



def   SCREAMING_SNAKE_CASE_       (  _snake_case  :list[list[int]]  ,      _snake_case  :list[list[int]]							)     ->    list[list[int]]:
   _A						     =					[]
   _A						     =					len(_snake_case							)
   for i in range(_snake_case							):
      _A						     =					[]
      for j in range(_snake_case							):
         _A						     =					0
         for k in range(_snake_case							):
            _A						     =					val + matrix_a[i][k] * matrix_b[k][j]
         list_a.append(_snake_case							)
      matrix_c.append(_snake_case							)
   return matrix_c

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

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int							)     ->    int:
   if n <= 1:
      return n
   _A						     =					identity(2							)
   _A						     =					[[1, 1], [1, 0]]
   _A						     =					n - 1
   while n > 0:
      if n % 2 == 1:
         _A						     =					multiply(_snake_case  ,      _snake_case							)
      _A						     =					multiply(_snake_case  ,      _snake_case							)
      _A						     =					int(n / 2							)
   return res_matrix[0][0]

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int							)     ->    int:
   if n <= 1:
      return n
   _A						     =					0
   _A						     =					1
   for _ in range(2  ,      n + 1							):
      _A     ,						_A						     =					fiba, fiba + fiba
   return fiba

def   SCREAMING_SNAKE_CASE_       (  )     ->    None:
   for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
      _A						     =					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(_snake_case							)} and using bruteforce is '''
          F'''{nth_fibonacci_bruteforce(_snake_case							)}\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()








from typing import TYPE_CHECKING

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


UpperCAmelCase_		   =				{
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_		   =				["""XLMProphetNetTokenizer"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_		   =				[
       """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMProphetNetDecoder""",
       """XLMProphetNetEncoder""",
       """XLMProphetNetForCausalLM""",
       """XLMProphetNetForConditionalGeneration""",
       """XLMProphetNetModel""",
       """XLMProphetNetPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm_prophetnet import (
          XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMProphetNetDecoder,
          XLMProphetNetEncoder,
          XLMProphetNetForCausalLM,
          XLMProphetNetForConditionalGeneration,
          XLMProphetNetModel,
          XLMProphetNetPreTrainedModel,
      )

else:
   import sys

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



