


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 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Any    =				fetch_california_housing()
  lowerCamelCase    ,	lowerCamelCase :					List[Any]    =				data_handling(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					Any    =				train_test_split(
      SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						test_size=0.25      ,						random_state=1	)
  lowerCamelCase :					List[str]    =				xgboost(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
  # Error printing
  print(f"""Mean Absolute Error : {mean_absolute_error(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)}"""	)
  print(f"""Mean Square Error  : {mean_squared_error(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)}"""	)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



