"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase     :		List[Any]											=					{
    "configuration_xlm_roberta_xl": [
        "XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaXLConfig",
        "XLMRobertaXLOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XLMRobertaXLForCausalLM",
           "XLMRobertaXLForMaskedLM",
           "XLMRobertaXLForMultipleChoice",
           "XLMRobertaXLForQuestionAnswering",
           "XLMRobertaXLForSequenceClassification",
           "XLMRobertaXLForTokenClassification",
           "XLMRobertaXLModel",
           "XLMRobertaXLPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_xlm_roberta_xl import (
           XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XLMRobertaXLConfig,
           XLMRobertaXLOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlm_roberta_xl import (
                  XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMRobertaXLForCausalLM,
                  XLMRobertaXLForMaskedLM,
                  XLMRobertaXLForMultipleChoice,
                  XLMRobertaXLForQuestionAnswering,
                  XLMRobertaXLForSequenceClassification,
                  XLMRobertaXLForTokenClassification,
                  XLMRobertaXLModel,
                  XLMRobertaXLPreTrainedModel,
              )

else:
       import sys

       _UpperCamelCase     :		Union[str, Any]											=					_LazyModule(__name__, globals()["__file__"], _import_structure)


"""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()

