


'''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 lowerCamelCase__      (						__lowerCamelCase	:							dict							):







   '''simple docstring'''




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





def lowerCamelCase__      (						__lowerCamelCase	:							np.ndarray	,   __lowerCamelCase	:							np.ndarray	,   __lowerCamelCase	:							np.ndarray							):







   '''simple docstring'''




   _UpperCAmelCase       :       Union[str, Any]							   =XGBRegressor(verbosity=0	,   random_state=4_2							)
   xgb.fit(__lowerCamelCase	,   __lowerCamelCase							)
   # Predict target for test data
   _UpperCAmelCase       :       Union[str, Any]							   =xgb.predict(__lowerCamelCase							)
   _UpperCAmelCase       :       Optional[Any]							   =predictions.reshape(len(__lowerCamelCase							)	,   1							)
   return predictions





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =fetch_california_housing()
   _UpperCAmelCase   ,	_UpperCAmelCase       :       List[str]							   =data_handling(__lowerCamelCase							)
   _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       Union[str, Any]							   =train_test_split(
       __lowerCamelCase	,   __lowerCamelCase	,   test_size=0.25	,   random_state=1							)
   _UpperCAmelCase       :       Optional[int]							   =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()





'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase							=logging.get_logger(__name__)

lowercase							='T5Config'





def lowerCamelCase__      (						__lowerCamelCase	:							jnp.array	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =jnp.zeros_like(__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]							)
   _UpperCAmelCase       :       List[Any]							   =shifted_input_ids.at[:, 0].set(__lowerCamelCase							)

   _UpperCAmelCase       :       str							   =jnp.where(shifted_input_ids == -1_0_0	,   __lowerCamelCase	,   __lowerCamelCase							)
   return shifted_input_ids






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig

