
import timeit







class 	lowerCamelCase						:





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





     def __mul__(self :       List[str]							,       _A :       Any  )		->		List[str]:
         snake_case						=   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 		lowercase_     (  A__					,						A__						)				->				Any:
    """simple docstring"""

    snake_case						=   Matrix([[1, 0], [0, 1]]						)
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix



def 		lowercase_     (  A__					,						A__					,						A__						)				->				List[Any]:
    """simple docstring"""

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



def 		lowercase_     (  A__					,						A__					,						A__						)				->				Union[str, Any]:
    """simple docstring"""

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

    snake_case						=   fa
    snake_case						=   fa
    n -= 2

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

    return fn_a



def 		lowercase_     (  )				->				str:
    """simple docstring"""

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



def 		lowercase_     (  )				->				Optional[Any]:
    """simple docstring"""

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



def 		lowercase_     (  )				->				Optional[Any]:
    """simple docstring"""

    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
   main()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A       =					{
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					["SplinterTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					[
       "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SplinterForQuestionAnswering",
       "SplinterForPreTraining",
       "SplinterLayer",
       "SplinterModel",
       "SplinterPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_splinter_fast import SplinterTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_splinter import (
          SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
          SplinterForPreTraining,
          SplinterForQuestionAnswering,
          SplinterLayer,
          SplinterModel,
          SplinterPreTrainedModel,
      )


else:
   import sys

   _A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

