

"""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       lowerCamelCase  (UpperCAmelCase__	,unittest.TestCase	):
			lowerCamelCase__      :						Optional[Any]    						=							BarthezTokenizer
			lowerCamelCase__      :						Tuple    						=							BarthezTokenizerFast
			lowerCamelCase__      :						Optional[int]    						=							True
			lowerCamelCase__      :						Optional[Any]    						=							True

			def     SCREAMING_SNAKE_CASE (    self :  Tuple				)      ->			Dict:
						super().setUp()

						SCREAMING_SNAKE_CASE__									=					BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""				)
						tokenizer.save_pretrained(self.tmpdirname				)
						tokenizer.save_pretrained(self.tmpdirname			, legacy_format=UpperCAmelCase__				)
						SCREAMING_SNAKE_CASE__									=					tokenizer

			def     SCREAMING_SNAKE_CASE (    self :  Optional[Any]				)      ->			Union[str, Any]:
						SCREAMING_SNAKE_CASE__									=					"""<pad>"""
						SCREAMING_SNAKE_CASE__									=					1

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

			def     SCREAMING_SNAKE_CASE (    self :  Tuple				)      ->			Tuple:
						SCREAMING_SNAKE_CASE__									=					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__				)			, 1_0_1_1_2_2				)

			def     SCREAMING_SNAKE_CASE (    self :  Optional[int]				)      ->			Any:
						self.assertEqual(self.get_tokenizer().vocab_size			, 1_0_1_1_2_2				)

			@require_torch
			def     SCREAMING_SNAKE_CASE (    self :  List[str]				)      ->			str:
						SCREAMING_SNAKE_CASE__									=					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
						SCREAMING_SNAKE_CASE__									=					[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

						SCREAMING_SNAKE_CASE__									=					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				)
						SCREAMING_SNAKE_CASE__									=					batch.input_ids.tolist()[0]
						self.assertListEqual(UpperCAmelCase__			, UpperCAmelCase__				)

			def     SCREAMING_SNAKE_CASE (    self :  Tuple				)      ->			Optional[Any]:

						if not self.test_rust_tokenizer:
									return

						SCREAMING_SNAKE_CASE__									=					self.get_tokenizer()
						SCREAMING_SNAKE_CASE__									=					self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE__									=					"""I was born in 92000, and this is falsé."""

						SCREAMING_SNAKE_CASE__									=					tokenizer.tokenize(UpperCAmelCase__				)
						SCREAMING_SNAKE_CASE__									=					rust_tokenizer.tokenize(UpperCAmelCase__				)
						self.assertListEqual(UpperCAmelCase__			, UpperCAmelCase__				)

						SCREAMING_SNAKE_CASE__									=					tokenizer.encode(UpperCAmelCase__			, add_special_tokens=UpperCAmelCase__				)
						SCREAMING_SNAKE_CASE__									=					rust_tokenizer.encode(UpperCAmelCase__			, add_special_tokens=UpperCAmelCase__				)
						self.assertListEqual(UpperCAmelCase__			, UpperCAmelCase__				)

						SCREAMING_SNAKE_CASE__									=					self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__									=					tokenizer.encode(UpperCAmelCase__				)
						SCREAMING_SNAKE_CASE__									=					rust_tokenizer.encode(UpperCAmelCase__				)
						self.assertListEqual(UpperCAmelCase__			, UpperCAmelCase__				)





			@slow
			def     SCREAMING_SNAKE_CASE (    self :  Union[str, Any]				)      ->			int:
						SCREAMING_SNAKE_CASE__									=					{"""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.
						SCREAMING_SNAKE_CASE__									=					[
						    """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__			, )








def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Any:



       """simple docstring"""

       A__       =    [0] * len(lowercase_						)
       A__       =    []
       A__       =    [1] * len(lowercase_						)

       for values in graph.values():
              for i in values:
                     indegree[i] += 1

       for i in range(len(lowercase_						)						):
              if indegree[i] == 0:
                     queue.append(lowercase_						)

       while queue:
              A__       =    queue.pop(0						)
              for x in graph[vertex]:
                     indegree[x] -= 1

                     if long_dist[vertex] + 1 > long_dist[x]:
                            A__       =    long_dist[vertex] + 1

                     if indegree[x] == 0:
                            queue.append(lowercase_						)

       print(max(lowercase_						)						)


# Adjacency list of Graph
_lowerCamelCase	:		Optional[int]							       =   {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)


