

"""simple docstring"""




def 						UpperCAmelCase__ (					A__						,     A__ )   ->   list[list[int]]:




   """simple docstring"""
   lowerCamelCase__						       =      []
   lowerCamelCase__						       =      len(A__ )
   for i in range(A__ ):
      lowerCamelCase__						       =      []
      for j in range(A__ ):
         lowerCamelCase__						       =      0
         for k in range(A__ ):
            lowerCamelCase__						       =      val + matrix_a[i][k] * matrix_b[k][j]
         list_a.append(A__ )
      matrix_c.append(A__ )
   return matrix_c



def 						UpperCAmelCase__ (					A__ )   ->   list[list[int]]:




   """simple docstring"""
   return [[int(row == column ) for column in range(A__ )] for row in range(A__ )]



def 						UpperCAmelCase__ (					A__ )   ->   int:




   """simple docstring"""
   if n <= 1:
      return n
   lowerCamelCase__						       =      identity(2 )
   lowerCamelCase__						       =      [[1, 1], [1, 0]]
   lowerCamelCase__						       =      n - 1
   while n > 0:
      if n % 2 == 1:
         lowerCamelCase__						       =      multiply(A__						,     A__ )
      lowerCamelCase__						       =      multiply(A__						,     A__ )
      lowerCamelCase__						       =      int(n / 2 )
   return res_matrix[0][0]



def 						UpperCAmelCase__ (					A__ )   ->   int:




   """simple docstring"""
   if n <= 1:
      return n
   lowerCamelCase__						       =      0
   lowerCamelCase__						       =      1
   for _ in range(2						,     n + 1 ):
      lowerCamelCase__       ,							lowerCamelCase__						       =      fiba, fiba + fiba
   return fiba



def 						UpperCAmelCase__ (					)   ->   None:




   """simple docstring"""
   for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
      lowerCamelCase__						       =      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(A__ )} and using bruteforce is '
          f'{nth_fibonacci_bruteforce(A__ )}\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"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_  :      Tuple							=      {
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_  :      int							=      [
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_  :      Dict							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




