


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





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Union[str, Any]						) ->   int:





	"""simple docstring"""







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

	A								=				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.
_lowercase       :      Optional[Any]	=	direct_transformers_import(TRANSFORMERS_PATH)

_lowercase       :      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`).
_lowercase       :      int	=	{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   List[str]:





	"""simple docstring"""







	A								=				TASK_GUIDE_TO_MODELS[task_guide]
	A								=				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCamelCase__							,					set()						)
	A								=				{
	    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				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     int=False						) ->   Optional[Any]:





	"""simple docstring"""







	A					,			A					,			A					,			A								=				_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-->"""							,					)

	A								=				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__":
				_lowercase       :      Tuple	=	argparse.ArgumentParser()
				parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
				_lowercase       :      int	=	parser.parse_args()

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







from __future__ import annotations

import requests





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   dict:





	"""simple docstring"""







	A								=				f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
	return requests.get(UpperCamelCase__						).json()





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   list[dict]:





	"""simple docstring"""







	A								=				"""https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
	A								=				requests.get(UpperCamelCase__						).json()[:max_stories]
	return [get_hackernews_story(UpperCamelCase__						) for story_id in story_ids]





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   str:





	"""simple docstring"""







	A								=				hackernews_top_stories(UpperCamelCase__						)
	return "\n".join("""* [{title}]({url})""".format(**UpperCamelCase__						) for story in stories						)


if __name__ == "__main__":
				print(hackernews_top_stories_as_markdown())



