


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
snake_case__			 =						'''src/transformers'''
snake_case__			 =						'''docs/source/en/tasks'''


def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   List[str]	,  a	:   Optional[Any]				)    ->	Optional[Any]:





			"""simple docstring"""
			with open(a	,  "r"	,  encoding="utf-8"	,  newline="\n"				) as f:
						a__ :Any						  =    f.readlines()
			# Find the start prompt.
			a__ :List[str]						  =    0
			while not lines[start_index].startswith(a				):
						start_index += 1
			start_index += 1

			a__ :Tuple						  =    start_index
			while not lines[end_index].startswith(a				):
						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.
snake_case__			 =						direct_transformers_import(TRANSFORMERS_PATH)

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`).
snake_case__			 =						{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}


def 				lowerCamelCase__	(      a	:   Optional[int]				)    ->	Dict:





			"""simple docstring"""
			a__ :List[str]						  =    TASK_GUIDE_TO_MODELS[task_guide]
			a__ :Any						  =    SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a	,  set()				)
			a__ :Dict						  =    {
			    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__	(      a	:   List[Any]	,  a	:   Union[str, Any]=False				)    ->	str:





			"""simple docstring"""
			a__  ,  a__  ,  a__  ,  a__ :str						  =    _find_text_in_file(
			    filename=os.path.join(a	,  a				)	,  start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"	,  end_prompt="<!--End of the generated tip-->"	,  )

			a__ :List[str]						  =    get_model_list_for_task(a				)

			if current_list != new_list:
						if overwrite:
									with open(os.path.join(a	,  a				)	,  "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__":
		snake_case__			 =						argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		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 sys
from collections import defaultdict


class 					lowerCAmelCase_ :




						def __init__(	self						:   Optional[int]						)      ->Any:






									"""simple docstring"""






									a__ :Optional[Any]						  =    []




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   List[Any]						)      ->List[str]:






									"""simple docstring"""






									return self.node_position[vertex]




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   str   ,     __A						:   Any						)      ->Dict:






									"""simple docstring"""






									a__ :Dict						  =    pos




						def  _snake_case					(	self						:   str   ,     __A						:   Union[str, Any]   ,     __A						:   Union[str, Any]   ,     __A						:   Optional[Any]   ,     __A						:   Optional[int]						)      ->List[Any]:






									"""simple docstring"""






									if start > size // 2 - 1:
												return
									else:
												if 2 * start + 2 >= size:
															a__ :str						  =    2 * start + 1
												else:
															if heap[2 * start + 1] < heap[2 * start + 2]:
																		a__ :Optional[int]						  =    2 * start + 1
															else:
																		a__ :List[Any]						  =    2 * start + 2
												if heap[smallest_child] < heap[start]:
															a__  ,  a__ :Optional[Any]						  =    heap[smallest_child], positions[smallest_child]
															a__  ,  a__ :int						  =    (
															    heap[start],
															    positions[start],
															)
															a__  ,  a__ :List[Any]						  =    temp, tempa

															a__ :Any						  =    self.get_position(positions[smallest_child]						)
															self.set_position(
															    positions[smallest_child]   ,     self.get_position(positions[start]						)						)
															self.set_position(positions[start]   ,     __A						)

															self.top_to_bottom(__A   ,     __A   ,     __A   ,     __A						)




						def  _snake_case					(	self						:   List[str]   ,     __A						:   Any   ,     __A						:   List[str]   ,     __A						:   Any   ,     __A						:   str						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Optional[Any]						  =    position[index]

									while index != 0:
												a__ :str						  =    int((index - 2) / 2						) if index % 2 == 0 else int((index - 1) / 2						)

												if val < heap[parent]:
															a__ :int						  =    heap[parent]
															a__ :Optional[Any]						  =    position[parent]
															self.set_position(position[parent]   ,     __A						)
												else:
															a__ :List[Any]						  =    val
															a__ :List[Any]						  =    temp
															self.set_position(__A   ,     __A						)
															break
												a__ :Union[str, Any]						  =    parent
									else:
												a__ :int						  =    val
												a__ :Dict						  =    temp
												self.set_position(__A   ,     0						)




						def  _snake_case					(	self						:   Tuple   ,     __A						:   int   ,     __A						:   int						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Tuple						  =    len(__A						) // 2 - 1
									for i in range(__A   ,     -1   ,     -1						):
												self.top_to_bottom(__A   ,     __A   ,     len(__A						)   ,     __A						)






						def  _snake_case					(	self						:   List[Any]   ,     __A						:   List[Any]   ,     __A						:   int						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Any						  =    positions[0]
									a__ :str						  =    sys.maxsize
									self.top_to_bottom(__A   ,     0   ,     len(__A						)   ,     __A						)
									return temp






def 				lowerCamelCase__	(      a	:   Any				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Tuple						  =    Heap()

			a__ :List[Any]						  =    [0] * len(a				)
			a__ :str						  =    [-1] * len(a				)  # Neighboring Tree Vertex of selected vertex
			# Minimum Distance of explored vertex with neighboring vertex of partial tree
			# formed in graph
			a__ :Any						  =    []  # Heap of Distance of vertices from their neighboring vertex
			a__ :int						  =    []

			for vertex in range(len(a				)				):
						distance_tv.append(sys.maxsize				)
						positions.append(a				)
						heap.node_position.append(a				)

			a__ :Tuple						  =    []
			a__ :Any						  =    1
			a__ :int						  =    sys.maxsize
			for neighbor, distance in adjacency_list[0]:
						a__ :int						  =    0
						a__ :List[str]						  =    distance
			heap.heapify(a	,  a				)

			for _ in range(1	,  len(a				)				):
						a__ :Dict						  =    heap.delete_minimum(a	,  a				)
						if visited[vertex] == 0:
									tree_edges.append((nbr_tv[vertex], vertex)				)
									a__ :Optional[int]						  =    1
									for neighbor, distance in adjacency_list[vertex]:
												if (
												    visited[neighbor] == 0
												    and distance < distance_tv[heap.get_position(a				)]
												):
															a__ :List[str]						  =    distance
															heap.bottom_to_top(
															    a	,  heap.get_position(a				)	,  a	,  a				)
															a__ :str						  =    vertex
			return tree_edges


if __name__ == "__main__":  # pragma: no cover
		# < --------- Prims Algorithm --------- >
		snake_case__			 =						int(input('''Enter number of edges: ''').strip())
		snake_case__			 =						defaultdict(list)
		for _ in range(edges_number):
				snake_case__			 =						[int(x) for x in input().strip().split()]
				adjacency_list[edge[0]].append([edge[1], edge[2]])
				adjacency_list[edge[1]].append([edge[0], edge[2]])
		print(prisms_algorithm(adjacency_list))


