






import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
							import torch


__snake_case      :      Dict             =       random.Random()







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   int=1.0      ,    UpperCamelCase_    :   Optional[int]=None      ,    UpperCamelCase_    :   Union[str, Any]=None						)					->				int:

						"""simple docstring"""



						if rng is None:
												lowerCAmelCase__        =  global_rng

						lowerCAmelCase__        =  []
						for batch_idx in range(shape[0]						):
												values.append([]						)
												for _ in range(shape[1]						):
																		values[-1].append(rng.random() * scale						)

						return values


@require_torch
class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=7       ,  _UpperCamelCase=4_00       ,  _UpperCamelCase=20_00       ,  _UpperCamelCase=1       ,  _UpperCamelCase=0.0       ,  _UpperCamelCase=1_60_00       ,  _UpperCamelCase=True       ,  _UpperCamelCase=80       ,  _UpperCamelCase=16       ,  _UpperCamelCase=64       ,  _UpperCamelCase="hann_window"       ,  _UpperCamelCase=80       ,  _UpperCamelCase=76_00       ,  _UpperCamelCase=1E-10       ,  _UpperCamelCase=True       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  parent
												lowerCAmelCase__        =  batch_size
												lowerCAmelCase__        =  min_seq_length
												lowerCAmelCase__        =  max_seq_length
												lowerCAmelCase__        =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												lowerCAmelCase__        =  feature_size
												lowerCAmelCase__        =  padding_value
												lowerCAmelCase__        =  sampling_rate
												lowerCAmelCase__        =  do_normalize
												lowerCAmelCase__        =  num_mel_bins
												lowerCAmelCase__        =  hop_length
												lowerCAmelCase__        =  win_length
												lowerCAmelCase__        =  win_function
												lowerCAmelCase__        =  fmin
												lowerCAmelCase__        =  fmax
												lowerCAmelCase__        =  mel_floor
												lowerCAmelCase__        =  return_attention_mask






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return {
												    "feature_size": self.feature_size,
												    "padding_value": self.padding_value,
												    "sampling_rate": self.sampling_rate,
												    "do_normalize": self.do_normalize,
												    "num_mel_bins": self.num_mel_bins,
												    "hop_length": self.hop_length,
												    "win_length": self.win_length,
												    "win_function": self.win_function,
												    "fmin": self.fmin,
												    "fmax": self.fmax,
												    "mel_floor": self.mel_floor,
												    "return_attention_mask": self.return_attention_mask,
												}






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase=False       ,  _UpperCamelCase=False				):






												"""simple docstring"""
												def _flatten(_UpperCamelCase				):
																		return list(itertools.chain(*_UpperCamelCase				)				)

												if equal_length:
																		lowerCAmelCase__        =  floats_list((self.batch_size, self.max_seq_length)				)
												else:
																		# make sure that inputs increase in size
																		lowerCAmelCase__        =  [
																		    _flatten(floats_list((x, self.feature_size)				)				)
																		    for x in range(self.min_seq_length       ,  self.max_seq_length       ,  self.seq_length_diff				)
																		]

												if numpify:
																		lowerCAmelCase__        =  [np.asarray(_UpperCamelCase				) for x in speech_inputs]

												return speech_inputs







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase=False       ,  _UpperCamelCase=False				):






												"""simple docstring"""
												if equal_length:
																		lowerCAmelCase__        =  [floats_list((self.max_seq_length, self.num_mel_bins)				) for _ in range(self.batch_size				)]
												else:
																		# make sure that inputs increase in size
																		lowerCAmelCase__        =  [
																		    floats_list((x, self.num_mel_bins)				)
																		    for x in range(self.min_seq_length       ,  self.max_seq_length       ,  self.seq_length_diff				)
																		]

												if numpify:
																		lowerCAmelCase__        =  [np.asarray(_UpperCamelCase				) for x in speech_inputs]

												return speech_inputs




@require_torch
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   SpeechTaFeatureExtractor






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  SpeechTaFeatureExtractionTester(self				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												self.assertTrue(np.all(np.mean(_UpperCamelCase       ,  axis=0				) < 1E-3				)				)
												self.assertTrue(np.all(np.abs(np.var(_UpperCamelCase       ,  axis=0				) - 1				) < 1E-3				)				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												# create three inputs of length 800, 1000, and 1200
												lowerCAmelCase__        =  [floats_list((1, x)				)[0] for x in range(8_00       ,  14_00       ,  2_00				)]
												lowerCAmelCase__        =  [np.asarray(_UpperCamelCase				) for speech_input in speech_inputs]

												# Test not batched input
												lowerCAmelCase__        =  feat_extract(speech_inputs[0]       ,  return_tensors='np'				).input_values
												lowerCAmelCase__        =  feat_extract(np_speech_inputs[0]       ,  return_tensors='np'				).input_values
												self.assertTrue(np.allclose(_UpperCamelCase       ,  _UpperCamelCase       ,  atol=1E-3				)				)

												# Test batched
												lowerCAmelCase__        =  feat_extract(_UpperCamelCase       ,  return_tensors='np'				).input_values
												lowerCAmelCase__        =  feat_extract(_UpperCamelCase       ,  return_tensors='np'				).input_values
												for enc_seq_a, enc_seq_a in zip(_UpperCamelCase       ,  _UpperCamelCase				):
																		self.assertTrue(np.allclose(_UpperCamelCase       ,  _UpperCamelCase       ,  atol=1E-3				)				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												lowerCAmelCase__        =  [floats_list((1, x)				)[0] for x in range(8_00       ,  14_00       ,  2_00				)]

												lowerCAmelCase__        =  ['longest', 'max_length', 'do_not_pad']
												lowerCAmelCase__        =  [None, 16_00, None]
												for max_length, padding in zip(_UpperCamelCase       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  feat_extract(_UpperCamelCase       ,  padding=_UpperCamelCase       ,  max_length=_UpperCamelCase       ,  return_tensors='np'				)
																		lowerCAmelCase__        =  processed.input_values

																		self._check_zero_mean_unit_variance(input_values[0][:8_00]				)
																		self.assertTrue(input_values[0][8_00:].sum() < 1E-6				)
																		self._check_zero_mean_unit_variance(input_values[1][:10_00]				)
																		self.assertTrue(input_values[0][10_00:].sum() < 1E-6				)
																		self._check_zero_mean_unit_variance(input_values[2][:12_00]				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												lowerCAmelCase__        =  range(8_00       ,  14_00       ,  2_00				)
												lowerCAmelCase__        =  [floats_list((1, x)				)[0] for x in lengths]

												lowerCAmelCase__        =  ['longest', 'max_length', 'do_not_pad']
												lowerCAmelCase__        =  [None, 16_00, None]

												for max_length, padding in zip(_UpperCamelCase       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  feat_extract(_UpperCamelCase       ,  max_length=_UpperCamelCase       ,  padding=_UpperCamelCase				)
																		lowerCAmelCase__        =  processed.input_values

																		self._check_zero_mean_unit_variance(input_values[0][:8_00]				)
																		self._check_zero_mean_unit_variance(input_values[1][:10_00]				)
																		self._check_zero_mean_unit_variance(input_values[2][:12_00]				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												lowerCAmelCase__        =  [floats_list((1, x)				)[0] for x in range(8_00       ,  14_00       ,  2_00				)]
												lowerCAmelCase__        =  feat_extract(
												    _UpperCamelCase       ,  truncation=_UpperCamelCase       ,  max_length=10_00       ,  padding='max_length'       ,  return_tensors='np'				)
												lowerCAmelCase__        =  processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_00]				)
												self._check_zero_mean_unit_variance(input_values[1]				)
												self._check_zero_mean_unit_variance(input_values[2]				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												lowerCAmelCase__        =  [floats_list((1, x)				)[0] for x in range(8_00       ,  14_00       ,  2_00				)]
												lowerCAmelCase__        =  feat_extract(
												    _UpperCamelCase       ,  truncation=_UpperCamelCase       ,  max_length=10_00       ,  padding='longest'       ,  return_tensors='np'				)
												lowerCAmelCase__        =  processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_00]				)
												self._check_zero_mean_unit_variance(input_values[1, :10_00]				)
												self._check_zero_mean_unit_variance(input_values[2]				)

												# make sure that if max_length < longest -> then pad to max_length
												self.assertTrue(input_values.shape == (3, 10_00)				)

												lowerCAmelCase__        =  [floats_list((1, x)				)[0] for x in range(8_00       ,  14_00       ,  2_00				)]
												lowerCAmelCase__        =  feat_extract(
												    _UpperCamelCase       ,  truncation=_UpperCamelCase       ,  max_length=20_00       ,  padding='longest'       ,  return_tensors='np'				)
												lowerCAmelCase__        =  processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_00]				)
												self._check_zero_mean_unit_variance(input_values[1, :10_00]				)
												self._check_zero_mean_unit_variance(input_values[2]				)

												# make sure that if max_length > longest -> then pad to longest
												self.assertTrue(input_values.shape == (3, 12_00)				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												lowerCAmelCase__        =  np.random.rand(1_00				).astype(np.floataa				)
												lowerCAmelCase__        =  np_speech_inputs.tolist()

												for inputs in [py_speech_inputs, np_speech_inputs]:
																		lowerCAmelCase__        =  feature_extractor.pad([{'input_values': inputs}]       ,  return_tensors='np'				)
																		self.assertTrue(np_processed.input_values.dtype == np.floataa				)
																		lowerCAmelCase__        =  feature_extractor.pad([{'input_values': inputs}]       ,  return_tensors='pt'				)
																		self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												# create three inputs of length 800, 1000, and 1200
												lowerCAmelCase__        =  [floats_list((1, x)				)[0] for x in range(8_00       ,  14_00       ,  2_00				)]
												lowerCAmelCase__        =  [np.asarray(_UpperCamelCase				) for speech_input in speech_inputs]

												# Test feature size
												lowerCAmelCase__        =  feature_extractor(audio_target=_UpperCamelCase       ,  padding=_UpperCamelCase       ,  return_tensors='np'				).input_values
												self.assertTrue(input_values.ndim == 3				)
												self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins				)

												# Test not batched input
												lowerCAmelCase__        =  feature_extractor(speech_inputs[0]       ,  return_tensors='np'				).input_values
												lowerCAmelCase__        =  feature_extractor(np_speech_inputs[0]       ,  return_tensors='np'				).input_values
												self.assertTrue(np.allclose(_UpperCamelCase       ,  _UpperCamelCase       ,  atol=1E-3				)				)

												# Test batched
												lowerCAmelCase__        =  feature_extractor(_UpperCamelCase       ,  return_tensors='np'				).input_values
												lowerCAmelCase__        =  feature_extractor(_UpperCamelCase       ,  return_tensors='np'				).input_values
												for enc_seq_a, enc_seq_a in zip(_UpperCamelCase       ,  _UpperCamelCase				):
																		self.assertTrue(np.allclose(_UpperCamelCase       ,  _UpperCamelCase       ,  atol=1E-3				)				)

												# Test 2-D numpy arrays are batched.
												lowerCAmelCase__        =  [floats_list((1, x)				)[0] for x in (8_00, 8_00, 8_00)]
												lowerCAmelCase__        =  np.asarray(_UpperCamelCase				)
												lowerCAmelCase__        =  feature_extractor(_UpperCamelCase       ,  return_tensors='np'				).input_values
												lowerCAmelCase__        =  feature_extractor(_UpperCamelCase       ,  return_tensors='np'				).input_values
												for enc_seq_a, enc_seq_a in zip(_UpperCamelCase       ,  _UpperCamelCase				):
																		self.assertTrue(np.allclose(_UpperCamelCase       ,  _UpperCamelCase       ,  atol=1E-3				)				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feat_extract_tester.prepare_inputs_for_target()
												lowerCAmelCase__        =  self.feature_extraction_class(**self.feat_extract_dict				)
												lowerCAmelCase__        =  feat_extract.model_input_names[0]

												lowerCAmelCase__        =  BatchFeature({input_name: speech_inputs}				)

												self.assertTrue(all(len(_UpperCamelCase				) == len(_UpperCamelCase				) for x, y in zip(_UpperCamelCase       ,  processed_features[input_name]				)				)				)

												lowerCAmelCase__        =  self.feat_extract_tester.prepare_inputs_for_target(equal_length=_UpperCamelCase				)
												lowerCAmelCase__        =  BatchFeature({input_name: speech_inputs}       ,  tensor_type='np'				)

												lowerCAmelCase__        =  processed_features[input_name]

												if len(batch_features_input.shape				) < 3:
																		lowerCAmelCase__        =  batch_features_input[:, :, None]

												self.assertTrue(
												    batch_features_input.shape
												    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.num_mel_bins)				)






						@require_torch
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feat_extract_tester.prepare_inputs_for_target(equal_length=_UpperCamelCase				)
												lowerCAmelCase__        =  self.feature_extraction_class(**self.feat_extract_dict				)
												lowerCAmelCase__        =  feat_extract.model_input_names[0]

												lowerCAmelCase__        =  BatchFeature({input_name: speech_inputs}       ,  tensor_type='pt'				)

												lowerCAmelCase__        =  processed_features[input_name]

												if len(batch_features_input.shape				) < 3:
																		lowerCAmelCase__        =  batch_features_input[:, :, None]

												self.assertTrue(
												    batch_features_input.shape
												    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.num_mel_bins)				)






						@require_torch
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feature_extraction_class(**self.feat_extract_dict				)
												lowerCAmelCase__        =  self.feat_extract_tester.prepare_inputs_for_target()
												lowerCAmelCase__        =  feat_extract.model_input_names[0]

												lowerCAmelCase__        =  BatchFeature({input_name: speech_inputs}				)

												lowerCAmelCase__        =  feat_extract.num_mel_bins  # hack!

												lowerCAmelCase__        =  feat_extract.pad(_UpperCamelCase       ,  padding='longest'       ,  return_tensors='np'				)[input_name]
												lowerCAmelCase__        =  feat_extract.pad(_UpperCamelCase       ,  padding='longest'       ,  return_tensors='pt'				)[input_name]

												self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_pt.numpy().astype(np.floataa				).sum()				) < 1E-2				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feat_extract_dict
												lowerCAmelCase__        =  True
												lowerCAmelCase__        =  self.feature_extraction_class(**_UpperCamelCase				)
												lowerCAmelCase__        =  self.feat_extract_tester.prepare_inputs_for_target()
												lowerCAmelCase__        =  [len(_UpperCamelCase				) for x in speech_inputs]
												lowerCAmelCase__        =  feat_extract.model_input_names[0]

												lowerCAmelCase__        =  BatchFeature({input_name: speech_inputs}				)

												lowerCAmelCase__        =  feat_extract.num_mel_bins  # hack!

												lowerCAmelCase__        =  feat_extract.pad(_UpperCamelCase       ,  padding='longest'       ,  return_tensors='np'				)
												self.assertIn('attention_mask'       ,  _UpperCamelCase				)
												self.assertListEqual(list(processed.attention_mask.shape				)       ,  list(processed[input_name].shape[:2]				)				)
												self.assertListEqual(processed.attention_mask.sum(-1				).tolist()       ,  _UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.feat_extract_dict
												lowerCAmelCase__        =  True
												lowerCAmelCase__        =  self.feature_extraction_class(**_UpperCamelCase				)
												lowerCAmelCase__        =  self.feat_extract_tester.prepare_inputs_for_target()
												lowerCAmelCase__        =  [len(_UpperCamelCase				) for x in speech_inputs]
												lowerCAmelCase__        =  feat_extract.model_input_names[0]

												lowerCAmelCase__        =  BatchFeature({input_name: speech_inputs}				)
												lowerCAmelCase__        =  min(_UpperCamelCase				)

												lowerCAmelCase__        =  feat_extract.num_mel_bins  # hack!

												lowerCAmelCase__        =  feat_extract.pad(
												    _UpperCamelCase       ,  padding='max_length'       ,  max_length=_UpperCamelCase       ,  truncation=_UpperCamelCase       ,  return_tensors='np'				)
												self.assertIn('attention_mask'       ,  _UpperCamelCase				)
												self.assertListEqual(
												    list(processed_pad.attention_mask.shape				)       ,  [processed_pad[input_name].shape[0], max_length]				)
												self.assertListEqual(
												    processed_pad.attention_mask[:, :max_length].sum(-1				).tolist()       ,  [max_length for x in speech_inputs]				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												from datasets import load_dataset

												lowerCAmelCase__        =  load_dataset('hf-internal-testing/librispeech_asr_dummy'       ,  'clean'       ,  split='validation'				)
												# automatic decoding with librispeech
												lowerCAmelCase__        =  ds.sort('id'				).select(range(_UpperCamelCase				)				)[:num_samples]['audio']

												return [x["array"] for x in speech_samples]






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  torch.tensor(
												    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
												     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
												     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
												     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
												     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
												     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]				)
												# fmt: on

												lowerCAmelCase__        =  self._load_datasamples(1				)
												lowerCAmelCase__        =  SpeechTaFeatureExtractor()
												lowerCAmelCase__        =  feature_extractor(_UpperCamelCase       ,  return_tensors='pt'				).input_values
												self.assertEquals(input_values.shape       ,  (1, 9_36_80)				)
												self.assertTrue(torch.allclose(input_values[0, :30]       ,  _UpperCamelCase       ,  atol=1E-6				)				)







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  torch.tensor(
												    [-2.68_70, -3.01_04, -3.13_56, -3.53_52, -3.00_44, -3.03_53, -3.47_19, -3.67_77,
												     -3.15_20, -2.94_35, -2.65_53, -2.87_95, -2.99_44, -2.59_21, -3.02_79, -3.03_86,
												     -3.08_64, -3.12_91, -3.23_53, -2.74_44, -2.68_31, -2.72_87, -3.17_61, -3.15_71,
												     -3.27_26, -3.05_82, -3.10_07, -3.45_33, -3.46_95, -3.09_98]				)
												# fmt: on

												lowerCAmelCase__        =  self._load_datasamples(1				)
												lowerCAmelCase__        =  SpeechTaFeatureExtractor()
												lowerCAmelCase__        =  feature_extractor(audio_target=_UpperCamelCase       ,  return_tensors='pt'				).input_values
												self.assertEquals(input_values.shape       ,  (1, 3_66, 80)				)
												self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,  _UpperCamelCase       ,  atol=1E-4				)				)













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       ,  )





