



'''simple docstring'''

import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a							   =				get_tests_dir('fixtures/test_sentencepiece_no_bos.model')






@require_sentencepiece
@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		List[str]        =				PegasusTokenizer
							UpperCAmelCase			:		Union[str, Any]        =				PegasusTokenizerFast
							UpperCAmelCase			:		Tuple        =				True
							UpperCAmelCase			:		Optional[Any]        =				True


							def    UpperCamelCase     (				self   :						Any			)			->					Optional[int]:
										super().setUp()

										# We have a SentencePiece fixture for testing
										snake_case:    Optional[Any]      							=PegasusTokenizer(a_			)
										tokenizer.save_pretrained(self.tmpdirname			)


							@cached_property
							def    UpperCamelCase     (				self   :						Tuple			)			->					int:
										return PegasusTokenizer.from_pretrained('google/pegasus-large'			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						**a_   :						str			)			->					PegasusTokenizer:
										return PegasusTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						str      ,						a_   :						Any			)			->					Tuple:
										return ("This is a test", "This is a test")


							def    UpperCamelCase     (				self   :						Tuple			)			->					Union[str, Any]:
										snake_case:    str      							='</s>'
										snake_case:    List[Any]      							=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   :						Tuple			)			->					Any:
										snake_case:    Optional[int]      							=list(self.get_tokenizer().get_vocab().keys()			)

										self.assertEqual(vocab_keys[0]      ,						'<pad>'			)
										self.assertEqual(vocab_keys[1]      ,						'</s>'			)
										self.assertEqual(vocab_keys[-1]      ,						'v'			)
										self.assertEqual(len(a_			)      ,						1_1_0_3			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										self.assertEqual(self.get_tokenizer().vocab_size      ,						1_1_0_3			)


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Union[str, Any]:
										snake_case:    int      							=self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
										snake_case:    Tuple      							=self.tokenizer_class.from_pretrained(self.tmpdirname			)
										snake_case:    Optional[Any]      							=(
										    'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
										    ' </s> <pad> <pad> <pad>'
										)
										snake_case:    List[str]      							=rust_tokenizer([raw_input_str]      ,						return_tensors=a_      ,						add_special_tokens=a_			).input_ids[0]
										snake_case:    int      							=py_tokenizer([raw_input_str]      ,						return_tensors=a_      ,						add_special_tokens=a_			).input_ids[0]
										self.assertListEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					int:
										snake_case:    List[str]      							=self._large_tokenizer
										# <mask_1> masks whole sentence while <mask_2> masks single word
										snake_case:    Union[str, Any]      							='<mask_1> To ensure a <mask_2> flow of bank resolutions.'
										snake_case:    List[str]      							=[2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
										snake_case:    List[Any]      							=tokenizer([raw_input_str]      ,						return_tensors=a_			).input_ids[0]
										self.assertListEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    int      							=self._large_tokenizer
										# The tracebacks for the following asserts are **better** without messages or self.assertEqual
										assert tokenizer.vocab_size == 9_6_1_0_3
										assert tokenizer.pad_token_id == 0
										assert tokenizer.eos_token_id == 1
										assert tokenizer.offset == 1_0_3
										assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
										assert tokenizer.unk_token == "<unk>"
										assert tokenizer.model_max_length == 1_0_2_4
										snake_case:    Dict      							='To ensure a smooth flow of bank resolutions.'
										snake_case:    Dict      							=[4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
										snake_case:    Tuple      							=tokenizer([raw_input_str]      ,						return_tensors=a_			).input_ids[0]
										self.assertListEqual(a_      ,						a_			)
										assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]			) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]


							@require_torch
							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Optional[int]      							=['This is going to be way too long.' * 1_5_0, 'short example']
										snake_case:    List[str]      							=['not super long but more than 5 tokens', 'tiny']
										snake_case:    Union[str, Any]      							=self._large_tokenizer(a_      ,						padding=a_      ,						truncation=a_      ,						return_tensors='pt'			)
										snake_case:    Dict      							=self._large_tokenizer(
										    text_target=a_      ,						max_length=5      ,						padding=a_      ,						truncation=a_      ,						return_tensors='pt'			)

										assert batch.input_ids.shape == (2, 1_0_2_4)
										assert batch.attention_mask.shape == (2, 1_0_2_4)
										assert targets["input_ids"].shape == (2, 5)
										assert len(a_			) == 2  # input_ids, attention_mask.




							@slow
							def    UpperCamelCase     (				self   :						str			)			->					Tuple:
										# fmt: off
										snake_case:    Dict      							={'input_ids': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 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, 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_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=a_      ,						model_name='google/bigbird-pegasus-large-arxiv'      ,						revision='ba85d0851d708441f91440d509690f1ab6353415'      ,						)






@require_sentencepiece
@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Union[str, Any]        =				PegasusTokenizer
							UpperCAmelCase			:		List[Any]        =				PegasusTokenizerFast
							UpperCAmelCase			:		Optional[Any]        =				True
							UpperCAmelCase			:		Union[str, Any]        =				True


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										super().setUp()

										# We have a SentencePiece fixture for testing
										snake_case:    Union[str, Any]      							=PegasusTokenizer(a_      ,						offset=0      ,						mask_token_sent=a_      ,						mask_token='[MASK]'			)
										tokenizer.save_pretrained(self.tmpdirname			)


							@cached_property
							def    UpperCamelCase     (				self   :						str			)			->					Any:
										return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'			)


							def    UpperCamelCase     (				self   :						List[str]      ,						**a_   :						Union[str, Any]			)			->					PegasusTokenizer:
										return PegasusTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Optional[Any]			)			->					List[Any]:
										return ("This is a test", "This is a test")


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Optional[int]:
										snake_case:    Tuple      							=self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
										snake_case:    Any      							=self.tokenizer_class.from_pretrained(self.tmpdirname			)
										snake_case:    Optional[Any]      							=(
										    'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
										    ' <pad> <pad> <pad>'
										)
										snake_case:    Optional[int]      							=rust_tokenizer([raw_input_str]      ,						return_tensors=a_      ,						add_special_tokens=a_			).input_ids[0]
										snake_case:    Any      							=py_tokenizer([raw_input_str]      ,						return_tensors=a_      ,						add_special_tokens=a_			).input_ids[0]
										self.assertListEqual(a_      ,						a_			)


							@require_torch
							def    UpperCamelCase     (				self   :						Any			)			->					Optional[Any]:
										snake_case:    Dict      							=['This is going to be way too long.' * 1_0_0_0, 'short example']
										snake_case:    List[Any]      							=['not super long but more than 5 tokens', 'tiny']
										snake_case:    Any      							=self._large_tokenizer(a_      ,						padding=a_      ,						truncation=a_      ,						return_tensors='pt'			)
										snake_case:    Any      							=self._large_tokenizer(
										    text_target=a_      ,						max_length=5      ,						padding=a_      ,						truncation=a_      ,						return_tensors='pt'			)

										assert batch.input_ids.shape == (2, 4_0_9_6)
										assert batch.attention_mask.shape == (2, 4_0_9_6)
										assert targets["input_ids"].shape == (2, 5)
										assert len(a_			) == 2  # input_ids, attention_mask.




							def    UpperCamelCase     (				self   :						str			)			->					str:
										snake_case:    Tuple      							=(
										    'This is an example string that is used to test the original TF implementation against the HF'
										    ' implementation'
										)

										snake_case:    Dict      							=self._large_tokenizer(a_			).input_ids

										self.assertListEqual(
										    a_      ,						[1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]      ,						)









'''simple docstring'''

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch

if is_datasets_available():
	from datasets import load_dataset

a							   =				random.Random()







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=1.0				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None					)				->					List[str]:





			"""simple docstring"""







			if rng is None:
						snake_case:    Dict      							=global_rng

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

			return values






class 						a_		(     unittest.TestCase    ):


							def __init__(				self   :						int      ,						a_   :						Any      ,						a_   :						str=7      ,						a_   :						Tuple=4_0_0      ,						a_   :						List[Any]=2_0_0_0      ,						a_   :						str=2_0_4_8      ,						a_   :						List[str]=1_2_8      ,						a_   :						int=1      ,						a_   :						Tuple=5_1_2      ,						a_   :						Dict=3_0      ,						a_   :						Optional[int]=4_4_1_0_0      ,						)			->					Union[str, Any]:
										snake_case:    Union[str, Any]      							=parent
										snake_case:    Optional[Any]      							=batch_size
										snake_case:    Union[str, Any]      							=min_seq_length
										snake_case:    List[str]      							=max_seq_length
										snake_case:    str      							=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case:    int      							=spectrogram_length
										snake_case:    List[str]      							=feature_size
										snake_case:    Dict      							=num_audio_channels
										snake_case:    int      							=hop_length
										snake_case:    List[Any]      							=chunk_length
										snake_case:    Optional[Any]      							=sampling_rate


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										return {
										    "spectrogram_length": self.spectrogram_length,
										    "feature_size": self.feature_size,
										    "num_audio_channels": self.num_audio_channels,
										    "hop_length": self.hop_length,
										    "chunk_length": self.chunk_length,
										    "sampling_rate": self.sampling_rate,
										}




							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						Optional[Any]=False      ,						a_   :						Tuple=False			)			->					Optional[int]:
										def _flatten(a_   :						Dict			):
													return list(itertools.chain(*a_			)			)

										if equal_length:
													snake_case:    Union[str, Any]      							=[floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
										else:
													# make sure that inputs increase in size
													snake_case:    Union[str, Any]      							=[
													    floats_list((x, self.feature_size)			)
													    for x in range(self.min_seq_length      ,						self.max_seq_length      ,						self.seq_length_diff			)
													]
										if numpify:
													snake_case:    Any      							=[np.asarray(a_			) for x in speech_inputs]
										return speech_inputs






@require_torch
@require_torchaudio
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Optional[Any]        =				TvltFeatureExtractor


							def    UpperCamelCase     (				self   :						int			)			->					int:
										snake_case:    Union[str, Any]      							=TvltFeatureExtractionTester(self			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					str:
										snake_case:    int      							=self.feature_extraction_class(**self.feat_extract_dict			)
										self.assertTrue(hasattr(a_      ,						'spectrogram_length'			)			)
										self.assertTrue(hasattr(a_      ,						'feature_size'			)			)
										self.assertTrue(hasattr(a_      ,						'num_audio_channels'			)			)
										self.assertTrue(hasattr(a_      ,						'hop_length'			)			)
										self.assertTrue(hasattr(a_      ,						'chunk_length'			)			)
										self.assertTrue(hasattr(a_      ,						'sampling_rate'			)			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					Optional[int]:
										snake_case:    Tuple      							=self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case:    Optional[Any]      							=feat_extract_first.save_pretrained(a_			)[0]
													check_json_file_has_correct_format(a_			)
													snake_case:    str      							=self.feature_extraction_class.from_pretrained(a_			)

										snake_case:    Optional[int]      							=feat_extract_first.to_dict()
										snake_case:    List[str]      							=feat_extract_second.to_dict()
										snake_case:    Optional[int]      							=dict_first.pop('mel_filters'			)
										snake_case:    List[Any]      							=dict_second.pop('mel_filters'			)
										self.assertTrue(np.allclose(a_      ,						a_			)			)
										self.assertEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						str			)			->					str:
										snake_case:    Any      							=self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case:    int      							=os.path.join(a_      ,						'feat_extract.json'			)
													feat_extract_first.to_json_file(a_			)
													snake_case:    Union[str, Any]      							=self.feature_extraction_class.from_json_file(a_			)

										snake_case:    Optional[Any]      							=feat_extract_first.to_dict()
										snake_case:    Optional[Any]      							=feat_extract_second.to_dict()
										snake_case:    Union[str, Any]      							=dict_first.pop('mel_filters'			)
										snake_case:    Union[str, Any]      							=dict_second.pop('mel_filters'			)
										self.assertTrue(np.allclose(a_      ,						a_			)			)
										self.assertEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					List[str]:
										# Initialize feature_extractor
										snake_case:    Tuple      							=self.feature_extraction_class(**self.feat_extract_dict			)

										# create three inputs of length 800, 1000, and 1200
										snake_case:    Any      							=[floats_list((1, x)			)[0] for x in range(8_0_0      ,						1_4_0_0      ,						2_0_0			)]
										snake_case:    Optional[Any]      							=[np.asarray(a_			) for speech_input in speech_inputs]

										# Test not batched input
										snake_case:    List[Any]      							=feature_extractor(np_speech_inputs[0]      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test batched
										snake_case:    Any      							=feature_extractor(a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test audio masking
										snake_case:    Optional[Any]      							=feature_extractor(
										    a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0      ,						mask_audio=a_			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test 2-D numpy arrays are batched.
										snake_case:    List[Any]      							=[floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
										snake_case:    List[str]      							=np.asarray(a_			)
										snake_case:    str      							=feature_extractor(a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values
										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						str			)			->					Union[str, Any]:
										snake_case:    Dict      							=load_dataset('hf-internal-testing/librispeech_asr_dummy'      ,						'clean'      ,						split='validation'			)
										# automatic decoding with librispeech
										snake_case:    Optional[int]      							=ds.sort('id'			).select(range(a_			)			)[:num_samples]['audio']

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




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[int]:
										snake_case:    Tuple      							=self._load_datasamples(1			)
										snake_case:    Any      							=TvltFeatureExtractor()
										snake_case:    Any      							=feature_extractor(a_      ,						return_tensors='pt'			).audio_values

										self.assertEquals(audio_values.shape      ,						(1, 1, 1_9_2, 1_2_8)			)

										snake_case:    int      							=torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]			)
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,						a_      ,						atol=1E-4			)			)




