


import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 	a__						(    unittest.TestCase     ):

       def     UpperCAmelCase( self   :  List[str]     ):
            a_     :  Tuple				      =     {
                """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""": 5_5.1, """ROUGE-1""": 7_6},
            }

       def     UpperCAmelCase( self   :  int     ):
            a_     :  Tuple				      =     ModelCard.from_dict(self.inputs_dict     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""model_details"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""intended_use"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""factors"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""metrics"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""evaluation_data"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""training_data"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""quantitative_analyses"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""ethical_considerations"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""caveats_and_recommendations"""     )     )

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

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  List[str]				      =     ModelCard.from_dict(self.inputs_dict     )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 a_     :  List[Any]				      =     os.path.join(lowerCamelCase_   ,				"""modelcard.json"""     )
                 model_card_first.to_json_file(lowerCamelCase_     )
                 a_     :  Union[str, Any]				      =     ModelCard.from_json_file(lowerCamelCase_     )

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

       def     UpperCAmelCase( self   :  List[str]     ):
            a_     :  List[Any]				      =     ModelCard.from_dict(self.inputs_dict     )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model_card_first.save_pretrained(lowerCamelCase_     )
                 a_     :  Any				      =     ModelCard.from_pretrained(lowerCamelCase_     )

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











"""simple docstring"""



from string import ascii_uppercase




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = ascii_uppercase			)    ->str:
 UpperCAmelCase__ 						=				keyword.upper()
 UpperCAmelCase__ 						=				plaintext.upper()
 UpperCAmelCase__ 						=				set(_SCREAMING_SNAKE_CASE			)

 # create a list of unique characters in the keyword - their order matters
 # it determines how we will map plaintext characters to the ciphertext
 UpperCAmelCase__ 						=				[]
 for char in keyword:
  if char in alphabet_set and char not in unique_chars:
   unique_chars.append(_SCREAMING_SNAKE_CASE			)
    # the number of those unique characters will determine the number of rows
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)

 # create a shifted version of the alphabet
 UpperCAmelCase__ 						=				unique_chars + [
     char for char in alphabet if char not in unique_chars
 ]

 # create a modified alphabet by splitting the shifted alphabet into rows
 UpperCAmelCase__ 						=				[
     shifted_alphabet[k : k + num_unique_chars_in_keyword]
     for k in range(0       ,	2_6       ,	_SCREAMING_SNAKE_CASE			)
 ]

 # map the alphabet characters to the modified alphabet characters
 # going 'vertically' through the modified alphabet - consider columns first
 UpperCAmelCase__ 						=				{}
 UpperCAmelCase__ 						=				0
 for column in range(_SCREAMING_SNAKE_CASE			):
  for row in modified_alphabet:
   # if current row (the last one) is too short, break out of loop
   if len(_SCREAMING_SNAKE_CASE			) <= column:
    break

   # map current letter to letter in modified alphabet
   UpperCAmelCase__ 						=				row[column]
   letter_index += 1

 if verbose:
  print(_SCREAMING_SNAKE_CASE			)
 # create the encrypted text by mapping the plaintext to the modified alphabet
 return "".join(mapping[char] if char in mapping else char for char in plaintext			)


if __name__ == "__main__":
 # example use
 print(mixed_keyword('''college''', '''UNIVERSITY'''))



