


'''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__				(							a__  ):





							'''simple docstring'''






							lowercase__			:   List[str]							=      ["input_values", "attention_mask"]







							def __init__(		self					,    lowerCamelCase_=1					,    lowerCamelCase_=1_60_00					,    lowerCamelCase_=0.0					,    lowerCamelCase_=False					,    lowerCamelCase_=True					,    **lowerCamelCase_					,    )       ->							List[str]:
														super().__init__(feature_size=lowerCamelCase_					,    sampling_rate=lowerCamelCase_					,    padding_value=lowerCamelCase_					,    **lowerCamelCase_   )
														lowerCAmelCase__          =	return_attention_mask
														lowerCAmelCase__          =	do_normalize







							@staticmethod
							def   __SCREAMING_SNAKE_CASE							(		lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_ = 0.0   )       ->							List[np.ndarray]:
														if attention_mask is not None:
																					lowerCAmelCase__          =	np.array(lowerCamelCase_					,    np.intaa   )
																					lowerCAmelCase__          =	[]

																					for vector, length in zip(lowerCamelCase_					,    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(lowerCamelCase_   )
														else:
																					lowerCAmelCase__          =	[(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]

														return normed_input_values







							def __call__(		self					,    lowerCamelCase_					,    lowerCamelCase_ = False					,    lowerCamelCase_ = None					,    lowerCamelCase_ = False					,    lowerCamelCase_ = None					,    lowerCamelCase_ = None					,    lowerCamelCase_ = None					,    lowerCamelCase_ = None					,    **lowerCamelCase_					,    )       ->							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.'''   )

														lowerCAmelCase__          =	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}"""   )
														lowerCAmelCase__          =	is_batched_numpy or (
														    isinstance(lowerCamelCase_					,    (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(
														    lowerCamelCase_					,    padding=lowerCamelCase_					,    max_length=lowerCamelCase_					,    truncation=lowerCamelCase_					,    pad_to_multiple_of=lowerCamelCase_					,    return_attention_mask=lowerCamelCase_					,    )

														# convert input values to correct format
														lowerCAmelCase__          =	padded_inputs['''input_values''']
														if not isinstance(input_values[0]					,    np.ndarray   ):
																					lowerCAmelCase__          =	[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   )
														):
																					lowerCAmelCase__          =	[array.astype(np.floataa   ) for array in input_values]
														elif isinstance(lowerCamelCase_					,    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(lowerCamelCase_					,    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(lowerCamelCase_					,    max_length=lowerCamelCase_   ) is not PaddingStrategy.DO_NOT_PAD
																					    else None
																					)
																					lowerCAmelCase__          =	self.zero_mean_unit_var_norm(
																					    padded_inputs['''input_values''']					,    attention_mask=lowerCamelCase_					,    padding_value=self.padding_value   )

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

														return padded_inputs



'''simple docstring'''






def 					_snake_case   (     A ,    A       )						->    float:
							return a + b


if __name__ == "__main__":
		__UpperCAmelCase    				=							5
		__UpperCAmelCase    				=							6
		print(f"""The sum of {a} + {b} is {add(a, b)}""")