy0kEJN4+Gk/f39wSRuIOIJgCswPjfg1+fuqyzc9tSf7ZfsVoqSExKMC80BR0sOTgcH1YNfvonw166YWTukk4/RFfsm3yPILo3Ol2PmdlWzkWTe63wrCxkMBgMxzIvru6n7cFLx+44oTw4Y9uo/xGWT8uDb5eW6XnwwfcE1Ys7KEgTIFiB8L8Hf9F5D55dv8Wv2FkqSCbwoELArkZd7UbU1u1+urT0nBuW5E7stAcnl++7h3x0L0W10GCH2WlUF60Y1etL9aAJkAyGbqDf6gUzACzWGDpjx9jZ73b3fI4VRmxZ109KX7klfTp1QlcBOKusdFBVd88rnOJ3P7jpngHzn5iVt57CA5igIry4Zyz+tns6DrZmwWm1f1p9yfR7u3qf5XsnDSTQY0Q0HoDNzGsU8/Wn9V5hqjkZDIbjnhdX99P24KVjd5wwHpyxZUQ/krKcpKXtwbdLy46qB7/xUfFNzcV3PVGTcxaF7zdSqhX1B19A08F5CASbsN+e9tPq6fO67MFJ6/YOJInHyAp50OE1HOTrV4zu/aV70OxBMhi6h+kaYwIAPurmeRwzjNiyjgA8DmbdJgrjACweX7l1VlnpoIPdOLV2+iz5eC5J/59+ufNaPL63Ghfmr0CeVY/1jf3wdvVYNDUDrBgkAJKUczj3mtxrxTYAXztCUzcYDIZjjekaY04oD87YMoIAPM4Mv2bDiHEAFs+qHD/r7dKyo+LBH3/cZ+4ISX+S+36N5oNPYGePC9Eqc5HTsgFFtW9hcbAZDcywCEil/YflwRWjeh2zHjQBksHQPUzXGPPRjrGz41aV+YpyA4CzGZ1atR4D4J3xlVtnlpUO0izc0DVK3l/5TRL0x7ZVowPBXDy1N/K5bfkVgi3unlESlN2d8zEYDIbjnOkaYz66dOyOE86D6IIHZ1WOn/l2aVm3evD+lSXfzKcOD6Y5BzGk6i8dAwgYb1l4I+h60E/VX1kPmiINBsMRpt/qBekA4ubuRnHCpBSM2LKuGMCDAKCUA9W5vkCjALw7vnJrUXfMDQBK3l/5XQB/IY8a3hG50owTtiytwWAwJOPF1f2MB6OYsWVEuwehFLgLHpxVOb7bPHj/yhItDxYRIaX9/4mvrAfNCpLBcOSZjNCmVCEkAALAbT15wsctOfpTO/qEUuv+CKB9Kd52AvBRildPoYjLwA2SZpSVDtqbaNCQ9RsFGL8nQReDkA/GQVb8Lztg/2jbhFExr+xK3l9JAH4C4Bc6k2gvxarYhqCFupM3GAyGE4zJAPxWAMisFRAOwAJoTWW0pjEcX/u4JV/aDI8iodS6CA+yYwPk67QHZ1WOn/F2aVlCD/5k4xAhgd+3EF3sAPkSOJjG/K++tv2jG0dti/Hg/StLuuRBm9NsSc1fWQ+aIg0GwxGm3+oF9wph3SOEFffBFwqUFJF4jIhWA1gH4LMtI85uPOqTjcOw8pWFRPwXAFMApAA4yEyvAXTfxiETKjp7vRFb1s0B8Lfo40QCPqtTQRIAbAYwo6x00BfRH5y05rN0KeV6YYkB0Z8pR211gs7wLWNHtLcEDwVHDwD4vu7NmRmtja1gW11adeG0lzozcYPBYDhReHF1v3vz94h78vYKCI59xtsWI5DKKq2RHiPu8OCIG7ccEx78dfmwwt5Ef/EDU1KAlAbgYDXzazuA+x4asrHTHpyxZURcD4IIZHUqSAJCHny7tCzGg/d/dlL6ISnX14tYD2YqtbWn4wz//ogt7R4MBUed8mALM+a1OmhSpZfumPbxV9aDJkAyGI4wA9a+uklKv3cTtEgYwFa4klgHYDuAjwFs2DLi7KP2H+nwzz8ZBWAFEdKiP1OKAGAtQC8DWABg1cYhE5LObcSWdb0AbACQG+/zrgRJzNgN4NRV/QbtCj9+8meb3hVSTE90nh2w3/589PCzAKDk/ZUSbkO7G7VvDCDYGgzaLfaNVRdMfbYz5xkMBsOJxDtvDthUtEsengdtbAfh4/+/vbuPseus7wT+e+6dGSfOSxNRki1ENiYzgz2DIdPYy0vjkI43VLGaFARElapFqC1ayosQZVAVqd1/VupWlRf+CGXZFUL9g9L+0xUordOGYEKKqLLe2EnKzLieafGEcUmgzavjeO7LefaPeZJ47Hk5d2onNv58pJGVmefce+5Ec7/3e85znhPNmBr9zdlXLQf/dGZk+8aIhy5P6YwcnKmqOBrxWES8nINfGJ5edd/GZ0dXzcH1lKSc87GIeOe3Nx9akoN3HX7Ld443GrestN21nc79vz8yc2tExN7/d926cvD/tqv2gc41v/nDXY/8TOegggRn0eZHvrmxr++SF3o8GnSKFBGNOOWu2gsR+YspNT43MzJ+Tv9YR2cPXJ1zPJ5SXL7SmMWS9PJrezwWA+IbEfF308M3LrnjdZla95cR8f7VnnedJelERLrx0OY3H46I2Hb4yK6IeHDVbaocrRdb72+dWLg3Ir4WER+s/YQRUXWrp9oL7e0//ZVfOuOoHQCLvnlg88YtU30vNLvrzMGFiDgZkcr2uZEXYiC+mE6mz41MzJzTHNw3O3r1izk/fmlKK+bggaqKZ175zyU5+IXh6SU5WKbWrZmD6yxJJyLixm9vPnQ4IuJTR7atmYMDOcdoq/X+pxZa68rBn1TVU99pt7cf+KWf/sznoIIEZ9GWx/76pmaz/+/Wt3WKlJrL/iTn/GBKaXxmZLz779i9FY3OHhjIOb6VUty82ricI3Jedm2XZyLir2IxJP52evjG46Oz/3BnRKx6t+yXpNT4fn/fhremVH9luJzjJ4c2X3/ttsNHLs05P5pSGlp9fI6FFxaebp9sPxY5v6fu80RE5Cr/fUTsPvaenRfTaksAPfvuvVtu+vkfN9eXgycj0onlS0Ju5gdTN42PTMyckxzcNzs6UOX8rUZKq+bgkznHY8t/dl6Sg18Ynj4+PjtaOwcjpe+nvv4eczD/5NubD137qSPbLo2cH401cjDlHG9bWHj6uXb7sSpHTzl4Ii/m4H/deeyiyEEFCc6iLY/tu73Z7FvnRYvNVY8e5ZzvSSl9YGZkvL3ioHUYnT2QIuIrUeM0+yoF6VQLEfFgSv3vSqschTvF8xExumFg43+IiPsi4qoa25T9Sb9z8sXO9RExsfbYHK0Trai6VXQWevoVfj0iPjK/a8dZ/b0D/Cz63j1bbr/6p83ec7CKiGciUqySgynfk3L6wMjEzFl9P963eKanVg4+l3M8tPZn54VWxIOHU3pX7iEHGwMb1pGD+XdGFk7WysFGzvG2Vis6VRXPtzt1nyKi5ODEjvmLJgct8w1nUVV17sunLVVXT1rz1HpK6faI+MbQ1P6N69u7Ff1e9DgHeQ0bUmreWrMcRUR8dnJw+48Obrr+QETsjoin6z5Rzvm3I+J364ytOov/WxrNRjT7lz9Tt4wvR8R/Vo4A6nnqmuq+Tt86crC1ejmKiEh5MQen9g69ZjlY8/TVhh+ndGvNchQR8dn9g5M/un/TwZ5zsNlDDl5VVZEior/RiEubveXgxVSOIhQkOKvmbrhjIefuvb1vWXve8Z6IuHdoav9ZuTnb6OyBD0XEf687Pi+zGtGZVp4quIz7Y/GoXUREHNx0/cGIGI+If6u5/dujxvtYzjm6nVdirdnft/SeRsv7o4j4+PyuHesovAAXpzveMbdw4orcew7Wf6fdExH3Tu0dOis5uG92tKccfLLGzKtnI+KZ+tcTLcnB+zcd7CkHU80cjJzj9d1XcvDSvmb0rb2PfxQRH5/YMX/R5aCCBGdZVVV3VFX3QG9b9TTV9eaI2D80tf/ne3uOpUZnD7wzllt2dAV1Z+OmVPv2ai9ExEcnB5fen+jgpusfiYjxnKPGcq/1nqzb7kaulr6Avg39K4yOiIi75nftuGt+1w5zkAF6dPyq6o7jV1a95WDt42oRUXJwau/QvysH982O9pSDJ3KO+TXG5IiYr1+OXoiIj+4fnFySNfdvOlhyMK+Zg52Ivjrt5dpuNzaeFuSX9/etdnj2rokd83dN7Ji/KHPQNUhwjmx57K/fl1Lj0yml4Yj0cymly1bfYvVrkJYxHRG3zoyMH+t130ZnD2yJiIci4vV1xi9ee7RkBbsVpGg0Vi0ep/rE5OD2L630w7G5f/71iPznK/1KFt+6GtFa6ERVrfw+VlVVtF9cfmZAt92JbnvJhIkci2eNvrzm3gOwqgf+Zsv7Lj2ePt3fSsP9C/FzjbxKDlYR8WxEqjVT4WXTEXHryMRMzzm4b3a0pxxs5xwHahy5ezYijjZqn3/4xP7ByRVzcPfc2K9HxJ+v9Nngpc/wm1qtuHyVWY0bqiq2ttvLnhV5sdONF7tn5uDEjvmLOgcVJHgVDE7elyLiFyJie/l6W/l3JCIGFkf1NDXtJUdjsSTN1t1gdPbAVRHx/YjYVmd8/XIUkVKz7mv4bkSMTw5uX/EdfWzuaH9Efi6iuuT0bHipHEWkaLc70e0s/zA552ifbJ9x9mjJz198+Z553Yj48PyuHV+v8wIAqG/yq4Nr5+BCRHqh5+XBj8ZiSaqdg/tmR3vMwRwHc46naow9llL8a72Dnd+NiPH9g5Mr5uDuubH+iHguIi45vSSd+vn99Z32kulzpw2M4XY7Llvh836VczzTevkgYjciPjyxY/6iz0EFCV5Dg5P39UXEULwcFo07Umps7/FhnoiI986MjP/DWgNHZw/0R8S9sXgRaC05x8dybjwWEb8Wi/dyGF55dCMajdVnveWc2ymlkcnB7WuG2djc0e9E5FvKluW7acm/3W4V7dbyq/F02p3otla/pLZ1YiFiceW9D83v2nHPWvsEwNkz+dXBpTl4Iu5IJ9O6cnBkYmbNHNw3O7qOHMwfuz/nWjn4REQ8udYZpJzbkdLI/sHJNXNw99zYdyLiltXGXFZ1Y3N7+ZkSr+904rqVylPx1EIrouTgxI55ORgKEpx3hqb2fzgivhq9zch+OiJumxkZf2ilAWU57/8dEb/dw+PunRzc+blTv7HtyMNbI+J95esdS4evPcUu5+qPp4be/nt1nnxs7uh/i4jfX/3xciycPDMYcs5Pt060lr9z+Snbtl9sHY+I2+d37Xigzj4BcG5N7R1adw6OTMysmINlOe+ec3DP4OSSHPzMkW0r5uBzEfHDNQrSJTn/8b6hqVo5uHtubM0cTDnH1tbCGfM8mjk//dZW6+rV9ibnHE+32scj4vaJHfMP1Nmni4GCBOehoan974vFm8sN1NuiERGxkFLj8yk1Ho2ITiyeKu+89NVs9n8wpcZ/6WE3/k9EfGhycOeKp/+3HXn4DRFxeyyGxO6I6E+pL1Ja/u045+rJiHjj1NDba62UOjZ39Fci4m/WGrdwsh2nv5flnHcvHD/5tdRo/MJK23XbnZPddvfm+V07elxUA4BzaWrvUP0cHIiIDRHRiIV0efp8uiItm4PHNjU/uLAx9ZyDe1aZBveZI9uW5GCO6P9hSvH8CtPsNub8ZIp44z1DU7VycPfcWK0c3NJaiEtPy8FGzrvfenLha81GWjEHT3a6J090uzdP7JiXg6dQkOA8NTS1/z/F4h25V7ioNcViGalzbVAjms3aiydERByIiFsmB3eeqLvBtiMPXxkRt0XEnSn13Z7S0lNJOVfHcq5GpofHnqv7mGNzR6+MxaOCqx6Oa7c60e0uya8/md46/MnNDz2ytdFIh1Kjccnp2+Qqdzrtznt+9O5f/H7d/QHg1TO1d2j1HNwQkd6QIm1YOwePX5HiJ2/s6TrfAxFxy57Bydo5+Jkj266MiNtaEXf+S0q3P5vSkhy8LOdjV+Q88vXh6do5uHturFYOXttpx+uWTqX7k7uHpz/5Px/ZvLUvNQ41G+mMHOzm3DnR6bzn07/4Izl4GgUJzmNDU/vfGYtzpc+4q3ZK/TVXvUvRbNYdGxERj0fEOyYHdz5Rf0+X2nbk4YGUmr8VEWOxuO9fmhq64YH1PNbY3NFDEXHDamO6nW60X1mNbi4itk9vHX4+ImLT3x98c6PZ/GZqpNGUUso551zlH1Td6rbH3zXW88pHALx6pvYOLZ+DAxHpTanOPe2iNRBx7E3NyDXGFo9HxDv2DE6uOwc/c2TbwBMp/VY7YixHXNUf8aW/GJp6YD2PtXtubM0cvLLbjes6L083n4uI7XcPTz8fEXH3wU1vHmg2vtlMabSRUqpyzt2cf9Cqqts+Nfa4HFyGggTnuaGp/W+LiPsi4tqXvtfDanHRaPRFo1H7qNnzEfHuycGdP+h1P8+VsbmjX4yIT6w25rTrkN47vXX4W6ePue57Dw/0DTRfd/Q/3vDjc7CbAJwjU3uHzszBN6RIV9YrPE++oREvXFl76e3nI+LdewYnz5sc3D03tmYO9uUcQ69ch/Teu4enz8jBzz983cDGZt/rPnbDUTm4BjeKhfPczMj4YxFxUyweESrq/+n2UI66sXjN0XkTCsX31hpwytmxryxXjiIi5m+6saUcAVx4RiZmzszBy+ttW6XopRx1Y/GaowsuBzspRZlo/pXlylFExO/eON9SjupRkOACUO5zdFNEHD6HT/PJycGdf3sOH3+91gyG4scRMXEudwSA10a5z9ErOVhztlzu7ZPuJ/cMTl6wOZjl4FmjIMEFYmZkfD4ibo6Ig71sV3Ma7f+YHNx5Xt41+9DmN81XVT652pjyGj8+vXX42VdnrwB4tY1MzLySg621Ri9KVUTq1svBPYOT52UOfnvzofmBqlo1Bxs5R474+N3D03LwLFCQ4AIyMzL+04gYz7k7U3ebnFdfSTTn6pGIqHU/htdKVVX/a7Wfd7vVo9Nbh7/xau0PAK+NkYmZxRx8NtfKwUaOuOLZ1QtS/0I+73NwY149B6+suo9+eXhaDp4lChJcYGZGxp+NyDfmXB2pM76qupHz8rdwyLl6Muf8y5ODO2vdj+G10kjps91uNbXcz6pu9UTq4Y7oAFzYRiZmno2FuDE/l2vl4NX/VsWGE8uXpA0n8pMDC/mX9wxOntc52EqNz17e7S6bg5dW1RNVJDl4FlnFDi5gw9Pf/YOI9JGIdE351vGIaKeU+iJiyVej0RxIqdFcnLidn8s53x+R75weftd5HQqn2v5P//SHKaXfSCldEzk/k3O+r9utPjo5PFRzsgUAP0um/2z4D6IZH4mBuCZSRDTjeKQzc7BK0ffM6xoDJy5Lzc5ARF87nhtYyPf3tePOd79z+oLJwQ/88/Y/bKf0G62UrunP8Ux/zvddWXU/+qeDk3LwLFKQ4CLzlsPfS/+49SZ/+ABclA7/1VvS1l/9RznIihQkAACAwjVIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAKEDGr8AAAGJSURBVABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUChIAAAAhYIEAABQKEgAAACFggQAAFAoSAAAAIWCBAAAUPx/Db85YoU9zIwAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2022-09-14 19:58:27,969 Running model_4\n", + "2022-09-14 19:59:31,845 model_4 took 61.7s (3 recycles) with pLDDT 78 and ptmscore 0.757\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2022-09-14 20:00:45,235 Running model_5\n", + "2022-09-14 20:01:49,152 model_5 took 61.7s (3 recycles) with pLDDT 83.5 and ptmscore 0.832\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2022-09-14 20:03:01,711 reranking models by plddt\n", + "2022-09-14 20:03:04,582 Done\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KK7X9T44pWb7", + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "outputId": "6f3a5ba9-6d0e-4f19-93c7-a698db117759" + }, + "source": [ + "#@title Display 3D structure {run: \"auto\"}\n", + "import py3Dmol\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "from colabfold.colabfold import plot_plddt_legend\n", + "rank_num = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n", + "color = \"lDDT\" #@param [\"chain\", \"lDDT\", \"rainbow\"]\n", + "show_sidechains = False #@param {type:\"boolean\"}\n", + "show_mainchains = False #@param {type:\"boolean\"}\n", + "\n", + "jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n", + "if use_amber:\n", + " pdb_filename = f\"{jobname}{jobname_prefix}_relaxed_rank_{rank_num}_model_*.pdb\"\n", + "else:\n", + " pdb_filename = f\"{jobname}{jobname_prefix}_unrelaxed_rank_{rank_num}_model_*.pdb\"\n", + "\n", + "pdb_file = glob.glob(pdb_filename)\n", + "\n", + "def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color=\"lDDT\"):\n", + " model_name = f\"rank_{rank_num}\"\n", + " view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)\n", + " view.addModel(open(pdb_file[0],'r').read(),'pdb')\n", + "\n", + " if color == \"lDDT\":\n", + " view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})\n", + " elif color == \"rainbow\":\n", + " view.setStyle({'cartoon': {'color':'spectrum'}})\n", + " elif color == \"chain\":\n", + " chains = len(queries[0][1]) + 1 if is_complex else 1\n", + " for n,chain,color in zip(range(chains),list(\"ABCDEFGH\"),\n", + " [\"lime\",\"cyan\",\"magenta\",\"yellow\",\"salmon\",\"white\",\"blue\",\"orange\"]):\n", + " view.setStyle({'chain':chain},{'cartoon': {'color':color}})\n", + " if show_sidechains:\n", + " BB = ['C','O','N']\n", + " view.addStyle({'and':[{'resn':[\"GLY\",\"PRO\"],'invert':True},{'atom':BB,'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"GLY\"},{'atom':'CA'}]},\n", + " {'sphere':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"PRO\"},{'atom':['C','O'],'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}}) \n", + " if show_mainchains:\n", + " BB = ['C','O','N','CA']\n", + " view.addStyle({'atom':BB},{'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + "\n", + " view.zoomTo()\n", + " return view\n", + "\n", + "\n", + "show_pdb(rank_num,show_sidechains, show_mainchains, color).show()\n", + "if color == \"lDDT\":\n", + " plot_plddt_legend().show() " + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", + "text/html": [ + "
\n", + "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", + " jupyter labextension install jupyterlab_3dmol

\n", + "
\n", + "" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "11l8k--10q0C", + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a1d4c2f6-01d2-49e2-d2d9-d5d92d31adb1" + }, + "source": [ + "#@title Plots {run: \"auto\"}\n", + "from IPython.display import display, HTML\n", + "import base64\n", + "from html import escape\n", + "\n", + "# see: https://stackoverflow.com/a/53688522\n", + "def image_to_data_url(filename):\n", + " ext = filename.split('.')[-1]\n", + " prefix = f'data:image/{ext};base64,'\n", + " with open(filename, 'rb') as f:\n", + " img = f.read()\n", + " return prefix + base64.b64encode(img).decode('utf-8')\n", + "\n", + "pae = image_to_data_url(f\"{jobname}{jobname_prefix}_PAE.png\")\n", + "cov = image_to_data_url(f\"{jobname}{jobname_prefix}_coverage.png\")\n", + "plddt = image_to_data_url(f\"{jobname}{jobname_prefix}_plddt.png\")\n", + "display(HTML(f\"\"\"\n", + "\n", + "
\n", + "

Plots for {escape(jobname)}

\n", + " \n", + " \n", + " \n", + "
\n", + "\"\"\"))\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "\n", + "
\n", + "

Plots for bax_f85cf

\n", + " \n", + " \n", + " \n", + "
\n" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "33g5IIegij5R", + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "5b18f3dd-6a1e-4d0e-92ef-d465358aa6db" + }, + "source": [ + "#@title Package and download results\n", + "#@markdown If you are having issues downloading the result archive, try disabling your adblocker and run this cell again. If that fails click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \\\"Download\\\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).\n", + "\n", + "if msa_mode == \"custom\":\n", + " print(\"Don't forget to cite your custom MSA generation method.\")\n", + "\n", + "!zip -FSr $jobname\".result.zip\" config.json $jobname*\".json\" $jobname*\".a3m\" $jobname*\"relaxed_rank_\"*\".pdb\" \"cite.bibtex\" $jobname*\".png\"\n", + "files.download(f\"{jobname}.result.zip\")\n", + "\n", + "if save_to_google_drive == True and drive:\n", + " uploaded = drive.CreateFile({'title': f\"{jobname}.result.zip\"})\n", + " uploaded.SetContentFile(f\"{jobname}.result.zip\")\n", + " uploaded.Upload()\n", + " print(f\"Uploaded {jobname}.result.zip to Google Drive with ID {uploaded.get('id')}\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " adding: config.json (deflated 49%)\n", + " adding: bax_f85cf_predicted_aligned_error_v1.json (deflated 89%)\n", + " adding: bax_f85cf_unrelaxed_rank_1_model_3_scores.json (deflated 70%)\n", + " adding: bax_f85cf_unrelaxed_rank_2_model_5_scores.json (deflated 70%)\n", + " adding: bax_f85cf_unrelaxed_rank_3_model_1_scores.json (deflated 69%)\n", + " adding: bax_f85cf_unrelaxed_rank_4_model_2_scores.json (deflated 70%)\n", + " adding: bax_f85cf_unrelaxed_rank_5_model_4_scores.json (deflated 69%)\n", + " adding: bax_f85cf.a3m (deflated 66%)\n", + " adding: bax_f85cf_relaxed_rank_1_model_3.pdb (deflated 78%)\n", + " adding: bax_f85cf_relaxed_rank_2_model_5.pdb (deflated 78%)\n", + " adding: bax_f85cf_relaxed_rank_3_model_1.pdb (deflated 78%)\n", + " adding: bax_f85cf_relaxed_rank_4_model_2.pdb (deflated 78%)\n", + " adding: bax_f85cf_relaxed_rank_5_model_4.pdb (deflated 78%)\n", + " adding: bax_f85cf_unrelaxed_rank_1_model_3.pdb (deflated 78%)\n", + " adding: bax_f85cf_unrelaxed_rank_2_model_5.pdb (deflated 78%)\n", + " adding: bax_f85cf_unrelaxed_rank_3_model_1.pdb (deflated 78%)\n", + " adding: bax_f85cf_unrelaxed_rank_4_model_2.pdb (deflated 78%)\n", + " adding: bax_f85cf_unrelaxed_rank_5_model_4.pdb (deflated 78%)\n", + " adding: cite.bibtex (deflated 52%)\n", + " adding: bax_f85cf_coverage.png (deflated 8%)\n", + " adding: bax_f85cf_PAE.png (deflated 1%)\n", + " adding: bax_f85cf_plddt.png (deflated 7%)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_7da755a8-6d63-4435-bbc7-d8561d79003f\", \"bax_f85cf.result.zip\", 2332414)" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UGUBLzB3C6WN", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Instructions \n", + "**Quick start**\n", + "1. Paste your protein sequence(s) in the input field.\n", + "2. Press \"Runtime\" -> \"Run all\".\n", + "3. The pipeline consists of 5 steps. The currently running step is indicated by a circle with a stop sign next to it.\n", + "\n", + "**Result zip file contents**\n", + "\n", + "1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if `use_amber` is enabled).\n", + "2. Plots of the model quality.\n", + "3. Plots of the MSA coverage.\n", + "4. Parameter log file.\n", + "5. A3M formatted input MSA.\n", + "6. A `predicted_aligned_error_v1.json` using [AlphaFold-DB's format](https://alphafold.ebi.ac.uk/faq#faq-7) and a `scores.json` for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.\n", + "7. BibTeX file with citations for all used tools and databases.\n", + "\n", + "At the end of the job a download modal box will pop up with a `jobname.result.zip` file. Additionally, if the `save_to_google_drive` option was selected, the `jobname.result.zip` will be uploaded to your Google Drive.\n", + "\n", + "**MSA generation for complexes**\n", + "\n", + "For the complex prediction we use unpaired and paired MSAs. Unpaired MSA is generated the same way as for the protein structures prediction by searching the UniRef100 and environmental sequences three iterations each.\n", + "\n", + "The paired MSA is generated by searching the UniRef100 database and pairing the best hits sharing the same NCBI taxonomic identifier (=species or sub-species). We only pair sequences if all of the query sequences are present for the respective taxonomic identifier.\n", + "\n", + "**Using a custom MSA as input**\n", + "\n", + "To predict the structure with a custom MSA (A3M formatted): (1) Change the `msa_mode`: to \"custom\", (2) Wait for an upload box to appear at the end of the \"MSA options ...\" box. Upload your A3M. The first fasta entry of the A3M must be the query sequence without gaps. \n", + "\n", + "It is also possilbe to proide custom MSAs for complex predictions. Read more about the format [here](https://github.com/sokrypton/ColabFold/issues/76).\n", + "\n", + "As an alternative for MSA generation the [HHblits Toolkit server](https://toolkit.tuebingen.mpg.de/tools/hhblits) can be used. After submitting your query, click \"Query Template MSA\" -> \"Download Full A3M\". Download the A3M file and upload it in this notebook.\n", + "\n", + "**Using custom templates** \n", + "\n", + "To predict the structure with a custom template (PDB or mmCIF formatted): (1) change the `template_mode` to \"custom\" in the execute cell and (2) wait for an upload box to appear at the end of the \"Input Protein\" box. Select and upload your templates (multiple choices are possible).\n", + "\n", + "* Templates must follow the four letter PDB naming with lower case letters.\n", + "\n", + "* Templates in mmCIF format must contain `_entity_poly_seq`. An error is thrown if this field is not present. The field `_pdbx_audit_revision_history.revision_date` is automatically generated if it is not present.\n", + "\n", + "* Templates in PDB format are automatically converted to the mmCIF format. `_entity_poly_seq` and `_pdbx_audit_revision_history.revision_date` are automatically generated.\n", + "\n", + "If you encounter problems, please report them to this [issue](https://github.com/sokrypton/ColabFold/issues/177).\n", + "\n", + "**Comparison to the full AlphaFold2 and Alphafold2 colab**\n", + "\n", + "This notebook replaces the homology detection and MSA pairing of AlphaFold2 with MMseqs2. For a comparison against the [AlphaFold2 Colab](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb) and the full [AlphaFold2](https://github.com/deepmind/alphafold) system read our [preprint](https://www.biorxiv.org/content/10.1101/2021.08.15.456425v1). \n", + "\n", + "**Troubleshooting**\n", + "* Check that the runtime type is set to GPU at \"Runtime\" -> \"Change runtime type\".\n", + "* Try to restart the session \"Runtime\" -> \"Factory reset runtime\".\n", + "* Check your input sequence.\n", + "\n", + "**Known issues**\n", + "* Google Colab assigns different types of GPUs with varying amount of memory. Some might not have enough memory to predict the structure for a long sequence.\n", + "* Your browser can block the pop-up for downloading the result file. You can choose the `save_to_google_drive` option to upload to Google Drive instead or manually download the result file: Click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \\\"Download\\\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).\n", + "\n", + "**Limitations**\n", + "* Computing resources: Our MMseqs2 API can handle ~20-50k requests per day.\n", + "* MSAs: MMseqs2 is very precise and sensitive but might find less hits compared to HHblits/HMMer searched against BFD or MGnify.\n", + "* We recommend to additionally use the full [AlphaFold2 pipeline](https://github.com/deepmind/alphafold).\n", + "\n", + "**Description of the plots**\n", + "* **Number of sequences per position** - We want to see at least 30 sequences per position, for best performance, ideally 100 sequences.\n", + "* **Predicted lDDT per position** - model confidence (out of 100) at each position. The higher the better.\n", + "* **Predicted Alignment Error** - For homooligomers, this could be a useful metric to assess how confident the model is about the interface. The lower the better.\n", + "\n", + "**Bugs**\n", + "- If you encounter any bugs, please report the issue to https://github.com/sokrypton/ColabFold/issues\n", + "\n", + "**License**\n", + "\n", + "The source code of ColabFold is licensed under [MIT](https://raw.githubusercontent.com/sokrypton/ColabFold/main/LICENSE). Additionally, this notebook uses the AlphaFold2 source code and its parameters licensed under [Apache 2.0](https://raw.githubusercontent.com/deepmind/alphafold/main/LICENSE) and [CC BY 4.0](https://creativecommons.org/licenses/by-sa/4.0/) respectively. Read more about the AlphaFold license [here](https://github.com/deepmind/alphafold).\n", + "\n", + "**Acknowledgments**\n", + "- We thank the AlphaFold team for developing an excellent model and open sourcing the software. \n", + "\n", + "- [KOBIC](https://kobic.re.kr) and [Söding Lab](https://www.mpinat.mpg.de/soeding) for providing the computational resources for the MMseqs2 MSA server.\n", + "\n", + "- Richard Evans for helping to benchmark the ColabFold's Alphafold-multimer support.\n", + "\n", + "- [David Koes](https://github.com/dkoes) for his awesome [py3Dmol](https://3dmol.csb.pitt.edu/) plugin, without whom these notebooks would be quite boring!\n", + "\n", + "- Do-Yoon Kim for creating the ColabFold logo.\n", + "\n", + "- A colab by Sergey Ovchinnikov ([@sokrypton](https://twitter.com/sokrypton)), Milot Mirdita ([@milot_mirdita](https://twitter.com/milot_mirdita)) and Martin Steinegger ([@thesteinegger](https://twitter.com/thesteinegger)).\n" + ] + } + ] +} \ No newline at 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label.previousElementSibling.checked = true; + } + window.localStorage.setItem("sphinx-design-last-tab", syncId); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8549469 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,134 @@ +/* + * _sphinx_javascript_frameworks_compat.js + * ~~~~~~~~~~ + * + * Compatability shim for jQuery and underscores.js. + * + * WILL BE REMOVED IN Sphinx 6.0 + * xref RemovedInSphinx60Warning + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 0000000..18495ea --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,900 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 270px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; 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+} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/_static/check-solid.svg b/_static/check-solid.svg new file mode 100644 index 0000000..92fad4b --- /dev/null +++ b/_static/check-solid.svg @@ -0,0 +1,4 @@ + + + + diff --git a/_static/clipboard.min.js b/_static/clipboard.min.js new file mode 100644 index 0000000..54b3c46 --- /dev/null +++ b/_static/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.8 + * https://clipboardjs.com/ + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1 + + + + diff --git a/_static/copybutton.css b/_static/copybutton.css new file mode 100644 index 0000000..f1916ec --- /dev/null +++ b/_static/copybutton.css @@ -0,0 +1,94 @@ +/* Copy buttons */ +button.copybtn { + position: absolute; + display: flex; + top: .3em; + right: .3em; + width: 1.7em; + height: 1.7em; + opacity: 0; + transition: opacity 0.3s, border .3s, background-color .3s; + user-select: none; + padding: 0; + border: none; + outline: none; + border-radius: 0.4em; + /* The colors that GitHub uses */ + border: #1b1f2426 1px solid; + background-color: #f6f8fa; + color: #57606a; +} + +button.copybtn.success { + border-color: #22863a; + color: #22863a; +} + +button.copybtn svg { + stroke: currentColor; + width: 1.5em; + height: 1.5em; + padding: 0.1em; +} + +div.highlight { + position: relative; +} + +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { + opacity: 1; +} + +.highlight button.copybtn:hover { + background-color: rgb(235, 235, 235); +} + +.highlight button.copybtn:active { + background-color: rgb(187, 187, 187); +} + +/** + * A minimal CSS-only tooltip copied from: + * https://codepen.io/mildrenben/pen/rVBrpK + * + * To use, write HTML like the following: + * + *

Short

+ */ + .o-tooltip--left { + position: relative; + } + + .o-tooltip--left:after { + opacity: 0; + visibility: hidden; + position: absolute; + content: attr(data-tooltip); + padding: .2em; + font-size: .8em; + left: -.2em; + background: grey; + color: white; + white-space: nowrap; + z-index: 2; + border-radius: 2px; + transform: translateX(-102%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); +} + +.o-tooltip--left:hover:after { + display: block; + opacity: 1; + visibility: visible; + transform: translateX(-100%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); + transition-delay: .5s; +} + +/* By default the copy button shouldn't show up when printing a page */ +@media print { + button.copybtn { + display: none; + } +} diff --git a/_static/copybutton.js b/_static/copybutton.js new file mode 100644 index 0000000..2ea7ff3 --- /dev/null +++ b/_static/copybutton.js @@ -0,0 +1,248 @@ +// Localization support +const messages = { + 'en': { + 'copy': 'Copy', + 'copy_to_clipboard': 'Copy to clipboard', + 'copy_success': 'Copied!', + 'copy_failure': 'Failed to copy', + }, + 'es' : { + 'copy': 'Copiar', + 'copy_to_clipboard': 'Copiar al portapapeles', + 'copy_success': '¡Copiado!', + 'copy_failure': 'Error al copiar', + }, + 'de' : { + 'copy': 'Kopieren', + 'copy_to_clipboard': 'In die Zwischenablage kopieren', + 'copy_success': 'Kopiert!', + 'copy_failure': 'Fehler beim Kopieren', + }, + 'fr' : { + 'copy': 'Copier', + 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_success': 'Copié !', + 'copy_failure': 'Échec de la copie', + }, + 'ru': { + 'copy': 'Скопировать', + 'copy_to_clipboard': 'Скопировать в буфер', + 'copy_success': 'Скопировано!', + 'copy_failure': 'Не удалось скопировать', + }, + 'zh-CN': { + 'copy': '复制', + 'copy_to_clipboard': '复制到剪贴板', + 'copy_success': '复制成功!', + 'copy_failure': '复制失败', + }, + 'it' : { + 'copy': 'Copiare', + 'copy_to_clipboard': 'Copiato negli appunti', + 'copy_success': 'Copiato!', + 'copy_failure': 'Errore durante la copia', + } +} + +let locale = 'en' +if( document.documentElement.lang !== undefined + && messages[document.documentElement.lang] !== undefined ) { + locale = document.documentElement.lang +} + +let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; +if (doc_url_root == '#') { + doc_url_root = ''; +} + +/** + * SVG files for our copy buttons + */ +let iconCheck = ` + ${messages[locale]['copy_success']} + + +` + +// If the user specified their own SVG use that, otherwise use the default +let iconCopy = ``; +if (!iconCopy) { + iconCopy = ` + ${messages[locale]['copy_to_clipboard']} + + + +` +} + +/** + * Set up copy/paste for code blocks + */ + +const runWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} + +const codeCellId = index => `codecell${index}` + +// Clears selected text since ClipboardJS will select the text when copying +const clearSelection = () => { + if (window.getSelection) { + window.getSelection().removeAllRanges() + } else if (document.selection) { + document.selection.empty() + } +} + +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + +const temporarilyChangeTooltip = (el, oldText, newText) => { + el.setAttribute('data-tooltip', newText) + el.classList.add('success') + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) +} + +// Changes the copy button icon for two seconds, then changes it back +const temporarilyChangeIcon = (el) => { + el.innerHTML = iconCheck; + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) +} + +const addCopyButtonToCodeCells = () => { + // If ClipboardJS hasn't loaded, wait a bit and try again. This + // happens because we load ClipboardJS asynchronously. + if (window.ClipboardJS === undefined) { + setTimeout(addCopyButtonToCodeCells, 250) + return + } + + // Add copybuttons to all of our code cells + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) + codeCells.forEach((codeCell, index) => { + const id = codeCellId(index) + codeCell.setAttribute('id', id) + + const clipboardButton = id => + `` + codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) + }) + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} + + +var copyTargetText = (trigger) => { + var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') +} + + // Initialize with a callback so we can modify the text before copy + const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) + + // Update UI with error/success messages + clipboard.on('success', event => { + clearSelection() + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) + temporarilyChangeIcon(event.trigger) + }) + + clipboard.on('error', event => { + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) + }) +} + +runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js new file mode 100644 index 0000000..dbe1aaa --- /dev/null +++ b/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} diff --git a/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css new file mode 100644 index 0000000..3225661 --- /dev/null +++ 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window.localStorage.setItem("sphinx-design-last-tab", syncId); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 0000000..527b876 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 0000000..03c0d01 --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,14 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '', + NAVIGATION_WITH_KEYS: true, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/_static/file.png b/_static/file.png new file mode 100644 index 0000000..a858a41 Binary files /dev/null and b/_static/file.png differ diff --git a/_static/images/logo_binder.svg b/_static/images/logo_binder.svg new file mode 100644 index 0000000..45fecf7 --- /dev/null +++ b/_static/images/logo_binder.svg @@ -0,0 +1,19 @@ + + + + +logo + + + + + + + + diff --git a/_static/images/logo_colab.png b/_static/images/logo_colab.png new file mode 100644 index 0000000..b7560ec Binary files /dev/null and b/_static/images/logo_colab.png differ diff --git a/_static/images/logo_deepnote.svg b/_static/images/logo_deepnote.svg new file mode 100644 index 0000000..fa77ebf --- /dev/null +++ b/_static/images/logo_deepnote.svg @@ -0,0 +1 @@ + diff --git a/_static/images/logo_jupyterhub.svg b/_static/images/logo_jupyterhub.svg new file mode 100644 index 0000000..60cfe9f --- /dev/null +++ b/_static/images/logo_jupyterhub.svg @@ -0,0 +1 @@ +logo_jupyterhubHub diff --git a/_static/jquery-3.6.0.js b/_static/jquery-3.6.0.js new file mode 100644 index 0000000..fc6c299 --- /dev/null +++ b/_static/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.6.0", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +} +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the primary Deferred + primary = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
" ], + col: [ 2, "", "
" ], + tr: [ 2, "", "
" ], + td: [ 3, "", "
" ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? 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DefaultType(){return{}}static get NAME(){throw new Error('You have to implement the static method "NAME", for each component!')}_getConfig(t){return t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t}_mergeConfigObj(t,e){const i=Mt(e)?be.getDataAttribute(e,"config"):{};return{...this.constructor.Default,..."object"==typeof i?i:{},...Mt(e)?be.getDataAttributes(e):{},..."object"==typeof t?t:{}}}_typeCheckConfig(t,e=this.constructor.DefaultType){for(const n of Object.keys(e)){const s=e[n],o=t[n],r=Mt(o)?"element":null==(i=o)?`${i}`:Object.prototype.toString.call(i).match(/\s([a-z]+)/i)[1].toLowerCase();if(!new RegExp(s).test(r))throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option "${n}" provided type "${r}" but expected type "${s}".`)}var i}}class ye extends ve{constructor(t,e){super(),(t=Ht(t))&&(this._element=t,this._config=this._getConfig(e),ge.set(this._element,this.constructor.DATA_KEY,this))}dispose(){ge.remove(this._element,this.constructor.DATA_KEY),de.off(this._element,this.constructor.EVENT_KEY);for(const t of Object.getOwnPropertyNames(this))this[t]=null}_queueCallback(t,e,i=!0){Yt(t,e,i)}_getConfig(t){return t=this._mergeConfigObj(t,this._element),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}static getInstance(t){return ge.get(Ht(t),this.DATA_KEY)}static getOrCreateInstance(t,e={}){return this.getInstance(t)||new this(t,"object"==typeof e?e:null)}static get VERSION(){return"5.2.3"}static get DATA_KEY(){return`bs.${this.NAME}`}static get EVENT_KEY(){return`.${this.DATA_KEY}`}static eventName(t){return`${t}${this.EVENT_KEY}`}}const we=(t,e="hide")=>{const i=`click.dismiss${t.EVENT_KEY}`,n=t.NAME;de.on(document,i,`[data-bs-dismiss="${n}"]`,(function(i){if(["A","AREA"].includes(this.tagName)&&i.preventDefault(),Ft(this))return;const s=Pt(this)||this.closest(`.${n}`);t.getOrCreateInstance(s)[e]()}))},Ae=".bs.alert",Ee=`close${Ae}`,Ce=`closed${Ae}`;class Te extends ye{static get NAME(){return"alert"}close(){if(de.trigger(this._element,Ee).defaultPrevented)return;this._element.classList.remove("show");const t=this._element.classList.contains("fade");this._queueCallback((()=>this._destroyElement()),this._element,t)}_destroyElement(){this._element.remove(),de.trigger(this._element,Ce),this.dispose()}static jQueryInterface(t){return this.each((function(){const e=Te.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}we(Te,"close"),Qt(Te);const Oe='[data-bs-toggle="button"]';class xe extends ye{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=xe.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}de.on(document,"click.bs.button.data-api",Oe,(t=>{t.preventDefault();const e=t.target.closest(Oe);xe.getOrCreateInstance(e).toggle()})),Qt(xe);const ke={find:(t,e=document.documentElement)=>[].concat(...Element.prototype.querySelectorAll.call(e,t)),findOne:(t,e=document.documentElement)=>Element.prototype.querySelector.call(e,t),children:(t,e)=>[].concat(...t.children).filter((t=>t.matches(e))),parents(t,e){const i=[];let n=t.parentNode.closest(e);for(;n;)i.push(n),n=n.parentNode.closest(e);return i},prev(t,e){let i=t.previousElementSibling;for(;i;){if(i.matches(e))return[i];i=i.previousElementSibling}return[]},next(t,e){let i=t.nextElementSibling;for(;i;){if(i.matches(e))return[i];i=i.nextElementSibling}return[]},focusableChildren(t){const e=["a","button","input","textarea","select","details","[tabindex]",'[contenteditable="true"]'].map((t=>`${t}:not([tabindex^="-"])`)).join(",");return this.find(e,t).filter((t=>!Ft(t)&&Wt(t)))}},Le=".bs.swipe",De=`touchstart${Le}`,$e=`touchmove${Le}`,Se=`touchend${Le}`,Ie=`pointerdown${Le}`,Ne=`pointerup${Le}`,Pe={endCallback:null,leftCallback:null,rightCallback:null},je={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class Me extends ve{constructor(t,e){super(),this._element=t,t&&Me.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return Pe}static get DefaultType(){return je}static get NAME(){return"swipe"}dispose(){de.off(this._element,Le)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),Xt(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&Xt(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(de.on(this._element,Ie,(t=>this._start(t))),de.on(this._element,Ne,(t=>this._end(t))),this._element.classList.add("pointer-event")):(de.on(this._element,De,(t=>this._start(t))),de.on(this._element,$e,(t=>this._move(t))),de.on(this._element,Se,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const He=".bs.carousel",We=".data-api",Fe="next",Be="prev",ze="left",qe="right",Re=`slide${He}`,Ve=`slid${He}`,Ke=`keydown${He}`,Qe=`mouseenter${He}`,Xe=`mouseleave${He}`,Ye=`dragstart${He}`,Ue=`load${He}${We}`,Ge=`click${He}${We}`,Je="carousel",Ze="active",ti=".active",ei=".carousel-item",ii=ti+ei,ni={ArrowLeft:qe,ArrowRight:ze},si={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},oi={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class ri extends ye{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=ke.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===Je&&this.cycle()}static get Default(){return si}static get DefaultType(){return oi}static get NAME(){return"carousel"}next(){this._slide(Fe)}nextWhenVisible(){!document.hidden&&Wt(this._element)&&this.next()}prev(){this._slide(Be)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?de.one(this._element,Ve,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void de.one(this._element,Ve,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?Fe:Be;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&de.on(this._element,Ke,(t=>this._keydown(t))),"hover"===this._config.pause&&(de.on(this._element,Qe,(()=>this.pause())),de.on(this._element,Xe,(()=>this._maybeEnableCycle()))),this._config.touch&&Me.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of ke.find(".carousel-item img",this._element))de.on(t,Ye,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ze)),rightCallback:()=>this._slide(this._directionToOrder(qe)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new Me(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=ni[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=ke.findOne(ti,this._indicatorsElement);e.classList.remove(Ze),e.removeAttribute("aria-current");const i=ke.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Ze),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===Fe,s=e||Ut(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>de.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Re).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ke.findOne(ii,this._element)}_getItems(){return ke.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?Be:Fe:t===ze?Fe:Be}_orderToDirection(t){return Kt()?t===Be?ze:qe:t===Be?qe:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}de.on(document,Ge,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=Pt(this);if(!e||!e.classList.contains(Je))return;t.preventDefault();const i=ri.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===be.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),de.on(window,Ue,(()=>{const t=ke.find('[data-bs-ride="carousel"]');for(const e of t)ri.getOrCreateInstance(e)})),Qt(ri);const ai=".bs.collapse",li=`show${ai}`,ci=`shown${ai}`,hi=`hide${ai}`,ui=`hidden${ai}`,di=`click${ai}.data-api`,fi="show",pi="collapse",gi="collapsing",mi=`:scope .${pi} .${pi}`,_i='[data-bs-toggle="collapse"]',bi={parent:null,toggle:!0},vi={parent:"(null|element)",toggle:"boolean"};class yi extends ye{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=ke.find(_i);for(const t of i){const e=Nt(t),i=ke.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(de.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(gi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi,fi),this._element.style[e]="",de.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(de.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(gi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=Pt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi),de.trigger(this._element,ui)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=Pt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ke.find(mi,this._config.parent);return ke.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}de.on(document,di,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=Nt(this),i=ke.find(e);for(const t of i)yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ai=".bs.dropdown",Ei=".data-api",Ci="ArrowUp",Ti="ArrowDown",Oi=`hide${Ai}`,xi=`hidden${Ai}`,ki=`show${Ai}`,Li=`shown${Ai}`,Di=`click${Ai}${Ei}`,$i=`keydown${Ai}${Ei}`,Si=`keyup${Ai}${Ei}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,ji=".dropdown-menu",Mi=Kt()?"top-end":"top-start",Hi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",Fi=Kt()?"bottom-start":"bottom-end",Bi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",qi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ye{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ke.next(this._element,ji)[0]||ke.prev(this._element,ji)[0]||ke.findOne(ji,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return qi}static get DefaultType(){return Ri}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Ft(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!de.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),de.trigger(this._element,Li,t)}}hide(){if(Ft(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!de.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),be.removeDataAttribute(this._menu,"popper"),de.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Mt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Mt(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Hi:Mi:e?Fi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(be.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=ke.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Wt(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ke.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ci,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:ke.prev(this,Ni)[0]||ke.next(this,Ni)[0]||ke.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}de.on(document,$i,Ni,Vi.dataApiKeydownHandler),de.on(document,$i,ji,Vi.dataApiKeydownHandler),de.on(document,Di,Vi.clearMenus),de.on(document,Si,Vi.clearMenus),de.on(document,Di,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Ki=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",Qi=".sticky-top",Xi="padding-right",Yi="margin-right";class Ui{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,Xi,(e=>e+t)),this._setElementAttributes(Ki,Xi,(e=>e+t)),this._setElementAttributes(Qi,Yi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,Xi),this._resetElementAttributes(Ki,Xi),this._resetElementAttributes(Qi,Yi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&be.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=be.getDataAttribute(t,e);null!==i?(be.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Mt(t))e(t);else for(const i of ke.find(t,this._element))e(i)}}const Gi="backdrop",Ji="show",Zi=`mousedown.bs.${Gi}`,tn={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},en={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class nn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return tn}static get DefaultType(){return en}static get NAME(){return Gi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ji),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ji),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(de.off(this._element,Zi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),de.on(t,Zi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Yt(t,this._getElement(),this._config.isAnimated)}}const sn=".bs.focustrap",on=`focusin${sn}`,rn=`keydown.tab${sn}`,an="backward",ln={autofocus:!0,trapElement:null},cn={autofocus:"boolean",trapElement:"element"};class hn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return ln}static get DefaultType(){return cn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),de.off(document,sn),de.on(document,on,(t=>this._handleFocusin(t))),de.on(document,rn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,de.off(document,sn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ke.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===an?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?an:"forward")}}const un=".bs.modal",dn=`hide${un}`,fn=`hidePrevented${un}`,pn=`hidden${un}`,gn=`show${un}`,mn=`shown${un}`,_n=`resize${un}`,bn=`click.dismiss${un}`,vn=`mousedown.dismiss${un}`,yn=`keydown.dismiss${un}`,wn=`click${un}.data-api`,An="modal-open",En="show",Cn="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ye{constructor(t,e){super(t,e),this._dialog=ke.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Ui,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||de.trigger(this._element,gn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(An),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(de.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])de.off(t,un);this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new nn({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ke.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,de.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){de.on(this._element,yn,(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),de.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),de.on(this._element,vn,(t=>{de.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(An),this._resetAdjustments(),this._scrollBar.reset(),de.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(de.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Cn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Cn),this._queueCallback((()=>{this._element.classList.remove(Cn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}de.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=Pt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),de.one(e,gn,(t=>{t.defaultPrevented||de.one(e,pn,(()=>{Wt(this)&&this.focus()}))}));const i=ke.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),we(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Dn=`load${kn}${Ln}`,$n="show",Sn="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,jn=`shown${kn}`,Mn=`hide${kn}`,Hn=`hidePrevented${kn}`,Wn=`hidden${kn}`,Fn=`resize${kn}`,Bn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,qn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ye{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||de.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Ui).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Sn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add($n),this._element.classList.remove(Sn),de.trigger(this._element,jn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(de.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove($n,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Ui).reset(),de.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new nn({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():de.trigger(this._element,Hn)}:null})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_addEventListeners(){de.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():de.trigger(this._element,Hn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}de.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=Pt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this))return;de.one(e,Wn,(()=>{Wt(this)&&this.focus()}));const i=ke.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),de.on(window,Dn,(()=>{for(const t of ke.find(Nn))Vn.getOrCreateInstance(t).show()})),de.on(window,Fn,(()=>{for(const t of ke.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),we(Vn),Qt(Vn);const Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Xn=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Yn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)||Xn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Gn={allowList:Un,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Jn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Zn={entry:"(string|element|function|null)",selector:"(string|element)"};class ts extends ve{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Gn}static get DefaultType(){return Jn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Zn)}_setContent(t,e,i){const n=ke.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Mt(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Yn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const es=new Set(["sanitize","allowList","sanitizeFn"]),is="fade",ns="show",ss=".modal",os="hide.bs.modal",rs="hover",as="focus",ls={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},cs={allowList:Un,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},hs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class us extends ye{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return cs}static get DefaultType(){return hs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),de.off(this._element.closest(ss),os,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=de.trigger(this._element,this.constructor.eventName("show")),e=(Bt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),de.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._queueCallback((()=>{de.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!de.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[as]=!1,this._activeTrigger[rs]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),de.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(is,ns),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(is),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new ts({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(is)}_isShown(){return this.tip&&this.tip.classList.contains(ns)}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=ls[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)de.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===rs?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===rs?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");de.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?as:rs]=!0,e._enter()})),de.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?as:rs]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},de.on(this._element.closest(ss),os,this._hideModalHandler)}_fixTitle(){const 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t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(us);const ds={...us.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},fs={...us.DefaultType,content:"(null|string|element|function)"};class ps extends us{static get Default(){return ds}static get DefaultType(){return fs}static get 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= 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (process.env.NODE_ENV !== \"production\") {\n if (!isHTMLElement(arrowElement)) {\n console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n }\n\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n if (process.env.NODE_ENV !== \"production\") {\n var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n return transitionProperty.indexOf(property) >= 0;\n })) {\n console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n }\n }\n\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n }\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n }); // Validate the provided modifiers so that the consumer will get warned\n // if one of the modifiers is invalid for any reason\n\n if (process.env.NODE_ENV !== \"production\") {\n var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n var name = _ref.name;\n return name;\n });\n validateModifiers(modifiers);\n\n if (getBasePlacement(state.options.placement) === auto) {\n var flipModifier = state.orderedModifiers.find(function (_ref2) {\n var name = _ref2.name;\n return name === 'flip';\n });\n\n if (!flipModifier) {\n console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n }\n }\n\n var _getComputedStyle = getComputedStyle(popper),\n marginTop = _getComputedStyle.marginTop,\n marginRight = _getComputedStyle.marginRight,\n marginBottom = _getComputedStyle.marginBottom,\n marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n // cause bugs with positioning, so we'll warn the consumer\n\n\n if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n return parseFloat(margin);\n })) {\n console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n }\n }\n\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n var __debug_loops__ = 0;\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (process.env.NODE_ENV !== \"production\") {\n __debug_loops__ += 1;\n\n if (__debug_loops__ > 100) {\n console.error(INFINITE_LOOP_ERROR);\n break;\n }\n }\n\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref3) {\n var name = _ref3.name,\n _ref3$options = _ref3.options,\n options = _ref3$options === void 0 ? {} : _ref3$options,\n effect = _ref3.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n * Bootstrap v5.2.3 (https://getbootstrap.com/)\n * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend'; // Shout-out Angus Croll (https://goo.gl/pxwQGp)\n\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`;\n }\n\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n/**\n * Public Util API\n */\n\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID);\n } while (document.getElementById(prefix));\n\n return prefix;\n};\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target');\n\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href'); // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n\n if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n return null;\n } // Just in case some CMS puts out a full URL with the anchor appended\n\n\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n }\n\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n }\n\n return selector;\n};\n\nconst getSelectorFromElement = element => {\n const selector = getSelector(element);\n\n if (selector) {\n return document.querySelector(selector) ? selector : null;\n }\n\n return null;\n};\n\nconst getElementFromSelector = element => {\n const selector = getSelector(element);\n return selector ? document.querySelector(selector) : null;\n};\n\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0;\n } // Get transition-duration of the element\n\n\n let {\n transitionDuration,\n transitionDelay\n } = window.getComputedStyle(element);\n const floatTransitionDuration = Number.parseFloat(transitionDuration);\n const floatTransitionDelay = Number.parseFloat(transitionDelay); // Return 0 if element or transition duration is not found\n\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0;\n } // If multiple durations are defined, take the first\n\n\n transitionDuration = transitionDuration.split(',')[0];\n transitionDelay = transitionDelay.split(',')[0];\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\n\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END));\n};\n\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false;\n }\n\n if (typeof object.jquery !== 'undefined') {\n object = object[0];\n }\n\n return typeof object.nodeType !== 'undefined';\n};\n\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object;\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(object);\n }\n\n return null;\n};\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false;\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'; // Handle `details` element as its content may falsie appear visible when it is closed\n\n const closedDetails = element.closest('details:not([open])');\n\n if (!closedDetails) {\n return elementIsVisible;\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary');\n\n if (summary && summary.parentNode !== closedDetails) {\n return false;\n }\n\n if (summary === null) {\n return false;\n }\n }\n\n return elementIsVisible;\n};\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true;\n }\n\n if (element.classList.contains('disabled')) {\n return true;\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled;\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null;\n } // Can find the shadow root otherwise it'll return the document\n\n\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode();\n return root instanceof ShadowRoot ? root : null;\n }\n\n if (element instanceof ShadowRoot) {\n return element;\n } // when we don't find a shadow root\n\n\n if (!element.parentNode) {\n return null;\n }\n\n return findShadowRoot(element.parentNode);\n};\n\nconst noop = () => {};\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\n\n\nconst reflow = element => {\n element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery;\n }\n\n return null;\n};\n\nconst DOMContentLoadedCallbacks = [];\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback();\n }\n });\n }\n\n DOMContentLoadedCallbacks.push(callback);\n } else {\n callback();\n }\n};\n\nconst isRTL = () => document.documentElement.dir === 'rtl';\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery();\n /* istanbul ignore if */\n\n if ($) {\n const name = plugin.NAME;\n const JQUERY_NO_CONFLICT = $.fn[name];\n $.fn[name] = plugin.jQueryInterface;\n $.fn[name].Constructor = plugin;\n\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT;\n return plugin.jQueryInterface;\n };\n }\n });\n};\n\nconst execute = callback => {\n if (typeof callback === 'function') {\n callback();\n }\n};\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback);\n return;\n }\n\n const durationPadding = 5;\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n let called = false;\n\n const handler = ({\n target\n }) => {\n if (target !== transitionElement) {\n return;\n }\n\n called = true;\n transitionElement.removeEventListener(TRANSITION_END, handler);\n execute(callback);\n };\n\n transitionElement.addEventListener(TRANSITION_END, handler);\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement);\n }\n }, emulatedDuration);\n};\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\n\n\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length;\n let index = list.indexOf(activeElement); // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n }\n\n index += shouldGetNext ? 1 : -1;\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength;\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\n\nlet uidEvent = 1;\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element);\n element.uidEvent = uid;\n eventRegistry[uid] = eventRegistry[uid] || {};\n return eventRegistry[uid];\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, {\n delegateTarget: element\n });\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn);\n }\n\n return fn.apply(element, [event]);\n };\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector);\n\n for (let {\n target\n } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue;\n }\n\n hydrateObj(event, {\n delegateTarget: target\n });\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn);\n }\n\n return fn.apply(target, [event]);\n }\n }\n };\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'; // todo: tooltip passes `false` instead of selector, so we need to check\n\n const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n let typeEvent = getTypeEvent(originalTypeEvent);\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent;\n }\n\n return [isDelegated, callable, typeEvent];\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction); // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n return fn.call(this, event);\n }\n };\n };\n\n callable = wrapFunction(callable);\n }\n\n const events = getElementEvents(element);\n const handlers = events[typeEvent] || (events[typeEvent] = {});\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff;\n return;\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n fn.delegationSelector = isDelegated ? handler : null;\n fn.callable = callable;\n fn.oneOff = oneOff;\n fn.uidEvent = uid;\n handlers[uid] = fn;\n element.addEventListener(typeEvent, fn, isDelegated);\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector);\n\n if (!fn) {\n return;\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n delete events[typeEvent][fn.uidEvent];\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {};\n\n for (const handlerKey of Object.keys(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n const event = storeElementEvent[handlerKey];\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '');\n return customEvents[event] || event;\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false);\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true);\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n const inNamespace = typeEvent !== originalTypeEvent;\n const events = getElementEvents(element);\n const storeElementEvent = events[typeEvent] || {};\n const isNamespace = originalTypeEvent.startsWith('.');\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return;\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n return;\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n }\n }\n\n for (const keyHandlers of Object.keys(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '');\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n const event = storeElementEvent[keyHandlers];\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null;\n }\n\n const $ = getjQuery();\n const typeEvent = getTypeEvent(event);\n const inNamespace = event !== typeEvent;\n let jQueryEvent = null;\n let bubbles = true;\n let nativeDispatch = true;\n let defaultPrevented = false;\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args);\n $(element).trigger(jQueryEvent);\n bubbles = !jQueryEvent.isPropagationStopped();\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n defaultPrevented = jQueryEvent.isDefaultPrevented();\n }\n\n let evt = new Event(event, {\n bubbles,\n cancelable: true\n });\n evt = hydrateObj(evt, args);\n\n if (defaultPrevented) {\n evt.preventDefault();\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt);\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault();\n }\n\n return evt;\n }\n\n};\n\nfunction hydrateObj(obj, meta) {\n for (const [key, value] of Object.entries(meta || {})) {\n try {\n obj[key] = value;\n } catch (_unused) {\n Object.defineProperty(obj, key, {\n configurable: true,\n\n get() {\n return value;\n }\n\n });\n }\n }\n\n return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\nconst elementMap = new Map();\nconst Data = {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map());\n }\n\n const instanceMap = elementMap.get(element); // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n return;\n }\n\n instanceMap.set(key, instance);\n },\n\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null;\n }\n\n return null;\n },\n\n remove(element, key) {\n if (!elementMap.has(element)) {\n return;\n }\n\n const instanceMap = elementMap.get(element);\n instanceMap.delete(key); // free up element references if there are no instances left for an element\n\n if (instanceMap.size === 0) {\n elementMap.delete(element);\n }\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nfunction normalizeData(value) {\n if (value === 'true') {\n return true;\n }\n\n if (value === 'false') {\n return false;\n }\n\n if (value === Number(value).toString()) {\n return Number(value);\n }\n\n if (value === '' || value === 'null') {\n return null;\n }\n\n if (typeof value !== 'string') {\n return value;\n }\n\n try {\n return JSON.parse(decodeURIComponent(value));\n } catch (_unused) {\n return value;\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {};\n }\n\n const attributes = {};\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '');\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n attributes[pureKey] = normalizeData(element.dataset[key]);\n }\n\n return attributes;\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {};\n }\n\n static get DefaultType() {\n return {};\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!');\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n }\n\n _configAfterMerge(config) {\n return config;\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n return { ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n };\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const property of Object.keys(configTypes)) {\n const expectedTypes = configTypes[property];\n const value = config[property];\n const valueType = isElement(value) ? 'element' : toType(value);\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n }\n }\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst VERSION = '5.2.3';\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super();\n element = getElement(element);\n\n if (!element) {\n return;\n }\n\n this._element = element;\n this._config = this._getConfig(config);\n Data.set(this._element, this.constructor.DATA_KEY, this);\n } // Public\n\n\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY);\n EventHandler.off(this._element, this.constructor.EVENT_KEY);\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null;\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated);\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n } // Static\n\n\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY);\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n }\n\n static get VERSION() {\n return VERSION;\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`;\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`;\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n const name = component.NAME;\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n if (isDisabled(this)) {\n return;\n }\n\n const target = getElementFromSelector(this) || this.closest(`.${name}`);\n const instance = component.getOrCreateInstance(target); // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n\n instance[method]();\n });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$f;\n } // Public\n\n\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n\n if (closeEvent.defaultPrevented) {\n return;\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW$8);\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n } // Private\n\n\n _destroyElement() {\n this._element.remove();\n\n EventHandler.trigger(this._element, EVENT_CLOSED);\n this.dispose();\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](this);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Alert, 'close');\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$e;\n } // Public\n\n\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this);\n\n if (config === 'toggle') {\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n event.preventDefault();\n const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n const data = Button.getOrCreateInstance(button);\n data.toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector);\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector));\n },\n\n parents(element, selector) {\n const parents = [];\n let ancestor = element.parentNode.closest(selector);\n\n while (ancestor) {\n parents.push(ancestor);\n ancestor = ancestor.parentNode.closest(selector);\n }\n\n return parents;\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling;\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous];\n }\n\n previous = previous.previousElementSibling;\n }\n\n return [];\n },\n\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling;\n\n while (next) {\n if (next.matches(selector)) {\n return [next];\n }\n\n next = next.nextElementSibling;\n }\n\n return [];\n },\n\n focusableChildren(element) {\n const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n};\nconst DefaultType$c = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n};\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super();\n this._element = element;\n\n if (!element || !Swipe.isSupported()) {\n return;\n }\n\n this._config = this._getConfig(config);\n this._deltaX = 0;\n this._supportPointerEvents = Boolean(window.PointerEvent);\n\n this._initEvents();\n } // Getters\n\n\n static get Default() {\n return Default$c;\n }\n\n static get DefaultType() {\n return DefaultType$c;\n }\n\n static get NAME() {\n return NAME$d;\n } // Public\n\n\n dispose() {\n EventHandler.off(this._element, EVENT_KEY$9);\n } // Private\n\n\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX;\n return;\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX;\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX;\n }\n\n this._handleSwipe();\n\n execute(this._config.endCallback);\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX);\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return;\n }\n\n const direction = absDeltaX / this._deltaX;\n this._deltaX = 0;\n\n if (!direction) {\n return;\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n } // Static\n\n\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n};\nconst DefaultType$b = {\n interval: '(number|boolean)',\n // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._interval = null;\n this._activeElement = null;\n this._isSliding = false;\n this.touchTimeout = null;\n this._swipeHelper = null;\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n\n this._addEventListeners();\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$b;\n }\n\n static get DefaultType() {\n return DefaultType$b;\n }\n\n static get NAME() {\n return NAME$c;\n } // Public\n\n\n next() {\n this._slide(ORDER_NEXT);\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next();\n }\n }\n\n prev() {\n this._slide(ORDER_PREV);\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element);\n }\n\n this._clearInterval();\n }\n\n cycle() {\n this._clearInterval();\n\n this._updateInterval();\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return;\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n return;\n }\n\n this.cycle();\n }\n\n to(index) {\n const items = this._getItems();\n\n if (index > items.length - 1 || index < 0) {\n return;\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n return;\n }\n\n const activeIndex = this._getItemIndex(this._getActive());\n\n if (activeIndex === index) {\n return;\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n\n this._slide(order, items[index]);\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose();\n }\n\n super.dispose();\n } // Private\n\n\n _configAfterMerge(config) {\n config.defaultInterval = config.interval;\n return config;\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners();\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return;\n } // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n\n this.pause();\n\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout);\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n };\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n };\n this._swipeHelper = new Swipe(this._element, swipeConfig);\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return;\n }\n\n const direction = KEY_TO_DIRECTION[event.key];\n\n if (direction) {\n event.preventDefault();\n\n this._slide(this._directionToOrder(direction));\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element);\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return;\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n activeIndicator.removeAttribute('aria-current');\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n newActiveIndicator.setAttribute('aria-current', 'true');\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive();\n\n if (!element) {\n return;\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n this._config.interval = elementInterval || this._config.defaultInterval;\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return;\n }\n\n const activeElement = this._getActive();\n\n const isNext = order === ORDER_NEXT;\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n\n if (nextElement === activeElement) {\n return;\n }\n\n const nextElementIndex = this._getItemIndex(nextElement);\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n });\n };\n\n const slideEvent = triggerEvent(EVENT_SLIDE);\n\n if (slideEvent.defaultPrevented) {\n return;\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // todo: change tests that use empty divs to avoid this check\n return;\n }\n\n const isCycling = Boolean(this._interval);\n this.pause();\n this._isSliding = true;\n\n this._setActiveIndicatorElement(nextElementIndex);\n\n this._activeElement = nextElement;\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n nextElement.classList.add(orderClassName);\n reflow(nextElement);\n activeElement.classList.add(directionalClassName);\n nextElement.classList.add(directionalClassName);\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName);\n nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n this._isSliding = false;\n triggerEvent(EVENT_SLID);\n };\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n\n if (isCycling) {\n this.cycle();\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE);\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element);\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval);\n this._interval = null;\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config);\n\n if (typeof config === 'number') {\n data.to(config);\n return;\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n const target = getElementFromSelector(this);\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return;\n }\n\n event.preventDefault();\n const carousel = Carousel.getOrCreateInstance(target);\n const slideIndex = this.getAttribute('data-bs-slide-to');\n\n if (slideIndex) {\n carousel.to(slideIndex);\n\n carousel._maybeEnableCycle();\n\n return;\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next();\n\n carousel._maybeEnableCycle();\n\n return;\n }\n\n carousel.prev();\n\n carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel);\n }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n parent: null,\n toggle: true\n};\nconst DefaultType$a = {\n parent: '(null|element)',\n toggle: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isTransitioning = false;\n this._triggerArray = [];\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n\n for (const elem of toggleList) {\n const selector = getSelectorFromElement(elem);\n const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem);\n }\n }\n\n this._initializeChildren();\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n }\n\n if (this._config.toggle) {\n this.toggle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$a;\n }\n\n static get DefaultType() {\n return DefaultType$a;\n }\n\n static get NAME() {\n return NAME$b;\n } // Public\n\n\n toggle() {\n if (this._isShown()) {\n this.hide();\n } else {\n this.show();\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return;\n }\n\n let activeChildren = []; // find active children\n\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n toggle: false\n }));\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return;\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n\n if (startEvent.defaultPrevented) {\n return;\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide();\n }\n\n const dimension = this._getDimension();\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE);\n\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n this._element.style[dimension] = 0;\n\n this._addAriaAndCollapsedClass(this._triggerArray, true);\n\n this._isTransitioning = true;\n\n const complete = () => {\n this._isTransitioning = false;\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n this._element.style[dimension] = '';\n EventHandler.trigger(this._element, EVENT_SHOWN$6);\n };\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n const scrollSize = `scroll${capitalizedDimension}`;\n\n this._queueCallback(complete, this._element, true);\n\n this._element.style[dimension] = `${this._element[scrollSize]}px`;\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return;\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n\n if (startEvent.defaultPrevented) {\n return;\n }\n\n const dimension = this._getDimension();\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n reflow(this._element);\n\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n for (const trigger of this._triggerArray) {\n const element = getElementFromSelector(trigger);\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false);\n }\n }\n\n this._isTransitioning = true;\n\n const complete = () => {\n this._isTransitioning = false;\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n this._element.classList.add(CLASS_NAME_COLLAPSE);\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n };\n\n this._element.style[dimension] = '';\n\n this._queueCallback(complete, this._element, true);\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW$7);\n } // Private\n\n\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle); // Coerce string values\n\n config.parent = getElement(config.parent);\n return config;\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return;\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n\n for (const element of children) {\n const selected = getElementFromSelector(element);\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected));\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent); // remove children if greater depth\n\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return;\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n element.setAttribute('aria-expanded', isOpen);\n }\n } // Static\n\n\n static jQueryInterface(config) {\n const _config = {};\n\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false;\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config);\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n event.preventDefault();\n }\n\n const selector = getSelectorFromElement(this);\n const selectorElements = SelectorEngine.find(selector);\n\n for (const element of selectorElements) {\n Collapse.getOrCreateInstance(element, {\n toggle: false\n }).toggle();\n }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n};\nconst DefaultType$9 = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n};\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._popper = null;\n this._parent = this._element.parentNode; // dropdown wrapper\n // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n this._inNavbar = this._detectNavbar();\n } // Getters\n\n\n static get Default() {\n return Default$9;\n }\n\n static get DefaultType() {\n return DefaultType$9;\n }\n\n static get NAME() {\n return NAME$a;\n } // Public\n\n\n toggle() {\n return this._isShown() ? this.hide() : this.show();\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return;\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n };\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._createPopper(); // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n\n this._element.focus();\n\n this._element.setAttribute('aria-expanded', true);\n\n this._menu.classList.add(CLASS_NAME_SHOW$6);\n\n this._element.classList.add(CLASS_NAME_SHOW$6);\n\n EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return;\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n };\n\n this._completeHide(relatedTarget);\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy();\n }\n\n super.dispose();\n }\n\n update() {\n this._inNavbar = this._detectNavbar();\n\n if (this._popper) {\n this._popper.update();\n }\n } // Private\n\n\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n\n if (hideEvent.defaultPrevented) {\n return;\n } // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n\n if (this._popper) {\n this._popper.destroy();\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW$6);\n\n this._element.classList.remove(CLASS_NAME_SHOW$6);\n\n this._element.setAttribute('aria-expanded', 'false');\n\n Manipulator.removeDataAttribute(this._menu, 'popper');\n EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n }\n\n _getConfig(config) {\n config = super._getConfig(config);\n\n if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n }\n\n return config;\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n }\n\n let referenceElement = this._element;\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent;\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference);\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference;\n }\n\n const popperConfig = this._getPopperConfig();\n\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n }\n\n _getPlacement() {\n const parentDropdown = this._parent;\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER;\n } // We need to trim the value because custom properties can also include spaces\n\n\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null;\n }\n\n _getOffset() {\n const {\n offset\n } = this._config;\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n\n return offset;\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }; // Disable Popper if we have a static display or Dropdown is in Navbar\n\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // todo:v6 remove\n\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }];\n }\n\n return { ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n };\n }\n\n _selectMenuItem({\n key,\n target\n }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n\n if (!items.length) {\n return;\n } // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n\n\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n return;\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle);\n\n if (!context || context._config.autoClose === false) {\n continue;\n }\n\n const composedPath = event.composedPath();\n const isMenuTarget = composedPath.includes(context._menu);\n\n if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n continue;\n } // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n\n\n if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue;\n }\n\n const relatedTarget = {\n relatedTarget: context._element\n };\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event;\n }\n\n context._completeHide(relatedTarget);\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n const isInput = /input|textarea/i.test(event.target.tagName);\n const isEscapeEvent = event.key === ESCAPE_KEY$2;\n const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return;\n }\n\n if (isInput && !isEscapeEvent) {\n return;\n }\n\n event.preventDefault(); // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n const instance = Dropdown.getOrCreateInstance(getToggleButton);\n\n if (isUpOrDownEvent) {\n event.stopPropagation();\n instance.show();\n\n instance._selectMenuItem(event);\n\n return;\n }\n\n if (instance._isShown()) {\n // else is escape and we check if it is shown\n event.stopPropagation();\n instance.hide();\n getToggleButton.focus();\n }\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n event.preventDefault();\n Dropdown.getOrCreateInstance(this).toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body;\n } // Public\n\n\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth;\n return Math.abs(window.innerWidth - documentWidth);\n }\n\n hide() {\n const width = this.getWidth();\n\n this._disableOverFlow(); // give padding to element to balance the hidden scrollbar width\n\n\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width); // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n\n\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow');\n\n this._resetElementAttributes(this._element, PROPERTY_PADDING);\n\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n }\n\n isOverflowing() {\n return this.getWidth() > 0;\n } // Private\n\n\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow');\n\n this._element.style.overflow = 'hidden';\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth();\n\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return;\n }\n\n this._saveInitialAttribute(element, styleProperty);\n\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n };\n\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty);\n\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue);\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty); // We only want to remove the property if the value is `null`; the value can also be zero\n\n if (value === null) {\n element.style.removeProperty(styleProperty);\n return;\n }\n\n Manipulator.removeDataAttribute(element, styleProperty);\n element.style.setProperty(styleProperty, value);\n };\n\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector);\n return;\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel);\n }\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true,\n // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n\n};\nconst DefaultType$8 = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n};\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isAppended = false;\n this._element = null;\n } // Getters\n\n\n static get Default() {\n return Default$8;\n }\n\n static get DefaultType() {\n return DefaultType$8;\n }\n\n static get NAME() {\n return NAME$9;\n } // Public\n\n\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n\n this._append();\n\n const element = this._getElement();\n\n if (this._config.isAnimated) {\n reflow(element);\n }\n\n element.classList.add(CLASS_NAME_SHOW$5);\n\n this._emulateAnimation(() => {\n execute(callback);\n });\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n\n this._emulateAnimation(() => {\n this.dispose();\n execute(callback);\n });\n }\n\n dispose() {\n if (!this._isAppended) {\n return;\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN);\n\n this._element.remove();\n\n this._isAppended = false;\n } // Private\n\n\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div');\n backdrop.className = this._config.className;\n\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE$4);\n }\n\n this._element = backdrop;\n }\n\n return this._element;\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement);\n return config;\n }\n\n _append() {\n if (this._isAppended) {\n return;\n }\n\n const element = this._getElement();\n\n this._config.rootElement.append(element);\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback);\n });\n this._isAppended = true;\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n\n};\nconst DefaultType$7 = {\n autofocus: 'boolean',\n trapElement: 'element'\n};\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isActive = false;\n this._lastTabNavDirection = null;\n } // Getters\n\n\n static get Default() {\n return Default$7;\n }\n\n static get DefaultType() {\n return DefaultType$7;\n }\n\n static get NAME() {\n return NAME$8;\n } // Public\n\n\n activate() {\n if (this._isActive) {\n return;\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus();\n }\n\n EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n\n EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n this._isActive = true;\n }\n\n deactivate() {\n if (!this._isActive) {\n return;\n }\n\n this._isActive = false;\n EventHandler.off(document, EVENT_KEY$5);\n } // Private\n\n\n _handleFocusin(event) {\n const {\n trapElement\n } = this._config;\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return;\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement);\n\n if (elements.length === 0) {\n trapElement.focus();\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus();\n } else {\n elements[0].focus();\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return;\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n backdrop: true,\n focus: true,\n keyboard: true\n};\nconst DefaultType$6 = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._isShown = false;\n this._isTransitioning = false;\n this._scrollBar = new ScrollBarHelper();\n\n this._addEventListeners();\n } // Getters\n\n\n static get Default() {\n return Default$6;\n }\n\n static get DefaultType() {\n return DefaultType$6;\n }\n\n static get NAME() {\n return NAME$7;\n } // Public\n\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n relatedTarget\n });\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = true;\n this._isTransitioning = true;\n\n this._scrollBar.hide();\n\n document.body.classList.add(CLASS_NAME_OPEN);\n\n this._adjustDialog();\n\n this._backdrop.show(() => this._showElement(relatedTarget));\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = false;\n this._isTransitioning = true;\n\n this._focustrap.deactivate();\n\n this._element.classList.remove(CLASS_NAME_SHOW$4);\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n }\n\n dispose() {\n for (const htmlElement of [window, this._dialog]) {\n EventHandler.off(htmlElement, EVENT_KEY$4);\n }\n\n this._backdrop.dispose();\n\n this._focustrap.deactivate();\n\n super.dispose();\n }\n\n handleUpdate() {\n this._adjustDialog();\n } // Private\n\n\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop),\n // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n });\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element);\n }\n\n this._element.style.display = 'block';\n\n this._element.removeAttribute('aria-hidden');\n\n this._element.setAttribute('aria-modal', true);\n\n this._element.setAttribute('role', 'dialog');\n\n this._element.scrollTop = 0;\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n\n if (modalBody) {\n modalBody.scrollTop = 0;\n }\n\n reflow(this._element);\n\n this._element.classList.add(CLASS_NAME_SHOW$4);\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate();\n }\n\n this._isTransitioning = false;\n EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n relatedTarget\n });\n };\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n if (event.key !== ESCAPE_KEY$1) {\n return;\n }\n\n if (this._config.keyboard) {\n event.preventDefault();\n this.hide();\n return;\n }\n\n this._triggerBackdropTransition();\n });\n EventHandler.on(window, EVENT_RESIZE$1, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog();\n }\n });\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return;\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition();\n\n return;\n }\n\n if (this._config.backdrop) {\n this.hide();\n }\n });\n });\n }\n\n _hideModal() {\n this._element.style.display = 'none';\n\n this._element.setAttribute('aria-hidden', true);\n\n this._element.removeAttribute('aria-modal');\n\n this._element.removeAttribute('role');\n\n this._isTransitioning = false;\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN);\n\n this._resetAdjustments();\n\n this._scrollBar.reset();\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n });\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE$3);\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const initialOverflowY = this._element.style.overflowY; // return if the following background transition hasn't yet completed\n\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return;\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden';\n }\n\n this._element.classList.add(CLASS_NAME_STATIC);\n\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC);\n\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY;\n }, this._dialog);\n }, this._dialog);\n\n this._element.focus();\n }\n /**\n * The following methods are used to handle overflowing modals\n */\n\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n\n const scrollbarWidth = this._scrollBar.getWidth();\n\n const isBodyOverflowing = scrollbarWidth > 0;\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = '';\n this._element.style.paddingRight = '';\n } // Static\n\n\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](relatedTarget);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n const target = getElementFromSelector(this);\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return;\n }\n\n EventHandler.one(target, EVENT_HIDDEN$4, () => {\n if (isVisible(this)) {\n this.focus();\n }\n });\n }); // avoid conflict when clicking modal toggler while another one is open\n\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide();\n }\n\n const data = Modal.getOrCreateInstance(target);\n data.toggle(this);\n});\nenableDismissTrigger(Modal);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n backdrop: true,\n keyboard: true,\n scroll: false\n};\nconst DefaultType$5 = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isShown = false;\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n\n this._addEventListeners();\n } // Getters\n\n\n static get Default() {\n return Default$5;\n }\n\n static get DefaultType() {\n return DefaultType$5;\n }\n\n static get NAME() {\n return NAME$6;\n } // Public\n\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n relatedTarget\n });\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = true;\n\n this._backdrop.show();\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide();\n }\n\n this._element.setAttribute('aria-modal', true);\n\n this._element.setAttribute('role', 'dialog');\n\n this._element.classList.add(CLASS_NAME_SHOWING$1);\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate();\n }\n\n this._element.classList.add(CLASS_NAME_SHOW$3);\n\n this._element.classList.remove(CLASS_NAME_SHOWING$1);\n\n EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n relatedTarget\n });\n };\n\n this._queueCallback(completeCallBack, this._element, true);\n }\n\n hide() {\n if (!this._isShown) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n this._focustrap.deactivate();\n\n this._element.blur();\n\n this._isShown = false;\n\n this._element.classList.add(CLASS_NAME_HIDING);\n\n this._backdrop.hide();\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n\n this._element.removeAttribute('aria-modal');\n\n this._element.removeAttribute('role');\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset();\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n };\n\n this._queueCallback(completeCallback, this._element, true);\n }\n\n dispose() {\n this._backdrop.dispose();\n\n this._focustrap.deactivate();\n\n super.dispose();\n } // Private\n\n\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n\n this.hide();\n }; // 'static' option will be translated to true, and booleans will keep their value\n\n\n const isVisible = Boolean(this._config.backdrop);\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n });\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return;\n }\n\n if (!this._config.keyboard) {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n\n this.hide();\n });\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](this);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n const target = getElementFromSelector(this);\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n if (isDisabled(this)) {\n return;\n }\n\n EventHandler.one(target, EVENT_HIDDEN$3, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus();\n }\n }); // avoid conflict when clicking a toggler of an offcanvas, while another is open\n\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide();\n }\n\n const data = Offcanvas.getOrCreateInstance(target);\n data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show();\n }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide();\n }\n }\n});\nenableDismissTrigger(Offcanvas);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i;\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i;\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase();\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue) || DATA_URL_PATTERN.test(attribute.nodeValue));\n }\n\n return true;\n } // Check if a regular expression validates the attribute.\n\n\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\n\nconst DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml;\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml);\n }\n\n const domParser = new window.DOMParser();\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase();\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove();\n continue;\n }\n\n const attributeList = [].concat(...element.attributes);\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName);\n }\n }\n }\n\n return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n allowList: DefaultAllowlist,\n content: {},\n // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n};\nconst DefaultType$4 = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n};\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n};\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n } // Getters\n\n\n static get Default() {\n return Default$4;\n }\n\n static get DefaultType() {\n return DefaultType$4;\n }\n\n static get NAME() {\n return NAME$5;\n } // Public\n\n\n getContent() {\n return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n }\n\n hasContent() {\n return this.getContent().length > 0;\n }\n\n changeContent(content) {\n this._checkContent(content);\n\n this._config.content = { ...this._config.content,\n ...content\n };\n return this;\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div');\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector);\n }\n\n const template = templateWrapper.children[0];\n\n const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '));\n }\n\n return template;\n } // Private\n\n\n _typeCheckConfig(config) {\n super._typeCheckConfig(config);\n\n this._checkContent(config.content);\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({\n selector,\n entry: content\n }, DefaultContentType);\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template);\n\n if (!templateElement) {\n return;\n }\n\n content = this._resolvePossibleFunction(content);\n\n if (!content) {\n templateElement.remove();\n return;\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement);\n\n return;\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content);\n return;\n }\n\n templateElement.textContent = content;\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg(this) : arg;\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = '';\n templateElement.append(element);\n return;\n }\n\n templateElement.textContent = element.textContent;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 0],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' + '
' + '
' + '
',\n title: '',\n trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n};\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n }\n\n super(element, config); // Private\n\n this._isEnabled = true;\n this._timeout = 0;\n this._isHovered = null;\n this._activeTrigger = {};\n this._popper = null;\n this._templateFactory = null;\n this._newContent = null; // Protected\n\n this.tip = null;\n\n this._setListeners();\n\n if (!this._config.selector) {\n this._fixTitle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$3;\n }\n\n static get DefaultType() {\n return DefaultType$3;\n }\n\n static get NAME() {\n return NAME$4;\n } // Public\n\n\n enable() {\n this._isEnabled = true;\n }\n\n disable() {\n this._isEnabled = false;\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled;\n }\n\n toggle() {\n if (!this._isEnabled) {\n return;\n }\n\n this._activeTrigger.click = !this._activeTrigger.click;\n\n if (this._isShown()) {\n this._leave();\n\n return;\n }\n\n this._enter();\n }\n\n dispose() {\n clearTimeout(this._timeout);\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n }\n\n this._disposePopper();\n\n super.dispose();\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements');\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n const shadowRoot = findShadowRoot(this._element);\n\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return;\n } // todo v6 remove this OR make it optional\n\n\n this._disposePopper();\n\n const tip = this._getTipElement();\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n\n const {\n container\n } = this._config;\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip);\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n }\n\n this._popper = this._createPopper(tip);\n tip.classList.add(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n\n if (this._isHovered === false) {\n this._leave();\n }\n\n this._isHovered = false;\n };\n\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n\n hide() {\n if (!this._isShown()) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n const tip = this._getTipElement();\n\n tip.classList.remove(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false;\n this._activeTrigger[TRIGGER_FOCUS] = false;\n this._activeTrigger[TRIGGER_HOVER] = false;\n this._isHovered = null; // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return;\n }\n\n if (!this._isHovered) {\n this._disposePopper();\n }\n\n this._element.removeAttribute('aria-describedby');\n\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n };\n\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n\n update() {\n if (this._popper) {\n this._popper.update();\n }\n } // Protected\n\n\n _isWithContent() {\n return Boolean(this._getTitle());\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n }\n\n return this.tip;\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml(); // todo: remove this check on v6\n\n\n if (!tip) {\n return null;\n }\n\n tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2); // todo: on v6 the following can be achieved with CSS only\n\n tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n const tipId = getUID(this.constructor.NAME).toString();\n tip.setAttribute('id', tipId);\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE$2);\n }\n\n return tip;\n }\n\n setContent(content) {\n this._newContent = content;\n\n if (this._isShown()) {\n this._disposePopper();\n\n this.show();\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content);\n } else {\n this._templateFactory = new TemplateFactory({ ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n });\n }\n\n return this._templateFactory;\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n };\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n } // Private\n\n\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n }\n\n _isAnimated() {\n return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n }\n\n _createPopper(tip) {\n const placement = typeof this._config.placement === 'function' ? this._config.placement.call(this, tip, this._element) : this._config.placement;\n const attachment = AttachmentMap[placement.toUpperCase()];\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n }\n\n _getOffset() {\n const {\n offset\n } = this._config;\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n\n return offset;\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg.call(this._element) : arg;\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [{\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }, {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n }, {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n }\n }]\n };\n return { ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n };\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ');\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context.toggle();\n });\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n\n context._enter();\n });\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n\n context._leave();\n });\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide();\n }\n };\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title');\n\n if (!title) {\n return;\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title);\n }\n\n this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n\n\n this._element.removeAttribute('title');\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true;\n return;\n }\n\n this._isHovered = true;\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show();\n }\n }, this._config.delay.show);\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return;\n }\n\n this._isHovered = false;\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide();\n }\n }, this._config.delay.hide);\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout);\n this._timeout = setTimeout(handler, timeout);\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true);\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element);\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute];\n }\n }\n\n config = { ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n };\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container);\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n };\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString();\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString();\n }\n\n return config;\n }\n\n _getDelegateConfig() {\n const config = {};\n\n for (const key in this._config) {\n if (this.constructor.Default[key] !== this._config[key]) {\n config[key] = this._config[key];\n }\n }\n\n config.selector = false;\n config.trigger = 'manual'; // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n\n return config;\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy();\n\n this._popper = null;\n }\n\n if (this.tip) {\n this.tip.remove();\n this.tip = null;\n }\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = { ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' + '
' + '

' + '
' + '
',\n trigger: 'click'\n};\nconst DefaultType$2 = { ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n};\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default$2;\n }\n\n static get DefaultType() {\n return DefaultType$2;\n }\n\n static get NAME() {\n return NAME$3;\n } // Overrides\n\n\n _isWithContent() {\n return this._getTitle() || this._getContent();\n } // Private\n\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n };\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content);\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n offset: null,\n // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n offset: '(number|null)',\n // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n};\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config); // this._element is the observablesContainer and config.target the menu links wrapper\n\n this._targetLinks = new Map();\n this._observableSections = new Map();\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n this._activeTarget = null;\n this._observer = null;\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n };\n this.refresh(); // initialize\n } // Getters\n\n\n static get Default() {\n return Default$1;\n }\n\n static get DefaultType() {\n return DefaultType$1;\n }\n\n static get NAME() {\n return NAME$2;\n } // Public\n\n\n refresh() {\n this._initializeTargetsAndObservables();\n\n this._maybeEnableSmoothScroll();\n\n if (this._observer) {\n this._observer.disconnect();\n } else {\n this._observer = this._getNewObserver();\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section);\n }\n }\n\n dispose() {\n this._observer.disconnect();\n\n super.dispose();\n } // Private\n\n\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body; // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n }\n\n return config;\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return;\n } // unregister any previous listeners\n\n\n EventHandler.off(this._config.target, EVENT_CLICK);\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash);\n\n if (observableSection) {\n event.preventDefault();\n const root = this._rootElement || window;\n const height = observableSection.offsetTop - this._element.offsetTop;\n\n if (root.scrollTo) {\n root.scrollTo({\n top: height,\n behavior: 'smooth'\n });\n return;\n } // Chrome 60 doesn't support `scrollTo`\n\n\n root.scrollTop = height;\n }\n });\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n };\n return new IntersectionObserver(entries => this._observerCallback(entries), options);\n } // The logic of selection\n\n\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n\n this._process(targetElement(entry));\n };\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n this._previousScrollData.parentScrollTop = parentScrollTop;\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null;\n\n this._clearActiveClass(targetElement(entry));\n\n continue;\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop; // if we are scrolling down, pick the bigger offsetTop\n\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry); // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n\n if (!parentScrollTop) {\n return;\n }\n\n continue;\n } // if we are scrolling up, pick the smallest offsetTop\n\n\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry);\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map();\n this._observableSections = new Map();\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue;\n }\n\n const observableSection = SelectorEngine.findOne(anchor.hash, this._element); // ensure that the observableSection exists & is visible\n\n if (isVisible(observableSection)) {\n this._targetLinks.set(anchor.hash, anchor);\n\n this._observableSections.set(anchor.hash, observableSection);\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return;\n }\n\n this._clearActiveClass(this._config.target);\n\n this._activeTarget = target;\n target.classList.add(CLASS_NAME_ACTIVE$1);\n\n this._activateParents(target);\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n relatedTarget: target\n });\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n return;\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both
    and