






'''simple docstring'''







def  lowerCAmelCase__		(					a_  :     list[list[int]]			,						a_  :     list[list[int]]			)   -> list[list[int]]:
       UpperCAmelCase__	:   str												=	[]
       UpperCAmelCase__	:   Optional[Any]												=	len(a_			)
       for i in range(a_			):
              UpperCAmelCase__	:   Optional[Any]												=	[]
              for j in range(a_			):
                     UpperCAmelCase__	:   Tuple												=	0
                     for k in range(a_			):
                            UpperCAmelCase__	:   Any												=	val + matrix_a[i][k] * matrix_b[k][j]
                     list_a.append(a_			)
              matrix_c.append(a_			)
       return matrix_c






def  lowerCAmelCase__		(					a_  :     int			)   -> list[list[int]]:
       return [[int(row == column			) for column in range(a_			)] for row in range(a_			)]






def  lowerCAmelCase__		(					a_  :     int			)   -> int:
       if n <= 1:
              return n
       UpperCAmelCase__	:   int												=	identity(2			)
       UpperCAmelCase__	:   Optional[int]												=	[[1, 1], [1, 0]]
       UpperCAmelCase__	:   List[str]												=	n - 1
       while n > 0:
              if n % 2 == 1:
                     UpperCAmelCase__	:   Tuple												=	multiply(a_			,						a_			)
              UpperCAmelCase__	:   str												=	multiply(a_			,						a_			)
              UpperCAmelCase__	:   int												=	int(n / 2			)
       return res_matrix[0][0]






def  lowerCAmelCase__		(					a_  :     int			)   -> int:
       if n <= 1:
              return n
       UpperCAmelCase__	:   Dict												=	0
       UpperCAmelCase__	:   int												=	1
       for _ in range(2			,						n + 1			):
              UpperCAmelCase__							,							UpperCAmelCase__	:   Dict												=	fiba, fiba + fiba
       return fiba






def  lowerCAmelCase__		(					)   -> None:
       for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
              UpperCAmelCase__	:   str												=	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(a_			)} and using bruteforce is """
                  f"""{nth_fibonacci_bruteforce(a_			)}\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()







'''simple docstring'''







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Dict				       =					c_float




      @property
      def 							lowerCamelCase     (					self						):
             return sizeof(self.value						)






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     int			,						a_  :     int = 0			)   -> int:
       # < 0 is possible if using a dynamic axis
       if dimension <= 0:
              UpperCAmelCase__	:   List[Any]												=	fixed_dimension

       dimension -= num_token_to_add
       return dimension






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     ParameterFormat			)   -> int:
       return num_parameters * dtype.size






def  lowerCAmelCase__		(					a_  :     str			)   -> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(a_			)
       except (ValueError, OSError, KeyError):
              UpperCAmelCase__							,							UpperCAmelCase__	:   Dict												=	None, None
              try:
                     UpperCAmelCase__	:   Union[str, Any]												=	AutoTokenizer.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass
              try:
                     UpperCAmelCase__	:   List[str]												=	AutoFeatureExtractor.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass

              if tokenizer is not None and feature_extractor is not None:
                     raise ValueError(
                         f"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""			)
              elif tokenizer is None and feature_extractor is None:
                     return None
              elif tokenizer is not None:
                     return tokenizer
              else:
                     return feature_extractor