


'''simple docstring'''





from typing import List, Optional, Union

import numpy as np

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


__lowerCamelCase												=						logging.get_logger(__name__)




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["input_features", "attention_mask"]


 def __init__(      self							:	str			,       __lowerCamelCase							:	str=8_0			,       __lowerCamelCase							:	Union[str, Any]=1_6_0_0_0			,       __lowerCamelCase							:	Any=0.0			,       __lowerCamelCase							:	List[str]=1_0			,       __lowerCamelCase							:	Optional[int]=2_5			,       __lowerCamelCase							:	Dict="hamming_window"			,       __lowerCamelCase							:	Tuple=3_27_68.0			,       __lowerCamelCase							:	List[Any]=0.97			,       __lowerCamelCase							:	List[str]=1.0			,       __lowerCamelCase							:	str=True			,       __lowerCamelCase							:	Any=True			,       __lowerCamelCase							:	Tuple=False			,       **__lowerCamelCase							:	Optional[int]			,       ):
    super().__init__(feature_size=__lowerCamelCase			,       sampling_rate=__lowerCamelCase			,       padding_value=__lowerCamelCase			,       **__lowerCamelCase )

    UpperCAmelCase__	:Dict      =      feature_size
    UpperCAmelCase__	:List[str]      =      sampling_rate
    UpperCAmelCase__	:Optional[Any]      =      padding_value
    UpperCAmelCase__	:Optional[Any]      =      hop_length
    UpperCAmelCase__	:str      =      win_length
    UpperCAmelCase__	:Optional[int]      =      frame_signal_scale
    UpperCAmelCase__	:int      =      preemphasis_coeff
    UpperCAmelCase__	:Dict      =      mel_floor
    UpperCAmelCase__	:List[Any]      =      normalize_means
    UpperCAmelCase__	:Optional[int]      =      normalize_vars
    UpperCAmelCase__	:Tuple      =      win_function
    UpperCAmelCase__	:List[str]      =      return_attention_mask

    UpperCAmelCase__	:List[Any]      =      win_length * sampling_rate // 1_0_0_0
    UpperCAmelCase__	:int      =      hop_length * sampling_rate // 1_0_0_0

    UpperCAmelCase__	:Dict      =      optimal_fft_length(self.sample_size )
    UpperCAmelCase__	:Union[str, Any]      =      (self.n_fft // 2) + 1


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	np.array ):
    if self.win_function == "hamming_window":
       UpperCAmelCase__	:Optional[int]      =      window_function(window_length=self.sample_size			,       name=self.win_function			,       periodic=__lowerCamelCase )
    else:
       UpperCAmelCase__	:Dict      =      window_function(window_length=self.sample_size			,       name=self.win_function )

    UpperCAmelCase__	:Any      =      mel_filter_bank(
        num_frequency_bins=self.n_freqs			,       num_mel_filters=self.feature_size			,       min_frequency=0.0			,       max_frequency=self.sampling_rate / 2.0			,       sampling_rate=self.sampling_rate			,       )

    UpperCAmelCase__	:Union[str, Any]      =      spectrogram(
        one_waveform * self.frame_signal_scale			,       window=__lowerCamelCase			,       frame_length=self.sample_size			,       hop_length=self.sample_stride			,       fft_length=self.n_fft			,       center=__lowerCamelCase			,       preemphasis=self.preemphasis_coeff			,       mel_filters=__lowerCamelCase			,       mel_floor=self.mel_floor			,       log_mel='''log'''			,       )
    return msfc_features.T


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	List[Any] ):
    # make sure we normalize float32 arrays
    if self.normalize_means:
       UpperCAmelCase__	:Optional[int]      =      x[:input_length].mean(axis=0 )
       UpperCAmelCase__	:str      =      np.subtract(__lowerCamelCase			,       __lowerCamelCase )
    if self.normalize_vars:
       UpperCAmelCase__	:Optional[Any]      =      x[:input_length].std(axis=0 )
       UpperCAmelCase__	:Optional[int]      =      np.divide(__lowerCamelCase			,       __lowerCamelCase )

    if input_length < x.shape[0]:
       UpperCAmelCase__	:List[Any]      =      padding_value

    # make sure array is in float32
    UpperCAmelCase__	:List[str]      =      x.astype(np.floataa )

    return x


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	List[np.ndarray]			,       __lowerCamelCase							:	Optional[np.ndarray] = None ):
    UpperCAmelCase__	:int      =      attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
    return [self._normalize_one(__lowerCamelCase			,       __lowerCamelCase			,       self.padding_value ) for x, n in zip(__lowerCamelCase			,       __lowerCamelCase )]





 def __call__(      self							:	Any			,       __lowerCamelCase							:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,       __lowerCamelCase							:	Union[bool, str, PaddingStrategy] = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[bool] = None			,       __lowerCamelCase							:	Optional[Union[str, TensorType]] = None			,       __lowerCamelCase							:	Optional[int] = None			,       **__lowerCamelCase							:	Optional[Any]			,       ):

    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 `raw_speech` 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.''' )

    UpperCAmelCase__	:Any      =      isinstance(__lowerCamelCase			,       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}''' )
    UpperCAmelCase__	:Dict      =      is_batched_numpy or (
        isinstance(__lowerCamelCase			,       (list, tuple) ) and (isinstance(raw_speech[0]			,       (np.ndarray, tuple, list) ))
    )

    if is_batched:
       UpperCAmelCase__	:str      =      [np.asarray(__lowerCamelCase			,       dtype=np.floataa ) for speech in raw_speech]
    elif not is_batched and not isinstance(__lowerCamelCase			,       np.ndarray ):
       UpperCAmelCase__	:Tuple      =      np.asarray(__lowerCamelCase			,       dtype=np.floataa )
    elif isinstance(__lowerCamelCase			,       np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
       UpperCAmelCase__	:Tuple      =      raw_speech.astype(np.floataa )

    # always return batch
    if not is_batched:
       UpperCAmelCase__	:int      =      [raw_speech]

    # extract fbank features
    UpperCAmelCase__	:Union[str, Any]      =      [self._extract_mfsc_features(__lowerCamelCase ) for one_waveform in raw_speech]

    # convert into correct format for padding
    UpperCAmelCase__	:List[Any]      =      BatchFeature({'''input_features''': features} )

    UpperCAmelCase__	:Optional[int]      =      self.pad(
        __lowerCamelCase			,       padding=__lowerCamelCase			,       max_length=__lowerCamelCase			,       truncation=__lowerCamelCase			,       pad_to_multiple_of=__lowerCamelCase			,       return_attention_mask=__lowerCamelCase			,       **__lowerCamelCase			,       )
    # make sure list is in array format
    UpperCAmelCase__	:str      =      padded_inputs.get('''input_features''' )
    if isinstance(input_features[0]			,       __lowerCamelCase ):
       UpperCAmelCase__	:Any      =      [np.asarray(__lowerCamelCase			,       dtype=np.floataa ) for feature in input_features]

    UpperCAmelCase__	:Dict      =      padded_inputs.get('''attention_mask''' )
    if attention_mask is not None:
       UpperCAmelCase__	:str      =      [np.asarray(__lowerCamelCase			,       dtype=np.intaa ) for array in attention_mask]

    if self.normalize_means or self.normalize_vars:
       UpperCAmelCase__	:Optional[int]      =      (
           np.array(__lowerCamelCase			,       dtype=np.intaa )
           if self._get_padding_strategies(__lowerCamelCase			,       max_length=__lowerCamelCase ) is not PaddingStrategy.DO_NOT_PAD
           and padding
           else None
       )
       UpperCAmelCase__	:Optional[Any]      =      self.normalize(
           padded_inputs['''input_features''']			,       attention_mask=__lowerCamelCase )

    if return_tensors is not None:
       UpperCAmelCase__	:Optional[Any]      =      padded_inputs.convert_to_tensors(__lowerCamelCase )

    return padded_inputs







'''simple docstring'''





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__lowerCamelCase												=						Lock()


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      Any,   UpperCamelCase_						:      Any,   UpperCamelCase_						:      int,   UpperCamelCase_						:      Union[str, Any],   UpperCamelCase_						:      List[str],   UpperCamelCase_						:      Optional[int]						):
   global process_lock

   # we perform n swaps since after n swaps we know we are sorted
   # we *could* stop early if we are sorted already, but it takes as long to
   # find out we are sorted as it does to sort the list with this algorithm
   for i in range(0,   10						):
      if (i + position) % 2 == 0 and r_send is not None:
         # send your value to your right neighbor
         process_lock.acquire()
         r_send[1].send(UpperCamelCase_						)
         process_lock.release()

         # receive your right neighbor's value
         process_lock.acquire()
         UpperCAmelCase__	:Union[str, Any]      =      rr_cv[0].recv()
         process_lock.release()

         # take the lower value since you are on the left
         UpperCAmelCase__	:List[Any]      =      min(UpperCamelCase_,   UpperCamelCase_						)
      elif (i + position) % 2 != 0 and l_send is not None:
         # send your value to your left neighbor
         process_lock.acquire()
         l_send[1].send(UpperCamelCase_						)
         process_lock.release()

         # receive your left neighbor's value
         process_lock.acquire()
         UpperCAmelCase__	:List[str]      =      lr_cv[0].recv()
         process_lock.release()

         # take the higher value since you are on the right
         UpperCAmelCase__	:List[Any]      =      max(UpperCamelCase_,   UpperCamelCase_						)
    # after all swaps are performed, send the values back to main
   result_pipe[1].send(UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      int						):
   UpperCAmelCase__	:List[str]      =      []
   UpperCAmelCase__	:Any      =      []
   # initialize the list of pipes where the values will be retrieved
   for _ in arr:
      result_pipe.append(Pipe()						)
   # creates the processes
   # the first and last process only have one neighbor so they are made outside
   # of the loop
   UpperCAmelCase__	:str      =      Pipe()
   UpperCAmelCase__	:Dict      =      Pipe()
   process_array_.append(
       Process(
           target=UpperCamelCase_,   args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),   )						)
   UpperCAmelCase__	:Optional[Any]      =      temp_rs
   UpperCAmelCase__	:Any      =      temp_rr

   for i in range(1,   len(UpperCamelCase_						) - 1						):
      UpperCAmelCase__	:Optional[int]      =      Pipe()
      UpperCAmelCase__	:List[str]      =      Pipe()
      process_array_.append(
          Process(
              target=UpperCamelCase_,   args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),   )						)
      UpperCAmelCase__	:Optional[Any]      =      temp_rs
      UpperCAmelCase__	:str      =      temp_rr

   process_array_.append(
       Process(
           target=UpperCamelCase_,   args=(
               len(UpperCamelCase_						) - 1,
               arr[len(UpperCamelCase_						) - 1],
               temp_ls,
               None,
               temp_lr,
               None,
               result_pipe[len(UpperCamelCase_						) - 1],
           ),   )						)

   # start the processes
   for p in process_array_:
      p.start()

   # wait for the processes to end and write their values to the list
   for p in range(0,   len(UpperCamelCase_						)						):
      UpperCAmelCase__	:List[str]      =      result_pipe[p][0].recv()
      process_array_[p].join()
   return arr


def        a__     (					):
   UpperCAmelCase__	:Optional[Any]      =      list(range(10,   0,   -1						)						)
   print('''Initial List'''						)
   print(*UpperCamelCase_						)
   UpperCAmelCase__	:str      =      odd_even_transposition(UpperCamelCase_						)
   print('''Sorted List\n'''						)
   print(*UpperCamelCase_						)


if __name__ == "__main__":
    main()



