






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     __SCREAMING_SNAKE_CASE					(   __lowercase		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       List[str]   =					BarthezTokenizer
						_SCREAMING_SNAKE_CASE	:       Tuple   =					BarthezTokenizerFast
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					True
						_SCREAMING_SNAKE_CASE	:       Optional[Any]   =					True






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().setUp()

												lowerCAmelCase__    						=						BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'   )
												tokenizer.save_pretrained(self.tmpdirname   )
												tokenizer.save_pretrained(self.tmpdirname     ,						legacy_format=_UpperCamelCase   )
												lowerCAmelCase__    						=						tokenizer






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						'<pad>'
												lowerCAmelCase__    						=						1

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						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(_UpperCamelCase   )     ,						10_11_22   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												self.assertEqual(self.get_tokenizer().vocab_size     ,						10_11_22   )






						@require_torch
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
												lowerCAmelCase__    						=						[0, 57, 30_18, 7_03_07, 91, 2]

												lowerCAmelCase__    						=						self.tokenizer(
												    _UpperCamelCase     ,						max_length=len(_UpperCamelCase   )     ,						padding=_UpperCamelCase     ,						truncation=_UpperCamelCase     ,						return_tensors='pt'   )
												self.assertIsInstance(_UpperCamelCase     ,						_UpperCamelCase   )

												self.assertEqual((2, 6)     ,						batch.input_ids.shape   )
												self.assertEqual((2, 6)     ,						batch.attention_mask.shape   )
												lowerCAmelCase__    						=						batch.input_ids.tolist()[0]
												self.assertListEqual(_UpperCamelCase     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												if not self.test_rust_tokenizer:
																		return

												lowerCAmelCase__    						=						self.get_tokenizer()
												lowerCAmelCase__    						=						self.get_rust_tokenizer()

												lowerCAmelCase__    						=						'I was born in 92000, and this is falsé.'

												lowerCAmelCase__    						=						tokenizer.tokenize(_UpperCamelCase   )
												lowerCAmelCase__    						=						rust_tokenizer.tokenize(_UpperCamelCase   )
												self.assertListEqual(_UpperCamelCase     ,						_UpperCamelCase   )

												lowerCAmelCase__    						=						tokenizer.encode(_UpperCamelCase     ,						add_special_tokens=_UpperCamelCase   )
												lowerCAmelCase__    						=						rust_tokenizer.encode(_UpperCamelCase     ,						add_special_tokens=_UpperCamelCase   )
												self.assertListEqual(_UpperCamelCase     ,						_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_rust_tokenizer()
												lowerCAmelCase__    						=						tokenizer.encode(_UpperCamelCase   )
												lowerCAmelCase__    						=						rust_tokenizer.encode(_UpperCamelCase   )
												self.assertListEqual(_UpperCamelCase     ,						_UpperCamelCase   )







						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# fmt: off
												lowerCAmelCase__    						=						{'input_ids': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 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_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 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.
												lowerCAmelCase__    						=						[
												    '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=_UpperCamelCase     ,						model_name='moussaKam/mbarthez'     ,						revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'     ,						sequences=_UpperCamelCase     ,						)













from __future__ import annotations

__snake_case    :    Any         =     {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}


class     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						graph
												# mapping node to its parent in resulting breadth first tree
												lowerCAmelCase__    						=						{}
												lowerCAmelCase__    						=						source_vertex






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						{self.source_vertex}
												lowerCAmelCase__    						=						None
												lowerCAmelCase__    						=						[self.source_vertex]  # first in first out queue

												while queue:
																		lowerCAmelCase__    						=						queue.pop(0   )
																		for adjacent_vertex in self.graph[vertex]:
																								if adjacent_vertex not in visited:
																														visited.add(_UpperCamelCase   )
																														lowerCAmelCase__    						=						vertex
																														queue.append(_UpperCamelCase   )







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												if target_vertex == self.source_vertex:
																		return self.source_vertex

												lowerCAmelCase__    						=						self.parent.get(_UpperCamelCase   )
												if target_vertex_parent is None:
																		lowerCAmelCase__    						=						(
																		    F"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
																		)
																		raise ValueError(_UpperCamelCase   )

												return self.shortest_path(_UpperCamelCase   ) + F"->{target_vertex}"


if __name__ == "__main__":
					__snake_case    :    Optional[Any]         =     Graph(graph, """G""")
					g.breath_first_search()
					print(g.shortest_path("""D"""))
					print(g.shortest_path("""G"""))
					print(g.shortest_path("""Foo"""))





