






from diffusers.utils.testing_utils import require_onnxruntime


@require_onnxruntime
class       __SCREAMING_SNAKE_CASE   :
						pass













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   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Tuple						=   BarthezTokenizer
						_SCREAMING_SNAKE_CASE		:    int						=   BarthezTokenizerFast
						_SCREAMING_SNAKE_CASE		:    Dict						=   True
						_SCREAMING_SNAKE_CASE		:    Tuple						=   True






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().setUp()

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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  '<pad>'
												lowerCAmelCase__        =  1

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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  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_11_22				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.assertEqual(self.get_tokenizer().vocab_size       ,  10_11_22				)






						@require_torch
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
												lowerCAmelCase__        =  [0, 57, 30_18, 7_03_07, 91, 2]

												lowerCAmelCase__        =  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				)
												lowerCAmelCase__        =  batch.input_ids.tolist()[0]
												self.assertListEqual(_UpperCamelCase       ,  _UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if not self.test_rust_tokenizer:
																		return

												lowerCAmelCase__        =  self.get_tokenizer()
												lowerCAmelCase__        =  self.get_rust_tokenizer()

												lowerCAmelCase__        =  'I was born in 92000, and this is falsé.'

												lowerCAmelCase__        =  tokenizer.tokenize(_UpperCamelCase				)
												lowerCAmelCase__        =  rust_tokenizer.tokenize(_UpperCamelCase				)
												self.assertListEqual(_UpperCamelCase       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  tokenizer.encode(_UpperCamelCase       ,  add_special_tokens=_UpperCamelCase				)
												lowerCAmelCase__        =  rust_tokenizer.encode(_UpperCamelCase       ,  add_special_tokens=_UpperCamelCase				)
												self.assertListEqual(_UpperCamelCase       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  self.get_rust_tokenizer()
												lowerCAmelCase__        =  tokenizer.encode(_UpperCamelCase				)
												lowerCAmelCase__        =  rust_tokenizer.encode(_UpperCamelCase				)
												self.assertListEqual(_UpperCamelCase       ,  _UpperCamelCase				)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# fmt: off
												lowerCAmelCase__        =  {'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.
												lowerCAmelCase__        =  [
												    '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       ,  )





