





def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   str:


      '''simple docstring'''


      if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	):
            raise TypeError("""'float' object cannot be interpreted as an integer"""	)
      if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	):
            raise TypeError("""'str' object cannot be interpreted as an integer"""	)

      if num == 0:
            return "0b0"

      SCREAMING_SNAKE_CASE		=						False

      if num < 0:
            SCREAMING_SNAKE_CASE		=						True
            SCREAMING_SNAKE_CASE		=						-num

      SCREAMING_SNAKE_CASE		=						[]
      while num > 0:
            binary.insert(0 ,						num % 2	)
            num >>= 1

      if negative:
            return "-0b" + "".join(str(_SCREAMING_SNAKE_CASE	) for e in binary	)

      return "0b" + "".join(str(_SCREAMING_SNAKE_CASE	) for e in binary	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()









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    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=1.0 ,						_SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   Tuple:


      '''simple docstring'''


      if rng is None:
            SCREAMING_SNAKE_CASE		=						global_rng

      SCREAMING_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 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

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




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Optional[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 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]						,lowerCamelCase__						:    Tuple=False						,lowerCamelCase__						:    Union[str, Any]=False       )     ->		str:




           '''simple docstring'''


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

           if equal_length:
                 SCREAMING_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
                 SCREAMING_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:
                 SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__       ) for x in speech_inputs]
           return speech_inputs

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



     '''simple docstring'''

     __snake_case    :	List[Any]			=			TvltFeatureExtractor






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TvltFeatureExtractionTester(self       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Any:




           '''simple docstring'''


           SCREAMING_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 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Union[str, Any]:




           '''simple docstring'''


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

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

           SCREAMING_SNAKE_CASE		=						feat_extract_first.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_second.to_dict()
           SCREAMING_SNAKE_CASE		=						dict_first.pop("""mel_filters"""       )
           SCREAMING_SNAKE_CASE		=						dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__       )       )
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Any:




           '''simple docstring'''


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

           with tempfile.TemporaryDirectory() as tmpdirname:
                 SCREAMING_SNAKE_CASE		=						os.path.join(lowerCamelCase__						,"""feat_extract.json"""       )
                 feat_extract_first.to_json_file(lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						self.feature_extraction_class.from_json_file(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						feat_extract_first.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_second.to_dict()
           SCREAMING_SNAKE_CASE		=						dict_first.pop("""mel_filters"""       )
           SCREAMING_SNAKE_CASE		=						dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__       )       )
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__       )






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




           '''simple docstring'''


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

           # create three inputs of length 800, 1000, and 1200
           SCREAMING_SNAKE_CASE		=						[floats_list((1, x)       )[0] for x in range(800						,1400						,200       )]
           SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__       ) for speech_input in speech_inputs]

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						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		=						feature_extractor(lowerCamelCase__						,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		=						feature_extractor(
               lowerCamelCase__						,return_tensors="""np"""						,sampling_rate=44100						,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.
           SCREAMING_SNAKE_CASE		=						[floats_list((1, x)       )[0] for x in (800, 800, 800)]
           SCREAMING_SNAKE_CASE		=						np.asarray(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,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 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    str       )     ->		Any:




           '''simple docstring'''


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

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._load_datasamples(1       )
           SCREAMING_SNAKE_CASE		=						TvltFeatureExtractor()
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""pt"""       ).audio_values

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

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


