code: 
  
  import json
  import os
  import tempfile
  import unittest
  
  from transformers.modelcard import ModelCard
  
  
  class 							_lowerCAmelCase		(     unittest.TestCase	):
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: List[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     __a		( self							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: Any	     =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     __a		( self							)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: str	     =ModelCard.from_dict(self.inputs_dict							)
  											SCREAMING_SNAKE_CASE			: int	     =json.loads(modelcard.to_json_string()							)
  											for key, value in self.inputs_dict.items():
  																		self.assertEqual(obj[key]		,		a_							)
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: int	     =ModelCard.from_dict(self.inputs_dict							)
  
  											with tempfile.TemporaryDirectory() as tmpdirname:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =os.path.join(a_		,		'''modelcard.json'''							)
  																		model_card_first.to_json_file(a_							)
  																		SCREAMING_SNAKE_CASE			: List[str]	     =ModelCard.from_json_file(a_							)
  
  											self.assertEqual(model_card_second.to_dict()		,		model_card_first.to_dict()							)
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: List[str]	     =ModelCard.from_dict(self.inputs_dict							)
  
  											with tempfile.TemporaryDirectory() as tmpdirname:
  																		model_card_first.save_pretrained(a_							)
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =ModelCard.from_pretrained(a_							)
  
  											self.assertEqual(model_card_second.to_dict()		,		model_card_first.to_dict()							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from __future__ import annotations
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =m - 1, 0
  							while i >= 0 and j < n:
  														if key == mat[i][j]:
  																					print(f'Key {key} found at row- {i + 1} column- {j + 1}' )
  																					return
  														if key < mat[i][j]:
  																					i -= 1
  														else:
  																					j += 1
  							print(f'Key {key} not found' )
  def 						lowerCAmelCase_       (	)       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  							SCREAMING_SNAKE_CASE			: List[str]	     =int(input('''Enter the element to be searched:''' ) )
  							print(__a )
  							search_in_a_sorted_matrix(__a	,	len(__a )	,	len(mat[0] )	,	__a )
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  							main()
  
  
  
  
  
  
  