



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







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

		UpperCAmelCase_						=				start_index
		while not lines[end_index].startswith(__SCREAMING_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.
SCREAMING_SNAKE_CASE     				=						direct_transformers_import(TRANSFORMERS_PATH)

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







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							List[Any]:
		UpperCAmelCase_						=				TASK_GUIDE_TO_MODELS[task_guide]
		UpperCAmelCase_						=				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__SCREAMING_SNAKE_CASE			,     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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=False )		->							Dict:
		UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				_find_text_in_file(
		    filename=os.path.join(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )			,     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(__SCREAMING_SNAKE_CASE )

		if current_list != new_list:
				if overwrite:
						with open(os.path.join(__SCREAMING_SNAKE_CASE			,     __SCREAMING_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__":
			SCREAMING_SNAKE_CASE     				=						argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()

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






import heapq as hq
import math
from collections.abc import Iterator



class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				str(id_     )
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				[]
							UpperCAmelCase_						=				{}  # {vertex:distance}



					def __lt__(			self    ,	lowerCAmelCase     ):
							return self.key < other.key



					def __repr__(			self     ):
							return self.id



					def    A__		(			self    ,	lowerCAmelCase     ):
							self.neighbors.append(lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				weight







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[int]:
		# add the neighbors:
		graph[a - 1].add_neighbor(graph[b - 1] )
		graph[b - 1].add_neighbor(graph[a - 1] )
		# add the edges:
		graph[a - 1].add_edge(graph[b - 1]			,     __SCREAMING_SNAKE_CASE )
		graph[b - 1].add_edge(graph[a - 1]			,     __SCREAMING_SNAKE_CASE )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							list:
		UpperCAmelCase_						=				[]
		for u in graph:
				UpperCAmelCase_						=				math.inf
				UpperCAmelCase_						=				None
		UpperCAmelCase_						=				0
		UpperCAmelCase_						=				graph[:]
		while q:
				UpperCAmelCase_						=				min(__SCREAMING_SNAKE_CASE )
				q.remove(__SCREAMING_SNAKE_CASE )
				for v in u.neighbors:
						if (v in q) and (u.edges[v.id] < v.key):
								UpperCAmelCase_						=				u
								UpperCAmelCase_						=				u.edges[v.id]
		for i in range(1			,     len(__SCREAMING_SNAKE_CASE ) ):
				a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
		return a







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Iterator[tuple]:
		for u in graph:
				UpperCAmelCase_						=				math.inf
				UpperCAmelCase_						=				None
		UpperCAmelCase_						=				0

		UpperCAmelCase_						=				list(__SCREAMING_SNAKE_CASE )
		hq.heapify(__SCREAMING_SNAKE_CASE )

		while h:
				UpperCAmelCase_						=				hq.heappop(__SCREAMING_SNAKE_CASE )
				for v in u.neighbors:
						if (v in h) and (u.edges[v.id] < v.key):
								UpperCAmelCase_						=				u
								UpperCAmelCase_						=				u.edges[v.id]
								hq.heapify(__SCREAMING_SNAKE_CASE )

		for i in range(1			,     len(__SCREAMING_SNAKE_CASE ) ):
				yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)







def snake_case__  (			)		->							None:
		pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()

