def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[str]					,	__UpperCamelCase       :			Union[str, Any]      )				->   Union[str, Any]:







  """simple docstring"""





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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			str      )				->   Optional[int]:







  """simple docstring"""





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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[int]      )				->   Dict:







  """simple docstring"""





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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Union[str, Any]:







  """simple docstring"""





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



def       __SCREAMING_SNAKE_CASE    (   )				->   Union[str, Any]:







  """simple docstring"""





  for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
    SCREAMING_SNAKE_CASE__     				= 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()








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_								=	{
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       lowercase_								=	[
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

       lowercase_								=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




