




"""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


a							      =     logging.get_logger(__name__)





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Tuple     =      ['''input_features''', '''attention_mask''']

 def __init__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			int=80       ,				_UpperCAmelCase			:			Any=16_000       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			Optional[Any]=10       ,				_UpperCAmelCase			:			Optional[int]=25       ,				_UpperCAmelCase			:			List[str]="hamming_window"       ,				_UpperCAmelCase			:			Dict=3_2768.0       ,				_UpperCAmelCase			:			int=0.97       ,				_UpperCAmelCase			:			List[str]=1.0       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			Union[str, Any]=True       ,				_UpperCAmelCase			:			int=False       ,				**_UpperCAmelCase			:			List[Any]       ,				):
       super().__init__(feature_size=_UpperCAmelCase       ,				sampling_rate=_UpperCAmelCase       ,				padding_value=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       _A     =  feature_size
       _A     =  sampling_rate
       _A     =  padding_value
       _A     =  hop_length
       _A     =  win_length
       _A     =  frame_signal_scale
       _A     =  preemphasis_coeff
       _A     =  mel_floor
       _A     =  normalize_means
       _A     =  normalize_vars
       _A     =  win_function
       _A     =  return_attention_mask

       _A     =  win_length * sampling_rate // 1_000
       _A     =  hop_length * sampling_rate // 1_000

       _A     =  optimal_fft_length(self.sample_size						)
       _A     =  (self.n_fft // 2) + 1

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			np.array						):
       if self.win_function == "hamming_window":
             _A     =  window_function(window_length=self.sample_size       ,				name=self.win_function       ,				periodic=_UpperCAmelCase						)
       else:
             _A     =  window_function(window_length=self.sample_size       ,				name=self.win_function						)

       _A     =  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       ,				)

       _A     =  spectrogram(
           one_waveform * self.frame_signal_scale       ,				window=_UpperCAmelCase       ,				frame_length=self.sample_size       ,				hop_length=self.sample_stride       ,				fft_length=self.n_fft       ,				center=_UpperCAmelCase       ,				preemphasis=self.preemphasis_coeff       ,				mel_filters=_UpperCAmelCase       ,				mel_floor=self.mel_floor       ,				log_mel='log'       ,				)
       return msfc_features.T

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

       if input_length < x.shape[0]:
             _A     =  padding_value

       # make sure array is in float32
       _A     =  x.astype(np.floataa						)

       return x

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			List[np.ndarray]       ,				_UpperCAmelCase			:			Optional[np.ndarray] = None						):
       _A     =  attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [self._normalize_one(_UpperCAmelCase       ,				_UpperCAmelCase       ,				self.padding_value						) for x, n in zip(_UpperCAmelCase       ,				_UpperCAmelCase						)]



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

       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.'						)

       _A     =  isinstance(_UpperCAmelCase       ,				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}'''						)
       _A     =  is_batched_numpy or (
           isinstance(_UpperCAmelCase       ,				(list, tuple)						) and (isinstance(raw_speech[0]       ,				(np.ndarray, tuple, list)						))
       )

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

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

       # extract fbank features
       _A     =  [self._extract_mfsc_features(_UpperCAmelCase						) for one_waveform in raw_speech]

       # convert into correct format for padding
       _A     =  BatchFeature({'input_features': features}						)

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

       _A     =  padded_inputs.get('attention_mask'						)
       if attention_mask is not None:
             _A     =  [np.asarray(_UpperCAmelCase       ,				dtype=np.intaa						) for array in attention_mask]

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

       if return_tensors is not None:
             _A     =  padded_inputs.convert_to_tensors(_UpperCAmelCase						)

       return padded_inputs






'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if len(__A) == 0:
          return False
     _a           =    len(__A) // 2
     if a_list[midpoint] == item:
          return True
     if item < a_list[midpoint]:
          return binary_search(a_list[:midpoint]    ,      __A)
     else:
          return binary_search(a_list[midpoint + 1 :]    ,      __A)


if __name__ == "__main__":
  lowercase_									=    input("Enter numbers separated by comma:\n").strip()
  lowercase_									=    [int(item.strip()) for item in user_input.split(",")]
  lowercase_									=    int(input("Enter the number to be found in the list:\n").strip())
  lowercase_									=    "" if binary_search(sequence, target) else "not "
  print(F"""{target} was {not_str}found in {sequence}""")



