



'''simple docstring'''






import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowerCAmelCase: int							=					'src/transformers'
lowerCAmelCase: Dict							=					'docs/source/en/tasks'
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  with open(_A			,      'r'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    a						: int					       =					f.readlines()
  # Find the start prompt.
  a						: str					       =					0
  while not lines[start_index].startswith(_A    ):
    start_index += 1
  start_index += 1

  a						: Tuple					       =					start_index
  while not lines[end_index].startswith(_A    ):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]    ) <= 1:
    start_index += 1
  while len(lines[end_index]    ) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]    ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase: Optional[Any]							=					direct_transformers_import(TRANSFORMERS_PATH)

lowerCAmelCase: int							=					{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
lowerCAmelCase: str							=					{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}
def 				lowerCamelCase__	(						_A    ):
  a						: Dict					       =					TASK_GUIDE_TO_MODELS[task_guide]
  a						: List[Any]					       =					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_A			,      set()    )
  a						: Dict					       =					{
      code: name
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if (code in model_maping_names or code in special_model_types)
  }
  return ", ".join([f"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]    ) + "\n"
def 				lowerCamelCase__	(						_A			,      _A=False    ):
  a						: List[Any]					       =					_find_text_in_file(
      filename=os.path.join(_A			,      _A    )			,      start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'			,      end_prompt='<!--End of the generated tip-->'			,      )

  a						: str					       =					get_model_list_for_task(_A    )

  if current_list != new_list:
    if overwrite:
      with open(os.path.join(_A			,      _A    )			,      'w'			,      encoding='utf-8'			,      newline='\n'    ) as f:
        f.writelines(lines[:start_index] + [new_list] + lines[end_index:]    )
    else:
      raise ValueError(
          f"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
          ' to fix this.'    )


if __name__ == "__main__":
     lowerCAmelCase: Optional[int]							=					argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     lowerCAmelCase: Optional[int]							=					parser.parse_args()

     for task_guide in TASK_GUIDE_TO_MODELS.keys():
          check_model_list_for_task(task_guide, args.fix_and_overwrite)




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase: Any							=						{
    'configuration_poolformer': [
        'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'PoolFormerConfig',
        'PoolFormerOnnxConfig',
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: List[str]							=						['PoolFormerFeatureExtractor']
     lowerCAmelCase: Tuple							=						['PoolFormerImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: str							=						[
         'POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'PoolFormerForImageClassification',
         'PoolFormerModel',
         'PoolFormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_poolformer import (
         POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         PoolFormerConfig,
         PoolFormerOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_poolformer import PoolFormerFeatureExtractor
          from .image_processing_poolformer import PoolFormerImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_poolformer import (
              POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              PoolFormerForImageClassification,
              PoolFormerModel,
              PoolFormerPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase: Dict							=						_LazyModule(__name__, globals()['__file__'], _import_structure)