





"""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


UpperCamelCase_	 =logging.get_logger(__name__)




class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       ['''input_values''', '''attention_mask''']






				def __init__(					self						:       Dict,					lowerCAmelCase__						:       Optional[Any]=1,					lowerCAmelCase__						:       List[str]=1_6_0_0_0,					lowerCAmelCase__						:       int=0.0,					lowerCAmelCase__						:       str=False,					lowerCAmelCase__						:       Optional[Any]=True,					**lowerCAmelCase__						:       Any,					)      -> Optional[int]:




										'''simple docstring'''



										super().__init__(feature_size=lowerCAmelCase__,					sampling_rate=lowerCAmelCase__,					padding_value=lowerCAmelCase__,					**lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       return_attention_mask
										_UpperCamelCase     :   Optional[Any]      =       do_normalize






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




										'''simple docstring'''



										if attention_mask is not None:
																_UpperCamelCase     :   str      =       np.array(lowerCAmelCase__,					np.intaa				)
																_UpperCamelCase     :   Optional[int]      =       []

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

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

										return normed_input_values






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




										'''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.'''				)

										_UpperCamelCase     :   Optional[int]      =       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}"""				)
										_UpperCamelCase     :   Any      =       is_batched_numpy or (
										    isinstance(lowerCAmelCase__,					(list, tuple)				) and (isinstance(raw_speech[0],					(np.ndarray, tuple, list)				))
										)

										# always return batch
										if not is_batched:
																_UpperCamelCase     :   List[str]      =       [raw_speech]

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

										_UpperCamelCase     :   Optional[int]      =       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
										_UpperCamelCase     :   Optional[Any]      =       padded_inputs['''input_values''']
										if not isinstance(input_values[0],					np.ndarray				):
																_UpperCamelCase     :   Tuple      =       [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				)
										):
																_UpperCamelCase     :   str      =       [array.astype(np.floataa				) for array in input_values]
										elif isinstance(lowerCAmelCase__,					np.ndarray				) and input_values.dtype is np.dtype(np.floataa				):
																_UpperCamelCase     :   Any      =       input_values.astype(np.floataa				)

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

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

										if return_tensors is not None:
																_UpperCamelCase     :   Optional[Any]      =       padded_inputs.convert_to_tensors(lowerCAmelCase__				)

										return padded_inputs








"""simple docstring"""







def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				list:
							for i, _ in enumerate(lowerCamelCase__      ):
														if (i % 2 == 1) == (nums[i - 1] > nums[i]):
																					A   ,	A    =       nums[i], nums[i - 1]

							return nums


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



