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__  (			lowercase:     dict					)				->		int:
       return (data["data"], data["target"])



def A__  (			lowercase:     np.ndarray,		lowercase:     np.ndarray,		lowercase:     np.ndarray					)				->		Union[str, Any]:
       A						:     Any         =XGBRegressor(verbosity=0,		random_state=42					)
       xgb.fit(snake_case__,		snake_case__					)
       # Predict target for test data
       A						:     Optional[Any]         =xgb.predict(snake_case__					)
       A						:     Tuple         =predictions.reshape(len(snake_case__					),		1					)
       return predictions



def A__  (			)				->		Tuple:
       A						:     Optional[int]         =fetch_california_housing()
       A  ,  A						:     Dict         =data_handling(snake_case__					)
       A  ,  A  ,  A  ,  A						:     Any         =train_test_split(
           snake_case__,		snake_case__,		test_size=0.25,		random_state=1					)
       A						:     int         =xgboost(snake_case__,		snake_case__,		snake_case__					)
       # Error printing
       print(F'Mean Absolute Error : {mean_absolute_error(snake_case__,		snake_case__					)}'					)
       print(F'Mean Square Error  : {mean_squared_error(snake_case__,		snake_case__					)}'					)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()







_lowercase							:      Dict      							='''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
    from .utils import rich






