
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


_UpperCAmelCase      :				Union[str, Any]          = logging.get_logger(__name__)


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : List[Any]			  =       ["input_values", "padding_mask"]



		def __init__(				self       ,   A_ = 1       ,   A_ = 24_000       ,   A_ = 0.0       ,   A_ = None       ,   A_ = None       ,   **A_       ,   )						->      Union[str, Any]:
				"""simple docstring"""
				super().__init__(feature_size=A_       ,   sampling_rate=A_       ,   padding_value=A_       ,   **A_ )
				UpperCamelCase						=  chunk_length_s
				UpperCamelCase						=  overlap



		@property
		def __UpperCamelCase				(				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 __UpperCamelCase				(				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       ,   A_       ,   A_ = None       ,   A_ = False       ,   A_ = None       ,   A_ = None       ,   A_ = 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
						UpperCamelCase						=  True

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

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

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

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

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

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

				UpperCamelCase						=  input_values
				if return_tensors is not None:
						UpperCamelCase						=  padded_inputs.convert_to_tensors(A_ )

				return padded_inputs


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  int(lowercase				)
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(lowercase				)
		UpperCamelCase			,						UpperCamelCase						=  divmod(lowercase					, 2				)
		return binary_recursive(lowercase				) + str(lowercase				)


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  str(lowercase				).strip()
		if not number:
				raise ValueError('No input value was provided'				)
		UpperCamelCase						=  '-' if number.startswith('-'				) else ''
		UpperCamelCase						=  number.lstrip('-'				)
		if not number.isnumeric():
				raise ValueError('Input value is not an integer'				)
		return f'''{negative}0b{binary_recursive(int(lowercase				)				)}'''


if __name__ == "__main__":
					from doctest import testmod

					testmod()
