






"""simple docstring"""


def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







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



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







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



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







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



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







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



def    lowerCAmelCase							(       ):




  """simple docstring"""







  for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
    __A		     =					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(__UpperCamelCase					)} and using bruteforce is '
        f'{nth_fibonacci_bruteforce(__UpperCamelCase					)}\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()











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


__lowercase							:Optional[int]							=	logging.get_logger(__name__)



class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    ["input_values", "attention_mask"]

							def __init__(   self					:  List[str]				,						a					:  Dict=1				,						a					:  Tuple=1_60_00				,						a					:  Union[str, Any]=0.0				,						a					:  Dict=False				,						a					:  List[Any]=True				,						**a					:  Any				,						)						->Optional[int]:
													super().__init__(feature_size=a				,						sampling_rate=a				,						padding_value=a				,						**a     )
													SCREAMING_SNAKE_CASE__  :					int =       return_attention_mask
													SCREAMING_SNAKE_CASE__  :					str =       do_normalize

							@staticmethod
							def   A_	(   a					:  List[np.ndarray]				,						a					:  List[np.ndarray]				,						a					:  float = 0.0     )						->List[np.ndarray]:
													if attention_mask is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       np.array(a				,						np.intaa     )
																			SCREAMING_SNAKE_CASE__  :					List[str] =       []

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

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

													return normed_input_values






							def __call__(   self					:  Union[str, 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					:  Optional[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."     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       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}"""     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       is_batched_numpy or (
													    isinstance(a				,						(list, tuple)     ) and (isinstance(raw_speech[0]				,						(np.ndarray, tuple, list)     ))
													)

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

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

													SCREAMING_SNAKE_CASE__  :					Tuple =       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
													SCREAMING_SNAKE_CASE__  :					Any =       padded_inputs["input_values"]
													if not isinstance(input_values[0]				,						np.ndarray     ):
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       [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     )
													):
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [array.astype(np.floataa     ) for array in input_values]
													elif isinstance(a				,						np.ndarray     ) and input_values.dtype is np.dtype(np.floataa     ):
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       input_values.astype(np.floataa     )

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

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

													if return_tensors is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       padded_inputs.convert_to_tensors(a     )

													return padded_inputs