


"""simple docstring"""

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCAmelCase__  							=logging.get_logger(__name__)





class    lowerCamelCase__						(		_a    ):
      a :     Optional[Any]											=		["""input_features"""]
      def __init__(		self	:	Optional[Any]      ,       A_	:	List[Any]=8_0      ,       A_	:	Any=1_6_0_0_0      ,       A_	:	Any=1_6_0      ,       A_	:	int=3_0      ,       A_	:	Union[str, Any]=4_0_0      ,       A_	:	Tuple=0.0      ,       A_	:	Any=False      ,       **A_	:	List[str]      ,       ):






       '''simple docstring'''


       super().__init__(
           feature_size=A_      ,       sampling_rate=A_      ,       padding_value=A_      ,       return_attention_mask=A_      ,       **A_      ,       )
       __lowercase									=	n_fft
       __lowercase									=	hop_length
       __lowercase									=	chunk_length
       __lowercase									=	chunk_length * sampling_rate
       __lowercase									=	self.n_samples // hop_length
       __lowercase									=	sampling_rate
       __lowercase									=	mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2      ,       num_mel_filters=A_      ,       min_frequency=0.0      ,       max_frequency=80_00.0      ,       sampling_rate=A_      ,       norm="""slaney"""      ,       mel_scale="""slaney"""      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	np.array							):






       '''simple docstring'''


       __lowercase									=	spectrogram(
           A_      ,       window_function(self.n_fft      ,       """hann"""							)      ,       frame_length=self.n_fft      ,       hop_length=self.hop_length      ,       power=2.0      ,       mel_filters=self.mel_filters      ,       log_mel="""log10"""      ,       )
       __lowercase									=	log_spec[:, :-1]
       __lowercase									=	np.maximum(A_      ,       log_spec.max() - 8.0							)
       __lowercase									=	(log_spec + 4.0) / 4.0
       return log_spec
      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def SCREAMING_SNAKE_CASE_	(		A_	:	List[np.ndarray]      ,       A_	:	List[np.ndarray]      ,       A_	:	float = 0.0							):






       '''simple docstring'''


       if attention_mask is not None:
        __lowercase									=	np.array(A_      ,       np.intaa							)
        __lowercase									=	[]

        for vector, length in zip(A_      ,       attention_mask.sum(-1							)							):
         __lowercase									=	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7							)
         if length < normed_slice.shape[0]:
          __lowercase									=	padding_value

         normed_input_values.append(A_							)
       else:
        __lowercase									=	[(x - x.mean()) / np.sqrt(x.var() + 1e-7							) for x in input_values]

       return normed_input_values
      def __call__(		self	:	Any      ,       A_	:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,       A_	:	bool = True      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[Union[str, TensorType]] = None      ,       A_	:	Optional[bool] = None      ,       A_	:	Optional[str] = "max_length"      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[bool] = None      ,       **A_	:	Dict      ,       ):






       '''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.__class__.__name__} was trained using a'''
             F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
             F''' was sampled with {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."""							)

       __lowercase									=	isinstance(A_      ,       np.ndarray							) and len(raw_speech.shape							) > 1
       if is_batched_numpy and len(raw_speech.shape							) > 2:
        raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''							)
       __lowercase									=	is_batched_numpy or (
           isinstance(A_      ,       (list, tuple)							) and (isinstance(raw_speech[0]      ,       (np.ndarray, tuple, list)							))
       )

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

       # always return batch
       if not is_batched:
        __lowercase									=	[np.asarray([raw_speech]							).T]

       __lowercase									=	BatchFeature({"""input_features""": raw_speech}							)

       # convert into correct format for padding

       __lowercase									=	self.pad(
           A_      ,       padding=A_      ,       max_length=max_length if max_length else self.n_samples      ,       truncation=A_      ,       pad_to_multiple_of=A_      ,       return_attention_mask=return_attention_mask or do_normalize      ,       )

       # zero-mean and unit-variance normalization
       if do_normalize:
        __lowercase									=	self.zero_mean_unit_var_norm(
            padded_inputs["""input_features"""]      ,       attention_mask=padded_inputs["""attention_mask"""]      ,       padding_value=self.padding_value      ,       )
        __lowercase									=	np.stack(padded_inputs["""input_features"""]      ,       axis=0							)

       # make sure list is in array format
       __lowercase									=	padded_inputs.get("""input_features"""							).transpose(2      ,       0      ,       1							)

       __lowercase									=	[self._np_extract_fbank_features(A_							) for waveform in input_features[0]]

       if isinstance(input_features[0]      ,       A_							):
        __lowercase									=	[np.asarray(A_      ,       dtype=np.floataa							) for feature in input_features]
       else:
        __lowercase									=	input_features

       if return_attention_mask:
        # rescale from sample (48000) to feature (3000)
        __lowercase									=	padded_inputs["""attention_mask"""][:, :: self.hop_length]

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

       return padded_inputs






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	copy.deepcopy(self.__dict__							)
       __lowercase									=	self.__class__.__name__
       if "mel_filters" in output:
        del output["mel_filters"]
       return output




"""simple docstring"""

# Algorithm for the pigeonhole sorting




def lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	min(UpperCamelCase__ )  # min() finds the minimum value
 __lowercase									=	max(UpperCamelCase__ )  # max() finds the maximum value

 __lowercase									=	max_val - min_val + 1  # size is difference of max and min values plus one

 # list of pigeonholes of size equal to the variable size
 __lowercase									=	[0] * size

 # Populate the pigeonholes.
 for x in a:
  assert isinstance(UpperCamelCase__				,   UpperCamelCase__ ), "integers only please"
  holes[x - min_val] += 1

 # Putting the elements back into the array in an order.
 __lowercase									=	0
 for count in range(UpperCamelCase__ ):
  while holes[count] > 0:
   holes[count] -= 1
   __lowercase									=	count + min_val
   i += 1


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[8, 3, 2, 7, 4, 6, 8]
 pigeonhole_sort(UpperCamelCase__ )
 print("""Sorted order is:"""				,   """ """.join(UpperCamelCase__ ) )


if __name__ == "__main__":
       main()
