"""simple docstring"""





def     a_					(				_lowerCAmelCase  :			list[list[int]]       ,			_lowerCAmelCase  :			list[list[int]]					):





       '''simple docstring'''

       lowercase__      :		Dict          =     []
       lowercase__      :		Union[str, Any]          =     len(_lowerCAmelCase					)
       for i in range(_lowerCAmelCase					):
              lowercase__      :		str          =     []
              for j in range(_lowerCAmelCase					):
                     lowercase__      :		Optional[Any]          =     0
                     for k in range(_lowerCAmelCase					):
                            lowercase__      :		List[str]          =     val + matrix_a[i][k] * matrix_b[k][j]
                     list_a.append(_lowerCAmelCase					)
              matrix_c.append(_lowerCAmelCase					)
       return matrix_c





def     a_					(				_lowerCAmelCase  :			int					):





       '''simple docstring'''

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





def     a_					(				_lowerCAmelCase  :			int					):





       '''simple docstring'''

       if n <= 1:
              return n
       lowercase__      :		int          =     identity(2					)
       lowercase__      :		Tuple          =     [[1, 1], [1, 0]]
       lowercase__      :		Optional[int]          =     n - 1
       while n > 0:
              if n % 2 == 1:
                     lowercase__      :		List[str]          =     multiply(_lowerCAmelCase       ,			_lowerCAmelCase					)
              lowercase__      :		Any          =     multiply(_lowerCAmelCase       ,			_lowerCAmelCase					)
              lowercase__      :		int          =     int(n / 2					)
       return res_matrix[0][0]





def     a_					(				_lowerCAmelCase  :			int					):





       '''simple docstring'''

       if n <= 1:
              return n
       lowercase__      :		Any          =     0
       lowercase__      :		int          =     1
       for _ in range(2       ,			n + 1					):
              lowercase__   ,				lowercase__      :		List[Any]          =     fiba, fiba + fiba
       return fiba





def     a_					(				):





       '''simple docstring'''

       for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
              lowercase__      :		Optional[Any]          =     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(_lowerCAmelCase					)} and using bruteforce is """
                  f"""{nth_fibonacci_bruteforce(_lowerCAmelCase					)}\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_tokenizers_available, is_torch_available


_UpperCamelCase       :						int     =   {
    "configuration_retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig"],
    "tokenization_retribert": ["RetriBertTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						Tuple     =   ["RetriBertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						int     =   [
           "RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RetriBertModel",
           "RetriBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
       from .tokenization_retribert import RetriBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_retribert_fast import RetriBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_retribert import (
                  RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RetriBertModel,
                  RetriBertPreTrainedModel,
              )

else:
       import sys

       _UpperCamelCase       :						Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

