


'''simple docstring'''


import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""input_values""", """attention_mask"""]






  def __init__(							self :       int	,_a :       int = 1	,_a :       int = 16000	,_a :       float = 0.0	,_a :       bool = False	,_a :       int = 80	,_a :       int = 16	,_a :       int = 64	,_a :       str = "hann_window"	,_a :       float = 1.0	,_a :       float = 80	,_a :       float = 7600	,_a :       float = 1e-10	,_a :       int = 2	,_a :       bool = True	,**_a :       str	,):

        '''simple docstring'''




        super().__init__(feature_size=_a	,sampling_rate=_a	,padding_value=_a	,**_a )
        A_						:  Any          =				do_normalize
        A_						:  Union[str, Any]          =				return_attention_mask

        A_						:  Optional[Any]          =				num_mel_bins
        A_						:  str          =				hop_length
        A_						:  Dict          =				win_length
        A_						:  Dict          =				win_function
        A_						:  List[Any]          =				frame_signal_scale
        A_						:  List[str]          =				fmin
        A_						:  List[str]          =				fmax
        A_						:  Any          =				mel_floor
        A_						:  Optional[Any]          =				reduction_factor

        A_						:  List[Any]          =				win_length * sampling_rate // 1000
        A_						:  List[Any]          =				hop_length * sampling_rate // 1000
        A_						:  List[str]          =				optimal_fft_length(self.sample_size )
        A_						:  Optional[int]          =				(self.n_fft // 2) + 1

        A_						:  Dict          =				window_function(window_length=self.sample_size	,name=self.win_function	,periodic=_a )

        A_						:  Tuple          =				mel_filter_bank(
            num_frequency_bins=self.n_freqs	,num_mel_filters=self.num_mel_bins	,min_frequency=self.fmin	,max_frequency=self.fmax	,sampling_rate=self.sampling_rate	,norm="""slaney"""	,mel_scale="""slaney"""	,)

        if frame_signal_scale != 1.0:
              warnings.warn(
                  """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""	,_a	,)
        if reduction_factor != 2.0:
              warnings.warn(
                  """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""	,_a	,)






  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def        _a							(							_a :       List[np.ndarray]	,_a :       List[np.ndarray]	,_a :       float = 0.0 ):

        '''simple docstring'''




        if attention_mask is not None:
              A_						:  Optional[Any]          =				np.array(_a	,np.intaa )
              A_						:  List[str]          =				[]

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

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

        return normed_input_values






  def        _a							(							self :       List[Any]	,_a :       np.ndarray	,):

        '''simple docstring'''




        A_						:  Tuple          =				spectrogram(
            _a	,window=self.window	,frame_length=self.sample_size	,hop_length=self.sample_stride	,fft_length=self.n_fft	,mel_filters=self.mel_filters	,mel_floor=self.mel_floor	,log_mel="""log10"""	,)
        return log_mel_spec.T






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

        '''simple docstring'''




        if audio is None and audio_target is None:
              raise ValueError("""You must provide either `audio` or `audio_target` values.""" )

        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 audio is not None:
              A_						:  Optional[int]          =				self._process_audio(
                  _a	,_a	,_a	,_a	,_a	,_a	,_a	,_a	,**_a	,)
        else:
              A_						:  Union[str, Any]          =				None

        if audio_target is not None:
              A_						:  str          =				self._process_audio(
                  _a	,_a	,_a	,_a	,_a	,_a	,_a	,_a	,**_a	,)

              if inputs is None:
                    return inputs_target
              else:
                    A_						:  Optional[int]          =				inputs_target["""input_values"""]
                    A_						:  Tuple          =				inputs_target.get("""attention_mask""" )
                    if decoder_attention_mask is not None:
                          A_						:  Optional[int]          =				decoder_attention_mask

        return inputs






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

        '''simple docstring'''




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

        if is_batched:
              A_						:  Optional[Any]          =				[np.asarray(_a	,dtype=np.floataa ) for speech in speech]
        elif not is_batched and not isinstance(_a	,np.ndarray ):
              A_						:  Union[str, Any]          =				np.asarray(_a	,dtype=np.floataa )
        elif isinstance(_a	,np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
              A_						:  Optional[int]          =				speech.astype(np.floataa )

        # always return batch
        if not is_batched:
              A_						:  Optional[Any]          =				[speech]

        # needed to make pad() work on spectrogram inputs
        A_						:  Dict          =				self.feature_size

        # convert into correct format for padding
        if is_target:
              A_						:  Dict          =				[self._extract_mel_features(_a ) for waveform in speech]
              A_						:  Optional[int]          =				BatchFeature({"""input_values""": features} )
              A_						:  Optional[int]          =				self.num_mel_bins
        else:
              A_						:  List[str]          =				BatchFeature({"""input_values""": speech} )

        A_						:  int          =				self.pad(
            _a	,padding=_a	,max_length=_a	,truncation=_a	,pad_to_multiple_of=_a	,return_attention_mask=_a	,**_a	,)

        A_						:  str          =				feature_size_hack

        # convert input values to correct format
        A_						:  List[Any]          =				padded_inputs["""input_values"""]
        if not isinstance(input_values[0]	,np.ndarray ):
              A_						:  Optional[int]          =				[np.asarray(_a	,dtype=np.floataa ) for array in input_values]
        elif (
            not isinstance(_a	,np.ndarray )
            and isinstance(input_values[0]	,np.ndarray )
            and input_values[0].dtype is np.dtype(np.floataa )
        ):
              A_						:  Union[str, Any]          =				[array.astype(np.floataa ) for array in input_values]
        elif isinstance(_a	,np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
              A_						:  int          =				input_values.astype(np.floataa )

        # convert attention_mask to correct format
        A_						:  List[Any]          =				padded_inputs.get("""attention_mask""" )
        if attention_mask is not None:
              A_						:  Any          =				[np.asarray(_a	,dtype=np.intaa ) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if not is_target and self.do_normalize:
              A_						:  Tuple          =				(
                  attention_mask
                  if self._get_padding_strategies(_a	,max_length=_a ) is not PaddingStrategy.DO_NOT_PAD
                  else None
              )
              A_						:  List[str]          =				self.zero_mean_unit_var_norm(
                  padded_inputs["""input_values"""]	,attention_mask=_a	,padding_value=self.padding_value )

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

        return padded_inputs






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Dict          =				super().to_dict()

        # Don't serialize these as they are derived from the other properties.
        A_						:  List[Any]          =				["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
        for name in names:
              if name in output:
                    del output[name]

        return output




'''simple docstring'''


import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
     print('Googling.....')
     __magic_name__			=      'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
     __magic_name__			=      requests.get(url, headers={'UserAgent': UserAgent().random})
     # res.raise_for_status()
     with open('project1a.html', 'wb') as out_file:  # only for knowing the class
          for data in res.iter_content(10_000):
               out_file.write(data)
     __magic_name__			=      BeautifulSoup(res.text, 'html.parser')
     __magic_name__			=      list(soup.select('.eZt8xd'))[:5]

     print(len(links))
     for link in links:
          if link.text == "Maps":
               webbrowser.open(link.get('href'))
          else:
               webbrowser.open(f"""https://google.com{link.get('href')}""")
