


'''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
A__						:   Any				=    """src/transformers"""
A__						:   Union[str, Any]				=    """docs/source/en/tasks"""



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							Optional[int]				)	->						List[str]:
     with open(UpperCAmelCase_ ,       'r' ,       encoding='utf-8' ,       newline='\n'				) as f:
          __lowerCamelCase  :      Optional[int]														=						f.readlines()
     # Find the start prompt.
     __lowerCamelCase  :      str														=						0
     while not lines[start_index].startswith(UpperCAmelCase_				):
          start_index += 1
     start_index += 1

     __lowerCamelCase  :      List[str]														=						start_index
     while not lines[end_index].startswith(UpperCAmelCase_				):
          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.
A__						:   List[Any]				=    direct_transformers_import(TRANSFORMERS_PATH)

A__						:   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`).
A__						:   List[Any]				=    {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int]				)	->						List[str]:
     __lowerCamelCase  :      Dict														=						TASK_GUIDE_TO_MODELS[task_guide]
     __lowerCamelCase  :      Tuple														=						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase_ ,       set()				)
     __lowerCamelCase  :      str														=						{
         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      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							Dict=False				)	->						Any:
     __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      int														=						_find_text_in_file(
         filename=os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				) ,       start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->' ,       end_prompt='<!--End of the generated tip-->' ,       )

     __lowerCamelCase  :      Optional[Any]														=						get_model_list_for_task(UpperCAmelCase_				)

     if current_list != new_list:
          if overwrite:
               with open(os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				) ,       '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__":
      A__						:   Any				=    argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      A__						:   Optional[Any]				=    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_tokenizers_available, is_torch_available


lowerCAmelCase_      :  Dict				     =  {
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  [
     'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GraphormerForGraphClassification',
     'GraphormerModel',
     'GraphormerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  Tuple				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
