update to new init

src/transformers/models/got_ocr2/__init__.py

@@ -13,66 +13,19 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import (
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    is_tokenizers_available,
-    is_torch_available,
-    is_vision_available,
-)
-
-
-_import_structure = {
-    "configuration_got_ocr2": ["GotOcr2Config", "GotOcr2VisionConfig"],
-    "processing_got_ocr2": ["GotOcr2Processor"],
-}
-try:
-    if not is_vision_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["image_processing_got_ocr2"] = ["GotOcr2ImageProcessor"]
-
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_got_ocr2"] = [
-        "GotOcr2ForConditionalGeneration",
-        "GotOcr2Model",
-        "GotOcr2PreTrainedModel",
-    ]
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
 
 
 if TYPE_CHECKING:
-    from .configuration_got_ocr2 import GotOcr2Config, GotOcr2VisionConfig
-    from .processing_got_ocr2 import GotOcr2Processor
-
-    try:
-        if not is_vision_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .image_processing_got_ocr2 import GotOcr2ImageProcessor
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_got_ocr2 import (
-            GotOcr2ForConditionalGeneration,
-            GotOcr2Model,
-            GotOcr2PreTrainedModel,
-        )
+    from .configuration_got_ocr2 import *
+    from .image_processing_got_ocr2 import *
+    from .modeling_got_ocr2 import *
+    from .processing_got_ocr2 import *
 
 
 else:
     import sys
 
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

src/transformers/models/got_ocr2/configuration_got_ocr2.py

@@ -291,3 +291,6 @@ def __init__(
         rope_config_validation(self, ignore_keys={"mrope_section"})
 
         super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+__all__ = ["GotOcr2VisionConfig", "GotOcr2Config"]

src/transformers/models/got_ocr2/image_processing_got_ocr2.py

@@ -53,14 +53,6 @@
 )
 
 
-if is_vision_available():
-    import PIL
-
-
-if is_vision_available():
-    import PIL
-
-
 if is_vision_available():
     import PIL
 
@@ -413,3 +405,6 @@ def crop_image_to_patches(
             processed_images = processed_images_numpy
 
         return processed_images
+
+
+__all__ = ["GotOcr2ImageProcessor"]

src/transformers/models/got_ocr2/modeling_got_ocr2.py

@@ -44,6 +44,7 @@
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
     logging,
+    replace_return_docstrings,
 )
 from .configuration_got_ocr2 import GotOcr2Config, GotOcr2VisionConfig
 
@@ -54,6 +55,8 @@
 
 logger = logging.get_logger(__name__)
 
+_CONFIG_FOR_DOC = "GotOcr2Config"
+
 
 class GotOcr2MLPBlock(nn.Module):
     def __init__(self, config):
@@ -1585,6 +1588,8 @@ def _update_model_kwargs_for_generation(
 
         return model_kwargs
 
+    @add_start_docstrings_to_model_forward(GOT_OCR2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1772,3 +1777,6 @@ def prepare_inputs_for_generation(
             }
         )
         return model_inputs
+
+
+__all__ = ["GotOcr2PreTrainedModel", "GotOcr2Model", "GotOcr2ForConditionalGeneration"]

src/transformers/models/got_ocr2/modular_got_ocr2.py

@@ -898,3 +898,14 @@ def prepare_inputs_for_generation(
             }
         )
         return model_inputs
+
+
+__all__ = [
+    "GotOcr2VisionConfig",
+    "GotOcr2Config",
+    "GotOcr2Processor",
+    "GotOcr2PreTrainedModel",
+    "GotOcr2Model",
+    "GotOcr2ForConditionalGeneration",
+    "GotOcr2ImageProcessor",
+]

src/transformers/models/got_ocr2/processing_got_ocr2.py

@@ -30,10 +30,6 @@
 from ...utils import is_vision_available, logging
 
 
-if is_vision_available():
-    from ...image_utils import load_images
-
-
 if is_vision_available():
     from ...image_utils import load_images
 
@@ -147,6 +143,12 @@ def __call__(
                 If set, will enable multi-page inference. The model will return the OCR result across multiple pages.
             crop_to_patches (`bool`, *optional*):
                 If set, will crop the image to patches. The model will return the OCR result upon the patch reference.
+            min_patches (`int`, *optional*):
+                The minimum number of patches to be cropped from the image. Only used when `crop_to_patches` is set to
+                `True`.
+            max_patches (`int`, *optional*):
+                The maximum number of patches to be cropped from the image. Only used when `crop_to_patches` is set to
+                `True`.
 
             return_tensors (`str` or [`~utils.TensorType`], *optional*):
                 If set, will return tensors of a particular framework. Acceptable values are:
@@ -178,6 +180,8 @@ def __call__(
         color = output_kwargs["images_kwargs"].pop("color", None)
         multi_page = output_kwargs["images_kwargs"].pop("multi_page", False)
         crop_to_patches = output_kwargs["images_kwargs"].pop("crop_to_patches", False)
+        min_patches = output_kwargs["images_kwargs"].pop("min_patches")
+        max_patches = output_kwargs["images_kwargs"].pop("max_patches")
 
         if not isinstance(box, (list, tuple)):
             raise ValueError("`box` must be a list or tuple in the form [x1, y1, x2, y2].")
@@ -219,8 +223,8 @@ def __call__(
                     image_group = self.image_processor.crop_image_to_patches(
                         image_group,
                         size=output_kwargs["images_kwargs"].get("size"),
-                        min_num=output_kwargs["images_kwargs"].get("min_patches"),
-                        max_num=output_kwargs["images_kwargs"].get("max_patches"),
+                        min_num=min_patches,
+                        max_num=max_patches,
                     )
                     images[index] = image_group
                 num_images = len(image_group) if (multi_page or crop_to_patches) else 1
@@ -259,14 +263,14 @@ def __call__(
 
     def batch_decode(self, *args, **kwargs):
         """
-        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
         refer to the docstring of this method for more information.
         """
         return self.tokenizer.batch_decode(*args, **kwargs)
 
     def decode(self, *args, **kwargs):
         """
-        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
         the docstring of this method for more information.
         """
         return self.tokenizer.decode(*args, **kwargs)
@@ -276,3 +280,6 @@ def model_input_names(self):
         tokenizer_input_names = self.tokenizer.model_input_names
         image_processor_input_names = self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+__all__ = ["GotOcr2Processor"]