


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()     )







__SCREAMING_SNAKE_CASE	:							Optional[Any]           =  '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

__SCREAMING_SNAKE_CASE	:							Optional[int]           =  [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__SCREAMING_SNAKE_CASE	:							Optional[int]           =  {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}

