






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  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= BarthezTokenizer
	lowerCAmelCase													= BarthezTokenizerFast
	lowerCAmelCase													= True
	lowerCAmelCase													= True




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

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




	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								snake_case_				=							'<pad>'
								snake_case_				=							1

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




	def 							_UpperCamelCase			(       self					)       ->    str:
								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(a					)       ,			10_11_22					)




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								self.assertEqual(self.get_tokenizer().vocab_size       ,			10_11_22					)




	@require_torch
	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							['A long paragraph for summarization.', 'Another paragraph for summarization.']
								snake_case_				=							[0, 57, 30_18, 7_03_07, 91, 2]

								snake_case_				=							self.tokenizer(
								    a       ,			max_length=len(a					)       ,			padding=a       ,			truncation=a       ,			return_tensors='pt'					)
								self.assertIsInstance(a       ,			a					)

								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(a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								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(a					)
								snake_case_				=							rust_tokenizer.tokenize(a					)
								self.assertListEqual(a       ,			a					)

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

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






	@slow
	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								# fmt: off
								snake_case_				=							{'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.
								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=a       ,			model_name='moussaKam/mbarthez'       ,			revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'       ,			sequences=a       ,			)













from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''image_processor''', '''tokenizer''']
	lowerCAmelCase													= '''BlipImageProcessor'''
	lowerCAmelCase													= ('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(       self       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							False
								super().__init__(a       ,			a					)
								snake_case_				=							self.image_processor




	def __call__(       self       ,			a = None       ,			a = None       ,			a = True       ,			a = False       ,			a = None       ,			a = None       ,			a = 0       ,			a = None       ,			a = None       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = True       ,			a = None       ,			**a       ,			)       ->    BatchEncoding:
								if images is None and text is None:
															raise ValueError('You have to specify either images or text.'					)

								# Get only text
								if images is None:
															snake_case_				=							self.tokenizer
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
															return text_encoding

								# add pixel_values
								snake_case_				=							self.image_processor(a       ,			return_tensors=a					)

								if text is not None:
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
								else:
															snake_case_				=							None

								if text_encoding is not None:
															encoding_image_processor.update(a					)

								return encoding_image_processor




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    int:
								return self.tokenizer.batch_decode(*a       ,			**a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    Any:
								return self.tokenizer.decode(*a       ,			**a					)






	@property
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.tokenizer.model_input_names
								snake_case_				=							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





