




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__ :   str						=							random.Random()





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     str=1.0  ,							lowerCamelCase_ :     Optional[Any]=None  ,							lowerCamelCase_ :     Dict=None  )       ->  Dict:







			"""simple docstring"""





			if rng is None:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      global_rng

			SCREAMING_SNAKE_CASE_   : List[Any]					  =      []
			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      ,snake_case__      ,snake_case__=7      ,snake_case__=400      ,snake_case__=2000      ,snake_case__=2048      ,snake_case__=128      ,snake_case__=1      ,snake_case__=512      ,snake_case__=30      ,snake_case__=44100      ,):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      parent
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      batch_size
							SCREAMING_SNAKE_CASE_   : Dict					  =      min_seq_length
							SCREAMING_SNAKE_CASE_   : Any					  =      max_seq_length
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							SCREAMING_SNAKE_CASE_   : Tuple					  =      spectrogram_length
							SCREAMING_SNAKE_CASE_   : Tuple					  =      feature_size
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      num_audio_channels
							SCREAMING_SNAKE_CASE_   : int					  =      hop_length
							SCREAMING_SNAKE_CASE_   : str					  =      chunk_length
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      sampling_rate






				def 		snake_case	(			self ):
							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      ,snake_case__=False      ,snake_case__=False ):
							def _flatten(snake_case__ ):
										return list(itertools.chain(*snake_case__ ) )

							if equal_length:
										SCREAMING_SNAKE_CASE_   : Any					  =      [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_   : List[str]					  =      [
										    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[int]					  =      [np.asarray(snake_case__ ) for x in speech_inputs]
							return speech_inputs







@require_torch
@require_torchaudio
class 				lowerCAmelCase_       (					lowerCamelCase_						,     unittest.TestCase							):
				__a     :       int						=     TvltFeatureExtractor






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      TvltFeatureExtractionTester(self )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.feature_extraction_class(**self.feat_extract_dict )
							self.assertTrue(hasattr(snake_case__      ,'spectrogram_length' ) )
							self.assertTrue(hasattr(snake_case__      ,'feature_size' ) )
							self.assertTrue(hasattr(snake_case__      ,'num_audio_channels' ) )
							self.assertTrue(hasattr(snake_case__      ,'hop_length' ) )
							self.assertTrue(hasattr(snake_case__      ,'chunk_length' ) )
							self.assertTrue(hasattr(snake_case__      ,'sampling_rate' ) )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Any					  =      self.feature_extraction_class(**self.feat_extract_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      feat_extract_first.save_pretrained(snake_case__ )[0]
										check_json_file_has_correct_format(snake_case__ )
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.feature_extraction_class.from_pretrained(snake_case__ )

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






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.feature_extraction_class(**self.feat_extract_dict )

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

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






				def 		snake_case	(			self ):
							# Initialize feature_extractor
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.feature_extraction_class(**self.feat_extract_dict )

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

							# Test not batched input
							SCREAMING_SNAKE_CASE_   : Dict					  =      feature_extractor(np_speech_inputs[0]      ,return_tensors='np'      ,sampling_rate=44100 ).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_   : Union[str, Any]					  =      feature_extractor(snake_case__      ,return_tensors='np'      ,sampling_rate=44100 ).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_   : Optional[int]					  =      feature_extractor(
							    snake_case__      ,return_tensors='np'      ,sampling_rate=44100      ,mask_audio=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.
							SCREAMING_SNAKE_CASE_   : int					  =      [floats_list((1, x) )[0] for x in (800, 800, 800)]
							SCREAMING_SNAKE_CASE_   : Any					  =      np.asarray(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      feature_extractor(snake_case__      ,return_tensors='np'      ,sampling_rate=44100 ).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      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      load_dataset('hf-internal-testing/librispeech_asr_dummy'      ,'clean'      ,split='validation' )
							# automatic decoding with librispeech
							SCREAMING_SNAKE_CASE_   : Dict					  =      ds.sort('id' ).select(range(snake_case__ ) )[:num_samples]['audio']

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






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      self._load_datasamples(1 )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      TvltFeatureExtractor()
							SCREAMING_SNAKE_CASE_   : List[str]					  =      feature_extractor(snake_case__      ,return_tensors='pt' ).audio_values

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

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









import numpy as np
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      int(np.ceil((x_end - xa) / h				)				)
  lowerCAmelCase__										=      np.zeros((n + 1,)				)
  lowerCAmelCase__										=      ya
  lowerCAmelCase__										=      xa

  for k in range(__lowerCAmelCase				):
    lowerCAmelCase__										=      f(__lowerCAmelCase   , y[k]				)
    lowerCAmelCase__										=      f(x + 0.5 * h   , y[k] + 0.5 * h * ka				)
    lowerCAmelCase__										=      f(x + 0.5 * h   , y[k] + 0.5 * h * ka				)
    lowerCAmelCase__										=      f(x + h   , y[k] + h * ka				)
    lowerCAmelCase__										=      y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
    x += h

  return y


if __name__ == "__main__":
      import doctest

      doctest.testmod()

