



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


a_       :List[Any]				  =				logging.get_logger(__name__)




class  snake_case__						(	_A					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     ["""input_values""", """attention_mask"""]






     def __init__(	self  :  List[str],			_snake_case  :  Tuple=1,			_snake_case  :  List[str]=1_6_0_0_0,			_snake_case  :  Optional[int]=0.0,			_snake_case  :  Union[str, Any]=False,			_snake_case  :  Tuple=True,			**_snake_case  :  Tuple,			)       ->Any:
            super().__init__(feature_size=UpperCamelCase__,			sampling_rate=UpperCamelCase__,			padding_value=UpperCamelCase__,			**UpperCamelCase__ )
            snake_case__							:     List[Any]            =     return_attention_mask
            snake_case__							:     List[Any]            =     do_normalize






     @staticmethod
     def        lowercase_       (	_snake_case  :  List[np.ndarray],			_snake_case  :  List[np.ndarray],			_snake_case  :  float = 0.0 )       ->List[np.ndarray]:






            if attention_mask is not None:
                   snake_case__							:     Any            =     np.array(UpperCamelCase__,			np.intaa )
                   snake_case__							:     Optional[Any]            =     []

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

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

            return normed_input_values






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






            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.' )

            snake_case__							:     List[str]            =     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}''' )
            snake_case__							:     Any            =     is_batched_numpy or (
                isinstance(UpperCamelCase__,			(list, tuple) ) and (isinstance(raw_speech[0],			(np.ndarray, tuple, list) ))
            )

            # always return batch
            if not is_batched:
                   snake_case__							:     List[Any]            =     [raw_speech]

            # convert into correct format for padding
            snake_case__							:     List[str]            =     BatchFeature({'input_values': raw_speech} )

            snake_case__							:     str            =     self.pad(
                UpperCamelCase__,			padding=UpperCamelCase__,			max_length=UpperCamelCase__,			truncation=UpperCamelCase__,			pad_to_multiple_of=UpperCamelCase__,			return_attention_mask=UpperCamelCase__,			)

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

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

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

            if return_tensors is not None:
                   snake_case__							:     Tuple            =     padded_inputs.convert_to_tensors(UpperCamelCase__ )

            return padded_inputs











def 	lowercase_							(A			:			list				):
       for i, _ in enumerate(A				):
              if (i % 2 == 1) == (nums[i - 1] > nums[i]):
                     snake_case__     ,    snake_case__							:     List[str]            =     nums[i], nums[i - 1]

       return nums


if __name__ == "__main__":
 print("Enter the array elements:")
 a_       :Tuple				  =				list(map(int, input().split()))
 print("The unsorted array is:")
 print(array)
 print("Array after Wiggle sort:")
 print(wiggle_sort(array))






