






'''simple docstring'''






import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard




class a_     (     unittest.TestCase				):







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Dict  				=			{
          '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': 7_6},
      }







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






      __snake_case					:   int  				=			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      SCREAMING_SNAKE_CASE__       (self)      ->   Any:
      """simple docstring"""






      __snake_case					:   int  				=			ModelCard.from_dict(self.inputs_dict)
      __snake_case					:   Any  				=			json.loads(modelcard.to_json_string())
      for key, value in self.inputs_dict.items():
         self.assertEqual(obj[key]       , __a)







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






      __snake_case					:   Dict  				=			ModelCard.from_dict(self.inputs_dict)

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

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   Any  				=			ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
         model_card_first.save_pretrained(__a)
         __snake_case					:   Any  				=			ModelCard.from_pretrained(__a)

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







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A     						= {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

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