






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       =    logging.get_logger(__name__)





class __lowerCamelCase					( _snake_case):





	"""simple docstring"""






	UpperCamelCase__								= ["""input_values""", """attention_mask"""]

	def __init__(				self			,       UpperCAmelCase=1			,       UpperCAmelCase=1_6000			,       UpperCAmelCase=0.0			,       UpperCAmelCase=False			,       UpperCAmelCase=True			,       **UpperCAmelCase			,       ):
							"""simple docstring"""







							super().__init__(feature_size=lowercase_			,       sampling_rate=lowercase_			,       padding_value=lowercase_			,       **lowercase_       )
							_UpperCAmelCase							   =     return_attention_mask
							_UpperCAmelCase							   =     do_normalize

	@staticmethod
	def 			UpperCamelCase       (				UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase = 0.0       ):
							"""simple docstring"""







							if attention_mask is not None:
													_UpperCAmelCase							   =     np.array(lowercase_			,       np.intaa       )
													_UpperCAmelCase							   =     []

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

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

							return normed_input_values

	def __call__(				self			,       UpperCAmelCase			,       UpperCAmelCase = False			,       UpperCAmelCase = None			,       UpperCAmelCase = False			,       UpperCAmelCase = None			,       UpperCAmelCase = None			,       UpperCAmelCase = None			,       UpperCAmelCase = None			,       **UpperCAmelCase			,       ):
							"""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							   =     isinstance(lowercase_			,       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							   =     is_batched_numpy or (
							    isinstance(lowercase_			,       (list, tuple)       ) and (isinstance(raw_speech[0]			,       (np.ndarray, tuple, list)       ))
							)

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

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

							_UpperCAmelCase							   =     self.pad(
							    lowercase_			,       padding=lowercase_			,       max_length=lowercase_			,       truncation=lowercase_			,       pad_to_multiple_of=lowercase_			,       return_attention_mask=lowercase_			,       )

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

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

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

							if return_tensors is not None:
													_UpperCAmelCase							   =     padded_inputs.convert_to_tensors(lowercase_       )

							return padded_inputs



'''simple docstring'''

from __future__ import annotations




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 assert n > 1 and a > 0 and greatest_common_divisor(UpperCAmelCase_    ,			UpperCAmelCase_      ) == 1
 ((UpperCAmelCase)   ,					(UpperCAmelCase)   ,					(UpperCAmelCase))			:      Dict		   =					extended_gcd(UpperCAmelCase_    ,			UpperCAmelCase_      )  # Implemented below
 UpperCAmelCase			:      int		   =					(b * s) % n
 return x




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 ((UpperCAmelCase)   ,					(UpperCAmelCase))			:      int		   =					extended_euclid(UpperCAmelCase_    ,			UpperCAmelCase_      )  # Implemented below
 if b < 0:
  UpperCAmelCase			:      Optional[int]		   =					(b % n + n) % n
 return b




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Tuple		   =					invert_modulo(UpperCAmelCase_    ,			UpperCAmelCase_      )
 UpperCAmelCase			:      List[Any]		   =					(b * s) % n
 return x




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 assert a >= 0 and b >= 0

 if b == 0:
  UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      Dict		   =					a, 1, 0
 else:
  ((UpperCAmelCase)   ,					(UpperCAmelCase)   ,					(UpperCAmelCase))			:      Optional[int]		   =					extended_gcd(UpperCAmelCase_    ,			a % b      )
  UpperCAmelCase			:      Dict		   =					q
  UpperCAmelCase			:      int		   =					p - q * (a // b)

 assert a % d == 0 and b % d == 0
 assert d == a * x + b * y

 return (d, x, y)




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 if b == 0:
  return (1, 0)
 ((UpperCAmelCase)   ,					(UpperCAmelCase))			:      Tuple		   =					extended_euclid(UpperCAmelCase_    ,			a % b      )
 UpperCAmelCase			:      Tuple		   =					a // b
 return (y, x - k * y)




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 if a < b:
  UpperCAmelCase   ,					UpperCAmelCase			:      Union[str, Any]		   =					b, a

 while a % b != 0:
  UpperCAmelCase   ,					UpperCAmelCase			:      int		   =					b, a % b

 return b


if __name__ == "__main__":
 from doctest import testmod

 testmod(name="modular_division", verbose=True)
 testmod(name="modular_division2", verbose=True)
 testmod(name="invert_modulo", verbose=True)
 testmod(name="extended_gcd", verbose=True)
 testmod(name="extended_euclid", verbose=True)
 testmod(name="greatest_common_divisor", verbose=True)




