





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


_lowerCamelCase :    Optional[Any]  							= logging.get_logger(__name__)


class 						lowerCamelCase       (__lowerCamelCase      ):







       """simple docstring"""




       UpperCAmelCase_      						=   ["input_values", "padding_mask"]






       def __init__( self     :					List[Any],			_UpperCAmelCase     :					int = 1,			_UpperCAmelCase     :					int = 2_4_0_0_0,			_UpperCAmelCase     :					float = 0.0,			_UpperCAmelCase     :					float = None,			_UpperCAmelCase     :					float = None,			**_UpperCAmelCase     :					str,			)				->			Tuple:





              """simple docstring"""
              super().__init__(feature_size=_UpperCAmelCase,			sampling_rate=_UpperCAmelCase,			padding_value=_UpperCAmelCase,			**_UpperCAmelCase						)
              SCREAMING_SNAKE_CASE__       :      List[str]     =						chunk_length_s
              SCREAMING_SNAKE_CASE__       :      int     =						overlap






       @property
       def  A_     ( self     :					Optional[int]						)				->			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  A_     ( self     :					Optional[int]						)				->			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     :					Any,			_UpperCAmelCase     :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],			_UpperCAmelCase     :					Optional[Union[bool, str, PaddingStrategy]] = None,			_UpperCAmelCase     :					Optional[bool] = False,			_UpperCAmelCase     :					Optional[int] = None,			_UpperCAmelCase     :					Optional[Union[str, TensorType]] = None,			_UpperCAmelCase     :					Optional[int] = 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
                     SCREAMING_SNAKE_CASE__       :      str     =						True

              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						bool(
                  isinstance(_UpperCAmelCase,			(list, tuple)						) and (isinstance(raw_audio[0],			(np.ndarray, tuple, list)						))						)

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

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

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

        # normal padding on batch
              if padded_inputs is None:
                     SCREAMING_SNAKE_CASE__       :      List[str]     =						self.pad(
                         _UpperCAmelCase,			max_length=_UpperCAmelCase,			truncation=_UpperCAmelCase,			padding=_UpperCAmelCase,			return_attention_mask=_UpperCAmelCase,			)
                     if padding:
                            SCREAMING_SNAKE_CASE__       :      Optional[int]     =						padded_inputs.pop("attention_mask"						)

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

              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						input_values
              if return_tensors is not None:
                     SCREAMING_SNAKE_CASE__       :      int     =						padded_inputs.convert_to_tensors(_UpperCAmelCase						)

              return padded_inputs













def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    str:

							'''simple docstring'''






							if not all(char in "01" for char in bin_string       ):
														raise ValueError("Non-binary value was passed to the function"       )
							if not bin_string:
														raise ValueError("Empty string was passed to the function"       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						""
							while len(SCREAMING_SNAKE_CASE__       ) % 3 != 0:
														SCREAMING_SNAKE_CASE__       :      str     =						"0" + bin_string
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						[
							    bin_string[index : index + 3]
							    for index in range(len(SCREAMING_SNAKE_CASE__       )       )
							    if index % 3 == 0
							]
							for bin_group in bin_string_in_3_list:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						0
														for index, val in enumerate(SCREAMING_SNAKE_CASE__       ):
																					oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE__       )       )
														oct_string += str(SCREAMING_SNAKE_CASE__       )
							return oct_string


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

		testmod()






