"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	:		str   =   logging.get_logger(__name__)






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Tuple    =    ["input_values", "attention_mask"]







   def __init__(	self ,				_SCREAMING_SNAKE_CASE=1 ,				_SCREAMING_SNAKE_CASE=16000 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=False ,				_SCREAMING_SNAKE_CASE=True ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      super().__init__(feature_size=_SCREAMING_SNAKE_CASE ,				sampling_rate=_SCREAMING_SNAKE_CASE ,				padding_value=_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					return_attention_mask
      __lowerCamelCase    		=					do_normalize







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = 0.0	):


      '''simple docstring'''

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

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

            normed_input_values.append(_SCREAMING_SNAKE_CASE	)
      else:
         __lowerCamelCase    		=					[(x - x.mean()) / np.sqrt(x.var() + 1E-7	) for x in input_values]

      return normed_input_values







   def __call__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = False ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = False ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				**_SCREAMING_SNAKE_CASE ,				):


      '''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 `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.'	)

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

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

      # convert into correct format for padding
      __lowerCamelCase    		=					BatchFeature({'input_values': raw_speech}	)

      __lowerCamelCase    		=					self.pad(
          _SCREAMING_SNAKE_CASE ,				padding=_SCREAMING_SNAKE_CASE ,				max_length=_SCREAMING_SNAKE_CASE ,				truncation=_SCREAMING_SNAKE_CASE ,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,				return_attention_mask=_SCREAMING_SNAKE_CASE ,				)

      # convert input values to correct format
      __lowerCamelCase    		=					padded_inputs['input_values']
      if not isinstance(input_values[0] ,				np.ndarray	):
         __lowerCamelCase    		=					[np.asarray(_SCREAMING_SNAKE_CASE ,				dtype=np.floataa	) for array in input_values]
      elif (
          not isinstance(_SCREAMING_SNAKE_CASE ,				np.ndarray	)
          and isinstance(input_values[0] ,				np.ndarray	)
          and input_values[0].dtype is np.dtype(np.floataa	)
      ):
         __lowerCamelCase    		=					[array.astype(np.floataa	) for array in input_values]
      elif isinstance(_SCREAMING_SNAKE_CASE ,				np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
         __lowerCamelCase    		=					input_values.astype(np.floataa	)

      # convert attention_mask to correct format
      __lowerCamelCase    		=					padded_inputs.get('attention_mask'	)
      if attention_mask is not None:
         __lowerCamelCase    		=					[np.asarray(_SCREAMING_SNAKE_CASE ,				dtype=np.intaa	) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if self.do_normalize:
         __lowerCamelCase    		=					(
             attention_mask
             if self._get_padding_strategies(_SCREAMING_SNAKE_CASE ,				max_length=_SCREAMING_SNAKE_CASE	) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         __lowerCamelCase    		=					self.zero_mean_unit_var_norm(
             padded_inputs['input_values'] ,				attention_mask=_SCREAMING_SNAKE_CASE ,				padding_value=self.padding_value	)

      if return_tensors is not None:
         __lowerCamelCase    		=					padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE	)

      return padded_inputs







"""simple docstring"""







from collections import Counter



def       A__    (	lowercase_     :	list[int]						)  ->							int:
   return sum(socks_by_color // 2 for socks_by_color in Counter(lowercase_						).values()						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     _SCREAMING_SNAKE_CASE	:		int   =   [int(x) for x in input('''Enter socks by color :''').rstrip().split()]
     print(F"sock_merchant({colors}) = {sock_merchant(colors)}")






