



import timeit

class        __snake_case					:







							'''simple docstring'''






							def __init__(							self     ,     a_						):
											if isinstance(_A     ,     _A						):  # Initializes a matrix identical to the one provided.
															a__ 					=						arg
															a__ 					=						len(_A						)
											else:  # Initializes a square matrix of the given size and set values to zero.
															a__ 					=						arg
															a__ 					=						[[0 for _ in range(self.n						)] for _ in range(self.n						)]







							def __mul__(							self     ,     a_						):
											a__ 					=						Matrix(self.n						)
											for i in range(self.n						):
															for j in range(self.n						):
																			for k in range(self.n						):
																							matrix.t[i][j] += self.t[i][k] * b.t[k][j]
											return matrix




def 	A_					(		__a   : str       ,						__a   : Dict						):

				"""simple docstring"""
				a__ 					=						Matrix([[1, 0], [0, 1]]						)
				while b > 0:
								if b & 1:
												matrix *= a
								a *= a
								b >>= 1
				return matrix

def 	A_					(		__a   : Union[str, Any]       ,						__a   : Union[str, Any]       ,						__a   : str						):

				"""simple docstring"""

				if n == 1:
								return fa
				elif n == 2:
								return fa
				a__ 					=						Matrix([[1, 1], [1, 0]]						)
				a__ 					=						modular_exponentiation(A__       ,						n - 2						)
				return fa * matrix.t[0][0] + fa * matrix.t[0][1]

def 	A_					(		__a   : Any       ,						__a   : Tuple       ,						__a   : Dict						):

				"""simple docstring"""

				if n == 1:
								return fa
				elif n == 2:
								return fa

				a__ 					=						fa
				a__ 					=						fa
				n -= 2

				while n > 0:
								a__					,	a__ 					=						fn_a + fn_a, fn_a
								n -= 1

				return fn_a

def 	A_					(		):

				"""simple docstring"""
				a__ 					=						"""\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"""
				a__ 					=						"""fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
				a__ 					=						timeit.timeit(setup=A__       ,						stmt=A__       ,						number=100						)
				print("""With matrix exponentiation the average execution time is """       ,						exec_time / 100						)
				return exec_time

def 	A_					(		):

				"""simple docstring"""
				a__ 					=						"""\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"""
				a__ 					=						"""simple_fibonacci(randint(1,70000), 1, 1)"""
				a__ 					=						timeit.timeit(setup=A__       ,						stmt=A__       ,						number=100						)
				print(
				    """Without matrix exponentiation the average execution time is """       ,						exec_time / 100						)
				return exec_time

def 	A_					(		):

				"""simple docstring"""
				matrix_exponentiation_time()
				simple_fibonacci_time()


if __name__ == "__main__":
	main()



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		(  A_  ):
     UpperCAmelCase__				:							int           =		["input_values", "attention_mask"]





     def __init__(self      :    Tuple				,    _A      :    Optional[Any]=1				,    _A      :    Dict=1_6_0_0_0				,    _A      :    int=0.0				,    _A      :    List[str]=False				,    _A      :    Optional[int]=True				,    **_A      :    List[Any]				,    )						->						str:
         super().__init__(feature_size=_A				,    sampling_rate=_A				,    padding_value=_A				,    **_A   )
         snake_case      =					return_attention_mask
         snake_case      =					do_normalize





     @staticmethod
     def 		UpperCAmelCase(_A      :    List[np.ndarray]				,    _A      :    List[np.ndarray]				,    _A      :    float = 0.0   )						->						List[np.ndarray]:
         if attention_mask is not None:
             snake_case      =					np.array(_A				,    np.intaa   )
             snake_case      =					[]

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

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

         return normed_input_values





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

         snake_case      =					isinstance(_A				,    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}'   )
         snake_case      =					is_batched_numpy or (
             isinstance(_A				,    (list, tuple)   ) and (isinstance(raw_speech[0]				,    (np.ndarray, tuple, list)   ))
         )

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

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

         snake_case      =					self.pad(
             _A				,    padding=_A				,    max_length=_A				,    truncation=_A				,    pad_to_multiple_of=_A				,    return_attention_mask=_A				,    )

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

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

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

         if return_tensors is not None:
             snake_case      =					padded_inputs.convert_to_tensors(_A   )

         return padded_inputs

