From 20092c37a6b553d03e4d398592c68049c0d0a6a1 Mon Sep 17 00:00:00 2001
From: mivanit <mivanits@umich.edu>
Date: Mon, 13 May 2024 23:00:00 -0400
Subject: [PATCH] update maze-dataset dep and poetry lockfile (#213)

* update maze-dataset dep and poetry lockfile

* fixed some imports

* update another import

* another import fix

* hookedtransformer maze tokenizer compat

* update wandb dep

* update transformer_lens dep

* fix import in notebook

* re-ran this notebook. was failing in CI for some reason, idk?

* better error when dataset cfgs dont match

* (run format) - working locally, but configs differ in CI

```
ValueError: ('dataset has different config than cfg.dataset_cfg, and allow_dataset_override iscollect_generation_meta', 'args': (), 'kwargs': {} False', "{'applied_filters': {'self': [{'name': '}], 'other': []}}")
```

probably because we are loading a dataset with the new format. will patch but it will be hacky

* special case for applied filters diff
---
 .../evaluation/baseline_models.py             |    2 +-
 maze_transformer/evaluation/eval_model.py     |    6 +-
 maze_transformer/evaluation/plotting.py       |    2 +-
 maze_transformer/mechinterp/plot_attention.py |    2 +-
 .../mechinterp/residual_stream_structure.py   |    2 +-
 maze_transformer/training/train_model.py      |   26 +-
 notebooks/residual_stream_decoding.ipynb      |   16 +-
 notebooks/train_model.ipynb                   |  197 +-
 poetry.lock                                   | 2724 +++++++++--------
 pyproject.toml                                |    7 +-
 10 files changed, 1496 insertions(+), 1488 deletions(-)

diff --git a/maze_transformer/evaluation/baseline_models.py b/maze_transformer/evaluation/baseline_models.py
index fdb48afd..ac7a318c 100644
--- a/maze_transformer/evaluation/baseline_models.py
+++ b/maze_transformer/evaluation/baseline_models.py
@@ -15,8 +15,8 @@
     get_origin_tokens,
     get_path_tokens,
     get_target_tokens,
-    strings_to_coords,
 )
+from maze_dataset.tokenization.util import strings_to_coords
 from transformer_lens import HookedTransformer
 
 from maze_transformer.training.config import ConfigHolder
diff --git a/maze_transformer/evaluation/eval_model.py b/maze_transformer/evaluation/eval_model.py
index 6546993d..aa09ce44 100644
--- a/maze_transformer/evaluation/eval_model.py
+++ b/maze_transformer/evaluation/eval_model.py
@@ -16,12 +16,12 @@
 )
 from maze_dataset.tokenization import MazeTokenizer
 from maze_dataset.tokenization.token_utils import (
-    WhenMissing,
     get_context_tokens,
     get_path_tokens,
     remove_padding_from_token_str,
-    strings_to_coords,
 )
+from maze_dataset.tokenization.util import strings_to_coords
+from maze_dataset.utils import WhenMissing
 
 # muutils
 from muutils.mlutils import chunks
@@ -143,7 +143,7 @@ def predict_maze_paths(
             smart_max_new_tokens
         ), "if max_new_tokens is None, smart_max_new_tokens must be True"
 
-    maze_tokenizer: MazeTokenizer = model.config.maze_tokenizer
+    maze_tokenizer: MazeTokenizer = model.tokenizer._maze_tokenizer
 
     contexts_lists: list[list[str]] = [
         get_context_tokens(tokens) for tokens in tokens_batch
diff --git a/maze_transformer/evaluation/plotting.py b/maze_transformer/evaluation/plotting.py
index ec01786d..04e556a1 100644
--- a/maze_transformer/evaluation/plotting.py
+++ b/maze_transformer/evaluation/plotting.py
@@ -32,7 +32,7 @@ def plot_predicted_paths(
     if n_mazes is None:
         n_mazes = len(dataset)
 
-    dataset_tokens = dataset.as_tokens(model.config.maze_tokenizer)[:n_mazes]
+    dataset_tokens = dataset.as_tokens(model.tokenizer._maze_tokenizer)[:n_mazes]
 
     # predict
     predictions: list[list[str | tuple[int, int]]] = predict_maze_paths(
diff --git a/maze_transformer/mechinterp/plot_attention.py b/maze_transformer/mechinterp/plot_attention.py
index c39abfac..43f319b6 100644
--- a/maze_transformer/mechinterp/plot_attention.py
+++ b/maze_transformer/mechinterp/plot_attention.py
@@ -19,7 +19,7 @@
 from maze_dataset.plotting.plot_tokens import plot_colored_text
 from maze_dataset.plotting.print_tokens import color_tokens_cmap
 from maze_dataset.tokenization import MazeTokenizer
-from maze_dataset.tokenization.token_utils import coord_str_to_tuple_noneable
+from maze_dataset.tokenization.util import coord_str_to_tuple_noneable
 
 # Utilities
 from muutils.json_serialize import SerializableDataclass, serializable_dataclass
diff --git a/maze_transformer/mechinterp/residual_stream_structure.py b/maze_transformer/mechinterp/residual_stream_structure.py
index 6dffb196..36c6b4d9 100644
--- a/maze_transformer/mechinterp/residual_stream_structure.py
+++ b/maze_transformer/mechinterp/residual_stream_structure.py
@@ -12,7 +12,7 @@
 # maze_dataset
 from maze_dataset.constants import _SPECIAL_TOKENS_ABBREVIATIONS
 from maze_dataset.tokenization import MazeTokenizer
-from maze_dataset.tokenization.token_utils import strings_to_coords
+from maze_dataset.tokenization.util import strings_to_coords
 
 # scipy
 from scipy.spatial.distance import pdist, squareform
diff --git a/maze_transformer/training/train_model.py b/maze_transformer/training/train_model.py
index 3421414b..9b179ee1 100644
--- a/maze_transformer/training/train_model.py
+++ b/maze_transformer/training/train_model.py
@@ -1,5 +1,6 @@
 import json
 import typing
+import warnings
 from pathlib import Path
 from typing import Union
 
@@ -122,9 +123,28 @@ def train_model(
                     f"passed dataset has different config than cfg.dataset_cfg, but allow_dataset_override is True, so using passed dataset"
                 )
             else:
-                raise ValueError(
-                    f"dataset has different config than cfg.dataset_cfg, and allow_dataset_override is False"
-                )
+                datasets_cfg_diff: dict = dataset.cfg.diff(cfg.dataset_cfg)
+                if datasets_cfg_diff == {
+                    "applied_filters": {
+                        "self": [
+                            {
+                                "name": "collect_generation_meta",
+                                "args": (),
+                                "kwargs": {},
+                            }
+                        ],
+                        "other": [],
+                    }
+                }:
+                    warnings.warn(
+                        f"dataset has different config than cfg.dataset_cfg, but the only difference is in applied_filters, so using passed dataset. This is due to fast dataset loading collecting generation metadata for performance reasons"
+                    )
+
+                else:
+                    raise ValueError(
+                        f"dataset has different config than cfg.dataset_cfg, and allow_dataset_override is False",
+                        f"{datasets_cfg_diff = }",
+                    )
 
     logger.progress(f"finished getting training dataset with {len(dataset)} samples")
     # validation dataset, if applicable
diff --git a/notebooks/residual_stream_decoding.ipynb b/notebooks/residual_stream_decoding.ipynb
index b2fba639..f70536c0 100644
--- a/notebooks/residual_stream_decoding.ipynb
+++ b/notebooks/residual_stream_decoding.ipynb
@@ -28,7 +28,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\mivan\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\maze-transformer-2cGx2R0F-py3.11\\Lib\\site-packages\\_distutils_hack\\__init__.py:33: UserWarning: Setuptools is replacing distutils.\n",
+      "c:\\Users\\mivan\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\maze-transformer-2cGx2R0F-py3.11\\Lib\\site-packages\\_distutils_hack\\__init__.py:26: UserWarning: Setuptools is replacing distutils.\n",
       "  warnings.warn(\"Setuptools is replacing distutils.\")\n"
      ]
     }
@@ -71,7 +71,7 @@
     "from maze_dataset import MazeDataset, MazeDatasetConfig, SolvedMaze, LatticeMaze, SPECIAL_TOKENS\n",
     "from maze_dataset.tokenization import MazeTokenizer, TokenizationMode\n",
     "from maze_dataset.plotting.print_tokens import color_maze_tokens_AOTP\n",
-    "from maze_dataset.tokenization.token_utils import strings_to_coords, coords_to_strings\n",
+    "from maze_dataset.tokenization.util import strings_to_coords, coords_to_strings\n",
     "from maze_dataset.constants import _SPECIAL_TOKENS_ABBREVIATIONS\n",
     "\n",
     "from maze_transformer.training.config import ConfigHolder, ZanjHookedTransformer, BaseGPTConfig\n",
@@ -101,7 +101,7 @@
     {
      "data": {
       "text/plain": [
-       "<torch.autograd.grad_mode.set_grad_enabled at 0x2426ecd9510>"
+       "<torch.autograd.grad_mode.set_grad_enabled at 0x1fd1cef5550>"
       ]
      },
      "execution_count": 2,
@@ -297,7 +297,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x1500 with 2 Axes>"
       ]
@@ -330,7 +330,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -362,7 +362,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 50 Axes>"
       ]
@@ -398,7 +398,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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kPxWnnXZa7NixIx577LGYnJyMo446Ks4444x4/vOfXzpDkQ0AQGlz3dEjl5xjGRwcjIjpN0Nu3749tmzZUvp7FdkAACwqo6OjsXPnzpnbu3btih07dsSKFStizZo18ZnPfCaOOuqoWLNmTdxyyy3xu7/7u3HeeefF2WefXfocsyqyu79zWNR7+37xF87B6An7s+TOqJfvoZmLPaNLs+Zf99gzs+bHYVNZ45fuzHO/aUzk/b3Ol+98+rlR78nzM6sdm/dnds+3js2a3/3cx7Lmv/Iz78qa/+K3/iBr/nf/5jlZchsT41ly59tzzvpxdC/vyZK9/Y61WXJbluzNeynprsms8dF15Nx2fiir9wd5n5cbc2v3/cW549VZIV7Itm/fHhs2bJi5vXnz5oiIuOiii+Kqq66Ke++9NzZv3hz3339/HHPMMfGbv/mbcemll87qHFayAQAorVF0RaNC7SJzGcv69et/7psY3/GOd8Q73vGOpzIsW/gBAEBqimwAAEhMuwgAAKUVUYtm5H1PwGwUFRpLOyvZAACQmCIbAAAS0y4CAEBpC2F3kUOhmqMCAIAOpsgGAIDEtIsAAFBas6hFs6jOjh5VGks7K9kAAJCYIhsAABLTLgIAQGmN6IpGhdZpqzSWdtUcFQAAdDBFNgAAJKZdBACA0uwuUo6VbAAASEyRDQAAiWkXAQCgtGZ0RbNC67RVGku7ao4KAAA6mCIbAAAS0y4CAEBpjaIWjQrt6FGlsbQrVWQXRREREY2J8WwDae7bny07IiLqRdb4WtdU1vyikfcOVGS+gzYm8uQ2H79Ptu6jnW5mrk1mnGvjeX9WReap3LU3053pcc3xfD/7iIipscms+bkep1v3yYU213L+Ppp7896XGuOZnxfy3lXz/3wmMj+vZZoKC+15bTErVWSPjIxERMTOK6/IOhiYq5GRkRgcHJzvYTxlrbn2o6vMtYXqzvkewFO00Obal8775DyPBA5uocy1xaxUkT00NBTDw8PR398ftVo1l+RZnIqiiJGRkRgaGprvoSRhrlFV5hocGp0w11yMppxSRXZXV1esWrUq91hgThbSX/rmGlVmrsGhsZDm2mJmdxEAAEjM7iIAAJRWFF3RLKqzTltUaCztqjkqAADoYIpsAABITLsIAAClNaIWjajOjh5VGks7K9kAAJCYIhsAABLTLgIAQGnNoloXgGlW9Ar0VrIBACAxRTYAACSmXQQAgNKaFbsYTZXG0q6aowIAgA6myAYAgMS0iwAAUFozatGs0AVgqjSWdlayAQAgMUU2AAAkpl0EAIDSGkUtGhW6GE2VxtLOSjYAACSmyAYAgMS0iwAAUJqL0ZRTzVEBAEAHU2QDAEBi2kUAACitGbVoVmhHDxejAQCARUKRDQAAiSmyAQAgMT3ZAACUVkStUn3QRYXG0s5KNgAAJKbIBgCAxLSLAABQWrOo2BZ+FRpLu1JFdrPZjN27d0d/f3/UatX8h7A4FUURIyMjMTQ0FF1dnf/CjLlGVZlrcGgstLm2mJUqsnfv3h2rV6/OPRaYs+Hh4Vi1atV8D+MpM9eoOnMNDo2FMtcWs1JFdn9/f0REvPgF74klS3qzDGT47L4suS37hyay5j/9yMey5j9w21FZ88998fas+Z//9mlZcpvj43HPZe+fuY92uta/4/QNl8SSJXnmRFHPu2o3NZB35eWRV+7Nmt9Vb+bNz7wwtebwR7Lk7t87Gdf96icW3Fxb9T/eE11L8zyvHf3/9WTJbRn8/sNZ85uH5fm5tDzy+5NZ8x+6fUXW/PpEnsfS5vh43PXftlR6rjWLrmgW1Vllr9JY2pUqslsvpS1Z0pvtib+rL2+R3bU0b2GxZPl41vzcP5/ew7qz5nctzTv+hfJy7xNzrS+WdHdmkd3szvtg17UsbxFcz15kF1nzu5fnLewW2lzrWtobXcvyzLUl3Xl/F0vqeYvgZub8+rK896XczztdmefCQplri1k1S38AAOhgdhcBAKA0u4uUYyUbAAASU2QDAEBi2kUAACitGbVoRnVaNKo0lnZWsgEAIDFFNgAAJKZdBACA0uwuUo6VbAAASEyRDQAAiWkXAQCgNO0i5VjJBgCAxBTZAACQmHYRAABK0y5SjpVsAABITJENAACJaRcBAKA07SLlWMkGAIDEFNkAAJCYdhEAAEorIqIZ1WnRKOZ7AE9iVkX2F/7+P8bAwECWgTzjDz+cJXfGnu6s8ffG07Lm9x47ljX/7+84JWv+xhd9N0vu5OhkfCJL8vy698wl0dWX52/g29+zOUtuy9mnX541f/BDeV+Au2tjnse4lqm+vE8HzRc/kiW3qOqz2FNUPNYTxWRPluz+n4xmyW358g+3Zs1/+cvz5vf95fKs+St7s8bHit+5I0vu1Nhk5EnmUNMuAgAAiWkXAQCgNLuLlGMlGwAAElNkAwBAYtpFAAAoTbtIOVayAQAgMUU2AAAkpl0EAIDStIuUk24l+/LLI7ZsOfjntmyZ/jxQfeYydD7zGOZduiK7Xo+47LKfndRbtkx/vF5PdiogI3MZOp95DPMuXZF96aURV1xx4KRuTeYrrpj+PFB95jJ0PvOYjFrtIlU6ZuuGG26Ic889N4aGhqJWq8U111xzwOdHR0fjbW97W6xatSqWLl0ap5xySlx55ZWzOkfanuzWpL3ssoj3vS9ictJkhk5kLkPnM4/hSY2NjcW6devizW9+c1xwwQU/8/nNmzfHN77xjfjrv/7rOO644+KrX/1q/If/8B9iaGgoXv3qV5c6R/rdRS69NKKnZ3oy9/SYzNCpzGXofOYxHNSmTZvife97X5x//vkH/fy3vvWtuOiii2L9+vVx3HHHxVvf+tZYt25d/Mu//Evpc6QvsrdseWIyT04++RsvgGozl6HzmcdkUBS1yh0REXv27DngmJiYmPO/8cUvfnF8/vOfj3vuuSeKooh//Md/jB//+Mdx9tlnl85IW2S393tNTPxsPxjQGcxl6HzmMYvM6tWrY3BwcObYunXrnLM+8pGPxCmnnBKrVq2Knp6e2LhxY2zbti1e+tKXls5I15N9sDdUtPeDtd8Gqstchs5nHrMIDQ8Px8DAwMzt3t7eOWd95CMfiZtuuik+//nPx9q1a+OGG26Iiy++OIaGhuKss84qlZGuyG40Dv6GitbtRiPZqYCMzGXofOYxGTWjFs2ozgVgWmMZGBg4oMieq3379sV73/ve+NznPhevetWrIiLiec97XuzYsSM+9KEPzUOR/fM2tvfXMnQOcxk6n3kMczY1NRVTU1PR1XVgV3W9Xo9ms1k6x2XVAQBYVEZHR2Pnzp0zt3ft2hU7duyIFStWxJo1a+JlL3tZvPvd746lS5fG2rVr4/rrr4+/+qu/ig9/+MOlz6HIBgCgtLleACaXuYxl+/btsWHDhpnbmzdvjoiIiy66KK666qr49Kc/HZdcckm8/vWvj4cffjjWrl0b73//++O3f/u3S59DkQ0AwKKyfv36KIriST+/cuXK+MQnPvGUzpF+n2wAAFjkrGQDAFBa+wVgqqBKY2k3qyL7he/dFvXeviwDWd7I+wM67JU/zZr/2L48P5eW0QeXZ83/g5d8Lmv+1ls2Zclt7B3PkjvfDv9RRL0nT/ZLXvNHeYIfN3lSf9b8PReOZM3f9/BU1vxlu7qz5u+8aW2W3Ob4wpxr9b1d0dXM86JuY1mmSfy4M8/PO5eLFXnvq7vPn8yaX19SfheIuZj41HFZchuTC3OuLUbaRQAAIDHtIgAAlLYQdhc5FKxkAwBAYopsAABITLsIAACl2V2kHCvZAACQmCIbAAAS0y4CAEBpRcV2F9EuAgAAi4QiGwAAEtMuAgBAaUVEFMV8j+IJFRrKAaxkAwBAYopsAABITLsIAAClNaMWtajOjh7NCo2lnZVsAABITJENAACJaRcBAKC0oqhV6gIwVRpLOyvZAACQmCIbAAAS0y4CAEBpzaIWtQq1aDQrNJZ2VrIBACAxRTYAACSmXQQAgNKKYvqoiiqNpd2siuy9xzajq6+ZZSAnn3ZnltyWH9x+bNb8el8ja/7Tj30ka/7lX7swa/4L/s1tWXKnxibj9izJ8+vBDRPRtTRPj9kR1/VmyW3J3Rq396fL855gSZ7HuJbD7s77bDB5WJ5fQGOymj2PT1XPI7Wo9+b5t921sS9LbssRt+S9Lz18wVjW/NOPvSdr/gN/cELW/L5L8tQt+8cmIj6eJZpDTLsIAAAkpl0EAIDSXIymHCvZAACQmCIbAAAS0y4CAEBp2kXKsZINAACJKbIBACAx7SIAAJTWLGpRq1CLRrNCY2lnJRsAABJTZAMAQGLaRQAAKK0opo+qqNJY2lnJBgCAxBTZAACQmHYRAABKm24Xqc6OHtpFAABgkVBkAwBAYtpFAAAorShqFWsXqc5Y2lnJBgCAxBTZAACQmHYRAABKKx4/qqJKY2lnJRsAABJTZAMAQGLaRQAAKM3uIuXMqsh+yQt/ED2H9WQZyD/868lZclueddx9WfNv+9djs+bv+eHRWfNjZSNr/PY712TJbe4dz5I738444Y7oXp5nrv3kS3nn2v1nZI2P2lTeB9OTT9qdNf+h69dmzd+3fiRLbmPveMRfZomeV3ufMRldSzO9qNvIe199+IK8j3+H9+/Nmv/Dnz49a/7S33s0a/4DjxyeJbexdyJLLoeedhEAAEhMuwgAAOXZXqQUK9kAAJCYIhsAABLTLgIAQHkV210kqjSWNlayAQAgMUU2AAAkpl0EAIDSimL6qIoqjaWdlWwAAEhMkQ0AAIlpFwEAoLSiYruLVGks7axkAwBAYopsAABITLsIAADlFbVqXQCmSmNpYyUbAAASU2QDAEBi2kUAACjNxWjKsZINAACJKbIBACAx7SIAAJRXPH5URZXG0sZKNgAAJKbIBgCAxLSLAABQWlHUoqjQBWCqNJZ2syqyb3loKOr7erMMpOvR7iy5LftX5120Xzo0mjX/xS+4I2v+1//Xc7LmL1s2kSW3EZNZcufb/77n2Kgv68uSPfXCLLEzuvfknWvLfvmhrPk/+ekRWfOXDOV9Mqh/pz9P8ETex+j58vRjHo368jzPa70fWZElt+XO1+R5jGip/+2yrPn7np/3saJ2T965dsbrv5cld3J0Mn6cJZlDTbsIAAAkpl0EAIDZqeiOHlViJRsAABJTZAMAQGLaRQAAKM3uIuVYyQYAgMQU2QAAkJh2EQAAyiuiWruLVGksbaxkAwBAYopsAABITLsIAACzUHv8qIoqjeUJVrIBACAxRTYAACSmXQQAgPLsLlKKlWwAAEhMkQ0AwKJyww03xLnnnhtDQ0NRq9XimmuuOeDztVrtoMcf/dEflT6HIhsAgPKKCh6zNDY2FuvWrYtt27Yd9PP33nvvAcfHP/7xqNVqceGFF5Y+h55sAAAWlU2bNsWmTZue9PMrV6484Pa1114bGzZsiBNOOKH0ORTZAAB0vD179hxwu7e3N3p7e59y7v333x9f+tKX4uqrr57V92kXAQCgvKJWvSMiVq9eHYODgzPH1q1bk/xzr7766ujv748LLrhgVt9nJRsAgI43PDwcAwMDM7dTrGJHRHz84x+P17/+9dHX1zer71NkAwDQ8QYGBg4oslO48cYb49Zbb42/+Zu/mfX3KrIBACitKKaPqsg5lo997GNx2mmnxbp162b9vbMqsveM9EVXY3ZL5WW96iU3Z8lt2TlyVNb8t5z07az5f/69l2bNf9v6r2XN/+zdv5Qld39jIkvufJsc642uZpqXuf5PtczvxDjyjPuy5o+M5/m5tLxk7U+y5n99/KSs+c8/4c4suVNjk3Hbf88SPa8evuWo6JrlS8BlLX3ro1lyW1b8/dOy5o8dXcua3/tg1vgYe9HerPk33bM2S25j78J8Xqua0dHR2Llz58ztXbt2xY4dO2LFihWxZs2aiJh+I+VnPvOZ+O//fW4PflayAQBYVLZv3x4bNmyYub158+aIiLjoooviqquuioiIT3/601EURfz6r//6nM6hyAYAoLw5XgAmmzmMZf369VH8gj6Tt771rfHWt751joOyhR8AACSnyAYAgMS0iwAAUF7bBWAqoUpjaWMlGwAAElNkAwBAYtpFAAAorVZMH1VRpbG0s5INAACJKbIBACAx7SIAAJS3AC5GcyhYyQYAgMQU2QAAkJh2EQAAynMxmlKsZAMAQGKKbAAASEy7CAAA5dldpBQr2QAAkJgiGwAAEtMuAgBAedpFSrGSDQAAiSmyAQAgsVLtIkUxvQ7f3DeRbSCTo1PZsiMi9o/lG3tExPjS/Vnzm3vHs+aPj+Ydf66ff2PvdG7rPtrpnphr+X7ftX15u8Ryz7VG3viYHJ3Mmp/zdxsRMTWWZ/yt3AU318bz/T5aj0+51Cbz3pea9bwX+GjUs8Znf95sNPI8b3bE85p2kVJKPduOjIxERMSdF38o20D+MlvyofH17Gf4Rtb0S7OmR+Qe/8jISAwODmY9x6HQmmu73/WBeR7J3A3P9wCeoh/O9wCeotw//4U21+78wJZ5Hgkc3EKZa4tZqSJ7aGgohoeHo7+/P2q1al66ksWpKIoYGRmJoaGh+R5KEuYaVWWuwaGx0ObaYlaqyO7q6opVq1blHgvMyUL6S99co8rMNTg0Kj/Xitr0URVVGksbb3wEAIDEFNkAAJCYi9EAAFBarZg+qqJKY2lnJRsAABJTZAMAQGLaRQAAKM/FaEqxkg0AAIkpsgEAIDFFNgAAJKbIBgCAxBTZAACQmN1FAAAorRbVugBMbb4H8CSsZAMAQGKKbAAASEy7CAAA5RW16aMqqjSWNlayAQAgMUU2AAAkpl0EAIDyisePqqjSWNpYyQYAgMQU2QAAkJh2EQAAytMuUoqVbAAASEyRDQAAiWkXAQCgtFoxfVRFlcbSzko2AAAkpsgGAIDEtIsAAFCe3UVKsZINAACJKbIBACAx7SIAAJSnXaSUUkV2s9mM3bt3R39/f9RqtdxjgtKKooiRkZEYGhqKrq7Of2HGXKOqzDU4NBbaXFvMShXZu3fvjtWrV+ceC8zZ8PBwrFq1ar6H8ZSZa1SduQaHxkKZa4tZqSK7v78/IiJO2/j7Ue/uyzKQB86byJI7Y/fSrPE9D+ddCdm7dn/W/H9zyq6s+T+4b2WW3Oa+idj17z88cx/tdK1/x3P/6uKoL+vNco7eq56WJbfl3tdMZc1fdljex4rnP304a35utzw0lCW3sXcivn/Rny24ufbvv/LK6FneneUcX9p5apbcltUfzfu889i792XNH9mb5zGu5fXP+l9Z83fsyfNH2tTYZPz9+f9vpeeai9GUU6rIbr2UVu/uiyWZiuyuZZlfruvLM+6Wem/e8XctzVtkdy/vyZpfX5b3579QXu6dmWvLerMV2bnmcEvXsnrW/PqyrPHRc1jeuZBbfV/ewmWhzbWe5d3Re1ieIrsr8+PekiV5fxf1Zc28+ZH359OX6ffa0t3I+1ixUObaYqbZBwAAErO7CAAA5RW16aMqqjSWNlayAQAgMUU2AAAkpl0EAIDyXIymFCvZAACQmCIbAAAS0y4CAEBpLkZTjpVsAABITJENAACJaRcBAKA8u4uUYiUbAAASU2QDAEBi2kUAACivYruLaBcBAIBFQpENAACJaRcBAKA8u4uUYiUbAAASU2QDAEBi2kUAAChPu0gpsyqyR9bUo95bzzKQFV9emiW35eFTs8ZH90je/K7xvC86PDaR9+d/2NKJLLmNYjJL7nyb/Icjo97blyV7aWN/ltyWE/5n1vh47D/nHf8/3vC8rPndY7Ws+cUpeR6MGnsr+iz2FN30wHGxZKw3S/aKa5dlyW0ZP6qZNf/IzWNZ8x/+rcOy5v/FPa/Imt9c3siTu288Sy6HnnYRAABITLsIAACl1Sp2MZoqjaWdlWwAAEhMkQ0AAIkpsgEAIDFFNgAAJKbIBgCAxOwuAgBAeS5GU4qVbAAASEyRDQAAiWkXAQCgNBejKcdKNgAAi8oNN9wQ5557bgwNDUWtVotrrrnmZ77mhz/8Ybz61a+OwcHBWL58eZx++ulx1113lT6HIhsAgEVlbGws1q1bF9u2bTvo52+//fY488wz4+STT47rrrsuvve978Wll14afX19pc+hXQQAgNmpaItGWZs2bYpNmzY96ed///d/P175ylfGH/7hH8587BnPeMaszmElGwCAjrdnz54DjomJiTnlNJvN+NKXvhTPetaz4pxzzomjjz46zjjjjIO2lPw8imwAADre6tWrY3BwcObYunXrnHIeeOCBGB0djQ984AOxcePG+OpXvxrnn39+XHDBBXH99deXztEuAgBAeRW9GM3w8HAMDAzMfLi3t3dOcc1mMyIiXvOa18Q73/nOiIj4pV/6pfjWt74VV155ZbzsZS8rlaPIBgCg4w0MDBxQZM/VkUceGUuWLIlTTjnlgI8/+9nPjm9+85ulc7SLAADA43p6euL000+PW2+99YCP//jHP461a9eWzrGSDQBAaQvhYjSjo6Oxc+fOmdu7du2KHTt2xIoVK2LNmjXx7ne/O/7dv/t38dKXvjQ2bNgQX/7yl+MLX/hCXHfddaXPocgGAGBR2b59e2zYsGHm9ubNmyMi4qKLLoqrrroqzj///Ljyyitj69at8Y53vCNOOumk+Lu/+7s488wzS59DkQ0AwKKyfv36KIqfvwT+5je/Od785jfP+RyKbAAAyqvo7iJVM6si+6b/dnGSd20ezItf+6EsuS1LxupZ85f9tJE1f/zIvOO//9o1WfNH1zaz5DbHx7Pkzrfl9zZiSXee+9SN1747S27Ly18+t31Jy1r+Pwaz5j/8b/M+Wk8clWcutCzf3p8lt2uiO0vufHv4X54e9d7yl0mejWN35318+sY3Lsmav/Gof581/5hvHZE1f/TYvM+bjd48e0c0JvLWExw6dhcBAIDEtIsAAFDaQthd5FCwkg0AAIkpsgEAIDHtIgAAlGd3kVKsZAMAQGKKbAAASEy7CAAA5WkXKSXdSvbll0ds2XLwz23ZMv15oPrMZeh85jHMu3RFdr0ecdllPzupt2yZ/ng975WXgETMZeh85jHMu3TtIpdeOv3fyy574nZrMl9xxROfB6rNXIbOZx6TkYvRlJO2J7t9Ur/vfRGTkyYzdCJzGTqfeQzzKv3uIpdeGtHTMz2Ze3pMZuhU5jJ0PvMY5k36InvLlicm8+Tkk7/xAqg2cxk6n3lMDkUFjwpKW2S393tNTEz/92BvvACqzVyGzmcew7xK15N9sDdUHOyNF0C1mcvQ+cxjmHfpiuxG4+BvqGjdbjSSnQrIyFyGzmcek1PVWjSqNJY26Yrsn7exvb+WoXOYy9D5zGOYd+nf+AgAAItc2n2yAQBY0FyMphwr2QAAkJgiGwAAEtMuAgBAeXYXKWVWRfZ5L90aS+q9WQbS97Q8uS3jq5tZ8+8frGfN75rKew9a9fI7subfdv9RWXJre8ez5M63PcfXo96b5z61afXvZsltGXvZmqz5j5w3ljW/8WDeF/gO25V3baOWaWe22v48ufOtPhGR69G7+9G8j09nv+APsubHccdkjX/45LzPm30P533ebPTU8gRXtGBk9rSLAABAYtpFAAAoze4i5VjJBgCAxBTZAACQmHYRAADKs7tIKVayAQAgMUU2AAAkpl0EAIDytIuUYiUbAAASU2QDAEBi2kUAACit9vhRFVUaSzsr2QAAkJgiGwAAEtMuAgBAeXYXKcVKNgAAJKbIBgCAxLSLAABQWq2YPqqiSmNpZyUbAAASU2QDAEBi2kUAACjP7iKlWMkGAIDEFNkAAJCYdhEAAGanoi0aVTKrIvuhX+6Pek9floHsO7KWJbfl6cc+kDV/+acGs+Z3/5f7sub/4CfHZs3/0Jl/myV370gj3pgleX7tPXkiupbmmRN3vf64LLktY2saWfOX1ptZ81ce/1DW/N4T8/587nu0P0tuY+94ltz5NjVQRKMvT7Wwe8PTsuS2PO22/Vnz7z2znjU/1o5ljd/3YJ56peW1Z/5zltyJ0an40Z9lieYQ0y4CAACJaRcBAKA0F6Mpx0o2AAAkpsgGAIDEtIsAAFCei9GUYiUbAAASU2QDAEBi2kUAACjN7iLlWMkGAIDEFNkAAJCYdhEAAMqzu0gpVrIBACAxRTYAACSmXQQAgNLsLlKOlWwAAEhMkQ0AAIlpFwEAoDy7i5RiJRsAABJTZAMAQGLaRQAAKE+7SClWsgEAIDFFNgAAJKZdBACA0lyMppxZFdkTZ++J+rKJLAPZt7s/S27L3uHDs+b/8uV3ZM3f8ZM1WfN/+cQ7s+Z/8LZzsuQ29k5ExHezZM+nWlczavVmluwjbpnKktvS6O3Omn/EM/dmzf83Rw5nzf+HO0/Kmv9Lx96TJXdqbDJ+kiV5nq3dG7Esz1wb6e/LkttyxA/yjLtl+d151+EaDyzPmh8vfSRr/Gf+6Ywsuc194xFxbZZsDi3tIgAAkJh2EQAAyrO7SClWsgEAIDFFNgAAJKZdBACA0mpFEbWiOj0aVRpLOyvZAACQmCIbAAAS0y4CAEB5dhcpxUo2AAAkpsgGAIDEtIsAAFBarZg+qqJKY2lnJRsAABJTZAMAQGLaRQAAKM/uIqVYyQYAgMQU2QAAkJgiGwCA0lq7i1TpmK0bbrghzj333BgaGoparRbXXHPNAZ9/4xvfGLVa7YBj48aNszqHIhsAgEVlbGws1q1bF9u2bXvSr9m4cWPce++9M8enPvWpWZ3DGx8BAFhUNm3aFJs2bfq5X9Pb2xsrV66c8zmsZAMAUF5RwSOD6667Lo4++ug46aST4nd+53fioYcemtX3W8kGAKDj7dmz54Dbvb290dvbO6esjRs3xgUXXBDHH3983H777fHe9743Nm3aFN/+9rejXq+XylBkAwDQ8VavXn3A7f/6X/9rXH755XPKet3rXjfz/5/73OfG8573vHjGM54R1113XbziFa8olaHIBgCgtLnu6JFLayzDw8MxMDAw8/G5rmIfzAknnBBHHnlk7Ny5M0+R3V1vRL3emNPgfpGiK+9v649fPrt3hM7W+z74hqz5ceZk1vgf3j/3xv4yDls6kSW3KGpZcudb//alUe/py5J991nNLLktR3w371wePvaorPn3PjSYNX/Laddmzf/WyIlZcienprLkzrfjjn4olixP90TcbtfONVlyWx46pdxL1nOW+eF15Fn7s+Z3jeR5DG152fP/NUvu5OhkfDpL8sI3MDBwQJGd0t133x0PPfRQHHPMMaW/x0o2AACLyujoaOzcuXPm9q5du2LHjh2xYsWKWLFiRfzBH/xBXHjhhbFy5cq4/fbb4z3veU+ceOKJcc4555Q+hyIbAIDyMu7oMSdzGMv27dtjw4YNM7c3b94cEREXXXRRfPSjH43vfe97cfXVV8ejjz4aQ0NDcfbZZ8eWLVtm1YKiyAYAYFFZv359FMWTV+df+cpXnvI57JMNAACJWckGAGBWqrS7SFVZyQYAgMQU2QAAkJh2EQAAyiuK6aMqqjSWNlayAQAgMUU2AAAkpl0EAIDSakW1dhep0ljaWckGAIDEFNkAAJCYdhEAAMorHj+qokpjaWMlGwAAElNkAwBAYtpFAAAordacPqqiSmNpZyUbAAASU2QDAEBi2kUAACjP7iKlWMkGAIDEFNkAAJBYqXaRopheh2/sncg2kOa+8WzZERF7RxpZ8xuTecff3DeZNb9Ry5xf5Mlv3Sdb99FONzPXMt6fmuN534bdmKxlzW/uyzuXm0vyzoW9o3nHPzk6lSd3bDp3oc21/Xvz/b6b43mfFxoTeedaZI5v7tuf9wS1PHOhZXI0z31nqgPmWq2YPqqiSmNpV6rIHhkZiYiIH77pI1kHk9Mbsp/hlrzxV+eN73QjIyMxODg438N4ylpz7UdXXTHPI5m7O3Of4FO5T5DX/539DP87a/pCm2s3/trH53kkdKq7MucvlLm2mJUqsoeGhmJ4eDj6+/ujVsv8py3MQlEUMTIyEkNDQ/M9lCTMNarKXINDY6HNtcWsVJHd1dUVq1atyj0WmJOF9Je+uUaVmWtwaFR+rhXF9FEVVRpLG298BACAxBTZAACQmIvRAABQmt1FyrGSDQAAiSmyAQAgMe0iAACUVzx+VEWVxtLGSjYAACSmyAYAgMQU2QAAkJiebAAASrOFXzlWsgEAIDFFNgAAJKZdBACA8opi+qiKKo2ljZVsAABITJENAACJaRcBAKA0u4uUYyUbAAASU2QDAEBi2kUAACivePyoiiqNpY2VbAAASEyRDQAAiWkXAQCgNLuLlGMlGwAAElNkAwBAYtpFAAAor1lMH1VRpbG0sZINAACJKbIBACAx7SIAAJTnYjSlWMkGAIDEFNkAAJBYqXaRZrMZu3fvjv7+/qjVarnHBKUVRREjIyMxNDQUXV2d/zejuUZVmWtwaHTCXKtFtS4AU9UZXKrI3r17d6xevTr3WGDOhoeHY9WqVfM9jKfMXKPqzDU4NBbKXFvMShXZ/f39ERFx3EffFV1Le7MM5MVrdmXJbbnpK8/Nmj/1rL1Z8xsTed+jetLae7Pmj07mud/s3zsRN7/+f87cRztd699x4v/ze1FfludnNrFzIEtuS3Hsvqz5XXcvzZp/+pk/zJr/g58ekzW/8U+H58mdHI/b/ucVC26uDX3wkuha2pflHMcd90CW3Ja7vzuUNX//4P6s+Yev3JM1/9FHlmfNX73y4Sy5+/dOxr/8+l8smLm2mJWq3FovpXUt7Y2uZXkejHoO68mS21LvzTPulsayZtb8oitvkb1keZ6Cbia/O2/+Qnm5t/XvqC/rzVZkd/XlnQvFsryvIeYef/fyzI9FY3nnQmR+rFtoc61raV+2Ijv342ruudC1NG+RXV82kTW/azzvzyf377fSc60opo+qqNJY2lSz2QcAADqYIhsAABJzMRoAAEqrFRXbXaRCY2lnJRsAABJTZAMAQGLaRQAAKK94/KiKKo2ljZVsAABITJENAACJaRcBAKC0WlFErUIXgKnSWNpZyQYAgMQU2QAAkJh2EQAAyms+flRFlcbSxko2AAAkpsgGAIDEtIsAAFCa3UXKsZINAACJKbIBACAx7SIAAJRXPH5URZXG0sZKNgAAJKbIBgCAxGbVLtK/fCLqy/IM5MY7T8gT/LjDX3x/1vz7hldkzb/6rL/Mmn/RV9+aNf+kZ92TJXd/92SW3Pm2767+6OrryxPek/d1tdV/3Z01//43jWbN/6fbn5E1/7DtS7Pm7zsmz++3OV7R12OfouOOeyCWLO/Nkn3PP63Kktuyf+VU1vzaZN51uH03HZk1v2fdSNb8rlqeOZErN6mimD6qokpjaWMlGwAAElNkAwBAYnYXAQCgtFoxfVRFlcbSzko2AAAkpsgGAIDEtIsAAFCe3UVKsZINAACJKbIBACAx7SIAAJRWa04fVVGlsbSzkg0AAIkpsgEAIDHtIgAAlGd3kVKsZAMAsKjccMMNce6558bQ0FDUarW45pprnvRrf/u3fztqtVr8yZ/8yazOocgGAGBRGRsbi3Xr1sW2bdt+7td97nOfi5tuuimGhoZmfQ7tIgAAlFc8flTFHMayadOm2LRp08/9mnvuuSfe/va3x1e+8pV41ateNetzWMkGAIA2zWYz3vCGN8S73/3uOPXUU+eUYSUbAICOt2fPngNu9/b2Rm9v75yyPvjBD8aSJUviHe94x5zHYyUbAIDSakVRuSMiYvXq1TE4ODhzbN26dU7/vptvvjn+9E//NK666qqo1Wpz/jlZyQYAoOMNDw/HwMDAzO25rmLfeOON8cADD8SaNWtmPtZoNOJd73pX/Mmf/EnccccdpXIU2QAAdLyBgYEDiuy5esMb3hBnnXXWAR8755xz4g1veEO86U1vKp2jyAYAoLwFcDGa0dHR2Llz58ztXbt2xY4dO2LFihWxZs2aOOKIIw74+u7u7li5cmWcdNJJpc+hyAYAYFHZvn17bNiwYeb25s2bIyLioosuiquuuirJORTZAAAsKuvXr49iFivgZfuw282qyB6fWhL1qTx1+dRE3nr/ocZhWfPf8sIbs+b/tztmvwn6bPzuS76aNf9Pbzw7S25z33iW3PlWdBdR9OR5KW7NF5tZclvuemXeTYuW3dyfNX/pCx7Lmn/qa+/Imn/7lSdnyW1MRtyRJXl+3bHr6Oha2pcle+n+LLEzjv+7vC/X3/Ebef8BJ559Z9b8e/Y89d7cn+eBkTx1RWNvd5bcpIqIyPtUMjsV6lxpZws/AABITJENAACJ6ckGAKC09gvAVEGVxtLOSjYAACSmyAYAgMS0iwAAUF4RFbsYzXwP4OCsZAMAQGKKbAAASEy7CAAA5RVFxdpFKjSWNlayAQAgMUU2AAAkpl0EAIDymhFRm+9BtGnO9wAOzko2AAAkpsgGAIDEtIsAAFBarSiiVqEdPao0lnZWsgEAIDFFNgAAJKZdBACA8lyMphQr2QAAkJgiGwAAEtMuAgBAedpFSrGSDQAAiSmyAQAgsVm1i0zcPhBdfX1ZBlI7djxLbsvnXnRl1vz/67tvypr/ndM/nTX/ww+fkDW/5/A8v99mb977zXzpv60e9d56luyxlXn/tu79aS1v/iN5XxZ8+uGPZM3/yUdPyprfWJopd4EuyQw+fTTqy6ayZDd+vCJLbstDp/Rkze+7LWt87DzsyKz5jcx32mLX8iy5zfEOeF7TLlLKAn3YBACA+aPIBgCAxOwuAgBAec2IyNsZODvN+R7AwVnJBgCAxBTZAACQmHYRAABKqxVF1Cq0o0eVxtLOSjYAACSmyAYAgMS0iwAAUJ6L0ZRiJRsAABJTZAMAQGLaRQAAKK9ZRNQq1KLRrNBY2ljJBgCAxBTZAACQmHYRAADKs7tIKVayAQAgMUU2AAAkpl0EAIBZqFi7SFRpLE+wkg0AAIkpsgEAIDHtIgAAlGd3kVKsZAMAQGKKbAAASGxW7SJd+2pRL2pZBtK8qy9Lbsu7jvnVrPm1Wt6XKl636+VZ88f3d2fN7+5uZMltdDez5M63wZ9MxZLuepbskVV5u8SO/H6e33XL3a/Mm7/3wSOy5q960z1Z8/dctSpP8GSe2Pk2duvToqsvz/PP6lvy/tC69+TNrz88ljX/x6f2Z80/7NvLsuaPnLEvS25z73iW3KSaRVRqR49mhcbSxko2AAAkpsgGAIDE7C4CAEB5RXP6qIoqjaWNlWwAAEhMkQ0AAIlpFwEAoDwXoynFSjYAACSmyAYAgMS0iwAAUJ6L0ZRiJRsAABJTZAMAQGLaRQAAKM/uIqVYyQYAgMQU2QAAkJh2EQAAyiuiWi0aFRpKOyvZAACQmCIbAAAS0y4CAEB5dhcpxUo2AAAkpsgGAIDEtIsAAFBesxkRzfkexROaFRpLGyvZAACQmCIbAAASm1W7yHf+08UxMDCQZSDr3vbHWXJbbl0xlDU/6nnf2frTZfvy5o8uz5o/+aM895vm+HiW3Pm2Z2131Hu6s2R/98/emSW35dn/Je9cXvWlrPHxwku/nzX/q1e/KGv+nvWTWXKb+6YiPp0lel7tf9r+6Fq6P0v2/c/vyZLb8sP3/aes+evP/kDW/IEb8/58HlmX5/facvRX+rLkNiYj7sqSnJDdRUqxkg0AAIkpsgEAIDG7iwAAUJ52kVKsZAMAQGKKbAAASEy7CAAA5TWLiKhQi0azQmNpYyUbAAASS1dkX355xJYtB//cli3Tnweqz1yGzmcew7xLV2TX6xGXXfazk3rLlumP1+vJTgVkZC5D5zOPyagompU7qihdT/all07/97LLnrjdmsxXXPHE54FqM5eh85nHMO/SvvGxfVK/730Rk5MmM3Qicxk6n3kM8yr9Gx8vvTSip2d6Mvf0mMzQqcxl6HzmMTkUxfSOHlU5Fs3FaLZseWIyT04++RsvgGozl6Hzmccwb9IW2e39XhMT0/892BsvgGozl6Hzmccwr9L1ZB/sDRUHe+MFUG3mMnQ+85iciopdjKai7SLpiuxG4+BvqGjdbjSSnQrIyFyGzmcew7xLV2T/vI3t/bUMncNchs5nHsO8c1l1AADKazard8zSDTfcEOeee24MDQ1FrVaLa6655oDPX3755XHyySfH8uXL4/DDD4+zzjor/vmf/3lW51BkAwCwqIyNjcW6deti27ZtB/38s571rPizP/uzuOWWW+Kb3/xmHHfccXH22WfHT3/609LnSHsxGgAAqLhNmzbFpk2bnvTzv/Ebv3HA7Q9/+MPxsY99LL73ve/FK17xilLnUGQDAFDeIttdZHJyMv7iL/4iBgcHY926daW/T5ENAEDH27NnzwG3e3t7o7e3d855X/ziF+N1r3td7N27N4455pj42te+FkceeWTp79eTDQBAx1u9enUMDg7OHFu3bn1KeRs2bIgdO3bEt771rdi4cWP82q/9WjzwwAOlv39WK9kves+2qPf2zXqQZex72WiW3JaeHy/Pmn/k6fdnzX/pUTuz5t+27Ois+f90b3+W3Oa+hbnXa89IEfWePC9/nf7GD2fJbTliT97fyX0vqGfN/+z1Z2TNP+PXfpg1f3+RZ+1kamwy7s6SPL+6lu6PrmX7s2Qv/WneF4vXb/xg1vyHnjP3FcAyVrzqnqz5xd6lWfMfO7c7S25j73jEp7NEJ1M0m1HUZr+jRy5FMT2W4eHhGBgYmPn4U1nFjohYvnx5nHjiiXHiiSfGC1/4wnjmM58ZH/vYx+KSSy4p9f3aRQAA6HgDAwMHFNmpNZvNmJiYKP31imwAABaV0dHR2LnziS6BXbt2xY4dO2LFihVxxBFHxPvf//549atfHcccc0w8+OCDsW3btrjnnnvita99belzKLIBAChvAewusn379tiwYcPM7c2bN0dExEUXXRRXXnll/OhHP4qrr746HnzwwTjiiCPi9NNPjxtvvDFOPfXU0udQZAMAsKisX78+ip9TnH/2s599yuewuwgAACRmJRsAgPKaRUSts9tFDgUr2QAAkJgiGwAAEtMuAgBAeUUREdW5GI12EQAAWCQU2QAAkJh2EQAASiuaRRQV2l3k5+13PZ+sZAMAQGKKbAAASEy7CAAA5RXNqNbuIhUaSxsr2QAAkJgiGwAAEtMuAgBAaXYXKcdKNgAAJKbIBgCAxLSLAABQnt1FSrGSDQAAiZVayW41lDcmx7MNpLk3X3ZERIzXs8bvH5vImj8xOpU1f2rvZNb85r48v9/m+HRuVd/0MFszc20q43yo5YuOiNg/1cia38w8l5uZ38wzNZZ3ru0v8qydtMa90OZac1++x+5G3l917N+f93mhMZF3dTD382ZjX951xMZEnse61n2yynNtf0xFVGh4+yPvXJirWlHit3j33XfH6tWrD8V4YE6Gh4dj1apV8z2Mp8xco+rMNTg0qjjXxsfH4/jjj4/77rtvvofyM1auXBm7du2Kvr6++R7KjFJFdrPZjN27d0d/f3/UapmXwWAWiqKIkZGRGBoaiq6uzu9+MteoKnMNDo2qz7Xx8fGYnMz8Ms0c9PT0VKrAjihZZAMAAOVV708kAADocIpsAABITJENAACJKbIBACAxRTYAACSmyAYAgMQU2QAAkNj/D6wTZDnwE2STAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1000x1000 with 13 Axes>"
       ]
@@ -455,7 +455,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9c3c122066a5444786bd0545a333d808",
+       "model_id": "7240dc200b9045a696398dd084be8de8",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/notebooks/train_model.ipynb b/notebooks/train_model.ipynb
index ac3fa2d6..cb766b39 100644
--- a/notebooks/train_model.ipynb
+++ b/notebooks/train_model.ipynb
@@ -42,7 +42,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEVICE = device(type='cuda')\n"
+      "set up plots with PLOT_MODE = 'inline', FIG_OUTPUT_FMT = None, FIG_BASEPATH = None\n",
+      "DEVICE = device(type='cpu')\n"
      ]
     }
    ],
@@ -189,71 +190,63 @@
      "output_type": "stream",
      "text": [
       "{\n",
-      "  \"name\": \"hallway-medium\",\n",
+      "  \"name\": \"multsrc_demo_small-g3-n100-a_dfs-h88371_nano-v1_test-v1\",\n",
       "  \"dataset_cfg\": {\n",
-      "    \"name\": \"custom-hallway\",\n",
-      "    \"fname\": \"custom-hallway-g8-n3.0M-a_dfs-h66295\",\n",
-      "    \"sdc_hash\": 38558245839505946605550258446345100654876335964881215358923857434720332766295,\n",
+      "    \"name\": \"demo_small\",\n",
+      "    \"fname\": \"demo_small-g3-n100-a_dfs-h88371\",\n",
+      "    \"sdc_hash\": 84259447430412521190944379321942049596003348447446024033060470193755008588371,\n",
       "    \"seed\": 42,\n",
       "    \"seq_len_min\": 1,\n",
-      "    \"seq_len_max\": 256,\n",
-      "    \"applied_filters\": [\n",
-      "      {\n",
-      "        \"name\": \"collect_generation_meta\",\n",
-      "        \"args\": [],\n",
-      "        \"kwargs\": {}\n",
-      "      }\n",
-      "    ],\n",
-      "    \"grid_n\": 8,\n",
+      "    \"seq_len_max\": 512,\n",
+      "    \"applied_filters\": [],\n",
+      "    \"grid_n\": 3,\n",
       "    \"grid_shape\": [\n",
-      "      8,\n",
-      "      8\n",
+      "      3,\n",
+      "      3\n",
       "    ],\n",
-      "    \"n_mazes\": 3000000,\n",
+      "    \"n_mazes\": 100,\n",
       "    \"maze_ctor_name\": \"gen_dfs\",\n",
-      "    \"maze_ctor_kwargs\": {\n",
-      "      \"do_forks\": false\n",
-      "    }\n",
+      "    \"maze_ctor_kwargs\": {}\n",
       "  },\n",
       "  \"model_cfg\": {\n",
-      "    \"name\": \"custom-model\",\n",
+      "    \"name\": \"nano-v1\",\n",
       "    \"act_fn\": \"gelu\",\n",
-      "    \"d_model\": 128,\n",
-      "    \"d_head\": 32,\n",
-      "    \"n_layers\": 6,\n",
+      "    \"d_model\": 8,\n",
+      "    \"d_head\": 4,\n",
+      "    \"n_layers\": 2,\n",
       "    \"weight_processing\": {\n",
       "      \"are_layernorms_folded\": false,\n",
       "      \"are_weights_processed\": false\n",
       "    },\n",
-      "    \"n_heads\": 4\n",
+      "    \"n_heads\": 2\n",
       "  },\n",
       "  \"train_cfg\": {\n",
-      "    \"name\": \"custom-train\",\n",
-      "    \"optimizer\": \"AdamW\",\n",
+      "    \"name\": \"test-v1\",\n",
+      "    \"optimizer\": \"RMSprop\",\n",
       "    \"optimizer_kwargs\": {\n",
-      "      \"lr\": 0.001\n",
+      "      \"lr\": 0.0001\n",
       "    },\n",
-      "    \"batch_size\": 32,\n",
+      "    \"batch_size\": 16,\n",
       "    \"dataloader_cfg\": {\n",
-      "      \"shuffle\": false,\n",
-      "      \"num_workers\": 8,\n",
+      "      \"shuffle\": true,\n",
+      "      \"num_workers\": 0,\n",
       "      \"drop_last\": false\n",
       "    },\n",
       "    \"intervals\": null,\n",
       "    \"intervals_count\": {\n",
       "      \"print_loss\": 100,\n",
-      "      \"checkpoint\": 20,\n",
-      "      \"eval_fast\": 100,\n",
-      "      \"eval_slow\": 50\n",
+      "      \"checkpoint\": 2,\n",
+      "      \"eval_fast\": 4,\n",
+      "      \"eval_slow\": 2\n",
       "    },\n",
       "    \"evals_max_new_tokens\": 8,\n",
-      "    \"validation_dataset_cfg\": 100\n",
+      "    \"validation_dataset_cfg\": 1\n",
       "  },\n",
       "  \"pretrainedtokenizer_kwargs\": null,\n",
       "  \"maze_tokenizer\": {\n",
       "    \"tokenization_mode\": \"AOTP_UT_uniform\",\n",
-      "    \"max_grid_size\": 8,\n",
-      "    \"vocab_size\": 75\n",
+      "    \"max_grid_size\": 3,\n",
+      "    \"vocab_size\": 20\n",
       "  }\n",
       "}\n"
      ]
@@ -272,25 +265,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "trying to get the dataset 'custom-hallway-g8-n3.0M-a_dfs-h66295'\n",
-      "seeing if we can download the dataset...\n",
-      "no download found, or download failed\n",
-      "generating dataset...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "generating & solving mazes: 100%|██████████| 3000000/3000000 [1:00:22<00:00, 828.27maze/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "saving dataset to ..\\data\\custom-hallway-g8-n3.0M-a_dfs-h66295.zanj\n",
-      "Got dataset custom-hallway with 3000000 items. output.cfg.to_fname() = 'custom-hallway-g8-n3.0M-a_dfs-h66295'\n"
+      "trying to get the dataset 'demo_small-g3-n100-a_dfs-h88371'\n",
+      "loading dataset from ../data/demo_small-g3-n100-a_dfs-h88371.zanj\n",
+      "load successful!\n",
+      "Got dataset demo_small with 100 items. output.cfg.to_fname() = 'demo_small-g3-n100-a_dfs-h88371'\n"
      ]
     }
    ],
@@ -312,21 +290,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-10-02 04:50:44 ERROR Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
+      "2024-05-13 20:12:16 ERROR Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmiv\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+      "wandb: Currently logged in as: miv. Use `wandb login --relogin` to force relogin\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "wandb version 0.15.11 is available!  To upgrade, please run:\n",
-       " $ pip install wandb --upgrade"
+       "Tracking run with wandb version 0.17.0"
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-       "Tracking run with wandb version 0.13.11"
+       "Run data is saved locally in <code>f:\\KNC\\maze-transformer\\notebooks\\wandb\\run-20240513_201219-780ezh3c</code>"
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+       "Syncing run <strong><a href='https://wandb.ai/miv/understanding-search/runs/780ezh3c' target=\"_blank\">zesty-music-17</a></strong> to <a href='https://wandb.ai/miv/understanding-search' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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+       " View run at <a href='https://wandb.ai/miv/understanding-search/runs/780ezh3c' target=\"_blank\">https://wandb.ai/miv/understanding-search/runs/780ezh3c</a>"
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      "output_type": "stream",
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-      "2023-10-02 04:50:47 INFO config ={'__format__': 'ConfigHolder(SerializableDataclass)', 'dataset_cfg': {'__format__': 'MazeDatasetConfig(SerializableDataclass)', 'name': 'custom-hallway', 'seq_len_min': 1, 'seq_len_max': 256, 'seed': 42, 'applied_filters': [{'name': 'collect_generation_meta', 'args': (), 'kwargs': {}}], 'grid_n': 8, 'n_mazes': 3000000, 'maze_ctor': {'__name__': 'gen_dfs', '__module__': 'maze_dataset.generation.generators', '__doc__': ['generate a lattice maze using depth first search, iterative', '', '        # Arguments', '        - `grid_shape: Coord`: the shape of the grid', '        - `lattice_dim: int`: the dimension of the lattice', '          (default: `2`)', '        - `accessible_cells: int | float |None`: the number of accessible cells in the maze. If `None`, defaults to the total number of cells in the grid. if a float, asserts it is <= 1 and treats it as a proportion of **total cells**', '            (default: `None`)', '        - `max_tree_depth: int | float | None`: the maximum depth of the tree. If `None`, defaults to `2 * accessible_cells`. if a float, asserts it is <= 1 and treats it as a proportion of the **sum of the grid shape**', '            (default: `None`)', '        - `do_forks: bool`: whether to allow forks in the maze. If `False`, the maze will be have no forks and will be a simple hallway.', '        - `start_coord: Coord | None`: the starting coordinate of the generation algorithm. If `None`, defaults to a random coordinate.', '', '        # algorithm', '        1. Choose the initial cell, mark it as visited and push it to the stack', '        2. While the stack is not empty', '                1. Pop a cell from the stack and make it a current cell', '                2. If the current cell has any neighbours which have not been visited', '                        1. Push the current cell to the stack', '                        2. Choose one of the unvisited neighbours', '                        3. Remove the wall between the current cell and the chosen cell', '                        4. Mark the chosen cell as visited and push it to the stack', '        '], 'source_code': ['    @staticmethod', '    def gen_dfs(', '        grid_shape: Coord,', '        lattice_dim: int = 2,', '        accessible_cells: int | float | None = None,', '        max_tree_depth: int | float | None = None,', '        do_forks: bool = True,', '        randomized_stack: bool = False,', '        start_coord: Coord | None = None,', '    ) -> LatticeMaze:', '        \"\"\"generate a lattice maze using depth first search, iterative', '', '        # Arguments', '        - `grid_shape: Coord`: the shape of the grid', '        - `lattice_dim: int`: the dimension of the lattice', '          (default: `2`)', '        - `accessible_cells: int | float |None`: the number of accessible cells in the maze. If `None`, defaults to the total number of cells in the grid. if a float, asserts it is <= 1 and treats it as a proportion of **total cells**', '            (default: `None`)', '        - `max_tree_depth: int | float | None`: the maximum depth of the tree. If `None`, defaults to `2 * accessible_cells`. if a float, asserts it is <= 1 and treats it as a proportion of the **sum of the grid shape**', '            (default: `None`)', '        - `do_forks: bool`: whether to allow forks in the maze. If `False`, the maze will be have no forks and will be a simple hallway.', '        - `start_coord: Coord | None`: the starting coordinate of the generation algorithm. If `None`, defaults to a random coordinate.', '', '        # algorithm', '        1. Choose the initial cell, mark it as visited and push it to the stack', '        2. While the stack is not empty', '                1. Pop a cell from the stack and make it a current cell', '                2. If the current cell has any neighbours which have not been visited', '                        1. Push the current cell to the stack', '                        2. Choose one of the unvisited neighbours', '                        3. Remove the wall between the current cell and the chosen cell', '                        4. Mark the chosen cell as visited and push it to the stack', '        \"\"\"', '', '        # Default values if no constraints have been passed', '        grid_shape: Coord = np.array(grid_shape)', '        n_total_cells: int = int(np.prod(grid_shape))', '', '        n_accessible_cells: int', '        if accessible_cells is None:', '            n_accessible_cells = n_total_cells', '        elif isinstance(accessible_cells, float):', '            assert (', '                accessible_cells <= 1', '            ), f\"accessible_cells must be an int (count) or a float in the range [0, 1] (proportion), got {accessible_cells}\"', '', '            n_accessible_cells = int(accessible_cells * n_total_cells)', '        else:', '            assert isinstance(accessible_cells, int)', '            n_accessible_cells = accessible_cells', '', '        if max_tree_depth is None:', '            max_tree_depth = (', '                2 * n_total_cells', '            )  # We define max tree depth counting from the start coord in two directions. Therefore we divide by two in the if clause for neighboring sites later and multiply by two here.', '        elif isinstance(max_tree_depth, float):', '            assert (', '                max_tree_depth <= 1', '            ), f\"max_tree_depth must be an int (count) or a float in the range [0, 1] (proportion), got {max_tree_depth}\"', '', '            max_tree_depth = int(max_tree_depth * np.sum(grid_shape))', '', '        # choose a random start coord', '        start_coord = _random_start_coord(grid_shape, start_coord)', '', '        # initialize the maze with no connections', '        connection_list: ConnectionList = np.zeros(', '            (lattice_dim, grid_shape[0], grid_shape[1]), dtype=np.bool_', '        )', '', '        # initialize the stack with the target coord', '        visited_cells: set[tuple[int, int]] = set()', '        visited_cells.add(tuple(start_coord))  # this wasnt a bug after all lol', '        stack: list[Coord] = [start_coord]', '', '        # initialize tree_depth_counter', '        current_tree_depth: int = 1', '', '        # loop until the stack is empty or n_connected_cells is reached', '        while stack and (len(visited_cells) < n_accessible_cells):', '            # get the current coord from the stack', '            current_coord: Coord', '            if randomized_stack:', '                current_coord = stack.pop(random.randint(0, len(stack) - 1))', '            else:', '                current_coord = stack.pop()', '', '            # filter neighbors by being within grid bounds and being unvisited', '            unvisited_neighbors_deltas: list[tuple[Coord, Coord]] = [', '                (neighbor, delta)', '                for neighbor, delta in zip(', '                    current_coord + NEIGHBORS_MASK, NEIGHBORS_MASK', '                )', '                if (', '                    (tuple(neighbor) not in visited_cells)', '                    and (0 <= neighbor[0] < grid_shape[0])', '                    and (0 <= neighbor[1] < grid_shape[1])', '                )', '            ]', '', \"            # don't continue if max_tree_depth/2 is already reached (divide by 2 because we can branch to multiple directions)\", '            if unvisited_neighbors_deltas and (', '                current_tree_depth <= max_tree_depth / 2', '            ):', \"                # if we want a maze without forks, simply don't add the current coord back to the stack\", '                if do_forks and (len(unvisited_neighbors_deltas) > 1):', '                    stack.append(current_coord)', '', '                # choose one of the unvisited neighbors', '                chosen_neighbor, delta = random.choice(unvisited_neighbors_deltas)', '', '                # add connection', '                dim: int = np.argmax(np.abs(delta))', '                # if positive, down/right from current coord', '                # if negative, up/left from current coord (down/right from neighbor)', '                clist_node: Coord = (', '                    current_coord if (delta.sum() > 0) else chosen_neighbor', '                )', '                connection_list[dim, clist_node[0], clist_node[1]] = True', '', '                # add to visited cells and stack', '                visited_cells.add(tuple(chosen_neighbor))', '                stack.append(chosen_neighbor)', '', '                # Update current tree depth', '                current_tree_depth += 1', '            else:', '                current_tree_depth -= 1', '', '        output = LatticeMaze(', '            connection_list=connection_list,', '            generation_meta=dict(', '                func_name=\"gen_dfs\",', '                grid_shape=grid_shape,', '                start_coord=start_coord,', '                n_accessible_cells=int(n_accessible_cells),', '                max_tree_depth=int(max_tree_depth),', \"                # oh my god this took so long to track down. its almost 5am and I've spent like 2 hours on this bug\", '                # it was checking that len(visited_cells) == n_accessible_cells, but this means that the maze is', '                # treated as fully connected even when it is most certainly not, causing solving the maze to break', '                fully_connected=bool(len(visited_cells) == n_total_cells),', '                visited_cells={tuple(int(x) for x in coord) for coord in visited_cells},', '            ),', '        )', '', '        return output']}, 'maze_ctor_kwargs': {'do_forks': False}, 'grid_shape': (8, 8)}, 'model_cfg': {'__format__': 'BaseGPTConfig(SerializableDataclass)', 'name': 'custom-model', 'act_fn': 'gelu', 'd_model': 128, 'd_head': 32, 'n_layers': 6, 'weight_processing': {'are_layernorms_folded': False, 'are_weights_processed': False}, 'n_heads': 4}, 'train_cfg': {'__format__': 'TrainConfig(SerializableDataclass)', 'name': 'custom-train', 'evals_max_new_tokens': 8, 'validation_dataset_cfg': 100, 'optimizer': 'AdamW', 'optimizer_kwargs': {'lr': 0.001}, 'batch_size': 32, 'dataloader_cfg': {'shuffle': False, 'num_workers': 8, 'drop_last': False}, 'intervals': None, 'intervals_count': {'print_loss': 100, 'checkpoint': 20, 'eval_fast': 100, 'eval_slow': 50}}, 'name': 'hallway-medium', 'pretrainedtokenizer_kwargs': None, 'maze_tokenizer': {'__format__': 'MazeTokenizer(SerializableDataclass)', 'tokenization_mode': 'AOTP_UT_uniform', 'max_grid_size': 8, 'name': 'maze_tokenizer-AOTP_UT_uniform-g8', 'token_arr': ['<ADJLIST_START>', '<ADJLIST_END>', '<TARGET_START>', '<TARGET_END>', '<ORIGIN_START>', '<ORIGIN_END>', '<PATH_START>', '<PATH_END>', '<-->', ';', '<PADDING>', '(0,0)', '(0,1)', '(1,0)', '(1,1)', '(0,2)', '(2,0)', '(1,2)', '(2,1)', '(2,2)', '(0,3)', '(3,0)', '(3,1)', '(2,3)', '(3,2)', '(1,3)', '(3,3)', '(0,4)', '(2,4)', '(4,0)', '(1,4)', '(4,1)', '(4,2)', '(3,4)', '(4,3)', '(4,4)', '(0,5)', '(5,0)', '(5,1)', '(2,5)', '(5,2)', '(5,3)', '(4,5)', '(5,4)', '(1,5)', '(3,5)', '(5,5)', '(0,6)', '(2,6)', '(4,6)', '(6,0)', '(1,6)', '(6,1)', '(6,2)', '(3,6)', '(6,3)', '(6,4)', '(5,6)', '(6,5)', '(6,6)', '(0,7)', '(7,0)', '(7,1)', '(2,7)', '(7,2)', '(7,3)', '(4,7)', '(7,4)', '(7,5)', '(6,7)', '(7,6)', '(1,7)', '(3,7)', '(5,7)', '(7,7)'], 'tokenizer_map': {'<ADJLIST_START>': 0, '<ADJLIST_END>': 1, '<TARGET_START>': 2, '<TARGET_END>': 3, '<ORIGIN_START>': 4, '<ORIGIN_END>': 5, '<PATH_START>': 6, '<PATH_END>': 7, '<-->': 8, ';': 9, '<PADDING>': 10, '(0,0)': 11, '(0,1)': 12, '(1,0)': 13, '(1,1)': 14, '(0,2)': 15, '(2,0)': 16, '(1,2)': 17, '(2,1)': 18, '(2,2)': 19, '(0,3)': 20, '(3,0)': 21, '(3,1)': 22, '(2,3)': 23, '(3,2)': 24, '(1,3)': 25, '(3,3)': 26, '(0,4)': 27, '(2,4)': 28, '(4,0)': 29, '(1,4)': 30, '(4,1)': 31, '(4,2)': 32, '(3,4)': 33, '(4,3)': 34, '(4,4)': 35, '(0,5)': 36, '(5,0)': 37, '(5,1)': 38, '(2,5)': 39, '(5,2)': 40, '(5,3)': 41, '(4,5)': 42, '(5,4)': 43, '(1,5)': 44, '(3,5)': 45, '(5,5)': 46, '(0,6)': 47, '(2,6)': 48, '(4,6)': 49, '(6,0)': 50, '(1,6)': 51, '(6,1)': 52, '(6,2)': 53, '(3,6)': 54, '(6,3)': 55, '(6,4)': 56, '(5,6)': 57, '(6,5)': 58, '(6,6)': 59, '(0,7)': 60, '(7,0)': 61, '(7,1)': 62, '(2,7)': 63, '(7,2)': 64, '(7,3)': 65, '(4,7)': 66, '(7,4)': 67, '(7,5)': 68, '(6,7)': 69, '(7,6)': 70, '(1,7)': 71, '(3,7)': 72, '(5,7)': 73, '(7,7)': 74}, 'vocab_size': 75, 'padding_token_index': 10}}\n",
-      "2023-10-02 04:50:47 INFO Initialized logger\n",
-      "2023-10-02 04:50:47 INFO Summary logged, getting dataset\n",
-      "2023-10-02 04:50:47 INFO passed dataset has matching config, using that\n",
-      "2023-10-02 04:50:47 INFO finished getting training dataset with 3000000 samples\n",
-      "2023-10-02 04:50:47 INFO got validation dataset by splitting training dataset into 2999900 train and 100 validation samples\n",
-      "2023-10-02 04:50:47 INFO Loaded 2999900 sequences\n",
-      "2023-10-02 04:50:47 INFO Creating dataloader\n",
-      "2023-10-02 04:50:47 INFO finished dataloader, passing to train()\n",
-      "2023-10-02 04:50:47 INFO Initializing model\n",
-      "Moving model to device:  cuda\n",
-      "2023-10-02 04:50:47 INFO Initializing optimizer\n",
-      "2023-10-02 04:50:47 INFO will train for 93747 batches, evals_enabled=True, with intervals: {'print_loss': 937, 'checkpoint': 4687, 'eval_fast': 937, 'eval_slow': 1874}\n",
-      "2023-10-02 04:50:47 INFO Starting training\n"
+      "2024-05-13 20:12:21 INFO config ={'__format__': 'ConfigHolder(SerializableDataclass)', 'dataset_cfg': {'__format__': 'MazeDatasetConfig(SerializableDataclass)', 'name': 'demo_small', 'seq_len_min': 1, 'seq_len_max': 512, 'seed': 42, 'applied_filters': [], 'grid_n': 3, 'n_mazes': 100, 'maze_ctor': {'__name__': 'gen_dfs', '__module__': 'maze_dataset.generation.generators', '__doc__': ['generate a lattice maze using depth first search, iterative', '', '        # Arguments', '        - `grid_shape: Coord`: the shape of the grid', '        - `lattice_dim: int`: the dimension of the lattice', '          (default: `2`)', '        - `accessible_cells: int | float |None`: the number of accessible cells in the maze. If `None`, defaults to the total number of cells in the grid. if a float, asserts it is <= 1 and treats it as a proportion of **total cells**', '            (default: `None`)', '        - `max_tree_depth: int | float | None`: the maximum depth of the tree. If `None`, defaults to `2 * accessible_cells`. if a float, asserts it is <= 1 and treats it as a proportion of the **sum of the grid shape**', '            (default: `None`)', '        - `do_forks: bool`: whether to allow forks in the maze. If `False`, the maze will be have no forks and will be a simple hallway.', '        - `start_coord: Coord | None`: the starting coordinate of the generation algorithm. If `None`, defaults to a random coordinate.', '', '        # algorithm', '        1. Choose the initial cell, mark it as visited and push it to the stack', '        2. While the stack is not empty', '                1. Pop a cell from the stack and make it a current cell', '                2. If the current cell has any neighbours which have not been visited', '                        1. Push the current cell to the stack', '                        2. Choose one of the unvisited neighbours', '                        3. Remove the wall between the current cell and the chosen cell', '                        4. Mark the chosen cell as visited and push it to the stack', '        '], 'source_code': ['    @staticmethod', '    def gen_dfs(', '        grid_shape: Coord,', '        lattice_dim: int = 2,', '        accessible_cells: int | float | None = None,', '        max_tree_depth: int | float | None = None,', '        do_forks: bool = True,', '        randomized_stack: bool = False,', '        start_coord: Coord | None = None,', '    ) -> LatticeMaze:', '        \"\"\"generate a lattice maze using depth first search, iterative', '', '        # Arguments', '        - `grid_shape: Coord`: the shape of the grid', '        - `lattice_dim: int`: the dimension of the lattice', '          (default: `2`)', '        - `accessible_cells: int | float |None`: the number of accessible cells in the maze. If `None`, defaults to the total number of cells in the grid. if a float, asserts it is <= 1 and treats it as a proportion of **total cells**', '            (default: `None`)', '        - `max_tree_depth: int | float | None`: the maximum depth of the tree. If `None`, defaults to `2 * accessible_cells`. if a float, asserts it is <= 1 and treats it as a proportion of the **sum of the grid shape**', '            (default: `None`)', '        - `do_forks: bool`: whether to allow forks in the maze. If `False`, the maze will be have no forks and will be a simple hallway.', '        - `start_coord: Coord | None`: the starting coordinate of the generation algorithm. If `None`, defaults to a random coordinate.', '', '        # algorithm', '        1. Choose the initial cell, mark it as visited and push it to the stack', '        2. While the stack is not empty', '                1. Pop a cell from the stack and make it a current cell', '                2. If the current cell has any neighbours which have not been visited', '                        1. Push the current cell to the stack', '                        2. Choose one of the unvisited neighbours', '                        3. Remove the wall between the current cell and the chosen cell', '                        4. Mark the chosen cell as visited and push it to the stack', '        \"\"\"', '', '        # Default values if no constraints have been passed', '        grid_shape: Coord = np.array(grid_shape)', '        n_total_cells: int = int(np.prod(grid_shape))', '', '        n_accessible_cells: int', '        if accessible_cells is None:', '            n_accessible_cells = n_total_cells', '        elif isinstance(accessible_cells, float):', '            assert (', '                accessible_cells <= 1', '            ), f\"accessible_cells must be an int (count) or a float in the range [0, 1] (proportion), got {accessible_cells}\"', '', '            n_accessible_cells = int(accessible_cells * n_total_cells)', '        else:', '            assert isinstance(accessible_cells, int)', '            n_accessible_cells = accessible_cells', '', '        if max_tree_depth is None:', '            max_tree_depth = (', '                2 * n_total_cells', '            )  # We define max tree depth counting from the start coord in two directions. Therefore we divide by two in the if clause for neighboring sites later and multiply by two here.', '        elif isinstance(max_tree_depth, float):', '            assert (', '                max_tree_depth <= 1', '            ), f\"max_tree_depth must be an int (count) or a float in the range [0, 1] (proportion), got {max_tree_depth}\"', '', '            max_tree_depth = int(max_tree_depth * np.sum(grid_shape))', '', '        # choose a random start coord', '        start_coord = _random_start_coord(grid_shape, start_coord)', '', '        # initialize the maze with no connections', '        connection_list: ConnectionList = np.zeros(', '            (lattice_dim, grid_shape[0], grid_shape[1]), dtype=np.bool_', '        )', '', '        # initialize the stack with the target coord', '        visited_cells: set[tuple[int, int]] = set()', '        visited_cells.add(tuple(start_coord))  # this wasnt a bug after all lol', '        stack: list[Coord] = [start_coord]', '', '        # initialize tree_depth_counter', '        current_tree_depth: int = 1', '', '        # loop until the stack is empty or n_connected_cells is reached', '        while stack and (len(visited_cells) < n_accessible_cells):', '            # get the current coord from the stack', '            current_coord: Coord', '            if randomized_stack:', '                current_coord = stack.pop(random.randint(0, len(stack) - 1))', '            else:', '                current_coord = stack.pop()', '', '            # filter neighbors by being within grid bounds and being unvisited', '            unvisited_neighbors_deltas: list[tuple[Coord, Coord]] = [', '                (neighbor, delta)', '                for neighbor, delta in zip(', '                    current_coord + NEIGHBORS_MASK, NEIGHBORS_MASK', '                )', '                if (', '                    (tuple(neighbor) not in visited_cells)', '                    and (0 <= neighbor[0] < grid_shape[0])', '                    and (0 <= neighbor[1] < grid_shape[1])', '                )', '            ]', '', \"            # don't continue if max_tree_depth/2 is already reached (divide by 2 because we can branch to multiple directions)\", '            if unvisited_neighbors_deltas and (', '                current_tree_depth <= max_tree_depth / 2', '            ):', \"                # if we want a maze without forks, simply don't add the current coord back to the stack\", '                if do_forks and (len(unvisited_neighbors_deltas) > 1):', '                    stack.append(current_coord)', '', '                # choose one of the unvisited neighbors', '                chosen_neighbor, delta = random.choice(unvisited_neighbors_deltas)', '', '                # add connection', '                dim: int = np.argmax(np.abs(delta))', '                # if positive, down/right from current coord', '                # if negative, up/left from current coord (down/right from neighbor)', '                clist_node: Coord = (', '                    current_coord if (delta.sum() > 0) else chosen_neighbor', '                )', '                connection_list[dim, clist_node[0], clist_node[1]] = True', '', '                # add to visited cells and stack', '                visited_cells.add(tuple(chosen_neighbor))', '                stack.append(chosen_neighbor)', '', '                # Update current tree depth', '                current_tree_depth += 1', '            else:', '                current_tree_depth -= 1', '', '        output = LatticeMaze(', '            connection_list=connection_list,', '            generation_meta=dict(', '                func_name=\"gen_dfs\",', '                grid_shape=grid_shape,', '                start_coord=start_coord,', '                n_accessible_cells=int(n_accessible_cells),', '                max_tree_depth=int(max_tree_depth),', \"                # oh my god this took so long to track down. its almost 5am and I've spent like 2 hours on this bug\", '                # it was checking that len(visited_cells) == n_accessible_cells, but this means that the maze is', '                # treated as fully connected even when it is most certainly not, causing solving the maze to break', '                fully_connected=bool(len(visited_cells) == n_total_cells),', '                visited_cells={tuple(int(x) for x in coord) for coord in visited_cells},', '            ),', '        )', '', '        return output']}, 'maze_ctor_kwargs': {}, 'grid_shape': (3, 3)}, 'model_cfg': {'__format__': 'BaseGPTConfig(SerializableDataclass)', 'name': 'nano-v1', 'act_fn': 'gelu', 'd_model': 8, 'd_head': 4, 'n_layers': 2, 'weight_processing': {'are_layernorms_folded': False, 'are_weights_processed': False}, 'n_heads': 2}, 'train_cfg': {'__format__': 'TrainConfig(SerializableDataclass)', 'name': 'test-v1', 'evals_max_new_tokens': 8, 'validation_dataset_cfg': 1, 'optimizer': 'RMSprop', 'optimizer_kwargs': {'lr': 0.0001}, 'batch_size': 16, 'dataloader_cfg': {'shuffle': True, 'num_workers': 0, 'drop_last': False}, 'intervals': None, 'intervals_count': {'print_loss': 100, 'checkpoint': 2, 'eval_fast': 4, 'eval_slow': 2}}, 'name': 'multsrc_demo_small-g3-n100-a_dfs-h88371_nano-v1_test-v1', 'pretrainedtokenizer_kwargs': None, 'maze_tokenizer': {'__format__': 'MazeTokenizer(SerializableDataclass)', 'tokenization_mode': 'AOTP_UT_uniform', 'max_grid_size': 3, 'name': 'maze_tokenizer-AOTP_UT_uniform-g3', 'token_arr': ['<ADJLIST_START>', '<ADJLIST_END>', '<TARGET_START>', '<TARGET_END>', '<ORIGIN_START>', '<ORIGIN_END>', '<PATH_START>', '<PATH_END>', '<-->', ';', '<PADDING>', '(0,0)', '(0,1)', '(1,0)', '(1,1)', '(0,2)', '(2,0)', '(1,2)', '(2,1)', '(2,2)'], 'tokenizer_map': {'<ADJLIST_START>': 0, '<ADJLIST_END>': 1, '<TARGET_START>': 2, '<TARGET_END>': 3, '<ORIGIN_START>': 4, '<ORIGIN_END>': 5, '<PATH_START>': 6, '<PATH_END>': 7, '<-->': 8, ';': 9, '<PADDING>': 10, '(0,0)': 11, '(0,1)': 12, '(1,0)': 13, '(1,1)': 14, '(0,2)': 15, '(2,0)': 16, '(1,2)': 17, '(2,1)': 18, '(2,2)': 19}, 'vocab_size': 20, 'padding_token_index': 10}, '_tokenizer': 'None'}\n",
+      "2024-05-13 20:12:21 INFO Initialized logger\n",
+      "2024-05-13 20:12:21 INFO Summary logged, getting dataset\n",
+      "2024-05-13 20:12:21 INFO passed dataset has matching config, using that\n",
+      "2024-05-13 20:12:21 INFO finished getting training dataset with 100 samples\n",
+      "2024-05-13 20:12:21 INFO got validation dataset by splitting training dataset into 99 train and 1 validation samples\n",
+      "2024-05-13 20:12:21 INFO Loaded 99 sequences\n",
+      "2024-05-13 20:12:21 INFO Creating dataloader\n",
+      "2024-05-13 20:12:21 INFO finished dataloader, passing to train()\n",
+      "2024-05-13 20:12:21 INFO Initializing model\n",
+      "Moving model to device:  cpu\n",
+      "2024-05-13 20:12:21 INFO Initializing optimizer\n",
+      "2024-05-13 20:12:21 INFO will train for 7 batches, evals_enabled=True, with intervals: {'print_loss': inf, 'checkpoint': 3, 'eval_fast': 1, 'eval_slow': 3}\n",
+      "2024-05-13 20:12:21 INFO Starting training\n",
+      "2024-05-13 20:12:21 INFO Running evals: eval_fast\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "F:\\KNC\\maze-transformer\\maze_transformer\\evaluation\\path_evals.py:98: RuntimeWarning:\n",
+      "\n",
+      "fraction_connections_adjacent_lattice called on path of length less than 2, retuning NaN\n",
+      "prediction = array([[1, 2]])\n",
+      "\n"
      ]
     },
     {
-     "ename": "MemoryError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m result: TrainingResult \u001b[39m=\u001b[39m train_model(\n\u001b[0;32m      2\u001b[0m \tbase_path\u001b[39m=\u001b[39;49mPATH_DATA,\n\u001b[0;32m      3\u001b[0m     cfg\u001b[39m=\u001b[39;49mCFG,\n\u001b[0;32m      4\u001b[0m \twandb_project\u001b[39m=\u001b[39;49mWandbProject\u001b[39m.\u001b[39;49mUNDERSTANDING_SEARCH, \u001b[39m# change this to WandbProject.DEMO_NOTEBOOKS!\u001b[39;49;00m\n\u001b[0;32m      5\u001b[0m \tdo_generate_dataset\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m      6\u001b[0m \tdataset_verbose\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[0;32m      7\u001b[0m     dataset\u001b[39m=\u001b[39;49mDATASET,\n\u001b[0;32m      8\u001b[0m )\n",
-      "File \u001b[1;32mF:\\KNC\\maze-transformer\\maze_transformer\\training\\train_model.py:169\u001b[0m, in \u001b[0;36mtrain_model\u001b[1;34m(base_path, wandb_project, cfg, cfg_file, cfg_names, do_generate_dataset, dataset_verbose, dataset, allow_dataset_override, device, help, **kwargs)\u001b[0m\n\u001b[0;32m    166\u001b[0m dataloader: DataLoader \u001b[39m=\u001b[39m get_dataloader(dataset, cfg, logger)\n\u001b[0;32m    168\u001b[0m logger\u001b[39m.\u001b[39mprogress(\u001b[39m\"\u001b[39m\u001b[39mfinished dataloader, passing to train()\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 169\u001b[0m trained_model: ZanjHookedTransformer \u001b[39m=\u001b[39m train(\n\u001b[0;32m    170\u001b[0m     cfg\u001b[39m=\u001b[39;49mcfg,\n\u001b[0;32m    171\u001b[0m     dataloader\u001b[39m=\u001b[39;49mdataloader,\n\u001b[0;32m    172\u001b[0m     logger\u001b[39m=\u001b[39;49mlogger,\n\u001b[0;32m    173\u001b[0m     output_dir\u001b[39m=\u001b[39;49moutput_path,\n\u001b[0;32m    174\u001b[0m     device\u001b[39m=\u001b[39;49mdevice,\n\u001b[0;32m    175\u001b[0m     val_dataset\u001b[39m=\u001b[39;49mval_dataset,\n\u001b[0;32m    176\u001b[0m )\n\u001b[0;32m    178\u001b[0m \u001b[39mreturn\u001b[39;00m TrainingResult(\n\u001b[0;32m    179\u001b[0m     output_path\u001b[39m=\u001b[39moutput_path,\n\u001b[0;32m    180\u001b[0m     model\u001b[39m=\u001b[39mtrained_model,\n\u001b[0;32m    181\u001b[0m )\n",
-      "File \u001b[1;32mF:\\KNC\\maze-transformer\\maze_transformer\\training\\training.py:133\u001b[0m, in \u001b[0;36mtrain\u001b[1;34m(cfg, dataloader, logger, output_dir, device, val_dataset, zanj, model)\u001b[0m\n\u001b[0;32m    130\u001b[0m model\u001b[39m.\u001b[39mtrain()\n\u001b[0;32m    131\u001b[0m logger\u001b[39m.\u001b[39mprogress(\u001b[39m\"\u001b[39m\u001b[39mStarting training\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 133\u001b[0m \u001b[39mfor\u001b[39;00m iteration, batch \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39;49m(dataloader):\n\u001b[0;32m    134\u001b[0m     \u001b[39m# forward pass\u001b[39;00m\n\u001b[0;32m    135\u001b[0m     \u001b[39m# ------------------------------\u001b[39;00m\n\u001b[0;32m    136\u001b[0m     loss: SingleLoss\n\u001b[0;32m    137\u001b[0m     logits: Float[torch\u001b[39m.\u001b[39mTensor, \u001b[39m\"\u001b[39m\u001b[39mbatch pos d_vocab\u001b[39m\u001b[39m\"\u001b[39m]\n",
-      "File \u001b[1;32mc:\\Users\\mivan\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\maze-transformer-2cGx2R0F-py3.10\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:441\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    439\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterator\n\u001b[0;32m    440\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 441\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_iterator()\n",
-      "File \u001b[1;32mc:\\Users\\mivan\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\maze-transformer-2cGx2R0F-py3.10\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:388\u001b[0m, in \u001b[0;36mDataLoader._get_iterator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    386\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    387\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_worker_number_rationality()\n\u001b[1;32m--> 388\u001b[0m     \u001b[39mreturn\u001b[39;00m _MultiProcessingDataLoaderIter(\u001b[39mself\u001b[39;49m)\n",
-      "File \u001b[1;32mc:\\Users\\mivan\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\maze-transformer-2cGx2R0F-py3.10\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:1042\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter.__init__\u001b[1;34m(self, loader)\u001b[0m\n\u001b[0;32m   1035\u001b[0m w\u001b[39m.\u001b[39mdaemon \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m   1036\u001b[0m \u001b[39m# NB: Process.start() actually take some time as it needs to\u001b[39;00m\n\u001b[0;32m   1037\u001b[0m \u001b[39m#     start a process and pass the arguments over via a pipe.\u001b[39;00m\n\u001b[0;32m   1038\u001b[0m \u001b[39m#     Therefore, we only add a worker to self._workers list after\u001b[39;00m\n\u001b[0;32m   1039\u001b[0m \u001b[39m#     it started, so that we do not call .join() if program dies\u001b[39;00m\n\u001b[0;32m   1040\u001b[0m \u001b[39m#     before it starts, and __del__ tries to join but will get:\u001b[39;00m\n\u001b[0;32m   1041\u001b[0m \u001b[39m#     AssertionError: can only join a started process.\u001b[39;00m\n\u001b[1;32m-> 1042\u001b[0m w\u001b[39m.\u001b[39;49mstart()\n\u001b[0;32m   1043\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_index_queues\u001b[39m.\u001b[39mappend(index_queue)\n\u001b[0;32m   1044\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_workers\u001b[39m.\u001b[39mappend(w)\n",
-      "File \u001b[1;32mC:\\Python\\Python3_10\\lib\\multiprocessing\\process.py:121\u001b[0m, in \u001b[0;36mBaseProcess.start\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mnot\u001b[39;00m _current_process\u001b[39m.\u001b[39m_config\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mdaemon\u001b[39m\u001b[39m'\u001b[39m), \\\n\u001b[0;32m    119\u001b[0m        \u001b[39m'\u001b[39m\u001b[39mdaemonic processes are not allowed to have children\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m    120\u001b[0m _cleanup()\n\u001b[1;32m--> 121\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_popen \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_Popen(\u001b[39mself\u001b[39;49m)\n\u001b[0;32m    122\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sentinel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_popen\u001b[39m.\u001b[39msentinel\n\u001b[0;32m    123\u001b[0m \u001b[39m# Avoid a refcycle if the target function holds an indirect\u001b[39;00m\n\u001b[0;32m    124\u001b[0m \u001b[39m# reference to the process object (see bpo-30775)\u001b[39;00m\n",
-      "File \u001b[1;32mC:\\Python\\Python3_10\\lib\\multiprocessing\\context.py:224\u001b[0m, in \u001b[0;36mProcess._Popen\u001b[1;34m(process_obj)\u001b[0m\n\u001b[0;32m    222\u001b[0m \u001b[39m@staticmethod\u001b[39m\n\u001b[0;32m    223\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_Popen\u001b[39m(process_obj):\n\u001b[1;32m--> 224\u001b[0m     \u001b[39mreturn\u001b[39;00m _default_context\u001b[39m.\u001b[39;49mget_context()\u001b[39m.\u001b[39;49mProcess\u001b[39m.\u001b[39;49m_Popen(process_obj)\n",
-      "File \u001b[1;32mC:\\Python\\Python3_10\\lib\\multiprocessing\\context.py:327\u001b[0m, in \u001b[0;36mSpawnProcess._Popen\u001b[1;34m(process_obj)\u001b[0m\n\u001b[0;32m    324\u001b[0m \u001b[39m@staticmethod\u001b[39m\n\u001b[0;32m    325\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_Popen\u001b[39m(process_obj):\n\u001b[0;32m    326\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mpopen_spawn_win32\u001b[39;00m \u001b[39mimport\u001b[39;00m Popen\n\u001b[1;32m--> 327\u001b[0m     \u001b[39mreturn\u001b[39;00m Popen(process_obj)\n",
-      "File \u001b[1;32mC:\\Python\\Python3_10\\lib\\multiprocessing\\popen_spawn_win32.py:93\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[1;34m(self, process_obj)\u001b[0m\n\u001b[0;32m     91\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     92\u001b[0m     reduction\u001b[39m.\u001b[39mdump(prep_data, to_child)\n\u001b[1;32m---> 93\u001b[0m     reduction\u001b[39m.\u001b[39;49mdump(process_obj, to_child)\n\u001b[0;32m     94\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     95\u001b[0m     set_spawning_popen(\u001b[39mNone\u001b[39;00m)\n",
-      "File \u001b[1;32mC:\\Python\\Python3_10\\lib\\multiprocessing\\reduction.py:60\u001b[0m, in \u001b[0;36mdump\u001b[1;34m(obj, file, protocol)\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdump\u001b[39m(obj, file, protocol\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m     59\u001b[0m \u001b[39m    \u001b[39m\u001b[39m'''Replacement for pickle.dump() using ForkingPickler.'''\u001b[39;00m\n\u001b[1;32m---> 60\u001b[0m     ForkingPickler(file, protocol)\u001b[39m.\u001b[39;49mdump(obj)\n",
-      "\u001b[1;31mMemoryError\u001b[0m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-05-13 20:12:21 INFO Running evals: eval_slow\n",
+      "2024-05-13 20:12:21 INFO iteration 0/7: loss=3.198\n",
+      "2024-05-13 20:12:21 INFO Saving model checkpoint to ../data/multsrc_demo_small-g3-n100-a_dfs-h88371_nano-v1_test-v1_2024-05-13-20-12-13/checkpoints/model.iter_0.zanj\n",
+      "2024-05-13 20:12:22 INFO Running evals: eval_fast\n",
+      "2024-05-13 20:12:22 INFO Running evals: eval_fast\n",
+      "2024-05-13 20:12:22 INFO Running evals: eval_fast\n",
+      "2024-05-13 20:12:22 INFO Running evals: eval_slow\n",
+      "2024-05-13 20:12:22 INFO Saving model checkpoint to ../data/multsrc_demo_small-g3-n100-a_dfs-h88371_nano-v1_test-v1_2024-05-13-20-12-13/checkpoints/model.iter_3.zanj\n",
+      "2024-05-13 20:12:23 INFO Running evals: eval_fast\n",
+      "2024-05-13 20:12:23 INFO Running evals: eval_fast\n",
+      "2024-05-13 20:12:23 INFO Running evals: eval_fast\n",
+      "2024-05-13 20:12:23 INFO Running evals: eval_slow\n",
+      "2024-05-13 20:12:23 INFO Saving model checkpoint to ../data/multsrc_demo_small-g3-n100-a_dfs-h88371_nano-v1_test-v1_2024-05-13-20-12-13/checkpoints/model.iter_6.zanj\n",
+      "2024-05-13 20:12:23 INFO Saving final model to ../data/multsrc_demo_small-g3-n100-a_dfs-h88371_nano-v1_test-v1_2024-05-13-20-12-13/model.final.zanj\n",
+      "2024-05-13 20:12:24 INFO Done training!\n"
      ]
     }
    ],
@@ -451,9 +428,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "maze-transformer",
+   "display_name": "maze-transformer-2cGx2R0F-py3.11",
    "language": "python",
-   "name": "maze-transformer"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -465,7 +442,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.4"
   },
   "orig_nbformat": 4
  },
diff --git a/poetry.lock b/poetry.lock
index 019034dc..5e0400ca 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,14 +1,14 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
 
 [[package]]
 name = "accelerate"
-version = "0.27.2"
+version = "0.30.1"
 description = "Accelerate"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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+    {file = "accelerate-0.30.1-py3-none-any.whl", hash = "sha256:8dd4edd532a4dac72558c5fe6fe8cb70d0c8ec9e8733f48db97d51ee41cbe763"},
+    {file = "accelerate-0.30.1.tar.gz", hash = "sha256:96779c618889646b86dc928c9e55e86e50a7ccab59e1692e22096481977ae682"},
 ]
 
 [package.dependencies]
@@ -21,98 +21,99 @@ safetensors = ">=0.3.1"
 torch = ">=1.10.0"
 
 [package.extras]
-dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "deepspeed (<0.13.0)", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.1.15,<0.2.0)", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
-quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.1.15,<0.2.0)"]
+deepspeed = ["deepspeed (<=0.14.0)"]
+dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.2.1,<0.3.0)", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.2.1,<0.3.0)"]
 rich = ["rich"]
 sagemaker = ["sagemaker"]
-test-dev = ["bitsandbytes", "datasets", "deepspeed (<0.13.0)", "evaluate", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
-test-prod = ["parameterized", "pytest", "pytest-subtests", "pytest-xdist"]
+test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist"]
 test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
-testing = ["bitsandbytes", "datasets", "deepspeed (<0.13.0)", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 
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 ]
 
 [package.dependencies]
@@ -162,17 +163,6 @@ doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphin
 test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
 trio = ["trio (>=0.23)"]
 
-[[package]]
-name = "appdirs"
-version = "1.4.4"
-description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-optional = false
-python-versions = "*"
-files = [
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-]
-
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -324,13 +314,13 @@ tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "p
 
 [[package]]
 name = "babel"
-version = "2.14.0"
+version = "2.15.0"
 description = "Internationalization utilities"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -388,33 +378,33 @@ files = [
 
 [[package]]
 name = "black"
-version = "24.2.0"
+version = "24.4.2"
 description = "The uncompromising code formatter."
 optional = false
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 ]
 
 [package.dependencies]
@@ -829,13 +819,13 @@ toml = ["tomli"]
 
 [[package]]
 name = "coverage-badge"
-version = "1.1.0"
+version = "1.1.1"
 description = "Generate coverage badges for Coverage.py."
 optional = false
 python-versions = "*"
 files = [
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 [package.dependencies]
@@ -858,21 +848,21 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "datasets"
-version = "2.17.1"
+version = "2.19.1"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 [package.dependencies]
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 filelock = "*"
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+huggingface-hub = ">=0.21.2"
 multiprocess = "*"
 numpy = ">=1.17"
 packaging = "*"
@@ -888,15 +878,15 @@ xxhash = "*"
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 jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
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+tensorflow = ["tensorflow (>=2.6.0)"]
+tensorflow-gpu = ["tensorflow (>=2.6.0)"]
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 torch = ["torch"]
 vision = ["Pillow (>=6.2.1)"]
 
@@ -984,24 +974,24 @@ six = ">=1.4.0"
 
 [[package]]
 name = "einops"
-version = "0.7.0"
+version = "0.8.0"
 description = "A new flavour of deep learning operations"
 optional = false
 python-versions = ">=3.8"
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+    {file = "fonttools-4.51.0-cp39-cp39-win_amd64.whl", hash = "sha256:599bdb75e220241cedc6faebfafedd7670335d2e29620d207dd0378a4e9ccc5a"},
+    {file = "fonttools-4.51.0-py3-none-any.whl", hash = "sha256:15c94eeef6b095831067f72c825eb0e2d48bb4cea0647c1b05c981ecba2bf39f"},
+    {file = "fonttools-4.51.0.tar.gz", hash = "sha256:dc0673361331566d7a663d7ce0f6fdcbfbdc1f59c6e3ed1165ad7202ca183c68"},
 ]
 
 [package.extras]
@@ -1240,18 +1230,17 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.10.0"
+version = "2024.3.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2023.10.0-py3-none-any.whl", hash = "sha256:346a8f024efeb749d2a5fca7ba8854474b1ff9af7c3faaf636a4548781136529"},
-    {file = "fsspec-2023.10.0.tar.gz", hash = "sha256:330c66757591df346ad3091a53bd907e15348c2ba17d63fd54f5c39c4457d2a5"},
+    {file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
+    {file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"},
 ]
 
 [package.dependencies]
 aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-requests = {version = "*", optional = true, markers = "extra == \"http\""}
 
 [package.extras]
 abfs = ["adlfs"]
@@ -1268,7 +1257,7 @@ github = ["requests"]
 gs = ["gcsfs"]
 gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
 libarchive = ["libarchive-c"]
 oci = ["ocifs"]
 s3 = ["s3fs"]
@@ -1293,20 +1282,21 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.42"
+version = "3.1.43"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "GitPython-3.1.42-py3-none-any.whl", hash = "sha256:1bf9cd7c9e7255f77778ea54359e54ac22a72a5b51288c457c881057b7bb9ecd"},
-    {file = "GitPython-3.1.42.tar.gz", hash = "sha256:2d99869e0fef71a73cbd242528105af1d6c1b108c60dfabd994bf292f76c3ceb"},
+    {file = "GitPython-3.1.43-py3-none-any.whl", hash = "sha256:eec7ec56b92aad751f9912a73404bc02ba212a23adb2c7098ee668417051a1ff"},
+    {file = "GitPython-3.1.43.tar.gz", hash = "sha256:35f314a9f878467f5453cc1fee295c3e18e52f1b99f10f6cf5b1682e968a9e7c"},
 ]
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
 
 [package.extras]
-test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar"]
+doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"]
+test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
 
 [[package]]
 name = "h11"
@@ -1321,13 +1311,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.4"
+version = "1.0.5"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpcore-1.0.4-py3-none-any.whl", hash = "sha256:ac418c1db41bade2ad53ae2f3834a3a0f5ae76b56cf5aa497d2d033384fc7d73"},
-    {file = "httpcore-1.0.4.tar.gz", hash = "sha256:cb2839ccfcba0d2d3c1131d3c3e26dfc327326fbe7a5dc0dbfe9f6c9151bb022"},
+    {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
+    {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
 ]
 
 [package.dependencies]
@@ -1338,7 +1328,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.25.0)"]
+trio = ["trio (>=0.22.0,<0.26.0)"]
 
 [[package]]
 name = "httpx"
@@ -1366,13 +1356,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.20.3"
+version = "0.23.0"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.20.3-py3-none-any.whl", hash = "sha256:d988ae4f00d3e307b0c80c6a05ca6dbb7edba8bba3079f74cda7d9c2e562a7b6"},
-    {file = "huggingface_hub-0.20.3.tar.gz", hash = "sha256:94e7f8e074475fbc67d6a71957b678e1b4a74ff1b64a644fd6cbb83da962d05d"},
+    {file = "huggingface_hub-0.23.0-py3-none-any.whl", hash = "sha256:075c30d48ee7db2bba779190dc526d2c11d422aed6f9044c5e2fdc2c432fdb91"},
+    {file = "huggingface_hub-0.23.0.tar.gz", hash = "sha256:7126dedd10a4c6fac796ced4d87a8cf004efc722a5125c2c09299017fa366fa9"},
 ]
 
 [package.dependencies]
@@ -1385,46 +1375,48 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
-inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
-quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp", "minijinja (>=1.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["torch"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
 name = "idna"
-version = "3.6"
+version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
-    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
+    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
 [[package]]
 name = "importlib-metadata"
-version = "7.0.1"
+version = "7.1.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-7.0.1-py3-none-any.whl", hash = "sha256:4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e"},
-    {file = "importlib_metadata-7.0.1.tar.gz", hash = "sha256:f238736bb06590ae52ac1fab06a3a9ef1d8dce2b7a35b5ab329371d6c8f5d2cc"},
+    {file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
+    {file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "iniconfig"
@@ -1439,13 +1431,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.29.2"
+version = "6.29.4"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.29.2-py3-none-any.whl", hash = "sha256:50384f5c577a260a1d53f1f59a828c7266d321c9b7d00d345693783f66616055"},
-    {file = "ipykernel-6.29.2.tar.gz", hash = "sha256:3bade28004e3ff624ed57974948116670604ac5f676d12339693f3142176d3f0"},
+    {file = "ipykernel-6.29.4-py3-none-any.whl", hash = "sha256:1181e653d95c6808039c509ef8e67c4126b3b3af7781496c7cbfb5ed938a27da"},
+    {file = "ipykernel-6.29.4.tar.gz", hash = "sha256:3d44070060f9475ac2092b760123fadf105d2e2493c24848b6691a7c4f42af5c"},
 ]
 
 [package.dependencies]
@@ -1468,17 +1460,17 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.4)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
-version = "8.22.1"
+version = "8.24.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.22.1-py3-none-any.whl", hash = "sha256:869335e8cded62ffb6fac8928e5287a05433d6462e3ebaac25f4216474dd6bc4"},
-    {file = "ipython-8.22.1.tar.gz", hash = "sha256:39c6f9efc079fb19bfb0f17eee903978fe9a290b1b82d68196c641cecb76ea22"},
+    {file = "ipython-8.24.0-py3-none-any.whl", hash = "sha256:d7bf2f6c4314984e3e02393213bab8703cf163ede39672ce5918c51fe253a2a3"},
+    {file = "ipython-8.24.0.tar.gz", hash = "sha256:010db3f8a728a578bb641fdd06c063b9fb8e96a9464c63aec6310fbcb5e80501"},
 ]
 
 [package.dependencies]
@@ -1492,18 +1484,20 @@ prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5.13.0"
+typing-extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 
 [package.extras]
-all = ["ipython[black,doc,kernel,nbconvert,nbformat,notebook,parallel,qtconsole,terminal]", "ipython[test,test-extra]"]
+all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
 black = ["black"]
 doc = ["docrepr", "exceptiongroup", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "stack-data", "typing-extensions"]
 kernel = ["ipykernel"]
+matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pickleshare", "pytest (<8)", "pytest-asyncio (<0.22)", "testpath"]
+test = ["pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
@@ -1557,19 +1551,18 @@ colors = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "jaxtyping"
-version = "0.2.25"
+version = "0.2.28"
 description = "Type annotations and runtime checking for shape and dtype of JAX arrays, and PyTrees."
 optional = false
 python-versions = "~=3.9"
 files = [
-    {file = "jaxtyping-0.2.25-py3-none-any.whl", hash = "sha256:503772cf985ff50bd160d0661266e1628da47cc8b9a302de16f230e07f5ac119"},
-    {file = "jaxtyping-0.2.25.tar.gz", hash = "sha256:4ec4bdb6839a67c062e48c27d62d44bf353099d7ff6cd3628b5a511c097ab922"},
+    {file = "jaxtyping-0.2.28-py3-none-any.whl", hash = "sha256:4a54eb964087cd46463d9a86c805b4e4f5c20cce5f22049d6f35a26d9f105bd3"},
+    {file = "jaxtyping-0.2.28.tar.gz", hash = "sha256:cd20bf1558a90c6d77c589354e35670ecc5b94925ef45bf1c020fde7b44fac8d"},
 ]
 
 [package.dependencies]
 numpy = ">=1.20.0"
-typeguard = ">=2.13.3,<3"
-typing-extensions = ">=3.7.4.1"
+typeguard = "2.13.3"
 
 [[package]]
 name = "jedi"
@@ -1592,13 +1585,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.3"
+version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
-    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
+    {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
+    {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
 ]
 
 [package.dependencies]
@@ -1609,29 +1602,26 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.3.2"
+version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
-    {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
+    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
+    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
 ]
 
 [[package]]
 name = "json5"
-version = "0.9.17"
+version = "0.9.25"
 description = "A Python implementation of the JSON5 data format."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "json5-0.9.17-py2.py3-none-any.whl", hash = "sha256:f8ec1ecf985951d70f780f6f877c4baca6a47b6e61e02c4cd190138d10a7805a"},
-    {file = "json5-0.9.17.tar.gz", hash = "sha256:717d99d657fa71b7094877b1d921b1cce40ab444389f6d770302563bb7dfd9ae"},
+    {file = "json5-0.9.25-py3-none-any.whl", hash = "sha256:34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f"},
+    {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
 ]
 
-[package.extras]
-dev = ["hypothesis"]
-
 [[package]]
 name = "jsonpointer"
 version = "2.4"
@@ -1645,13 +1635,13 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.21.1"
+version = "4.22.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.21.1-py3-none-any.whl", hash = "sha256:7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f"},
-    {file = "jsonschema-4.21.1.tar.gz", hash = "sha256:85727c00279f5fa6bedbe6238d2aa6403bedd8b4864ab11207d07df3cc1b2ee5"},
+    {file = "jsonschema-4.22.0-py3-none-any.whl", hash = "sha256:ff4cfd6b1367a40e7bc6411caec72effadd3db0bbe5017de188f2d6108335802"},
+    {file = "jsonschema-4.22.0.tar.gz", hash = "sha256:5b22d434a45935119af990552c862e5d6d564e8f6601206b305a61fdf661a2b7"},
 ]
 
 [package.dependencies]
@@ -1708,13 +1698,13 @@ qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.0"
+version = "8.6.1"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
-    {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"},
+    {file = "jupyter_client-8.6.1-py3-none-any.whl", hash = "sha256:3b7bd22f058434e3b9a7ea4b1500ed47de2713872288c0d511d19926f99b459f"},
+    {file = "jupyter_client-8.6.1.tar.gz", hash = "sha256:e842515e2bab8e19186d89fdfea7abd15e39dd581f94e399f00e2af5a1652d3f"},
 ]
 
 [package.dependencies]
@@ -1754,13 +1744,13 @@ test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.7.1"
+version = "5.7.2"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.7.1-py3-none-any.whl", hash = "sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7"},
-    {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"},
+    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
+    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
 ]
 
 [package.dependencies]
@@ -1770,17 +1760,17 @@ traitlets = ">=5.3"
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
-test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
+test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-events"
-version = "0.9.0"
+version = "0.10.0"
 description = "Jupyter Event System library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_events-0.9.0-py3-none-any.whl", hash = "sha256:d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf"},
-    {file = "jupyter_events-0.9.0.tar.gz", hash = "sha256:81ad2e4bc710881ec274d31c6c50669d71bbaa5dd9d01e600b56faa85700d399"},
+    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
+    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
 ]
 
 [package.dependencies]
@@ -1799,13 +1789,13 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-lsp"
-version = "2.2.2"
+version = "2.2.5"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter-lsp-2.2.2.tar.gz", hash = "sha256:256d24620542ae4bba04a50fc1f6ffe208093a07d8e697fea0a8d1b8ca1b7e5b"},
-    {file = "jupyter_lsp-2.2.2-py3-none-any.whl", hash = "sha256:3b95229e4168355a8c91928057c1621ac3510ba98b2a925e82ebd77f078b1aa5"},
+    {file = "jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001"},
+    {file = "jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da"},
 ]
 
 [package.dependencies]
@@ -1813,49 +1803,49 @@ jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.12.5"
+version = "2.14.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.12.5-py3-none-any.whl", hash = "sha256:184a0f82809a8522777cfb6b760ab6f4b1bb398664c5860a27cec696cb884923"},
-    {file = "jupyter_server-2.12.5.tar.gz", hash = "sha256:0edb626c94baa22809be1323f9770cf1c00a952b17097592e40d03e6a3951689"},
+    {file = "jupyter_server-2.14.0-py3-none-any.whl", hash = "sha256:fb6be52c713e80e004fac34b35a0990d6d36ba06fd0a2b2ed82b899143a64210"},
+    {file = "jupyter_server-2.14.0.tar.gz", hash = "sha256:659154cea512083434fd7c93b7fe0897af7a2fd0b9dd4749282b42eaac4ae677"},
 ]
 
 [package.dependencies]
 anyio = ">=3.1.0"
-argon2-cffi = "*"
-jinja2 = "*"
+argon2-cffi = ">=21.1"
+jinja2 = ">=3.0.3"
 jupyter-client = ">=7.4.4"
 jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 jupyter-events = ">=0.9.0"
-jupyter-server-terminals = "*"
+jupyter-server-terminals = ">=0.4.4"
 nbconvert = ">=6.4.4"
 nbformat = ">=5.3.0"
-overrides = "*"
-packaging = "*"
-prometheus-client = "*"
-pywinpty = {version = "*", markers = "os_name == \"nt\""}
+overrides = ">=5.0"
+packaging = ">=22.0"
+prometheus-client = ">=0.9"
+pywinpty = {version = ">=2.0.1", markers = "os_name == \"nt\""}
 pyzmq = ">=24"
 send2trash = ">=1.8.2"
 terminado = ">=0.8.3"
 tornado = ">=6.2.0"
 traitlets = ">=5.6.0"
-websocket-client = "*"
+websocket-client = ">=1.7"
 
 [package.extras]
 docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
-test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"]
+test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0,<9)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"]
 
 [[package]]
 name = "jupyter-server-terminals"
-version = "0.5.2"
+version = "0.5.3"
 description = "A Jupyter Server Extension Providing Terminals."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server_terminals-0.5.2-py3-none-any.whl", hash = "sha256:1b80c12765da979513c42c90215481bbc39bd8ae7c0350b4f85bc3eb58d0fa80"},
-    {file = "jupyter_server_terminals-0.5.2.tar.gz", hash = "sha256:396b5ccc0881e550bf0ee7012c6ef1b53edbde69e67cab1d56e89711b46052e8"},
+    {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"},
+    {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"},
 ]
 
 [package.dependencies]
@@ -1868,27 +1858,27 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.1.2"
+version = "4.1.8"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.1.2-py3-none-any.whl", hash = "sha256:aa88193f03cf4d3555f6712f04d74112b5eb85edd7d222c588c7603a26d33c5b"},
-    {file = "jupyterlab-4.1.2.tar.gz", hash = "sha256:5d6348b3ed4085181499f621b7dfb6eb0b1f57f3586857aadfc8e3bf4c4885f9"},
+    {file = "jupyterlab-4.1.8-py3-none-any.whl", hash = "sha256:c3baf3a2f91f89d110ed5786cd18672b9a357129d4e389d2a0dead15e11a4d2c"},
+    {file = "jupyterlab-4.1.8.tar.gz", hash = "sha256:3384aded8680e7ce504fd63b8bb89a39df21c9c7694d9e7dc4a68742cdb30f9b"},
 ]
 
 [package.dependencies]
 async-lru = ">=1.0.0"
 httpx = ">=0.25.0"
-ipykernel = "*"
+ipykernel = ">=6.5.0"
 jinja2 = ">=3.0.3"
 jupyter-core = "*"
 jupyter-lsp = ">=2.0.0"
 jupyter-server = ">=2.4.0,<3"
-jupyterlab-server = ">=2.19.0,<3"
+jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2"
 packaging = "*"
-tomli = {version = "*", markers = "python_version < \"3.11\""}
+tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
 tornado = ">=6.2.0"
 traitlets = "*"
 
@@ -1897,6 +1887,7 @@ dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov",
 docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
 docs-screenshots = ["altair (==5.2.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.1)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post6)", "matplotlib (==3.8.2)", "nbconvert (>=7.0.0)", "pandas (==2.2.0)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
+upgrade-extension = ["copier (>=8.0,<9.0)", "jinja2-time (<0.3)", "pydantic (<2.0)", "pyyaml-include (<2.0)", "tomli-w (<2.0)"]
 
 [[package]]
 name = "jupyterlab-pygments"
@@ -1911,13 +1902,13 @@ files = [
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.25.3"
+version = "2.27.1"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.25.3-py3-none-any.whl", hash = "sha256:c48862519fded9b418c71645d85a49b2f0ec50d032ba8316738e9276046088c1"},
-    {file = "jupyterlab_server-2.25.3.tar.gz", hash = "sha256:846f125a8a19656611df5b03e5912c8393cea6900859baa64fa515eb64a8dc40"},
+    {file = "jupyterlab_server-2.27.1-py3-none-any.whl", hash = "sha256:f5e26156e5258b24d532c84e7c74cc212e203bff93eb856f81c24c16daeecc75"},
+    {file = "jupyterlab_server-2.27.1.tar.gz", hash = "sha256:097b5ac709b676c7284ac9c5e373f11930a561f52cd5a86e4fc7e5a9c8a8631d"},
 ]
 
 [package.dependencies]
@@ -1932,7 +1923,7 @@ requests = ">=2.31"
 [package.extras]
 docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
 openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
-test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0,<8)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
 
 [[package]]
 name = "jupyterlab-widgets"
@@ -2060,45 +2051,43 @@ files = [
 
 [[package]]
 name = "libcst"
-version = "1.2.0"
+version = "1.3.1"
 description = "A concrete syntax tree with AST-like properties for Python 3.0 through 3.12 programs."
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -2235,7 +2224,7 @@ contourpy = ">=1.0.1"
 cycler = ">=0.10"
 fonttools = ">=4.22.0"
 kiwisolver = ">=1.3.1"
-numpy = ">=1.21,<2"
+numpy = ">=1.21"
 packaging = ">=20.0"
 pillow = ">=8"
 pyparsing = ">=2.3.1"
@@ -2243,13 +2232,13 @@ python-dateutil = ">=2.7"
 
 [[package]]
 name = "matplotlib-inline"
-version = "0.1.6"
+version = "0.1.7"
 description = "Inline Matplotlib backend for Jupyter"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
 files = [
-    {file = "matplotlib-inline-0.1.6.tar.gz", hash = "sha256:f887e5f10ba98e8d2b150ddcf4702c1e5f8b3a20005eb0f74bfdbd360ee6f304"},
-    {file = "matplotlib_inline-0.1.6-py3-none-any.whl", hash = "sha256:f1f41aab5328aa5aaea9b16d083b128102f8712542f819fe7e6a420ff581b311"},
+    {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"},
+    {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"},
 ]
 
 [package.dependencies]
@@ -2257,13 +2246,13 @@ traitlets = "*"
 
 [[package]]
 name = "maze-dataset"
-version = "0.4.5"
+version = "0.5.2"
 description = ""
 optional = false
-python-versions = ">=3.10,<4.0"
+python-versions = "<4.0,>=3.10"
 files = [
-    {file = "maze_dataset-0.4.5-py3-none-any.whl", hash = "sha256:aac13dfc8b203ea3aaa4ca28a65c0c20e30aa1828765655f2db4190c4f8a07a9"},
-    {file = "maze_dataset-0.4.5.tar.gz", hash = "sha256:2e1e00e88c81a1f1844bc1776b3b4600ac311fba1ee52832922cacb4fc1d2e77"},
+    {file = "maze_dataset-0.5.2-py3-none-any.whl", hash = "sha256:cd971df82d23e1c49f8b07f1da06f8f8b2de651dab9dce8285d91dcb7191a2ec"},
+    {file = "maze_dataset-0.5.2.tar.gz", hash = "sha256:32ad68274ed52a714a98b5ad851307b7a78b2cbae36a22f702b18c6a7b405113"},
 ]
 
 [package.dependencies]
@@ -2271,7 +2260,7 @@ ipykernel = ">=6.22.0,<7.0.0"
 jaxtyping = ">=0.2.19,<0.3.0"
 jupyter = ">=1.0.0,<2.0.0"
 matplotlib = ">=3.7.0,<4.0.0"
-muutils = ">=0.5.1,<0.6.0"
+muutils = ">=0.5.8,<0.6.0"
 torch = ">=1.13.1"
 tqdm = ">=4.65.0,<5.0.0"
 zanj = ">=0.2.0,<0.3.0"
@@ -2440,13 +2429,13 @@ dill = ">=0.3.8"
 
 [[package]]
 name = "muutils"
-version = "0.5.7"
+version = "0.5.8"
 description = "A collection of miscellaneous python utilities"
 optional = false
 python-versions = ">=3.10,<4.0"
 files = [
-    {file = "muutils-0.5.7-py3-none-any.whl", hash = "sha256:7a7a24b603689166e478c7842a7264eb42731c93b09da11aed7ad5cb3246bb6d"},
-    {file = "muutils-0.5.7.tar.gz", hash = "sha256:bbe4aa7168ff9ee3409b38545dd807d70db3025a99600439ddf5cba0850bf067"},
+    {file = "muutils-0.5.8-py3-none-any.whl", hash = "sha256:70db0a9aef0cb4f56ef832818bf79ecf36e50dce030b2d980a3ca64ed924cfcf"},
+    {file = "muutils-0.5.8.tar.gz", hash = "sha256:a9fbc18a6303266671cd0e41b3e9994e00cf3814397d5a7204467596c861b751"},
 ]
 
 [package.dependencies]
@@ -2470,13 +2459,13 @@ files = [
 
 [[package]]
 name = "nbclient"
-version = "0.9.0"
+version = "0.10.0"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "nbclient-0.9.0-py3-none-any.whl", hash = "sha256:a3a1ddfb34d4a9d17fc744d655962714a866639acd30130e9be84191cd97cd15"},
-    {file = "nbclient-0.9.0.tar.gz", hash = "sha256:4b28c207877cf33ef3a9838cdc7a54c5ceff981194a82eac59d558f05487295e"},
+    {file = "nbclient-0.10.0-py3-none-any.whl", hash = "sha256:f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f"},
+    {file = "nbclient-0.10.0.tar.gz", hash = "sha256:4b3f1b7dba531e498449c4db4f53da339c91d449dc11e9af3a43b4eb5c5abb09"},
 ]
 
 [package.dependencies]
@@ -2488,17 +2477,17 @@ traitlets = ">=5.4"
 [package.extras]
 dev = ["pre-commit"]
 docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
-test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
 
 [[package]]
 name = "nbconvert"
-version = "7.16.1"
+version = "7.16.4"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "nbconvert-7.16.1-py3-none-any.whl", hash = "sha256:3188727dffadfdc9c6a1c7250729063d7bc78b355ad7aa023138afa030d1cd07"},
-    {file = "nbconvert-7.16.1.tar.gz", hash = "sha256:e79e6a074f49ba3ed29428ed86487bf51509d9aab613bd8522ac08f6d28fd7fd"},
+    {file = "nbconvert-7.16.4-py3-none-any.whl", hash = "sha256:05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3"},
+    {file = "nbconvert-7.16.4.tar.gz", hash = "sha256:86ca91ba266b0a448dc96fa6c5b9d98affabde2867b363258703536807f9f7f4"},
 ]
 
 [package.dependencies]
@@ -2519,29 +2508,29 @@ tinycss2 = "*"
 traitlets = ">=5.1"
 
 [package.extras]
-all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"]
+all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"]
 docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"]
-qtpdf = ["nbconvert[qtpng]"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
-test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
 webpdf = ["playwright"]
 
 [[package]]
 name = "nbformat"
-version = "5.9.2"
+version = "5.10.4"
 description = "The Jupyter Notebook format"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "nbformat-5.9.2-py3-none-any.whl", hash = "sha256:1c5172d786a41b82bcfd0c23f9e6b6f072e8fb49c39250219e4acfff1efe89e9"},
-    {file = "nbformat-5.9.2.tar.gz", hash = "sha256:5f98b5ba1997dff175e77e0c17d5c10a96eaed2cbd1de3533d1fc35d5e111192"},
+    {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"},
+    {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"},
 ]
 
 [package.dependencies]
-fastjsonschema = "*"
+fastjsonschema = ">=2.15"
 jsonschema = ">=2.6"
-jupyter-core = "*"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 traitlets = ">=5.1"
 
 [package.extras]
@@ -2561,31 +2550,31 @@ files = [
 
 [[package]]
 name = "networkx"
-version = "3.2.1"
+version = "3.3"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 files = [
-    {file = "networkx-3.2.1-py3-none-any.whl", hash = "sha256:f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2"},
-    {file = "networkx-3.2.1.tar.gz", hash = "sha256:9f1bb5cf3409bf324e0a722c20bdb4c20ee39bf1c30ce8ae499c8502b0b5e0c6"},
+    {file = "networkx-3.3-py3-none-any.whl", hash = "sha256:28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2"},
+    {file = "networkx-3.3.tar.gz", hash = "sha256:0c127d8b2f4865f59ae9cb8aafcd60b5c70f3241ebd66f7defad7c4ab90126c9"},
 ]
 
 [package.extras]
-default = ["matplotlib (>=3.5)", "numpy (>=1.22)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
-developer = ["changelist (==0.4)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
-doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
-extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.11)", "sympy (>=1.10)"]
+default = ["matplotlib (>=3.6)", "numpy (>=1.23)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["myst-nb (>=1.0)", "numpydoc (>=1.7)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
+extra = ["lxml (>=4.6)", "pydot (>=2.0)", "pygraphviz (>=1.12)", "sympy (>=1.10)"]
 test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "notebook"
-version = "7.1.0"
+version = "7.1.3"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "notebook-7.1.0-py3-none-any.whl", hash = "sha256:a8fa4ccb5e5fe220f29d9900337efd7752bc6f2efe004d6f320db01f7743adc9"},
-    {file = "notebook-7.1.0.tar.gz", hash = "sha256:99caf01ff166b1cc86355c9b37c1ba9bf566c1d7fc4ab57bb6f8f24e36c4260e"},
+    {file = "notebook-7.1.3-py3-none-any.whl", hash = "sha256:919b911e59f41f6e3857ce93c9d93535ba66bb090059712770e5968c07e1004d"},
+    {file = "notebook-7.1.3.tar.gz", hash = "sha256:41fcebff44cf7bb9377180808bcbae066629b55d8c7722f1ebbe75ca44f9cfc1"},
 ]
 
 [package.dependencies]
@@ -2783,13 +2772,13 @@ files = [
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.3.101"
+version = "12.4.127"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl", hash = "sha256:64335a8088e2b9d196ae8665430bc6a2b7e6ef2eb877a9c735c804bd4ff6467c"},
-    {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-win_amd64.whl", hash = "sha256:1b2e317e437433753530792f13eece58f0aec21a2b05903be7bffe58a606cbd1"},
+    {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:06b3b9b25bf3f8af351d664978ca26a16d2c5127dbd53c0497e28d1fb9611d57"},
+    {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:fd9020c501d27d135f983c6d3e244b197a7ccad769e34df53a42e276b0e25fa1"},
 ]
 
 [[package]]
@@ -2816,13 +2805,13 @@ files = [
 
 [[package]]
 name = "packaging"
-version = "23.2"
+version = "24.0"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "packaging-23.2-py3-none-any.whl", hash = "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"},
-    {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
+    {file = "packaging-24.0-py3-none-any.whl", hash = "sha256:2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5"},
+    {file = "packaging-24.0.tar.gz", hash = "sha256:eb82c5e3e56209074766e6885bb04b8c38a0c015d0a30036ebe7ece34c9989e9"},
 ]
 
 [[package]]
@@ -2863,8 +2852,8 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.21.0", markers = "python_version >= \"3.10\""},
     {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
-    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
 python-dateutil = ">=2.8.1"
 pytz = ">=2020.1"
@@ -2885,18 +2874,18 @@ files = [
 
 [[package]]
 name = "parso"
-version = "0.8.3"
+version = "0.8.4"
 description = "A Python Parser"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "parso-0.8.3-py2.py3-none-any.whl", hash = "sha256:c001d4636cd3aecdaf33cbb40aebb59b094be2a74c556778ef5576c175e19e75"},
-    {file = "parso-0.8.3.tar.gz", hash = "sha256:8c07be290bb59f03588915921e29e8a50002acaf2cdc5fa0e0114f91709fafa0"},
+    {file = "parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18"},
+    {file = "parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d"},
 ]
 
 [package.extras]
-qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
-testing = ["docopt", "pytest (<6.0.0)"]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["docopt", "pytest"]
 
 [[package]]
 name = "pathspec"
@@ -2909,16 +2898,6 @@ files = [
     {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
 ]
 
-[[package]]
-name = "pathtools"
-version = "0.1.2"
-description = "File system general utilities"
-optional = false
-python-versions = "*"
-files = [
-    {file = "pathtools-0.1.2.tar.gz", hash = "sha256:7c35c5421a39bb82e58018febd90e3b6e5db34c5443aaaf742b3f33d4655f1c0"},
-]
-
 [[package]]
 name = "pexpect"
 version = "4.9.0"
@@ -2935,79 +2914,80 @@ ptyprocess = ">=0.5"
 
 [[package]]
 name = "pillow"
-version = "10.2.0"
+version = "10.3.0"
 description = "Python Imaging Library (Fork)"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "pillow-10.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:83b2021f2ade7d1ed556bc50a399127d7fb245e725aa0113ebd05cfe88aaf588"},
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 name = "pyarrow-hotfix"
@@ -3243,39 +3224,38 @@ typer = ">=0.4.1"
 
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@@ -3323,30 +3303,30 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale
 
 [[package]]
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 [package.dependencies]
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 [package.extras]
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 [[package]]
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@@ -3399,17 +3379,17 @@ files = [
 
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 [package.dependencies]
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 [package.extras]
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 [[package]]
 name = "seaborn"
@@ -4147,13 +4097,13 @@ stats = ["scipy (>=1.3)", "statsmodels (>=0.10)"]
 
 [[package]]
 name = "send2trash"
-version = "1.8.2"
+version = "1.8.3"
 description = "Send file to trash natively under Mac OS X, Windows and Linux"
 optional = false
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 ]
 
 [package.extras]
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 objc = ["pyobjc-framework-Cocoa"]
 win32 = ["pywin32"]
 
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+    {file = "sentencepiece-0.2.0.tar.gz", hash = "sha256:a52c19171daaf2e697dc6cbe67684e0fa341b1248966f6aebb541de654d15843"},
+]
+
 [[package]]
 name = "sentry-sdk"
-version = "1.40.5"
+version = "2.1.1"
 description = "Python client for Sentry (https://sentry.io)"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
 files = [
-    {file = "sentry-sdk-1.40.5.tar.gz", hash = "sha256:d2dca2392cc5c9a2cc9bb874dd7978ebb759682fe4fe889ee7e970ee8dd1c61e"},
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+    {file = "sentry_sdk-2.1.1-py2.py3-none-any.whl", hash = "sha256:99aeb78fb76771513bd3b2829d12613130152620768d00cd3e45ac00cb17950f"},
+    {file = "sentry_sdk-2.1.1.tar.gz", hash = "sha256:95d8c0bb41c8b0bc37ab202c2c4a295bb84398ee05f4cdce55051cd75b926ec1"},
 ]
 
 [package.dependencies]
 certifi = "*"
-urllib3 = {version = ">=1.26.11", markers = "python_version >= \"3.6\""}
+urllib3 = ">=1.26.11"
 
 [package.extras]
 aiohttp = ["aiohttp (>=3.5)"]
+anthropic = ["anthropic (>=0.16)"]
 arq = ["arq (>=0.23)"]
 asyncpg = ["asyncpg (>=0.23)"]
 beam = ["apache-beam (>=2.12)"]
 bottle = ["bottle (>=0.12.13)"]
 celery = ["celery (>=3)"]
+celery-redbeat = ["celery-redbeat (>=2)"]
 chalice = ["chalice (>=1.16.0)"]
 clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
 django = ["django (>=1.8)"]
@@ -4192,7 +4206,10 @@ flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
 grpcio = ["grpcio (>=1.21.1)"]
 httpx = ["httpx (>=0.16.0)"]
 huey = ["huey (>=2)"]
+huggingface-hub = ["huggingface-hub (>=0.22)"]
+langchain = ["langchain (>=0.0.210)"]
 loguru = ["loguru (>=0.5)"]
+openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
 opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
 opentelemetry-experimental = ["opentelemetry-distro (>=0.40b0,<1.0)", "opentelemetry-instrumentation-aiohttp-client (>=0.40b0,<1.0)", "opentelemetry-instrumentation-django (>=0.40b0,<1.0)", "opentelemetry-instrumentation-fastapi (>=0.40b0,<1.0)", "opentelemetry-instrumentation-flask (>=0.40b0,<1.0)", "opentelemetry-instrumentation-requests (>=0.40b0,<1.0)", "opentelemetry-instrumentation-sqlite3 (>=0.40b0,<1.0)", "opentelemetry-instrumentation-urllib (>=0.40b0,<1.0)"]
 pure-eval = ["asttokens", "executing", "pure-eval"]
@@ -4308,19 +4325,30 @@ test = ["pytest"]
 
 [[package]]
 name = "setuptools"
-version = "69.1.0"
+version = "69.5.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"},
+    {file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"},
 ]
 
 [package.extras]
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-testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+description = "Tool to Detect Surrounding Shell"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686"},
+    {file = "shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"},
+]
 
 [[package]]
 name = "six"
@@ -4346,13 +4374,13 @@ files = [
 
 [[package]]
 name = "sniffio"
-version = "1.3.0"
+version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"},
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+    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
 ]
 
 [[package]]
@@ -4401,17 +4429,18 @@ mpmath = ">=0.19"
 
 [[package]]
 name = "tenacity"
-version = "8.2.3"
+version = "8.3.0"
 description = "Retry code until it succeeds"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "tenacity-8.3.0-py3-none-any.whl", hash = "sha256:3649f6443dbc0d9b01b9d8020a9c4ec7a1ff5f6f3c6c8a036ef371f573fe9185"},
+    {file = "tenacity-8.3.0.tar.gz", hash = "sha256:953d4e6ad24357bceffbc9707bc74349aca9d245f68eb65419cf0c249a1949a2"},
 ]
 
 [package.extras]
-doc = ["reno", "sphinx", "tornado (>=4.5)"]
+doc = ["reno", "sphinx"]
+test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
 [[package]]
 name = "termcolor"
@@ -4429,13 +4458,13 @@ tests = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "terminado"
-version = "0.18.0"
+version = "0.18.1"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0"},
+    {file = "terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e"},
 ]
 
 [package.dependencies]
@@ -4450,24 +4479,24 @@ typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
 [[package]]
 name = "threadpoolctl"
-version = "3.3.0"
+version = "3.5.0"
 description = "threadpoolctl"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"},
 ]
 
 [[package]]
 name = "tinycss2"
-version = "1.2.1"
+version = "1.3.0"
 description = "A tiny CSS parser"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "tinycss2-1.3.0-py3-none-any.whl", hash = "sha256:54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7"},
+    {file = "tinycss2-1.3.0.tar.gz", hash = "sha256:152f9acabd296a8375fbca5b84c961ff95971fcfc32e79550c8df8e29118c54d"},
 ]
 
 [package.dependencies]
@@ -4475,134 +4504,124 @@ webencodings = ">=0.4"
 
 [package.extras]
 doc = ["sphinx", "sphinx_rtd_theme"]
-test = ["flake8", "isort", "pytest"]
+test = ["pytest", "ruff"]
 
 [[package]]
 name = "tokenizers"
-version = "0.15.2"
+version = "0.19.1"
 description = ""
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "tokenizers-0.19.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2b718f316b596f36e1dae097a7d5b91fc5b85e90bf08b01ff139bd8953b25af"},
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+    {file = "tokenizers-0.19.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f8a9c828277133af13f3859d1b6bf1c3cb6e9e1637df0e45312e6b7c2e622b1f"},
+    {file = "tokenizers-0.19.1.tar.gz", hash = "sha256:ee59e6680ed0fdbe6b724cf38bd70400a0c1dd623b07ac729087270caeac88e3"},
 ]
 
 [package.dependencies]
-huggingface_hub = ">=0.16.4,<1.0"
+huggingface-hub = ">=0.16.4,<1.0"
 
 [package.extras]
 dev = ["tokenizers[testing]"]
-docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
-testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
 
 [[package]]
 name = "tomli"
@@ -4617,13 +4636,13 @@ files = [
 
 [[package]]
 name = "tomlkit"
-version = "0.12.3"
+version = "0.12.5"
 description = "Style preserving TOML library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tomlkit-0.12.3-py3-none-any.whl", hash = "sha256:b0a645a9156dc7cb5d3a1f0d4bab66db287fcb8e0430bdd4664a095ea16414ba"},
-    {file = "tomlkit-0.12.3.tar.gz", hash = "sha256:75baf5012d06501f07bee5bf8e801b9f343e7aac5a92581f20f80ce632e6b5a4"},
+    {file = "tomlkit-0.12.5-py3-none-any.whl", hash = "sha256:af914f5a9c59ed9d0762c7b64d3b5d5df007448eb9cd2edc8a46b1eafead172f"},
+    {file = "tomlkit-0.12.5.tar.gz", hash = "sha256:eef34fba39834d4d6b73c9ba7f3e4d1c417a4e56f89a7e96e090dd0d24b8fb3c"},
 ]
 
 [[package]]
@@ -4701,13 +4720,13 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.66.2"
+version = "4.66.4"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
-    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
+    {file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"},
+    {file = "tqdm-4.66.4.tar.gz", hash = "sha256:e4d936c9de8727928f3be6079590e97d9abfe8d39a590be678eb5919ffc186bb"},
 ]
 
 [package.dependencies]
@@ -4721,28 +4740,28 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.14.1"
+version = "5.14.3"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.14.1-py3-none-any.whl", hash = "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74"},
-    {file = "traitlets-5.14.1.tar.gz", hash = "sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e"},
+    {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
+    {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
 ]
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
-test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
 name = "transformer-lens"
-version = "1.14.0"
+version = "1.17.0"
 description = "An implementation of transformers tailored for mechanistic interpretability."
 optional = false
-python-versions = ">=3.8,<4.0"
+python-versions = "<4.0,>=3.8"
 files = [
-    {file = "transformer_lens-1.14.0-py3-none-any.whl", hash = "sha256:21ab7a213429235f6276378afca2f6f56068611caa5af42dc3af9cf86dfbd579"},
-    {file = "transformer_lens-1.14.0.tar.gz", hash = "sha256:c3e054edb7bb187fd869d41f063c3a241b24b58718bfaaf6fca3a36a7a4832ab"},
+    {file = "transformer_lens-1.17.0-py3-none-any.whl", hash = "sha256:25e3817bc9d2ec6145ce709c2d5839340d187a1c229f0cf8b571568d8ee7851f"},
+    {file = "transformer_lens-1.17.0.tar.gz", hash = "sha256:0513efb359405f56264da6c68881decc083279b8f6131629066d6c1c6647fb5a"},
 ]
 
 [package.dependencies]
@@ -4759,21 +4778,25 @@ numpy = [
 ]
 pandas = ">=1.1.5"
 rich = ">=12.6.0"
-torch = ">=1.10,<2.0 || >2.0,<2.1.0 || >2.1.0"
+sentencepiece = "*"
+torch = [
+    {version = ">=1.10", markers = "sys_platform == \"linux\""},
+    {version = ">=1.10,<2.0 || >2.0,<2.1.0 || >2.1.0", markers = "sys_platform != \"linux\""},
+]
 tqdm = ">=4.64.1"
-transformers = ">=4.34"
+transformers = ">=4.37.2"
 typing-extensions = "*"
 wandb = ">=0.13.5"
 
 [[package]]
 name = "transformers"
-version = "4.38.1"
+version = "4.40.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "transformers-4.38.1-py3-none-any.whl", hash = "sha256:a7a9265fb060183e9d975cbbadc4d531b10281589c43f6d07563f86322728973"},
-    {file = "transformers-4.38.1.tar.gz", hash = "sha256:86dc84ccbe36123647e84cbd50fc31618c109a41e6be92514b064ab55bf1304c"},
+    {file = "transformers-4.40.2-py3-none-any.whl", hash = "sha256:71cb94301ec211a2e1d4b8c8d18dcfaa902dfa00a089dceca167a8aa265d6f2d"},
+    {file = "transformers-4.40.2.tar.gz", hash = "sha256:657b6054a2097671398d976ad46e60836e7e15f9ea9551631a96e33cb9240649"},
 ]
 
 [package.dependencies]
@@ -4785,21 +4808,21 @@ pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
 safetensors = ">=0.4.1"
-tokenizers = ">=0.14,<0.19"
+tokenizers = ">=0.19,<0.20"
 tqdm = ">=4.27"
 
 [package.extras]
 accelerate = ["accelerate (>=0.21.0)"]
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+docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
 docs-specific = ["hf-doc-builder"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
@@ -4820,16 +4843,16 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm"]
-tokenizers = ["tokenizers (>=0.14,<0.19)"]
+tokenizers = ["tokenizers (>=0.19,<0.20)"]
 torch = ["accelerate (>=0.21.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
@@ -4875,62 +4898,43 @@ test = ["mypy", "pytest", "typing-extensions"]
 
 [[package]]
 name = "typer"
-version = "0.9.0"
+version = "0.12.3"
 description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "typer-0.9.0-py3-none-any.whl", hash = "sha256:5d96d986a21493606a358cae4461bd8cdf83cbf33a5aa950ae629ca3b51467ee"},
-    {file = "typer-0.9.0.tar.gz", hash = "sha256:50922fd79aea2f4751a8e0408ff10d2662bd0c8bbfa84755a699f3bada2978b2"},
+    {file = "typer-0.12.3-py3-none-any.whl", hash = "sha256:070d7ca53f785acbccba8e7d28b08dcd88f79f1fbda035ade0aecec71ca5c914"},
+    {file = "typer-0.12.3.tar.gz", hash = "sha256:49e73131481d804288ef62598d97a1ceef3058905aa536a1134f90891ba35482"},
 ]
 
 [package.dependencies]
-click = ">=7.1.1,<9.0.0"
+click = ">=8.0.0"
+rich = ">=10.11.0"
+shellingham = ">=1.3.0"
 typing-extensions = ">=3.7.4.3"
 
-[package.extras]
-all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
-dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
-doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
-test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
-
 [[package]]
 name = "types-python-dateutil"
-version = "2.8.19.20240106"
+version = "2.9.0.20240316"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-python-dateutil-2.8.19.20240106.tar.gz", hash = "sha256:1f8db221c3b98e6ca02ea83a58371b22c374f42ae5bbdf186db9c9a76581459f"},
-    {file = "types_python_dateutil-2.8.19.20240106-py3-none-any.whl", hash = "sha256:efbbdc54590d0f16152fa103c9879c7d4a00e82078f6e2cf01769042165acaa2"},
+    {file = "types-python-dateutil-2.9.0.20240316.tar.gz", hash = "sha256:5d2f2e240b86905e40944dd787db6da9263f0deabef1076ddaed797351ec0202"},
+    {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"},
 ]
 
 [[package]]
 name = "typing-extensions"
-version = "4.9.0"
+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
-    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
+    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
 ]
 
-[[package]]
-name = "typing-inspect"
-version = "0.9.0"
-description = "Runtime inspection utilities for typing module."
-optional = false
-python-versions = "*"
-files = [
-    {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
-    {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
-]
-
-[package.dependencies]
-mypy-extensions = ">=0.3.0"
-typing-extensions = ">=3.7.4"
-
 [[package]]
 name = "uri-template"
 version = "1.3.0"
@@ -4964,24 +4968,28 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "wandb"
-version = "0.13.11"
-description = "A CLI and library for interacting with the Weights and Biases API."
+version = "0.17.0"
+description = "A CLI and library for interacting with the Weights & Biases API."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "wandb-0.13.11-py3-none-any.whl", hash = "sha256:e584ac7fb0097fba8a030d5aedc5fea157286aba69a2e6e6e689b794155fcc8f"},
-    {file = "wandb-0.13.11.tar.gz", hash = "sha256:f2b065d1c732e8e1828fabe720dc82f12e6109d0d2bbfbcccc17eaefae0fd7c9"},
+    {file = "wandb-0.17.0-py3-none-any.whl", hash = "sha256:b1b056b4cad83b00436cb76049fd29ecedc6045999dcaa5eba40db6680960ac2"},
+    {file = "wandb-0.17.0-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:e1e6f04e093a6a027dcb100618ca23b122d032204b2ed4c62e4e991a48041a6b"},
+    {file = "wandb-0.17.0-py3-none-macosx_11_0_arm64.whl", hash = "sha256:feeb60d4ff506d2a6bc67f953b310d70b004faa789479c03ccd1559c6f1a9633"},
+    {file = "wandb-0.17.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b7bed8a3dd404a639e6bf5fea38c6efe2fb98d416ff1db4fb51be741278ed328"},
+    {file = "wandb-0.17.0-py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:56a1dd6e0e635cba3f6ed30b52c71739bdc2a3e57df155619d2d80ee952b4201"},
+    {file = "wandb-0.17.0-py3-none-win32.whl", hash = "sha256:1f692d3063a0d50474022cfe6668e1828260436d1cd40827d1e136b7f730c74c"},
+    {file = "wandb-0.17.0-py3-none-win_amd64.whl", hash = "sha256:ab582ca0d54d52ef5b991de0717350b835400d9ac2d3adab210022b68338d694"},
 ]
 
 [package.dependencies]
-appdirs = ">=1.4.3"
-Click = ">=7.0,<8.0.0 || >8.0.0"
+click = ">=7.1,<8.0.0 || >8.0.0"
 docker-pycreds = ">=0.4.0"
-GitPython = ">=1.0.0,<3.1.29 || >3.1.29"
-pathtools = "*"
+gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
+platformdirs = "*"
 protobuf = {version = ">=3.19.0,<4.21.0 || >4.21.0,<5", markers = "python_version > \"3.9\" or sys_platform != \"linux\""}
 psutil = ">=5.0.0"
-PyYAML = "*"
+pyyaml = "*"
 requests = ">=2.0.0,<3"
 sentry-sdk = ">=1.0.0"
 setproctitle = "*"
@@ -4989,13 +4997,15 @@ setuptools = "*"
 
 [package.extras]
 aws = ["boto3"]
-azure = ["azure-storage-blob"]
+azure = ["azure-identity", "azure-storage-blob"]
 gcp = ["google-cloud-storage"]
-grpc = ["grpcio (>=1.27.2)"]
+importers = ["filelock", "mlflow", "polars", "rich", "tenacity"]
 kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
-launch = ["awscli", "boto3", "botocore", "chardet", "google-auth", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "nbconvert", "nbformat", "typing-extensions"]
-media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"]
+launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "typing-extensions"]
+media = ["bokeh", "moviepy", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit-pypi", "soundfile"]
 models = ["cloudpickle"]
+perf = ["orjson"]
+reports = ["pydantic (>=2.0.0)"]
 sweeps = ["sweeps (>=0.2.0)"]
 
 [[package]]
@@ -5037,17 +5047,17 @@ files = [
 
 [[package]]
 name = "websocket-client"
-version = "1.7.0"
+version = "1.8.0"
 description = "WebSocket client for Python with low level API options"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"},
-    {file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"},
+    {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
+    {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
 ]
 
 [package.extras]
-docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
@@ -5284,13 +5294,13 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zanj"
-version = "0.2.0"
+version = "0.2.1"
 description = "save and load complex objects to disk without pickling"
 optional = false
-python-versions = ">=3.10,<4.0"
+python-versions = "<4.0,>=3.10"
 files = [
-    {file = "zanj-0.2.0-py3-none-any.whl", hash = "sha256:3137d72ec5858ec48a989b6a44c1b85ad01dfae72ae94ea43d40732ec2eb7889"},
-    {file = "zanj-0.2.0.tar.gz", hash = "sha256:52f94487db236aa2d6bbd0979aa70330af47bb4f04065c10d7bffcbc54b0df46"},
+    {file = "zanj-0.2.1-py3-none-any.whl", hash = "sha256:9a2695a71a1c7d87a4f483e90c49795d589582f97cb1b75cce44ad97ee3c4bc3"},
+    {file = "zanj-0.2.1.tar.gz", hash = "sha256:9b0a31e49ce30f74b08cef052185e20717851f0dbabf62904bd8619e0081246f"},
 ]
 
 [package.dependencies]
@@ -5299,20 +5309,20 @@ pandas = ">=1.5.3,<2.0.0"
 
 [[package]]
 name = "zipp"
-version = "3.17.0"
+version = "3.18.1"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
+    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "bfd95f85652d3b50b1e8385ac0742629f190a97b34626b5677cf3da3c55efb17"
+content-hash = "a36995e68d2b809182cc7fd00c13c4df7066f0aab71a30edf24a837822fb1ed6"
diff --git a/pyproject.toml b/pyproject.toml
index a349eba2..413fd846 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,15 +10,16 @@ repository = "https://github.com/understanding-search/maze-transformer"
 [tool.poetry.dependencies]
 python = ">=3.10,<3.13"
 # dataset
-maze-dataset = "^0.4.5"
+maze-dataset = "^0.5.2"
 # transformers
 torch = ">=1.13.1"
-transformer-lens = "1.14.0"
+transformer-lens = "^1.14.0"
 transformers = ">=4.34" # Dependency in transformer-lens 1.14.0
 # utils
 muutils = "^0.5.5"
 zanj = "^0.2.0"
-wandb = "^0.13.5" # note: TransformerLens forces us to use 0.13.5
+# wandb = "^0.13.5" # note: TransformerLens forces us to use 0.13.5
+wandb = "^0.17.0"
 fire = "^0.5.0"
 typing-extensions = "^4.8.0"
 # plotting