






"""simple docstring"""
def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Any     				=						[]
 lowerCamelCase__     :			int     				=						len(_lowerCamelCase				)
 for i in range(_lowerCamelCase				):
  lowerCamelCase__     :			Union[str, Any]     				=						[]
  for j in range(_lowerCamelCase				):
   lowerCamelCase__     :			int     				=						0
   for k in range(_lowerCamelCase				):
    lowerCamelCase__     :			int     				=						val + matrix_a[i][k] * matrix_b[k][j]
   list_a.append(_lowerCamelCase				)
  matrix_c.append(_lowerCamelCase				)
 return matrix_c






def        lowerCamelCase_  (						_lowerCamelCase				):
 return [[int(row == column				) for column in range(_lowerCamelCase				)] for row in range(_lowerCamelCase				)]






def        lowerCamelCase_  (						_lowerCamelCase				):
 if n <= 1:
  return n
 lowerCamelCase__     :			int     				=						identity(2				)
 lowerCamelCase__     :			List[str]     				=						[[1, 1], [1, 0]]
 lowerCamelCase__     :			str     				=						n - 1
 while n > 0:
  if n % 2 == 1:
   lowerCamelCase__     :			Union[str, Any]     				=						multiply(_lowerCamelCase			,   _lowerCamelCase				)
  lowerCamelCase__     :			Optional[int]     				=						multiply(_lowerCamelCase			,   _lowerCamelCase				)
  lowerCamelCase__     :			Any     				=						int(n / 2				)
 return res_matrix[0][0]






def        lowerCamelCase_  (						_lowerCamelCase				):
 if n <= 1:
  return n
 lowerCamelCase__     :			str     				=						0
 lowerCamelCase__     :			int     				=						1
 for _ in range(2			,   n + 1				):
  lowerCamelCase__				,     lowerCamelCase__     :			Optional[int]     				=						fiba, fiba + fiba
 return fiba






def        lowerCamelCase_  (						):
 for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
  lowerCamelCase__     :			str     				=						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_torch_available


A_			: Dict				=			{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Tuple				=			[
          "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "QDQBertForMaskedLM",
          "QDQBertForMultipleChoice",
          "QDQBertForNextSentencePrediction",
          "QDQBertForQuestionAnswering",
          "QDQBertForSequenceClassification",
          "QDQBertForTokenClassification",
          "QDQBertLayer",
          "QDQBertLMHeadModel",
          "QDQBertModel",
          "QDQBertPreTrainedModel",
          "load_tf_weights_in_qdqbert",
      ]


if TYPE_CHECKING:
      from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_qdqbert import (
                QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                QDQBertForMaskedLM,
                QDQBertForMultipleChoice,
                QDQBertForNextSentencePrediction,
                QDQBertForQuestionAnswering,
                QDQBertForSequenceClassification,
                QDQBertForTokenClassification,
                QDQBertLayer,
                QDQBertLMHeadModel,
                QDQBertModel,
                QDQBertPreTrainedModel,
                load_tf_weights_in_qdqbert,
            )


else:
      import sys

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

