"""simple docstring"""
import math





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			bool:





						"""simple docstring"""






						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5	,     int(math.sqrt(_UpperCamelCase   ) + 1   )	,     6   ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True





def 				lowerCAmelCase__      (     _UpperCamelCase    :			float = 0.1   )   ->			int:





						"""simple docstring"""






						snake_case        =     3
						snake_case        =     3

						while primes / (2 * j - 1) >= ratio:
												for i in range(j * j + j + 1	,     (j + 2) * (j + 2)	,     j + 1   ):
																		primes += is_prime(_UpperCamelCase   )
												j += 2
						return j


if __name__ == "__main__":
					import doctest

					doctest.testmod()



"""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

SCREAMING_SNAKE_CASE__   =					random.Random()





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			Optional[Any]=1.0	,     _UpperCamelCase    :			Optional[int]=None	,     _UpperCamelCase    :			Optional[int]=None   )   ->			Optional[int]:





						"""simple docstring"""






						if rng is None:
												snake_case        =     global_rng

						snake_case        =     []
						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   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=7						,      lowerCAmelCase=4_00						,      lowerCAmelCase=20_00						,      lowerCAmelCase=20_48						,      lowerCAmelCase=1_28						,      lowerCAmelCase=1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=30						,      lowerCAmelCase=4_41_00						,      ):






										"""simple docstring"""

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

				def 						snake_case	(			self      ):






										"""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 						snake_case	(			self						,      lowerCAmelCase=False						,      lowerCAmelCase=False      ):






										"""simple docstring"""

										def _flatten(lowerCAmelCase      ):
																return list(itertools.chain(*lowerCAmelCase      )      )

										if equal_length:
																snake_case        =     [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        =     [
																    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        =     [np.asarray(lowerCAmelCase      ) for x in speech_inputs]
										return speech_inputs





@require_torch
@require_torchaudio
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  TvltFeatureExtractor

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TvltFeatureExtractionTester(self      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.feature_extraction_class(**self.feat_extract_dict      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'spectrogram_length'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'feature_size'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'num_audio_channels'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'hop_length'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'chunk_length'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'sampling_rate'      )      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

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

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

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

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

										# create three inputs of length 800, 1000, and 1200
										snake_case        =     [floats_list((1, x)      )[0] for x in range(8_00						,      14_00						,      2_00      )]
										snake_case        =     [np.asarray(lowerCAmelCase      ) for speech_input in speech_inputs]

										# Test not batched input
										snake_case        =     feature_extractor(np_speech_inputs[0]						,      return_tensors='np'						,      sampling_rate=4_41_00      ).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        =     feature_extractor(lowerCAmelCase						,      return_tensors='np'						,      sampling_rate=4_41_00      ).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        =     feature_extractor(
										    lowerCAmelCase						,      return_tensors='np'						,      sampling_rate=4_41_00						,      mask_audio=lowerCAmelCase      ).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        =     [floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
										snake_case        =     np.asarray(lowerCAmelCase      )
										snake_case        =     feature_extractor(lowerCAmelCase						,      return_tensors='np'						,      sampling_rate=4_41_00      ).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						,      lowerCAmelCase      ):






										"""simple docstring"""

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

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self._load_datasamples(1      )
										snake_case        =     TvltFeatureExtractor()
										snake_case        =     feature_extractor(lowerCAmelCase						,      return_tensors='pt'      ).audio_values

										self.assertEquals(audio_values.shape						,      (1, 1, 1_92, 1_28)      )

										snake_case        =     torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]      )
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]						,      lowerCAmelCase						,      atol=1E-4      )      )


