


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

lowerCAmelCase       =							random.Random()
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=1.0						,				SCREAMING_SNAKE_CASE=None						,				SCREAMING_SNAKE_CASE=None     ):


		"""simple docstring"""




		if rng is None:
				lowercase__            =      global_rng

		lowercase__            =      []
		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    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Tuple=7    ,						UpperCamelCase_:	Any=400    ,						UpperCamelCase_:	Optional[int]=2_000    ,						UpperCamelCase_:	Tuple=2_048    ,						UpperCamelCase_:	int=128    ,						UpperCamelCase_:	Optional[int]=1    ,						UpperCamelCase_:	List[str]=512    ,						UpperCamelCase_:	str=30    ,						UpperCamelCase_:	int=44_100    ,						)			->   Any:





									"""simple docstring"""







									lowercase__            =      parent
									lowercase__            =      batch_size
									lowercase__            =      min_seq_length
									lowercase__            =      max_seq_length
									lowercase__            =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									lowercase__            =      spectrogram_length
									lowercase__            =      feature_size
									lowercase__            =      num_audio_channels
									lowercase__            =      hop_length
									lowercase__            =      chunk_length
									lowercase__            =      sampling_rate

							def       lowerCamelCase_ (							self:	List[Any]							)			->   int:





									"""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       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	int=False    ,						UpperCamelCase_:	Optional[int]=False							)			->   str:





									"""simple docstring"""







									def _flatten(UpperCamelCase_:	List[Any]							):
											return list(itertools.chain(*UpperCamelCase_							)							)

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


@require_torch
@require_torchaudio
class        _a							(			UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			Optional[int]        = TvltFeatureExtractor

							def       lowerCamelCase_ (							self:	Optional[int]							)			->   str:





									"""simple docstring"""







									lowercase__            =      TvltFeatureExtractionTester(self							)

							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      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       lowerCamelCase_ (							self:	int							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      self.feature_extraction_class(**self.feat_extract_dict							)

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

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

							def       lowerCamelCase_ (							self:	int							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      self.feature_extraction_class(**self.feat_extract_dict							)

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

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

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      self.feature_extraction_class(**self.feat_extract_dict							)

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

									# Test not batched input
									lowercase__            =      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
									lowercase__            =      feature_extractor(UpperCamelCase_    ,						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
									lowercase__            =      feature_extractor(
									    UpperCamelCase_    ,						return_tensors='''np'''    ,						sampling_rate=44_100    ,						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.
									lowercase__            =      [floats_list((1, x)							)[0] for x in (800, 800, 800)]
									lowercase__            =      np.asarray(UpperCamelCase_							)
									lowercase__            =      feature_extractor(UpperCamelCase_    ,						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       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	str							)			->   List[str]:





									"""simple docstring"""







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

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


							def       lowerCamelCase_ (							self:	int							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      self._load_datasamples(1							)
									lowercase__            =      TvltFeatureExtractor()
									lowercase__            =      feature_extractor(UpperCamelCase_    ,						return_tensors='''pt'''							).audio_values

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

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






def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		if exponent == 1:
				return base
		if exponent % 2 == 0:
				lowercase__            =      _modexpt(SCREAMING_SNAKE_CASE						,				exponent // 2						,				SCREAMING_SNAKE_CASE     ) % modulo_value
				return (x * x) % modulo_value
		else:
				return (base * _modexpt(SCREAMING_SNAKE_CASE						,				exponent - 1						,				SCREAMING_SNAKE_CASE     )) % modulo_value
def 			_a (      SCREAMING_SNAKE_CASE = 17_77						,				SCREAMING_SNAKE_CASE = 18_55						,				SCREAMING_SNAKE_CASE = 8     ):


		"""simple docstring"""




		lowercase__            =      base
		for _ in range(1						,				SCREAMING_SNAKE_CASE     ):
				lowercase__            =      _modexpt(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				10**digits     )

		return result


if __name__ == "__main__":
			print(f"""{solution() = }""")


