






'''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

_UpperCAmelCase			:		Dict   				=    random.Random()





def 	lowerCAmelCase_		(lowercase__						:       int     ,					lowercase__						:       Optional[int]=1.0     ,					lowercase__						:       Tuple=None     ,					lowercase__						:       List[str]=None       )		->    Any:


				'''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
class  lowerCAmelCase_       (				unittest.TestCase					):




							def __init__(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=7 ,							SCREAMING_SNAKE_CASE_     :   List[str]=400 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=2_000 ,							SCREAMING_SNAKE_CASE_     :   List[str]=2_048 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=128 ,							SCREAMING_SNAKE_CASE_     :   Dict=1 ,							SCREAMING_SNAKE_CASE_     :   List[str]=512 ,							SCREAMING_SNAKE_CASE_     :   Any=30 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=44_100 ,							):
											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__      			=						spectrogram_length
											lowerCAmelCase__      			=						feature_size
											lowerCAmelCase__      			=						num_audio_channels
											lowerCAmelCase__      			=						hop_length
											lowerCAmelCase__      			=						chunk_length
											lowerCAmelCase__      			=						sampling_rate




							def 						__snake_case					(   self     :   Any	):
											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 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   List[str]=False ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=False	):
											def _flatten(SCREAMING_SNAKE_CASE_     :   int	):
															return list(itertools.chain(*SCREAMING_SNAKE_CASE_	)	)

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



@require_torch
@require_torchaudio
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:str		      =    TvltFeatureExtractor




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						TvltFeatureExtractionTester(self	)




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						self.feature_extraction_class(**self.feat_extract_dict	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''spectrogram_length'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''feature_size'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''num_audio_channels'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''hop_length'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''chunk_length'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''sampling_rate'''	)	)




							def 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						self.feature_extraction_class(**self.feat_extract_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
															lowerCAmelCase__      			=						feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE_	)[0]
															check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_	)
															lowerCAmelCase__      			=						self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						feat_extract_first.to_dict()
											lowerCAmelCase__      			=						feat_extract_second.to_dict()
											lowerCAmelCase__      			=						dict_first.pop('''mel_filters'''	)
											lowerCAmelCase__      			=						dict_second.pop('''mel_filters'''	)
											self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)	)
											self.assertEqual(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						self.feature_extraction_class(**self.feat_extract_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
															lowerCAmelCase__      			=						os.path.join(SCREAMING_SNAKE_CASE_ ,							'''feat_extract.json'''	)
															feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE_	)
															lowerCAmelCase__      			=						self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						feat_extract_first.to_dict()
											lowerCAmelCase__      			=						feat_extract_second.to_dict()
											lowerCAmelCase__      			=						dict_first.pop('''mel_filters'''	)
											lowerCAmelCase__      			=						dict_second.pop('''mel_filters'''	)
											self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)	)
											self.assertEqual(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[str]	):
											# Initialize feature_extractor
											lowerCAmelCase__      			=						self.feature_extraction_class(**self.feat_extract_dict	)

											# create three inputs of length 800, 1000, and 1200
											lowerCAmelCase__      			=						[floats_list((1, x)	)[0] for x in range(800 ,							1_400 ,							200	)]
											lowerCAmelCase__      			=						[np.asarray(SCREAMING_SNAKE_CASE_	) for speech_input in speech_inputs]

											# Test not batched input
											lowerCAmelCase__      			=						feature_extractor(np_speech_inputs[0] ,							return_tensors='''np''' ,							sampling_rate=44_100	).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
											lowerCAmelCase__      			=						feature_extractor(SCREAMING_SNAKE_CASE_ ,							return_tensors='''np''' ,							sampling_rate=44_100	).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
											lowerCAmelCase__      			=						feature_extractor(
											    SCREAMING_SNAKE_CASE_ ,							return_tensors='''np''' ,							sampling_rate=44_100 ,							mask_audio=SCREAMING_SNAKE_CASE_	).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.
											lowerCAmelCase__      			=						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
											lowerCAmelCase__      			=						np.asarray(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						feature_extractor(SCREAMING_SNAKE_CASE_ ,							return_tensors='''np''' ,							sampling_rate=44_100	).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 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Dict	):
											lowerCAmelCase__      			=						load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,							'''clean''' ,							split='''validation'''	)
											# automatic decoding with librispeech
											lowerCAmelCase__      			=						ds.sort('''id'''	).select(range(SCREAMING_SNAKE_CASE_	)	)[:num_samples]['''audio''']

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




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						self._load_datasamples(1	)
											lowerCAmelCase__      			=						TvltFeatureExtractor()
											lowerCAmelCase__      			=						feature_extractor(SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	).audio_values

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

											lowerCAmelCase__      			=						torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]	)
											self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)













import pytest

import datasets


# Import fixture modules as plugins
_UpperCAmelCase			:		Tuple   				=    ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,					lowercase__						:       int       )		->    Any:


				'''simple docstring'''




				for item in items:
								if any(marker in item.keywords for marker in ['''integration''', '''unit''']       ):
												continue
								item.add_marker(pytest.mark.unit       )





def 	lowerCAmelCase_		(lowercase__						:       str       )		->    List[str]:


				'''simple docstring'''




				config.addinivalue_line('''markers'''     ,					'''torchaudio_latest: mark test to run with torchaudio>=0.12'''       )





@pytest.fixture(autouse=lowercase__       )
def 	lowerCAmelCase_		(lowercase__						:       int     ,					lowercase__						:       Dict       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						tmp_path_factory.getbasetemp() / '''cache'''
				lowerCAmelCase__      			=						test_hf_cache_home / '''datasets'''
				lowerCAmelCase__      			=						test_hf_cache_home / '''metrics'''
				lowerCAmelCase__      			=						test_hf_cache_home / '''modules'''
				monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''     ,					str(lowercase__       )       )
				monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''     ,					str(lowercase__       )       )
				monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''     ,					str(lowercase__       )       )
				lowerCAmelCase__      			=						test_hf_datasets_cache / '''downloads'''
				monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''     ,					str(lowercase__       )       )
				lowerCAmelCase__      			=						test_hf_datasets_cache / '''downloads''' / '''extracted'''
				monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''     ,					str(lowercase__       )       )





@pytest.fixture(autouse=lowercase__     ,					scope='''session'''       )
def 	lowerCAmelCase_		()		->    int:


				'''simple docstring'''




				datasets.disable_progress_bar()





@pytest.fixture(autouse=lowercase__       )
def 	lowerCAmelCase_		(lowercase__						:       Dict       )		->    List[str]:


				'''simple docstring'''




				monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''     ,					lowercase__       )





@pytest.fixture
def 	lowerCAmelCase_		(lowercase__						:       List[str]       )		->    List[Any]:


				'''simple docstring'''




				monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''     ,					lowercase__       )





