


'''simple docstring'''






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 						__UpperCamelCase	(						_SCREAMING_SNAKE_CASE						,  unittest.TestCase							):
							lowercase			:		Optional[int]   		=  BarthezTokenizer
							lowercase			:		Dict   		=  BarthezTokenizerFast
							lowercase			:		Optional[Any]   		=  True
							lowercase			:		Dict   		=  True





							def 		a__   (  self		:str			):
									super().setUp()

									snake_case_		:							Any			       =						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""			)
									tokenizer.save_pretrained(self.tmpdirname			)
									tokenizer.save_pretrained(self.tmpdirname  ,legacy_format=A_			)
									snake_case_		:							str			       =						tokenizer





							def 		a__   (  self		:str			):
									snake_case_		:							Optional[int]			       =						"""<pad>"""
									snake_case_		:							Optional[int]			       =						1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_			)  ,A_			)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_			)  ,A_			)





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Union[str, Any]			       =						list(self.get_tokenizer().get_vocab().keys()			)

									self.assertEqual(vocab_keys[0]  ,"""<s>"""			)
									self.assertEqual(vocab_keys[1]  ,"""<pad>"""			)
									self.assertEqual(vocab_keys[-1]  ,"""<mask>"""			)
									self.assertEqual(len(A_			)  ,1_0_1_1_2_2			)





							def 		a__   (  self		:List[Any]			):
									self.assertEqual(self.get_tokenizer().vocab_size  ,1_0_1_1_2_2			)





							@require_torch
							def 		a__   (  self		:Dict			):
									snake_case_		:							Dict			       =						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									snake_case_		:							Tuple			       =						[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

									snake_case_		:							Tuple			       =						self.tokenizer(
									    A_  ,max_length=len(A_			)  ,padding=A_  ,truncation=A_  ,return_tensors="""pt"""			)
									self.assertIsInstance(A_  ,A_			)

									self.assertEqual((2, 6)  ,batch.input_ids.shape			)
									self.assertEqual((2, 6)  ,batch.attention_mask.shape			)
									snake_case_		:							Tuple			       =						batch.input_ids.tolist()[0]
									self.assertListEqual(A_  ,A_			)





							def 		a__   (  self		:Union[str, Any]			):
									if not self.test_rust_tokenizer:
											return

									snake_case_		:							str			       =						self.get_tokenizer()
									snake_case_		:							Dict			       =						self.get_rust_tokenizer()

									snake_case_		:							List[str]			       =						"""I was born in 92000, and this is falsé."""

									snake_case_		:							Union[str, Any]			       =						tokenizer.tokenize(A_			)
									snake_case_		:							int			       =						rust_tokenizer.tokenize(A_			)
									self.assertListEqual(A_  ,A_			)

									snake_case_		:							int			       =						tokenizer.encode(A_  ,add_special_tokens=A_			)
									snake_case_		:							Tuple			       =						rust_tokenizer.encode(A_  ,add_special_tokens=A_			)
									self.assertListEqual(A_  ,A_			)

									snake_case_		:							Optional[Any]			       =						self.get_rust_tokenizer()
									snake_case_		:							Tuple			       =						tokenizer.encode(A_			)
									snake_case_		:							Dict			       =						rust_tokenizer.encode(A_			)
									self.assertListEqual(A_  ,A_			)





							@slow
							def 		a__   (  self		:List[str]			):
									snake_case_		:							Tuple			       =						{"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									snake_case_		:							str			       =						[
									    """Le transformeur est un modèle d\'apprentissage profond introduit en 2017, """
									    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
									    """À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
									    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
									    """telles que la traduction et la synthèse de texte.""",
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=A_  ,model_name="""moussaKam/mbarthez"""  ,revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""  ,sequences=A_  ,)



'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

__A	:	str     =     tuple[int, int]
class 						__UpperCamelCase	:





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:set[int]  ,_UpperCamelCase		:Mapping[EdgeT, int]			):
									snake_case_		:							set[int]			       =						vertices
									snake_case_		:							dict[EdgeT, int]			       =						{
									    (min(_UpperCamelCase			), max(_UpperCamelCase			)): weight for edge, weight in edges.items()
									}





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:EdgeT  ,_UpperCamelCase		:int			):
									self.vertices.add(edge[0]			)
									self.vertices.add(edge[1]			)
									snake_case_		:							str			       =						weight





							def 		a__   (  self		:Tuple			):
									snake_case_		:							Graph			       =						Graph({min(self.vertices			)}  ,{}			)
									snake_case_		:							EdgeT
									snake_case_		:							int
									snake_case_		:							EdgeT
									snake_case_		:							int

									while len(subgraph.vertices			) < len(self.vertices			):
											snake_case_		:							int			       =						max(self.edges.values()			) + 1
											for edge, weight in self.edges.items():
													if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
															if weight < min_weight:
																	snake_case_		:							Dict			       =						edge
																	snake_case_		:							Dict			       =						weight

											subgraph.add_edge(_UpperCamelCase  ,_UpperCamelCase			)

									return subgraph





def      UpperCAmelCase      (		lowerCamelCase_  :str = "p107_network.txt" ):







		'''simple docstring'''
		snake_case_		:							str			       =						os.path.abspath(os.path.dirname(lowerCamelCase_ ) )
		snake_case_		:							str			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							dict[EdgeT, int]			       =						{}
		snake_case_		:							list[str]
		snake_case_		:							int
		snake_case_		:							int

		with open(lowerCamelCase_ ) as f:
				snake_case_		:							Optional[int]			       =						f.read().strip().split("""\n""" )

		snake_case_		:							Any			       =						[line.split(""",""" ) for line in data]

		for edgea in range(1				,      len(lowerCamelCase_ ) ):
				for edgea in range(lowerCamelCase_ ):
						if adjaceny_matrix[edgea][edgea] != "-":
								snake_case_		:							str			       =						int(adjaceny_matrix[edgea][edgea] )

		snake_case_		:							Graph			       =						Graph(set(range(len(lowerCamelCase_ ) ) )				,      lowerCamelCase_ )

		snake_case_		:							Graph			       =						graph.prims_algorithm()

		snake_case_		:							int			       =						sum(graph.edges.values() )
		snake_case_		:							int			       =						sum(subgraph.edges.values() )

		return initial_total - optimal_total


if __name__ == "__main__":
							print(F'{solution() = }')