






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_						:						Optional[Any]						      =					'src/transformers'
A_						:						Any						      =					'docs/source/en/tasks'

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

  UpperCamelCase_:   Union[str, Any]        =							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_						:						Union[str, 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_						:						List[Any]						      =					{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def     snake_case   (UpperCAmelCase__    )			->							Union[str, Any]:
  UpperCamelCase_:   List[str]        =							TASK_GUIDE_TO_MODELS[task_guide]
  UpperCamelCase_:   Union[str, Any]        =							SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__      ,       set()    )
  UpperCamelCase_:   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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__=False    )			->							Any:
  UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   str        =							_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-->'      ,       )

  UpperCamelCase_:   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_						:						Optional[int]						      =					argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   A_						:						str						      =					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


_lowerCAmelCase       =				{
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase       =				[
							    "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

							_lowerCAmelCase       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

