


'''simple docstring'''
import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard






class 					lowerCAmelCase_     (    unittest.TestCase  ):






       def      UpperCamelCase	(   self      )->			Optional[Any]:
            _A				=       {
                '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      UpperCamelCase	(   self      )->			List[Any]:
            _A				=       ModelCard.from_dict(self.inputs_dict      )
            self.assertTrue(hasattr(_UpperCamelCase ,    'model_details'      )      )
            self.assertTrue(hasattr(_UpperCamelCase ,    'intended_use'      )      )
            self.assertTrue(hasattr(_UpperCamelCase ,    'factors'      )      )
            self.assertTrue(hasattr(_UpperCamelCase ,    'metrics'      )      )
            self.assertTrue(hasattr(_UpperCamelCase ,    'evaluation_data'      )      )
            self.assertTrue(hasattr(_UpperCamelCase ,    'training_data'      )      )
            self.assertTrue(hasattr(_UpperCamelCase ,    'quantitative_analyses'      )      )
            self.assertTrue(hasattr(_UpperCamelCase ,    'ethical_considerations'      )      )
            self.assertTrue(hasattr(_UpperCamelCase ,    'caveats_and_recommendations'      )      )






       def      UpperCamelCase	(   self      )->			List[str]:
            _A				=       ModelCard.from_dict(self.inputs_dict      )
            _A				=       json.loads(modelcard.to_json_string()      )
            for key, value in self.inputs_dict.items():
                 self.assertEqual(obj[key] ,    _UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			int:
            _A				=       ModelCard.from_dict(self.inputs_dict      )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 _A				=       os.path.join(_UpperCamelCase ,    'modelcard.json'      )
                 model_card_first.to_json_file(_UpperCamelCase      )
                 _A				=       ModelCard.from_json_file(_UpperCamelCase      )

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






       def      UpperCamelCase	(   self      )->			Optional[int]:
            _A				=       ModelCard.from_dict(self.inputs_dict      )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model_card_first.save_pretrained(_UpperCamelCase      )
                 _A				=       ModelCard.from_pretrained(_UpperCamelCase      )

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









'''simple docstring'''
import numpy as np
import requests

def 				lowerCamelCase_   (   )				->  int:







     """simple docstring"""
     _A				=       requests.get(
         'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
         'master/Week1/ADRvsRating.csv'		)
     _A				=       response.text.splitlines()
     _A				=       []
     for item in lines:
          _A				=       item.split(','		)
          data.append(__UpperCamelCase		)
     data.pop(0		)  # This is for removing the labels from the list
     _A				=       np.matrix(__UpperCamelCase		)
     return dataset

def 				lowerCamelCase_   (   __UpperCamelCase				:					Union[str, Any]       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					int       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Dict		)				->  int:







     """simple docstring"""
     _A				=       len_data

     _A				=       np.dot(__UpperCamelCase       ,						data_x.transpose()		)
     prod -= data_y.transpose()
     _A				=       np.dot(__UpperCamelCase       ,						__UpperCamelCase		)
     _A				=       theta - (alpha / n) * sum_grad
     return theta

def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[int]       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					Optional[int]		)				->  Dict:







     """simple docstring"""
     _A				=       np.dot(__UpperCamelCase       ,						data_x.transpose()		)
     prod -= data_y.transpose()
     _A				=       np.sum(np.square(__UpperCamelCase		)		)
     _A				=       sum_elem / (2 * len_data)
     return error

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]		)				->  int:







     """simple docstring"""
     _A				=       1_0_0_0_0_0
     _A				=       0.0_00_15_50

     _A				=       data_x.shape[1]
     _A				=       data_x.shape[0] - 1

     _A				=       np.zeros((1, no_features)		)

     for i in range(0       ,						__UpperCamelCase		):
          _A				=       run_steep_gradient_descent(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
          _A				=       sum_of_square_error(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
          print(F'At Iteration {i + 1} - Error is {error:.5f}'		)

     return theta

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					Tuple		)				->  Union[str, Any]:







     """simple docstring"""
     _A				=       sum(abs(y - predicted_y[i]		) for i, y in enumerate(__UpperCamelCase		)		)
     return total / len(__UpperCamelCase		)

def 				lowerCamelCase_   (   )				->  Optional[Any]:







     """simple docstring"""
     _A				=       collect_dataset()

     _A				=       data.shape[0]
     _A				=       np.c_[np.ones(__UpperCamelCase		), data[:, :-1]].astype(__UpperCamelCase		)
     _A				=       data[:, -1].astype(__UpperCamelCase		)

     _A				=       run_linear_regression(__UpperCamelCase       ,						__UpperCamelCase		)
     _A				=       theta.shape[1]
     print('Resultant Feature vector : '		)
     for i in range(0       ,						__UpperCamelCase		):
          print(F'{theta[0, i]:.5f}'		)


if __name__ == "__main__":
    main()





