import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
					from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase	: List[str]    		=  get_tests_dir('fixtures/test_sentencepiece.model')

__UpperCamelCase	: Tuple    		=  {'target_lang': 'fi', 'source_lang': 'en'}
__UpperCamelCase	: Dict    		=  '>>zh<<'
__UpperCamelCase	: int    		=  'Helsinki-NLP/'

if is_torch_available():
					__UpperCamelCase	: List[str]    		=  'pt'
elif is_tf_available():
					__UpperCamelCase	: Union[str, Any]    		=  'tf'
else:
					__UpperCamelCase	: Union[str, Any]    		=  'jax'





@require_sentencepiece
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	MarianTokenizer
						UpperCamelCase_  =	False
						UpperCamelCase_  =	True


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												super().setUp()
												SCREAMING_SNAKE_CASE							:					Dict           =							['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
												SCREAMING_SNAKE_CASE							:					List[Any]           =							dict(zip(UpperCamelCase__					,	range(len(UpperCamelCase__		)		)		)		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							Path(self.tmpdirname		)
												save_json(UpperCamelCase__					,	save_dir / VOCAB_FILES_NAMES['''vocab''']		)
												save_json(UpperCamelCase__					,	save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']		)
												if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
																		copyfile(UpperCamelCase__					,	save_dir / VOCAB_FILES_NAMES['''source_spm''']		)
																		copyfile(UpperCamelCase__					,	save_dir / VOCAB_FILES_NAMES['''target_spm''']		)

												SCREAMING_SNAKE_CASE							:					int           =							MarianTokenizer.from_pretrained(self.tmpdirname		)
												tokenizer.save_pretrained(self.tmpdirname		)


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





												'''simple docstring'''





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


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												return (
												    "This is a test",
												    "This is a test",
												)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''</s>'''
												SCREAMING_SNAKE_CASE							:					Any           =							0

												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			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							list(self.get_tokenizer().get_vocab().keys()		)

												self.assertEqual(vocab_keys[0]					,	'''</s>'''		)
												self.assertEqual(vocab_keys[1]					,	'''<unk>'''		)
												self.assertEqual(vocab_keys[-1]					,	'''<pad>'''		)
												self.assertEqual(len(UpperCamelCase__		)					,	9		)


						def  __A ( self			:       str		):





												'''simple docstring'''





												self.assertEqual(self.get_tokenizer().vocab_size					,	9		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							en_de_tokenizer(['''I am a small frog''']					,	return_tensors=UpperCamelCase__		)
												self.assertIsInstance(UpperCamelCase__					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					int           =							[38, 121, 14, 697, 3_8848, 0]
												self.assertListEqual(UpperCamelCase__					,	batch.input_ids[0]		)

												SCREAMING_SNAKE_CASE							:					Dict           =							tempfile.mkdtemp()
												en_de_tokenizer.save_pretrained(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							[x.name for x in Path(UpperCamelCase__		).glob('''*'''		)]
												self.assertIn('''source.spm'''					,	UpperCamelCase__		)
												MarianTokenizer.from_pretrained(UpperCamelCase__		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tok(
												    ['''I am a small frog''' * 1000, '''I am a small frog''']					,	padding=UpperCamelCase__					,	truncation=UpperCamelCase__					,	return_tensors=UpperCamelCase__		)
												self.assertIsInstance(UpperCamelCase__					,	UpperCamelCase__		)
												self.assertEqual(batch.input_ids.shape					,	(2, 512)		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_tokenizer()
												SCREAMING_SNAKE_CASE							:					int           =							tok(['''I am a tiny frog''', '''I am a small frog''']					,	padding=UpperCamelCase__					,	return_tensors=UpperCamelCase__		)
												self.assertIsInstance(UpperCamelCase__					,	UpperCamelCase__		)
												self.assertEqual(batch_smaller.input_ids.shape					,	(2, 10)		)


						@slow
						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							{'''input_ids''': [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], '''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, 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, 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, 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, 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=UpperCamelCase__					,	model_name='''Helsinki-NLP/opus-mt-en-de'''					,	revision='''1a8c2263da11e68e50938f97e10cd57820bd504c'''					,	decode_kwargs={'''use_source_tokenizer''': True}					,	)



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							'''Tämä on testi'''
												SCREAMING_SNAKE_CASE							:					Dict           =							'''This is a test'''

												SCREAMING_SNAKE_CASE							:					List[Any]           =							[76, 7, 2047, 2]
												SCREAMING_SNAKE_CASE							:					int           =							[69, 12, 11, 940, 2]

												SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer(UpperCamelCase__		).input_ids
												self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							tokenizer(text_target=UpperCamelCase__		).input_ids
												self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							tokenizer.decode(UpperCamelCase__					,	skip_special_tokens=UpperCamelCase__		)
												self.assertEqual(UpperCamelCase__					,	UpperCamelCase__		)






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

__UpperCamelCase	: Dict    		=  random.Random()
def    A			(					_lowercase	,						_lowercase=1.0	,						_lowercase=None	,						_lowercase=None		):
						if rng is None:
												SCREAMING_SNAKE_CASE							:					Any           =							global_rng

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

						return values





class       lowercase__		(     unittest.TestCase):


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       str=7					,	UpperCamelCase__			:       Any=400					,	UpperCamelCase__			:       List[str]=2000					,	UpperCamelCase__			:       List[Any]=2048					,	UpperCamelCase__			:       Any=128					,	UpperCamelCase__			:       Union[str, Any]=1					,	UpperCamelCase__			:       List[Any]=512					,	UpperCamelCase__			:       str=30					,	UpperCamelCase__			:       Tuple=4_4100					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							parent
												SCREAMING_SNAKE_CASE							:					str           =							batch_size
												SCREAMING_SNAKE_CASE							:					str           =							min_seq_length
												SCREAMING_SNAKE_CASE							:					Dict           =							max_seq_length
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							spectrogram_length
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							feature_size
												SCREAMING_SNAKE_CASE							:					Tuple           =							num_audio_channels
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							hop_length
												SCREAMING_SNAKE_CASE							:					List[Any]           =							chunk_length
												SCREAMING_SNAKE_CASE							:					str           =							sampling_rate


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												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  __A ( self			:       Tuple					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       Optional[int]=False		):





												'''simple docstring'''





												def _flatten(UpperCamelCase__			:       str		):
																		return list(itertools.chain(*UpperCamelCase__		)		)

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






@require_torch
@require_torchaudio
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	TvltFeatureExtractor


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							TvltFeatureExtractionTester(self		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.feature_extraction_class(**self.feat_extract_dict		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''spectrogram_length'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''feature_size'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''num_audio_channels'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''hop_length'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''chunk_length'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''sampling_rate'''		)		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							feat_extract_first.save_pretrained(UpperCamelCase__		)[0]
																		check_json_file_has_correct_format(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							self.feature_extraction_class.from_pretrained(UpperCamelCase__		)

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


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							os.path.join(UpperCamelCase__					,	'''feat_extract.json'''		)
																		feat_extract_first.to_json_file(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[str]           =							self.feature_extraction_class.from_json_file(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							feat_extract_first.to_dict()
												SCREAMING_SNAKE_CASE							:					str           =							feat_extract_second.to_dict()
												SCREAMING_SNAKE_CASE							:					int           =							dict_first.pop('''mel_filters'''		)
												SCREAMING_SNAKE_CASE							:					Any           =							dict_second.pop('''mel_filters'''		)
												self.assertTrue(np.allclose(UpperCamelCase__					,	UpperCamelCase__		)		)
												self.assertEqual(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							self.feature_extraction_class(**self.feat_extract_dict		)

												# create three inputs of length 800, 1000, and 1200
												SCREAMING_SNAKE_CASE							:					List[Any]           =							[floats_list((1, x)		)[0] for x in range(800					,	1400					,	200		)]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[np.asarray(UpperCamelCase__		) for speech_input in speech_inputs]

												# Test not batched input
												SCREAMING_SNAKE_CASE							:					List[Any]           =							feature_extractor(np_speech_inputs[0]					,	return_tensors='''np'''					,	sampling_rate=4_4100		).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
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							feature_extractor(UpperCamelCase__					,	return_tensors='''np'''					,	sampling_rate=4_4100		).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
												SCREAMING_SNAKE_CASE							:					List[str]           =							feature_extractor(
												    UpperCamelCase__					,	return_tensors='''np'''					,	sampling_rate=4_4100					,	mask_audio=UpperCamelCase__		).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.
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[floats_list((1, x)		)[0] for x in (800, 800, 800)]
												SCREAMING_SNAKE_CASE							:					Dict           =							np.asarray(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							feature_extractor(UpperCamelCase__					,	return_tensors='''np'''					,	sampling_rate=4_4100		).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  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





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

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



						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self._load_datasamples(1		)
												SCREAMING_SNAKE_CASE							:					int           =							TvltFeatureExtractor()
												SCREAMING_SNAKE_CASE							:					List[Any]           =							feature_extractor(UpperCamelCase__					,	return_tensors='''pt'''		).audio_values

												self.assertEquals(audio_values.shape					,	(1, 1, 192, 128)		)

												SCREAMING_SNAKE_CASE							:					str           =							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
												self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					,	UpperCamelCase__					,	atol=1E-4		)		)





