

"""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 			lowercase    (	lowerCamelCase__     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[str]	      =      BarthezTokenizer
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      BarthezTokenizerFast
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      True
				__SCREAMING_SNAKE_CASE :			int	      =      True




				def    a							( self	):
											super().setUp()

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




				def    a							( self	):
											snake_case_          =   "<pad>"
											snake_case_          =   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	):
											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(snake_case	)				,       10_1122	)




				def    a							( self	):
											self.assertEqual(self.get_tokenizer().vocab_size				,       10_1122	)




				@require_torch
				def    a							( self	):
											snake_case_          =   ["A long paragraph for summarization.", "Another paragraph for summarization."]
											snake_case_          =   [0, 57, 3018, 7_0307, 91, 2]

											snake_case_          =   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	)
											snake_case_          =   batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case				,       snake_case	)




				def    a							( self	):




											if not self.test_rust_tokenizer:
																		return

											snake_case_          =   self.get_tokenizer()
											snake_case_          =   self.get_rust_tokenizer()

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

											snake_case_          =   tokenizer.tokenize(snake_case	)
											snake_case_          =   rust_tokenizer.tokenize(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   self.get_rust_tokenizer()
											snake_case_          =   tokenizer.encode(snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)




				@slow
				def    a							( self	):
											snake_case_          =   {"input_ids": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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_          =   [
											    "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				,       )







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel




