


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 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  List[Any]       =		['''input_values''', '''padding_mask''']
      def __init__( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : int = 2_4_0_0_0  ,							SCREAMING_SNAKE_CASE__       : float = 0.0  ,							SCREAMING_SNAKE_CASE__       : float = None  ,							SCREAMING_SNAKE_CASE__       : float = None  ,							**SCREAMING_SNAKE_CASE__       : Any  ,							)	->    Union[str, Any]:
             super().__init__(feature_size=SCREAMING_SNAKE_CASE__  ,							sampling_rate=SCREAMING_SNAKE_CASE__  ,							padding_value=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[int]							=						chunk_length_s
             a_      :	str							=						overlap
      @property
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Optional[int]:
             if self.chunk_length_s is None:
                    return None
             else:
                    return int(self.chunk_length_s * self.sampling_rate  )
      @property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[int]:
             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       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[bool, str, PaddingStrategy]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[bool] = False  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[str, TensorType]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							)	->    BatchFeature:
             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_      :	int							=						True

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

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

             # always return batch
             if not is_batched:
                    a_      :	Dict							=						[np.asarray(SCREAMING_SNAKE_CASE__  ).T]

             # verify inputs are valid
             for idx, example in enumerate(SCREAMING_SNAKE_CASE__  ):
                    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_      :	Dict							=						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_      :	Dict							=						min(array.shape[0] for array in raw_audio  )
                           a_      :	Union[str, Any]							=						int(np.floor(max_length / self.chunk_stride  )  )
                           a_      :	Any							=						(nb_step - 1) * self.chunk_stride + self.chunk_length
                    elif padding:
                           a_      :	Union[str, Any]							=						max(array.shape[0] for array in raw_audio  )
                           a_      :	Optional[int]							=						int(np.ceil(max_length / self.chunk_stride  )  )
                           a_      :	int							=						(nb_step - 1) * self.chunk_stride + self.chunk_length
                           a_      :	Optional[Any]							=						'max_length'
                    else:
                           a_      :	Union[str, Any]							=						input_values

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

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

             a_      :	List[Any]							=						input_values
             if return_tensors is not None:
                    a_      :	Optional[Any]							=						padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE__  )

             return padded_inputs






def  SCREAMING_SNAKE_CASE_		(    __A				:    str       )			->	list:

       """simple docstring"""

       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__A       )       )
           if txt[a].isalpha()
       ]


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


