"""simple docstring"""





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format


class   UpperCAmelCase_			:
      lowerCamelCase__	:					List[Any]										=						None


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Any     					=			self.feature_extraction_class(**self.feat_extract_dict							)
             lowercase__    :	Union[str, Any]     					=			json.loads(feat_extract.to_json_string()							)
             for key, value in self.feat_extract_dict.items():
                    self.assertEqual(obj[key]	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	Union[str, Any]     					=			self.feature_extraction_class(**self.feat_extract_dict							)

             with tempfile.TemporaryDirectory() as tmpdirname:
                    lowercase__    :	Any     					=			os.path.join(a	,    'feat_extract.json'							)
                    feat_extract_first.to_json_file(a							)
                    lowercase__    :	List[str]     					=			self.feature_extraction_class.from_json_file(a							)

             self.assertEqual(feat_extract_second.to_dict()	,    feat_extract_first.to_dict()							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			self.feature_extraction_class(**self.feat_extract_dict							)

             with tempfile.TemporaryDirectory() as tmpdirname:
                    lowercase__    :	List[str]     					=			feat_extract_first.save_pretrained(a							)[0]
                    check_json_file_has_correct_format(a							)
                    lowercase__    :	Tuple     					=			self.feature_extraction_class.from_pretrained(a							)

             self.assertEqual(feat_extract_second.to_dict()	,    feat_extract_first.to_dict()							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Union[str, Any]     					=			self.feature_extraction_class()
             self.assertIsNotNone(a							)


"""simple docstring"""





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def        a_  (   _lowerCAmelCase						:				dict  ):





       '''simple docstring'''

       return (data["data"], data["target"])





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			XGBRegressor(verbosity=0     ,				random_state=42  )
       xgb.fit(_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Predict target for test data
       lowercase__    :	str     					=			xgb.predict(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			predictions.reshape(len(_lowerCAmelCase  )     ,				1  )
       return predictions





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			fetch_california_housing()
       lowercase__					,   lowercase__    :	str     					=			data_handling(_lowerCAmelCase  )
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			train_test_split(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				test_size=0.2_5     ,				random_state=1  )
       lowercase__    :	Any     					=			xgboost(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Error printing
       print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )
       print(f"""Mean Square Error  : {mean_squared_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

