





'''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__							:							Union[str, Any] ='''src/transformers'''
a__							:							str ='''docs/source/en/tasks'''
def 				lowercase__   (   __lowercase						:  Optional[Any]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  str     ) ->      Tuple:



       """simple docstring"""






       with open(__lowercase				,  'r'				,  encoding='utf-8'				,  newline='\n'     ) as f:
              __UpperCamelCase       =  f.readlines()
       # Find the start prompt.
       __UpperCamelCase       =  0
       while not lines[start_index].startswith(__lowercase     ):
              start_index += 1
       start_index += 1

       __UpperCamelCase       =  start_index
       while not lines[end_index].startswith(__lowercase     ):
              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__							:							Dict =direct_transformers_import(TRANSFORMERS_PATH)

a__							:							Optional[Any] ={
    '''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__							:							Dict ={
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}
def 				lowercase__   (   __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  TASK_GUIDE_TO_MODELS[task_guide]
       __UpperCamelCase       =  SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__lowercase				,  set()     )
       __UpperCamelCase       =  {
           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 				lowercase__   (   __lowercase						:  Any				,  __lowercase						:  Tuple=False     ) ->      List[Any]:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  _find_text_in_file(
           filename=os.path.join(__lowercase				,  __lowercase     )				,  start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'				,  end_prompt='<!--End of the generated tip-->'				,  )

       __UpperCamelCase       =  get_model_list_for_task(__lowercase     )

       if current_list != new_list:
              if overwrite:
                     with open(os.path.join(__lowercase				,  __lowercase     )				,  '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__							:							str =argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    a__							:							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


a__							:							List[Any] ={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Optional[int] =[
        '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TimesformerModel''',
        '''TimesformerForVideoClassification''',
        '''TimesformerPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    a__							:							Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
