





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


lowercase      :       List[Any]									=  logging.get_logger(__name__)





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				int         =  ['''input_values''', '''padding_mask''']

   def __init__(					self					,  lowercase = 1					,  lowercase = 2_4000					,  lowercase = 0.0					,  lowercase = None					,  lowercase = None					,  **lowercase					,  )			->						Tuple:







          '''simple docstring'''

          super().__init__(feature_size=lowercase					,  sampling_rate=lowercase					,  padding_value=lowercase					,  **lowercase)
          a__						:	int =				chunk_length_s
          a__						:	Tuple =				overlap

   @property
   def 							__lowercase		(					self)			->						Optional[int]:







          '''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)			->						Optional[int]:







          '''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					,  lowercase					,  lowercase = None					,  lowercase = False					,  lowercase = None					,  lowercase = None					,  lowercase = None					,  )			->						BatchFeature:







          '''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
                 a__						:	Tuple =				True

          a__						:	int =				bool(
              isinstance(lowercase					,  (list, tuple)) and (isinstance(raw_audio[0]					,  (np.ndarray, tuple, list))))

          if is_batched:
                 a__						:	List[Any] =				[np.asarray(lowercase					,  dtype=np.floataa).T for audio in raw_audio]
          elif not is_batched and not isinstance(lowercase					,  np.ndarray):
                 a__						:	int =				np.asarray(lowercase					,  dtype=np.floataa)
          elif isinstance(lowercase					,  np.ndarray) and raw_audio.dtype is np.dtype(np.floataa):
                 a__						:	Optional[Any] =				raw_audio.astype(np.floataa)

          # always return batch
          if not is_batched:
                 a__						:	List[str] =				[np.asarray(lowercase).T]

          # verify inputs are valid
          for idx, example in enumerate(lowercase):
                 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')

          a__						:	str =				None
          a__						:	List[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:
                        a__						:	Tuple =				min(array.shape[0] for array in raw_audio)
                        a__						:	str =				int(np.floor(max_length / self.chunk_stride))
                        a__						:	Union[str, Any] =				(nb_step - 1) * self.chunk_stride + self.chunk_length
                 elif padding:
                        a__						:	Tuple =				max(array.shape[0] for array in raw_audio)
                        a__						:	Dict =				int(np.ceil(max_length / self.chunk_stride))
                        a__						:	Dict =				(nb_step - 1) * self.chunk_stride + self.chunk_length
                        a__						:	str =				'max_length'
                 else:
                        a__						:	List[str] =				input_values

        # normal padding on batch
          if padded_inputs is None:
                 a__						:	Optional[int] =				self.pad(
                     lowercase					,  max_length=lowercase					,  truncation=lowercase					,  padding=lowercase					,  return_attention_mask=lowercase					,  )
                 if padding:
                        a__						:	List[Any] =				padded_inputs.pop('attention_mask')

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

          a__						:	Optional[Any] =				input_values
          if return_tensors is not None:
                 a__						:	Tuple =				padded_inputs.convert_to_tensors(lowercase)

          return padded_inputs









def A_	(		A__		,      A__				)  ->		str:
       if a < 0 or b < 0:
              raise ValueError('the value of both inputs must be positive'				)

       a__						:	List[str] =				str(bin(A__				)				)[2:]  # remove the leading "0b"
       a__						:	Optional[int] =				str(bin(A__				)				)[2:]  # remove the leading "0b"

       a__						:	List[str] =				max(len(A__				)		,      len(A__				)				)

       return "0b" + "".join(
           str(int(char_a == '1' and char_b == '1'				)				)
           for char_a, char_b in zip(a_binary.zfill(A__				)		,      b_binary.zfill(A__				)				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


