


'''simple docstring'''


def 			__lowerCAmelCase			(__lowerCAmelCase = 10**6 ):

		if n <= 0:
				raise ValueError("Please enter an integer greater than 0" )

		_UpperCAmelCase		:							Optional[Any]             =   list(range(n + 1 ) )
		for number in range(2   ,			n + 1 ):
				if phi[number] == number:
						phi[number] -= 1
						for multiple in range(number * 2   ,			n + 1   ,			__lowerCAmelCase ):
								_UpperCAmelCase		:							int             =   (phi[multiple] // number) * (number - 1)

		_UpperCAmelCase		:							List[str]             =   1
		for number in range(1   ,			n + 1 ):
				if (answer / phi[answer]) < (number / phi[number]):
						_UpperCAmelCase		:							str             =   number

		return answer


if __name__ == "__main__":
						print(solution())




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()      )


