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

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

__UpperCamelCase	: Dict    		=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCamelCase	: Tuple    		=  [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if ' ' in file]
if space_files:
					print(f"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if '-' in file]
if hyphen_files:
					print(f"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

__UpperCamelCase	: Optional[Any]    		=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)





