






def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if not isinstance(snake_case,	snake_case			):
								raise TypeError('''only integers accepted as input'''			)
				else:
								__magic_name__		:List[Any]   =   str(abs(snake_case			)			)
								__magic_name__		:Dict   =   [list(snake_case			) for char in range(len(snake_case			)			)]
								for index in range(len(snake_case			)			):
												num_transpositions[index].pop(snake_case			)
								return max(
								    int(''''''.join(list(snake_case			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
					__import__("""doctest""").testmod()








'''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


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  ['''input_values''', '''padding_mask''']




						def __init__(   self       ,  _lowercase = 1       ,  _lowercase = 24_000       ,  _lowercase = 0.0       ,  _lowercase = None       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(feature_size=_lowercase       ,  sampling_rate=_lowercase       ,  padding_value=_lowercase       ,  **_lowercase )
												_lowerCAmelCase    		=					chunk_length_s
												_lowerCAmelCase    		=					overlap




						@property
						def    _lowercase      (   self ):

												"""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 ):

												"""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       ,  ):

												"""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
																		_lowerCAmelCase    		=					True

												_lowerCAmelCase    		=					bool(
												    isinstance(_lowercase       ,  (list, tuple) ) and (isinstance(raw_audio[0]       ,  (np.ndarray, tuple, list) )) )

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

												# always return batch
												if not is_batched:
																		_lowerCAmelCase    		=					[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' )

												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					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:
																								_lowerCAmelCase    		=					min(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.floor(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																		elif padding:
																								_lowerCAmelCase    		=					max(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.ceil(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																								_lowerCAmelCase    		=					"""max_length"""
																		else:
																								_lowerCAmelCase    		=					input_values

        # normal padding on batch
												if padded_inputs is None:
																		_lowerCAmelCase    		=					self.pad(
																		    _lowercase       ,  max_length=_lowercase       ,  truncation=_lowercase       ,  padding=_lowercase       ,  return_attention_mask=_lowercase       ,  )
																		if padding:
																								_lowerCAmelCase    		=					padded_inputs.pop("""attention_mask""" )

												_lowerCAmelCase    		=					[]
												for example in padded_inputs.pop("""input_values""" ):
																		if self.feature_size == 1:
																								_lowerCAmelCase    		=					example[..., None]
																		input_values.append(example.T )

												_lowerCAmelCase    		=					input_values
												if return_tensors is not None:
																		_lowerCAmelCase    		=					padded_inputs.convert_to_tensors(_lowercase )

												return padded_inputs
