






"""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


lowercase_								=	logging.get_logger(__name__)


class    snake_case						(				UpperCamelCase__		):


   '''simple docstring'''

   A_		:      Any							=					["""input_values""", """attention_mask"""]


   def __init__(      self					:	Dict,			_lowerCamelCase					:	str=1,			_lowerCamelCase					:	Dict=1_60_00,			_lowerCamelCase					:	str=0.0,			_lowerCamelCase					:	Union[str, Any]=False,			_lowerCamelCase					:	Dict=True,			**_lowerCamelCase					:	Union[str, Any],			):

     '''simple docstring'''
     super().__init__(feature_size=_lowerCamelCase,			sampling_rate=_lowerCamelCase,			padding_value=_lowerCamelCase,			**_lowerCamelCase    )
     __A		     =					return_attention_mask
     __A		     =					do_normalize


   @staticmethod
   def 							_SCREAMING_SNAKE_CASE							(      _lowerCamelCase					:	Tuple,			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Union[str, Any] = 0.0    ):

     '''simple docstring'''
     if attention_mask is not None:
       __A		     =					np.array(_lowerCamelCase,			np.intaa    )
       __A		     =					[]

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

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

     return normed_input_values






   def __call__(      self					:	str,			_lowerCamelCase					:	Optional[int],			_lowerCamelCase					:	Optional[Any] = False,			_lowerCamelCase					:	Dict = None,			_lowerCamelCase					:	str = False,			_lowerCamelCase					:	str = None,			_lowerCamelCase					:	List[str] = None,			_lowerCamelCase					:	Optional[Any] = None,			_lowerCamelCase					:	List[Any] = None,			**_lowerCamelCase					:	List[Any],			):

     '''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.'''    )

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

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

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

     __A		     =					self.pad(
         _lowerCamelCase,			padding=_lowerCamelCase,			max_length=_lowerCamelCase,			truncation=_lowerCamelCase,			pad_to_multiple_of=_lowerCamelCase,			return_attention_mask=_lowerCamelCase,			)

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

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

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

     if return_tensors is not None:
       __A		     =					padded_inputs.convert_to_tensors(_lowerCamelCase    )

     return padded_inputs









'''simple docstring'''



from __future__ import annotations

from random import randrange



def 	__lowerCAmelCase       (				lowerCamelCase :    list       ):






	'''simple docstring'''




	if len(lowerCamelCase       ) < 2:
		return collection
	__lowerCAmelCase       				=	randrange(len(lowerCamelCase       )       )  # Use random element as pivot
	__lowerCAmelCase       				=	collection[pivot_index]
	__lowerCAmelCase       				=	[]  # All elements greater than pivot
	__lowerCAmelCase       				=	[]  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	return [*quick_sort(lowerCamelCase       ), pivot, *quick_sort(lowerCamelCase       )]


if __name__ == "__main__":
	lowerCAmelCase						:		List[Any]						      = input('''Enter numbers separated by a comma:\n''').strip()
	lowerCAmelCase						:		Any						      = [int(item) for item in user_input.split(''',''')]
	print(quick_sort(unsorted))