



'''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


lowerCAmelCase						:		int						      = logging.get_logger(__name__)






class        UpperCAmelCase__						(		UpperCamelCase__			):
					a   :  Any   =				["""input_values""", """attention_mask"""]



					def __init__(     self      ,					UpperCamelCase=1      ,					UpperCamelCase=1_6000      ,					UpperCamelCase=0.0      ,					UpperCamelCase=False      ,					UpperCamelCase=True      ,					**UpperCamelCase      ,					)  ->      Tuple:
						super().__init__(feature_size=UpperCamelCase      ,					sampling_rate=UpperCamelCase      ,					padding_value=UpperCamelCase      ,					**UpperCamelCase  )
						__lowerCAmelCase       				=	return_attention_mask
						__lowerCAmelCase       				=	do_normalize



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

							for vector, length in zip(UpperCamelCase      ,					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(UpperCamelCase  )
						else:
							__lowerCAmelCase       				=	[(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      ,					)  ->      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(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}'''  )
						__lowerCAmelCase       				=	is_batched_numpy or (
						    isinstance(UpperCamelCase      ,					(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(
						    UpperCamelCase      ,					padding=UpperCamelCase      ,					max_length=UpperCamelCase      ,					truncation=UpperCamelCase      ,					pad_to_multiple_of=UpperCamelCase      ,					return_attention_mask=UpperCamelCase      ,					)

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

						if return_tensors is not None:
							__lowerCAmelCase       				=	padded_inputs.convert_to_tensors(UpperCamelCase  )

						return padded_inputs




'''simple docstring'''



def 	__lowerCAmelCase       (				lowerCamelCase :    list[list[int]]					,		lowerCamelCase :    list[list[int]]       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	[]
	__lowerCAmelCase       				=	len(lowerCamelCase       )
	for i in range(lowerCamelCase       ):
		__lowerCAmelCase       				=	[]
		for j in range(lowerCamelCase       ):
			__lowerCAmelCase       				=	0
			for k in range(lowerCamelCase       ):
				__lowerCAmelCase       				=	val + matrix_a[i][k] * matrix_b[k][j]
			list_a.append(lowerCamelCase       )
		matrix_c.append(lowerCamelCase       )
	return matrix_c



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	return [[int(row == column       ) for column in range(lowerCamelCase       )] for row in range(lowerCamelCase       )]



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if n <= 1:
		return n
	__lowerCAmelCase       				=	identity(2       )
	__lowerCAmelCase       				=	[[1, 1], [1, 0]]
	__lowerCAmelCase       				=	n - 1
	while n > 0:
		if n % 2 == 1:
			__lowerCAmelCase       				=	multiply(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	multiply(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	int(n / 2       )
	return res_matrix[0][0]



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if n <= 1:
		return n
	__lowerCAmelCase       				=	0
	__lowerCAmelCase       				=	1
	for _ in range(2					,		n + 1       ):
		__lowerCAmelCase				,							__lowerCAmelCase       				=	fiba, fiba + fiba
	return fiba



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
		__lowerCAmelCase       				=	int("".join(c for c in ordinal if c in "0123456789"       )       )  # 1000th --> 1000
		print(
		    f'''{ordinal} fibonacci number using matrix exponentiation is '''
		    f'''{nth_fibonacci_matrix(lowerCamelCase       )} and using bruteforce is '''
		    f'''{nth_fibonacci_bruteforce(lowerCamelCase       )}\n'''       )
	# from timeit import timeit
	# print(timeit("nth_fibonacci_matrix(1000000)",
	#              "from main import nth_fibonacci_matrix", number=5))
	# print(timeit("nth_fibonacci_bruteforce(1000000)",
	#              "from main import nth_fibonacci_bruteforce", number=5))
	# 2.3342058970001744
	# 57.256506615000035


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()