


import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard




class A  (       unittest.TestCase					):






      def 	lowerCamelCase	( self						:					List[Any]			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		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 	lowerCamelCase	( self						:					Any			) ->						List[Any]:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =ModelCard.from_dict(self.inputs_dict			)
             self.assertTrue(hasattr(lowercase_      ,       'model_details'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'intended_use'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'factors'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'metrics'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'evaluation_data'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'training_data'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'quantitative_analyses'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'ethical_considerations'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'caveats_and_recommendations'			)			)






      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Any          =ModelCard.from_dict(self.inputs_dict			)
             _lowerCamelCase	:		List[str]          =json.loads(modelcard.to_json_string()			)
             for key, value in self.inputs_dict.items():
                    self.assertEqual(obj[key]      ,       lowercase_			)






      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =ModelCard.from_dict(self.inputs_dict			)

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase	:		List[Any]          =os.path.join(lowercase_      ,       'modelcard.json'			)
                    model_card_first.to_json_file(lowercase_			)
                    _lowerCamelCase	:		Dict          =ModelCard.from_json_file(lowercase_			)

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






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =ModelCard.from_dict(self.inputs_dict			)

             with tempfile.TemporaryDirectory() as tmpdirname:
                    model_card_first.save_pretrained(lowercase_			)
                    _lowerCamelCase	:		List[str]          =ModelCard.from_pretrained(lowercase_			)

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase						 =	{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase						 =	[
     'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'VisualBertForMultipleChoice',
     'VisualBertForPreTraining',
     'VisualBertForQuestionAnswering',
     'VisualBertForRegionToPhraseAlignment',
     'VisualBertForVisualReasoning',
     'VisualBertLayer',
     'VisualBertModel',
     'VisualBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_visual_bert import (
      VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      VisualBertForMultipleChoice,
      VisualBertForPreTraining,
      VisualBertForQuestionAnswering,
      VisualBertForRegionToPhraseAlignment,
      VisualBertForVisualReasoning,
      VisualBertLayer,
      VisualBertModel,
      VisualBertPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase						 =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


