
"""simple docstring"""




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


A__   :					int							=   logging.get_logger(__name__)


class 			lowercase_       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		['''input_values''', '''padding_mask''']
       def __init__(     self ,				A_ = 1 ,				A_ = 2_4000 ,				A_ = 0.0 ,				A_ = None ,				A_ = None ,				**A_ ,				)     ->	List[str]:





         """simple docstring"""




         super().__init__(feature_size=A_ ,				sampling_rate=A_ ,				padding_value=A_ ,				**A_       )
         _lowercase:			Any       =     chunk_length_s
         _lowercase:			Optional[int]       =     overlap
       @property
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if self.chunk_length_s is None:
           return None
         else:
           return int(self.chunk_length_s * self.sampling_rate       )
       @property
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         if self.chunk_length_s is None or self.overlap is None:
           return None
         else:
           return max(1 ,				int((1.0 - self.overlap) * self.chunk_length       )       )



       def __call__(     self ,				A_ ,				A_ = None ,				A_ = False ,				A_ = None ,				A_ = None ,				A_ = None ,				)     ->	int:





         """simple docstring"""




         if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
             raise ValueError(
                 f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                 f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
                 f''' {self.sampling_rate} and not {sampling_rate}.'''       )
         else:
           logger.warning(
               '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
               '''Failing to do so can result in silent errors that might be hard to debug.'''       )

         if padding and truncation:
           raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''       )
         elif padding is None:
           # by default let's pad the inputs
           _lowercase:			Any       =     True

         _lowercase:			List[Any]       =     bool(
             isinstance(A_ ,				(list, tuple)       ) and (isinstance(raw_audio[0] ,				(np.ndarray, tuple, list)       ))       )

         if is_batched:
           _lowercase:			Tuple       =     [np.asarray(A_ ,				dtype=np.floataa       ).T for audio in raw_audio]
         elif not is_batched and not isinstance(A_ ,				np.ndarray       ):
           _lowercase:			str       =     np.asarray(A_ ,				dtype=np.floataa       )
         elif isinstance(A_ ,				np.ndarray       ) and raw_audio.dtype is np.dtype(np.floataa       ):
           _lowercase:			List[str]       =     raw_audio.astype(np.floataa       )

         # always return batch
         if not is_batched:
           _lowercase:			str       =     [np.asarray(A_       ).T]

         # verify inputs are valid
         for idx, example in enumerate(A_       ):
           if example.ndim > 2:
             raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}'''       )
           if self.feature_size == 1 and example.ndim != 1:
             raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels'''       )
           if self.feature_size == 2 and example.shape[-1] != 2:
             raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels'''       )

         _lowercase:			Union[str, Any]       =     None
         _lowercase:			Any       =     BatchFeature({'''input_values''': raw_audio}       )
         if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
           if truncation:
             _lowercase:			Any       =     min(array.shape[0] for array in raw_audio       )
             _lowercase:			int       =     int(np.floor(max_length / self.chunk_stride       )       )
             _lowercase:			Dict       =     (nb_step - 1) * self.chunk_stride + self.chunk_length
           elif padding:
             _lowercase:			str       =     max(array.shape[0] for array in raw_audio       )
             _lowercase:			Optional[int]       =     int(np.ceil(max_length / self.chunk_stride       )       )
             _lowercase:			Dict       =     (nb_step - 1) * self.chunk_stride + self.chunk_length
             _lowercase:			Any       =     'max_length'
           else:
             _lowercase:			List[str]       =     input_values

        # normal padding on batch
         if padded_inputs is None:
           _lowercase:			Any       =     self.pad(
               A_ ,				max_length=A_ ,				truncation=A_ ,				padding=A_ ,				return_attention_mask=A_ ,				)
           if padding:
             _lowercase:			str       =     padded_inputs.pop('''attention_mask'''       )

         _lowercase:			Optional[int]       =     []
         for example in padded_inputs.pop('''input_values'''       ):
           if self.feature_size == 1:
             _lowercase:			Any       =     example[..., None]
           input_values.append(example.T       )

         _lowercase:			List[Any]       =     input_values
         if return_tensors is not None:
           _lowercase:			List[str]       =     padded_inputs.convert_to_tensors(A_       )

         return padded_inputs


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return number | (1 << position)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return number & ~(1 << position)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return number ^ (1 << position)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return ((number >> position) & 1) == 1




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return int((number & (1 << position)) != 0						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
