



"""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 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							BarthezTokenizer
						__UpperCamelCase		    =							BarthezTokenizerFast
						__UpperCamelCase		    =							True
						__UpperCamelCase		    =							True






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Tuple:
												super().setUp()

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






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Union[str, Any]:
												UpperCAmelCase					=			'<pad>'
												UpperCAmelCase					=			1

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






						def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
												UpperCAmelCase					=			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(lowercase_ )       ,  10_11_22 )






						def      UpperCAmelCase__					( self			:Any )    ->      Optional[int]:
												self.assertEqual(self.get_tokenizer().vocab_size       ,  10_11_22 )






						@require_torch
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Dict:
												UpperCAmelCase					=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
												UpperCAmelCase					=			[0, 57, 30_18, 7_03_07, 91, 2]

												UpperCAmelCase					=			self.tokenizer(
												    lowercase_       ,  max_length=len(lowercase_ )       ,  padding=lowercase_       ,  truncation=lowercase_       ,  return_tensors='pt' )
												self.assertIsInstance(lowercase_       ,  lowercase_ )

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






						def      UpperCAmelCase__					( self			:Dict )    ->      str:
												if not self.test_rust_tokenizer:
																		return

												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_rust_tokenizer()

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

												UpperCAmelCase					=			tokenizer.tokenize(lowercase_ )
												UpperCAmelCase					=			rust_tokenizer.tokenize(lowercase_ )
												self.assertListEqual(lowercase_       ,  lowercase_ )

												UpperCAmelCase					=			tokenizer.encode(lowercase_       ,  add_special_tokens=lowercase_ )
												UpperCAmelCase					=			rust_tokenizer.encode(lowercase_       ,  add_special_tokens=lowercase_ )
												self.assertListEqual(lowercase_       ,  lowercase_ )

												UpperCAmelCase					=			self.get_rust_tokenizer()
												UpperCAmelCase					=			tokenizer.encode(lowercase_ )
												UpperCAmelCase					=			rust_tokenizer.encode(lowercase_ )
												self.assertListEqual(lowercase_       ,  lowercase_ )






						@slow
						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      str:
												# fmt: off
												UpperCAmelCase					=			{'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.
												UpperCAmelCase					=			[
												    '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=lowercase_       ,  model_name='moussaKam/mbarthez'       ,  revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'       ,  sequences=lowercase_       ,  )










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_    					=	{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """RemBertForCausalLM""",
				    """RemBertForMaskedLM""",
				    """RemBertForMultipleChoice""",
				    """RemBertForQuestionAnswering""",
				    """RemBertForSequenceClassification""",
				    """RemBertForTokenClassification""",
				    """RemBertLayer""",
				    """RemBertModel""",
				    """RemBertPreTrainedModel""",
				    """load_tf_weights_in_rembert""",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFRemBertForCausalLM""",
				    """TFRemBertForMaskedLM""",
				    """TFRemBertForMultipleChoice""",
				    """TFRemBertForQuestionAnswering""",
				    """TFRemBertForSequenceClassification""",
				    """TFRemBertForTokenClassification""",
				    """TFRemBertLayer""",
				    """TFRemBertModel""",
				    """TFRemBertPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert import RemBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert_fast import RemBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rembert import (
								    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RemBertForCausalLM,
								    RemBertForMaskedLM,
								    RemBertForMultipleChoice,
								    RemBertForQuestionAnswering,
								    RemBertForSequenceClassification,
								    RemBertForTokenClassification,
								    RemBertLayer,
								    RemBertModel,
								    RemBertPreTrainedModel,
								    load_tf_weights_in_rembert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rembert import (
								    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRemBertForCausalLM,
								    TFRemBertForMaskedLM,
								    TFRemBertForMultipleChoice,
								    TFRemBertForQuestionAnswering,
								    TFRemBertForSequenceClassification,
								    TFRemBertForTokenClassification,
								    TFRemBertLayer,
								    TFRemBertModel,
								    TFRemBertPreTrainedModel,
								)


else:
				import sys

				snake_case_    					=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





