





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:  Any   =   logging.get_logger(__name__)


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							["input_values", "attention_mask"]

	def __init__(	self:	List[Any],				lowerCamelCase_:	Any=1,				lowerCamelCase_:	Dict=16000,				lowerCamelCase_:	Optional[Any]=0.0,				lowerCamelCase_:	Tuple=False,				lowerCamelCase_:	Union[str, Any]=True,				**lowerCamelCase_:	Union[str, Any],				):
			super().__init__(feature_size=lowerCamelCase_,				sampling_rate=lowerCamelCase_,				padding_value=lowerCamelCase_,				**lowerCamelCase_ )
			lowercase__				:			Optional[int]            =		return_attention_mask
			lowercase__				:			Tuple            =		do_normalize

	@staticmethod
	def       snake_case__(	lowerCamelCase_:	List[np.ndarray],				lowerCamelCase_:	List[np.ndarray],				lowerCamelCase_:	float = 0.0 ):
			if attention_mask is not None:
					lowercase__				:			List[Any]            =		np.array(lowerCamelCase_,				np.intaa )
					lowercase__				:			Dict            =		[]

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

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

			return normed_input_values





	def __call__(	self:	Optional[int],				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_:	Union[str, Any],				):

			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.' )

			lowercase__				:			Union[str, Any]            =		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}""" )
			lowercase__				:			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:
					lowercase__				:			int            =		[raw_speech]

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

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

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

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

			if return_tensors is not None:
					lowercase__				:			Tuple            =		padded_inputs.convert_to_tensors(lowerCamelCase_ )

			return padded_inputs









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   list )      ->       list:




		'''simple docstring'''
		lowercase__				:			Any            =		len(_lowercase )
		for cycle_start in range(0      ,						array_len - 1 ):
				lowercase__				:			List[str]            =		array[cycle_start]

				lowercase__				:			str            =		cycle_start
				for i in range(cycle_start + 1      ,						_lowercase ):
						if array[i] < item:
								pos += 1

				if pos == cycle_start:
						continue

				while item == array[pos]:
						pos += 1

				lowercase__      ,							lowercase__				:			Optional[Any]            =		item, array[pos]
				while pos != cycle_start:
						lowercase__				:			str            =		cycle_start
						for i in range(cycle_start + 1      ,						_lowercase ):
								if array[i] < item:
										pos += 1

						while item == array[pos]:
								pos += 1

						lowercase__      ,							lowercase__				:			Tuple            =		item, array[pos]

		return array


if __name__ == "__main__":
			assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


