





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			:	List[Any]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :Optional[Any]		       =  ['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=A_			,				sampling_rate=A_			,				padding_value=A_			,				**A_  )
       UpperCamelCase      :   List[Any]			      =  chunk_length_s
       UpperCamelCase      :   Optional[int]			      =  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
        UpperCamelCase      :   List[Any]			      =  True

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

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

       # always return batch
       if not is_batched:
        UpperCamelCase      :   Any			      =  [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      :   Optional[Any]			      =  None
       UpperCamelCase      :   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:
         UpperCamelCase      :   Union[str, Any]			      =  min(array.shape[0] for array in raw_audio  )
         UpperCamelCase      :   Optional[Any]			      =  int(np.floor(max_length / self.chunk_stride  )  )
         UpperCamelCase      :   Tuple			      =  (nb_step - 1) * self.chunk_stride + self.chunk_length
        elif padding:
         UpperCamelCase      :   Union[str, Any]			      =  max(array.shape[0] for array in raw_audio  )
         UpperCamelCase      :   Tuple			      =  int(np.ceil(max_length / self.chunk_stride  )  )
         UpperCamelCase      :   Tuple			      =  (nb_step - 1) * self.chunk_stride + self.chunk_length
         UpperCamelCase      :   Optional[Any]			      =  "max_length"
        else:
         UpperCamelCase      :   Optional[int]			      =  input_values

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

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

       UpperCamelCase      :   Optional[int]			      =  input_values
       if return_tensors is not None:
        UpperCamelCase      :   Optional[Any]			      =  padded_inputs.convert_to_tensors(A_  )

       return padded_inputs







__lowerCamelCase			:	str					    =	6_5521



def 						A_							(	_lowerCAmelCase	)		->					int:
 UpperCamelCase      :   Any			      =  1
 UpperCamelCase      :   str			      =  0
 for plain_chr in plain_text:
  UpperCamelCase      :   List[Any]			      =  (a + ord(_lowerCAmelCase	)) % MOD_ADLER
  UpperCamelCase      :   List[Any]			      =  (b + a) % MOD_ADLER
 return (b << 16) | a
