






"""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         =							"src/transformers"
_lowerCAmelCase         =							"docs/source/en/tasks"






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	with open(snake_case__   ,   """r"""   ,   encoding="""utf-8"""   ,   newline="""\n"""					) as f:
		A_		:						Optional[int]	    =   f.readlines()
	# Find the start prompt.
	A_		:						Any	    =   0
	while not lines[start_index].startswith(snake_case__					):
		start_index += 1
	start_index += 1

	A_		:						List[Any]	    =   start_index
	while not lines[end_index].startswith(snake_case__					):
		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         =							direct_transformers_import(TRANSFORMERS_PATH)

_lowerCAmelCase         =							{
    "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         =							{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						int	    =   TASK_GUIDE_TO_MODELS[task_guide]
	A_		:						Any	    =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case__   ,   set()					)
	A_		:						Any	    =   {
	    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	(		snake_case__   ,   snake_case__=False					):
	A_    ,     A_    ,     A_    ,     A_		:						List[Any]	    =   _find_text_in_file(
	    filename=os.path.join(snake_case__   ,   snake_case__					)   ,   start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""   ,   end_prompt="""<!--End of the generated tip-->"""   ,   )

	A_		:						int	    =   get_model_list_for_task(snake_case__					)

	if current_list != new_list:
		if overwrite:
			with open(os.path.join(snake_case__   ,   snake_case__					)   ,   """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         =							argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							_lowerCAmelCase         =							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"""
_lowerCAmelCase         =							{"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_lowerCAmelCase         =							["a", "b", "c", "d", "e"]






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						int	    =   start
	# add current to visited
	visited.append(snake_case__					)
	A_		:						Any	    =   edges[current]
	for neighbor in neighbors:
		# if neighbor not in visited, visit
		if neighbor not in visited:
			A_		:						Optional[Any]	    =   topological_sort(snake_case__   ,   snake_case__   ,   snake_case__					)
    # if all neighbors visited add current to sort
	sort.append(snake_case__					)
	# if all vertices haven't been visited select a new one to visit
	if len(snake_case__					) != len(snake_case__					):
		for vertice in vertices:
			if vertice not in visited:
				A_		:						Optional[Any]	    =   topological_sort(snake_case__   ,   snake_case__   ,   snake_case__					)
    # return sort
	return sort


if __name__ == "__main__":
							_lowerCAmelCase         =							topological_sort("a", [], [])
							print(sort)

