





'''simple docstring'''

lowercase_			       =				{
    """A""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """B""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """C""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """D""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """E""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """F""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """G""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """H""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """I""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """J""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """K""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """L""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """M""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """N""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """O""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """P""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """Q""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """R""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """S""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """T""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """U""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """V""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """W""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """X""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """Y""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
    """Z""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
}





def lowerCamelCase		(  __lowerCamelCase				:					str					)		->list[tuple[str, str]]:
    return [alphabet[char] for char in key.upper()]





def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str					)		->str:
    _SCREAMING_SNAKE_CASE             =    """"""
    _SCREAMING_SNAKE_CASE             =    0
    _SCREAMING_SNAKE_CASE             =    generate_table(__lowerCamelCase					)
    for char in words.upper():
        cipher += get_opponent(table[count]      , __lowerCamelCase					)
        _SCREAMING_SNAKE_CASE             =    (count + 1) % len(__lowerCamelCase					)
    return cipher





def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str					)		->str:
    return encrypt(__lowerCamelCase      , __lowerCamelCase					)





def lowerCamelCase		(  __lowerCamelCase				:					tuple[str, str]      , __lowerCamelCase				:					str					)		->tuple[int, int]:
    _SCREAMING_SNAKE_CASE             =    0 if char in table[0] else 1
    _SCREAMING_SNAKE_CASE             =    table[row].index(__lowerCamelCase					)
    return row, col





def lowerCamelCase		(  __lowerCamelCase				:					tuple[str, str]      , __lowerCamelCase				:					str					)		->str:
    _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    get_position(__lowerCamelCase      , char.upper()					)
    if row == 1:
        return table[0][col]
    else:
        return table[1][col] if row == 0 else char


if __name__ == "__main__":
      import doctest

      doctest.testmod()  # Fist ensure that all our tests are passing...
      lowercase_			       =				input("""Enter key: """).strip()
      lowercase_			       =				input("""Enter text to encrypt: """).strip()
      lowercase_			       =				encrypt(key, text)

      print(f"""Encrypted: {cipher_text}""")
      print(f"""Decrypted with key: {decrypt(key, cipher_text)}""")










'''simple docstring'''

import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class       a_  (       unittest.TestCase ):

    '''simple docstring'''


    def     snake_case_(	self )	->     int:
        _SCREAMING_SNAKE_CASE             =    {
            """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     snake_case_(	self )	->     Tuple:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
        self.assertTrue(hasattr(A				,	"""model_details""" ) )
        self.assertTrue(hasattr(A				,	"""intended_use""" ) )
        self.assertTrue(hasattr(A				,	"""factors""" ) )
        self.assertTrue(hasattr(A				,	"""metrics""" ) )
        self.assertTrue(hasattr(A				,	"""evaluation_data""" ) )
        self.assertTrue(hasattr(A				,	"""training_data""" ) )
        self.assertTrue(hasattr(A				,	"""quantitative_analyses""" ) )
        self.assertTrue(hasattr(A				,	"""ethical_considerations""" ) )
        self.assertTrue(hasattr(A				,	"""caveats_and_recommendations""" ) )


    def     snake_case_(	self )	->     List[str]:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
        _SCREAMING_SNAKE_CASE             =    json.loads(modelcard.to_json_string() )
        for key, value in self.inputs_dict.items():
            self.assertEqual(obj[key]				,	A )


    def     snake_case_(	self )	->     Any:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )

        with tempfile.TemporaryDirectory() as tmpdirname:
            _SCREAMING_SNAKE_CASE             =    os.path.join(A				,	"""modelcard.json""" )
            model_card_first.to_json_file(A )
            _SCREAMING_SNAKE_CASE             =    ModelCard.from_json_file(A )

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





    def     snake_case_(	self )	->     List[Any]:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )

        with tempfile.TemporaryDirectory() as tmpdirname:
            model_card_first.save_pretrained(A )
            _SCREAMING_SNAKE_CASE             =    ModelCard.from_pretrained(A )

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



