




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    :		List[Any]			   =       random.Random()

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple     ,							lowercase     :	Optional[int]=1.0     ,							lowercase     :	Optional[int]=None     ,							lowercase     :	str=None					):


      '''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   A(						unittest.TestCase       ):



 '''simple docstring'''


 def __init__(    self :  Optional[Any]						,							A_ :  Optional[Any]						,							A_ :  Dict=7						,							A_ :  List[str]=400						,							A_ :  List[str]=2000						,							A_ :  str=2048						,							A_ :  Optional[Any]=128						,							A_ :  Tuple=1						,							A_ :  str=512						,							A_ :  Optional[int]=30						,							A_ :  int=44100						,							)       ->     Dict:

       """simple docstring"""






       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     a__			(    self :  Union[str, Any]  )       ->     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 :  int						,							A_ :  Tuple=False						,							A_ :  int=False  )       ->     Dict:

       """simple docstring"""






       def _flatten(A_ :  Dict  ):
             return list(itertools.chain(*A_  )  )

       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(A_  ) for x in speech_inputs]
       return speech_inputs







@require_torch
@require_torchaudio
class   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						TvltFeatureExtractor

 def     a__			(    self :  Optional[Any]  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   TvltFeatureExtractionTester(self  )

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

       """simple docstring"""






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

 def     a__			(    self :  Tuple  )       ->     Tuple:

       """simple docstring"""






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

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

       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(A_						,							A_  )  )
       self.assertEqual(A_						,							A_  )

 def     a__			(    self :  Tuple  )       ->     List[str]:

       """simple docstring"""






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

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

       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(A_						,							A_  )  )
       self.assertEqual(A_						,							A_  )

 def     a__			(    self :  Optional[int]  )       ->     Tuple:

       """simple docstring"""






       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						,							1400						,							200  )]
       lowerCamelCase_							=   [np.asarray(A_  ) for speech_input in speech_inputs]

       # Test not batched input
       lowerCamelCase_							=   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
       lowerCamelCase_							=   feature_extractor(A_						,							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
       lowerCamelCase_							=   feature_extractor(
           A_						,							return_tensors='np'						,							sampling_rate=44100						,							mask_audio=A_  ).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(A_  )
       lowerCamelCase_							=   feature_extractor(A_						,							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     a__			(    self :  Union[str, Any]						,							A_ :  str  )       ->     List[Any]:

       """simple docstring"""






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

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





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

       """simple docstring"""






       lowerCamelCase_							=   self._load_datasamples(1  )
       lowerCamelCase_							=   TvltFeatureExtractor()
       lowerCamelCase_							=   feature_extractor(A_						,							return_tensors='pt'  ).audio_values

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

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









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



      if not all(char in '01' for char in bin_string					):
            raise ValueError('Non-binary value was passed to the function'					)
      if not bin_string:
            raise ValueError('Empty string was passed to the function'					)
      lowerCamelCase_							=   ''
      while len(lowercase					) % 3 != 0:
            lowerCamelCase_							=   '0' + bin_string
      lowerCamelCase_							=   [
          bin_string[index : index + 3]
          for index in range(len(lowercase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            lowerCamelCase_							=   0
            for index, val in enumerate(lowercase					):
                  oct_val += int(2 ** (2 - index) * int(lowercase					)					)
            oct_string += str(lowercase					)
      return oct_string


if __name__ == "__main__":
 from doctest import testmod

 testmod()



