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__		(     UpperCamelCase_			,							unittest.TestCase):
	UpperCamelCase_  =	BarthezTokenizer
	UpperCamelCase_  =	BarthezTokenizerFast
	UpperCamelCase_  =	True
	UpperCamelCase_  =	True


	def  __A ( self			:       Dict		):





							'''simple docstring'''





							super().setUp()

							SCREAMING_SNAKE_CASE							:					Tuple           =							BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''		)
							tokenizer.save_pretrained(self.tmpdirname		)
							tokenizer.save_pretrained(self.tmpdirname					,	legacy_format=UpperCamelCase__		)
							SCREAMING_SNAKE_CASE							:					str           =							tokenizer


	def  __A ( self			:       List[str]		):





							'''simple docstring'''





							SCREAMING_SNAKE_CASE							:					Tuple           =							'''<pad>'''
							SCREAMING_SNAKE_CASE							:					str           =							1

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


	def  __A ( self			:       str		):





							'''simple docstring'''





							SCREAMING_SNAKE_CASE							:					str           =							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__		)					,	10_1122		)


	def  __A ( self			:       Optional[Any]		):





							'''simple docstring'''





							self.assertEqual(self.get_tokenizer().vocab_size					,	10_1122		)


	@require_torch
	def  __A ( self			:       Any		):





							'''simple docstring'''





							SCREAMING_SNAKE_CASE							:					Optional[Any]           =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							SCREAMING_SNAKE_CASE							:					List[str]           =							[0, 57, 3018, 7_0307, 91, 2]

							SCREAMING_SNAKE_CASE							:					Any           =							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							:					List[Any]           =							batch.input_ids.tolist()[0]
							self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)


	def  __A ( self			:       List[Any]		):





							'''simple docstring'''





							if not self.test_rust_tokenizer:
													return

							SCREAMING_SNAKE_CASE							:					List[str]           =							self.get_tokenizer()
							SCREAMING_SNAKE_CASE							:					List[str]           =							self.get_rust_tokenizer()

							SCREAMING_SNAKE_CASE							:					int           =							'''I was born in 92000, and this is falsé.'''

							SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer.tokenize(UpperCamelCase__		)
							SCREAMING_SNAKE_CASE							:					Tuple           =							rust_tokenizer.tokenize(UpperCamelCase__		)
							self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)

							SCREAMING_SNAKE_CASE							:					int           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
							SCREAMING_SNAKE_CASE							:					List[str]           =							rust_tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
							self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)

							SCREAMING_SNAKE_CASE							:					List[str]           =							self.get_rust_tokenizer()
							SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokenizer.encode(UpperCamelCase__		)
							SCREAMING_SNAKE_CASE							:					Any           =							rust_tokenizer.encode(UpperCamelCase__		)
							self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)



	@slow
	def  __A ( self			:       int		):





							'''simple docstring'''





							SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{'''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.
							SCREAMING_SNAKE_CASE							:					List[str]           =							[
							    '''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__					,	)






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	FunnelTokenizer
						UpperCamelCase_  =	FunnelTokenizerFast
						UpperCamelCase_  =	True
						UpperCamelCase_  =	True


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[
												    '''<unk>''',
												    '''<cls>''',
												    '''<sep>''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												SCREAMING_SNAKE_CASE							:					List[str]           =							os.path.join(self.tmpdirname					,	VOCAB_FILES_NAMES['''vocab_file''']		)
												with open(self.vocab_file					,	'''w'''					,	encoding='''utf-8'''		) as vocab_writer:
																		vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizer.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizerFast.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''UNwant\u00E9d,running'''
												SCREAMING_SNAKE_CASE							:					int           =							'''unwanted, running'''
												return input_text, output_text


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.tokenizer_class(self.vocab_file		)

												SCREAMING_SNAKE_CASE							:					int           =							tokenizer.tokenize('''UNwant\u00E9d,running'''		)
												self.assertListEqual(UpperCamelCase__					,	['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']		)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)					,	[7, 4, 5, 10, 8, 9]		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		SCREAMING_SNAKE_CASE							:					int           =							tokenizer('''UNwant\u00E9d,running'''		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(inputs['''input_ids''']		) - 1
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len		)

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer('''UNwant\u00E9d,running'''					,	'''UNwant\u00E9d,running'''		)
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len + [1] * sentence_len		)





