
def        lowerCAmelCase       (				snake_case__   :      str				,					snake_case__   :      str )->			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        lowerCAmelCase       (				snake_case__   :      Optional[Any] )->			list[list[int]]:
    return [[int(row == column ) for column in range(snake_case__ )] for row in range(snake_case__ )]





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			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        lowerCAmelCase       (				snake_case__   :      Any )->			int:
    if n <= 1:
        return n
    A_								=					0
    A_								=					1
    for _ in range(2				,					n + 1 ):
        A_								=					fiba, fiba + fiba
    return fiba





def        lowerCAmelCase       (				)->			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()







import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard






class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				{
            '''model_details''': {
                '''Organization''': '''testing''',
                '''Model date''': '''today''',
                '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
                '''Architecture''': '''Convolutional Neural Network.''',
            },
            '''metrics''': '''BLEU and ROUGE-1''',
            '''evaluation_data''': {
                '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
                '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
            },
            '''training_data''': {
                '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
                '''Preprocessing''': (
                    '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
                    ''' https://arxiv.org/pdf/1810.03993.pdf'''
                ),
            },
            '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 76},
        }
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Tuple	      =				ModelCard.from_dict(self.inputs_dict      )
        self.assertTrue(hasattr(__magic_name__ ,       '''model_details'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''intended_use'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''factors'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''metrics'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''evaluation_data'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''training_data'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''quantitative_analyses'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''ethical_considerations'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''caveats_and_recommendations'''      )      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				ModelCard.from_dict(self.inputs_dict      )
        A_							:       List[Any]	      =				json.loads(modelcard.to_json_string()      )
        for key, value in self.inputs_dict.items():
               self.assertEqual(obj[key] ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Dict	      =				ModelCard.from_dict(self.inputs_dict      )

        with tempfile.TemporaryDirectory() as tmpdirname:
               A_							:       str	      =				os.path.join(__magic_name__ ,       '''modelcard.json'''      )
               model_card_first.to_json_file(__magic_name__      )
               A_							:       str	      =				ModelCard.from_json_file(__magic_name__      )

        self.assertEqual(model_card_second.to_dict() ,       model_card_first.to_dict()      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				ModelCard.from_dict(self.inputs_dict      )

        with tempfile.TemporaryDirectory() as tmpdirname:
               model_card_first.save_pretrained(__magic_name__      )
               A_							:       Optional[int]	      =				ModelCard.from_pretrained(__magic_name__      )

        self.assertEqual(model_card_second.to_dict() ,       model_card_first.to_dict()      )


