


'''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     :						Tuple	=     logging.get_logger(__name__)





class 							__UpperCamelCase    (		lowercase__ ):
	A_           =   ['input_values', 'padding_mask']




	def __init__(       self						,    __a = 1						,    __a = 2_4000						,    __a = 0.0						,    __a = None						,    __a = None						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(feature_size=_UpperCamelCase						,    sampling_rate=_UpperCamelCase						,    padding_value=_UpperCamelCase						,    **_UpperCamelCase							)
						__a      :	Dict    =  chunk_length_s
						__a      :	str    =  overlap




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						if self.chunk_length_s is None:
											return None
						else:
											return int(self.chunk_length_s * self.sampling_rate							)




	@property
	def 				__UpperCAmelCase							(       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						,    __a						,    __a = None						,    __a = False						,    __a = None						,    __a = None						,    __a = 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
											__a      :	Tuple    =  True

						__a      :	str    =  bool(
						    isinstance(_UpperCamelCase						,    (list, tuple)							) and (isinstance(raw_audio[0]						,    (np.ndarray, tuple, list)							))							)

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

						# always return batch
						if not is_batched:
											__a      :	Optional[Any]    =  [np.asarray(_UpperCamelCase							).T]

						# verify inputs are valid
						for idx, example in enumerate(_UpperCamelCase							):
											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      :	Tuple    =  None
						__a      :	Optional[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      :	Union[str, Any]    =  min(array.shape[0] for array in raw_audio							)
																__a      :	Dict    =  int(np.floor(max_length / self.chunk_stride							)							)
																__a      :	Union[str, Any]    =  (nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
																__a      :	Any    =  max(array.shape[0] for array in raw_audio							)
																__a      :	List[Any]    =  int(np.ceil(max_length / self.chunk_stride							)							)
																__a      :	Any    =  (nb_step - 1) * self.chunk_stride + self.chunk_length
																__a      :	Union[str, Any]    =  """max_length"""
											else:
																__a      :	int    =  input_values

        # normal padding on batch
						if padded_inputs is None:
											__a      :	Optional[int]    =  self.pad(
											    _UpperCamelCase						,    max_length=_UpperCamelCase						,    truncation=_UpperCamelCase						,    padding=_UpperCamelCase						,    return_attention_mask=_UpperCamelCase						,    )
											if padding:
																__a      :	Tuple    =  padded_inputs.pop('attention_mask'							)

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

						__a      :	List[Any]    =  input_values
						if return_tensors is not None:
											__a      :	Tuple    =  padded_inputs.convert_to_tensors(_UpperCamelCase							)

						return padded_inputs







'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int       ):
					return number & 1 == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()



