import timeit

class 			lowerCamelCase_			:
      '''simple docstring'''

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





      def __mul__( self		:       str		,      A		:       Union[str, Any] ):
             _UpperCAmelCase  :						str							   =					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 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   Any					)					->					Any:


       """simple docstring"""







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

def 			UpperCamelCase_							(      _UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   List[Any]					)					->					Dict:


       """simple docstring"""







       if n == 1:
              return fa
       elif n == 2:
              return fa
       _UpperCAmelCase  :						int							   =					Matrix([[1, 1], [1, 0]]					)
       _UpperCAmelCase  :						List[Any]							   =					modular_exponentiation(lowercase_    ,			n - 2					)
       return fa * matrix.t[0][0] + fa * matrix.t[0][1]

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[int]    ,			_UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   str					)					->					int:


       """simple docstring"""







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

       _UpperCAmelCase  :						List[Any]							   =					fa
       _UpperCAmelCase  :						str							   =					fa
       n -= 2

       while n > 0:
              _UpperCAmelCase  :						Tuple							   =					fn_a + fn_a, fn_a
              n -= 1

       return fn_a

def 			UpperCamelCase_							(      )					->					Optional[int]:


       """simple docstring"""







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

def 			UpperCamelCase_							(      )					->					Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						Tuple							   =					'\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'
       _UpperCAmelCase  :						Optional[Any]							   =					'simple_fibonacci(randint(1,70000), 1, 1)'
       _UpperCAmelCase  :						str							   =					timeit.timeit(setup=lowercase_    ,			stmt=lowercase_    ,			number=100					)
       print(
           "Without matrix exponentiation the average execution time is "    ,			exec_time / 100					)
       return exec_time

def 			UpperCamelCase_							(      )					->					str:


       """simple docstring"""







       matrix_exponentiation_time()
       simple_fibonacci_time()


if __name__ == "__main__":
  main()


'''simple docstring'''




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						{
    """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:
  __SCREAMING_SNAKE_CASE :  Optional[int]  =						[
      """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTSanJapaneseForConditionalGeneration""",
      """GPTSanJapaneseModel""",
      """GPTSanJapanesePreTrainedModel""",
  ]
  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						[
      """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

  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

