code: 
  '''simple docstring'''
  
  import json
  import os
  import tempfile
  import unittest
  
  from transformers.modelcard import ModelCard
  
  class 			UpperCAmelCase				(					unittest.TestCase ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def _lowercase  (    self     )				->    int:
  				_UpperCamelCase       : str    =      {
  				    '''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 _lowercase  (    self     )				->    Tuple:
  				_UpperCamelCase       : List[Any]    =      ModelCard.from_dict(self.inputs_dict     )
  				self.assertTrue(hasattr(_snake_case  ,					'''model_details'''     )     )
  				self.assertTrue(hasattr(_snake_case  ,					'''intended_use'''     )     )
  				self.assertTrue(hasattr(_snake_case  ,					'''factors'''     )     )
  				self.assertTrue(hasattr(_snake_case  ,					'''metrics'''     )     )
  				self.assertTrue(hasattr(_snake_case  ,					'''evaluation_data'''     )     )
  				self.assertTrue(hasattr(_snake_case  ,					'''training_data'''     )     )
  				self.assertTrue(hasattr(_snake_case  ,					'''quantitative_analyses'''     )     )
  				self.assertTrue(hasattr(_snake_case  ,					'''ethical_considerations'''     )     )
  				self.assertTrue(hasattr(_snake_case  ,					'''caveats_and_recommendations'''     )     )
  		def _lowercase  (    self     )				->    List[str]:
  				_UpperCamelCase       : Dict    =      ModelCard.from_dict(self.inputs_dict     )
  				_UpperCamelCase       : Dict    =      json.loads(modelcard.to_json_string()     )
  				for key, value in self.inputs_dict.items():
  						self.assertEqual(obj[key]  ,					_snake_case     )
  		def _lowercase  (    self     )				->    Any:
  				_UpperCamelCase       : Optional[int]    =      ModelCard.from_dict(self.inputs_dict     )
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  						_UpperCamelCase       : Dict    =      os.path.join(_snake_case  ,					'''modelcard.json'''     )
  						model_card_first.to_json_file(_snake_case     )
  						_UpperCamelCase       : Tuple    =      ModelCard.from_json_file(_snake_case     )
  
  				self.assertEqual(model_card_second.to_dict()  ,					model_card_first.to_dict()     )
  
  
  
  
  		def _lowercase  (    self     )				->    List[Any]:
  				_UpperCamelCase       : Dict    =      ModelCard.from_dict(self.inputs_dict     )
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  						model_card_first.save_pretrained(_snake_case     )
  						_UpperCamelCase       : List[str]    =      ModelCard.from_pretrained(_snake_case     )
  
  				self.assertEqual(model_card_second.to_dict()  ,					model_card_first.to_dict()     )
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _UpperCAmelCase   :      int 						=  {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	_UpperCAmelCase   :      int 						=  [
  	    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """GPTSanJapaneseForConditionalGeneration""",
  	    """GPTSanJapaneseModel""",
  	    """GPTSanJapanesePreTrainedModel""",
  	]
  	_UpperCAmelCase   :      str 						=  [
  	    """GPTSanJapaneseTokenizer""",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_gptsan_japanese import (
  		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    GPTSanJapaneseForConditionalGeneration,
  		    GPTSanJapaneseModel,
  		    GPTSanJapanesePreTrainedModel,
  		)
  		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
  	import sys
  
  	_UpperCAmelCase   :      List[str] 						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  