



'''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        SCREAMING_SNAKE_CASE(							A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= BarthezTokenizer
	lowerCamelCase__    				= BarthezTokenizerFast
	lowerCamelCase__    				= True
	lowerCamelCase__    				= True







	def   A			(		self    :  List[str]	)    ->	Union[str, Any]:
								super().setUp()

								UpperCAmelCase				: Tuple											=	BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''	)
								tokenizer.save_pretrained(self.tmpdirname	)
								tokenizer.save_pretrained(self.tmpdirname	,    legacy_format=__snake_case	)
								UpperCAmelCase				: Any											=	tokenizer







	def   A			(		self    :  str	)    ->	List[str]:
								UpperCAmelCase				: Any											=	'''<pad>'''
								UpperCAmelCase				: List[str]											=	1

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







	def   A			(		self    :  List[Any]	)    ->	Any:
								UpperCAmelCase				: Dict											=	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(__snake_case	)	,    101122	)







	def   A			(		self    :  int	)    ->	Dict:
								self.assertEqual(self.get_tokenizer().vocab_size	,    101122	)







	@require_torch
	def   A			(		self    :  List[str]	)    ->	Union[str, Any]:
								UpperCAmelCase				: Optional[int]											=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								UpperCAmelCase				: List[Any]											=	[0, 57, 3018, 70307, 91, 2]

								UpperCAmelCase				: List[Any]											=	self.tokenizer(
								    __snake_case	,    max_length=len(__snake_case	)	,    padding=__snake_case	,    truncation=__snake_case	,    return_tensors='''pt'''	)
								self.assertIsInstance(__snake_case	,    __snake_case	)

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







	def   A			(		self    :  List[Any]	)    ->	List[Any]:
								if not self.test_rust_tokenizer:
															return

								UpperCAmelCase				: int											=	self.get_tokenizer()
								UpperCAmelCase				: Optional[int]											=	self.get_rust_tokenizer()

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

								UpperCAmelCase				: Tuple											=	tokenizer.tokenize(__snake_case	)
								UpperCAmelCase				: int											=	rust_tokenizer.tokenize(__snake_case	)
								self.assertListEqual(__snake_case	,    __snake_case	)

								UpperCAmelCase				: List[Any]											=	tokenizer.encode(__snake_case	,    add_special_tokens=__snake_case	)
								UpperCAmelCase				: str											=	rust_tokenizer.encode(__snake_case	,    add_special_tokens=__snake_case	)
								self.assertListEqual(__snake_case	,    __snake_case	)

								UpperCAmelCase				: Optional[int]											=	self.get_rust_tokenizer()
								UpperCAmelCase				: Dict											=	tokenizer.encode(__snake_case	)
								UpperCAmelCase				: str											=	rust_tokenizer.encode(__snake_case	)
								self.assertListEqual(__snake_case	,    __snake_case	)







	@slow
	def   A			(		self    :  str	)    ->	Optional[Any]:
								# fmt: off
								UpperCAmelCase				: Tuple											=	{'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 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				: Tuple											=	[
								    '''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=__snake_case	,    model_name='''moussaKam/mbarthez'''	,    revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''	,    sequences=__snake_case	,    )










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__:     Union[str, Any]										=    {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:     List[str]										=    [
						    "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LiltForQuestionAnswering",
						    "LiltForSequenceClassification",
						    "LiltForTokenClassification",
						    "LiltModel",
						    "LiltPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__:     Optional[Any]										=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





