



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase:      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:
     lowerCAmelCase:      str		 =		[
         '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

     lowerCAmelCase:      List[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    lowerCamelCase__ (		_A       ):
  return (data["data"], data["target"])
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Any       =  XGBRegressor(verbosity=0				,    random_state=42       )
  xgb.fit(_A				,    _A       )
  # Predict target for test data
  a		:      Any       =  xgb.predict(_A       )
  a		:      Any       =  predictions.reshape(len(_A       )				,    1       )
  return predictions
def    lowerCamelCase__ (		):
  a		:      Optional[int]       =  fetch_california_housing()
  a     ,			a		:      Optional[Any]       =  data_handling(_A       )
  a     ,			a     ,			a     ,			a		:      Dict       =  train_test_split(
      _A				,    _A				,    test_size=0.25				,    random_state=1       )
  a		:      List[str]       =  xgboost(_A				,    _A				,    _A       )
  # Error printing
  print(f"""Mean Absolute Error : {mean_absolute_error(_A				,    _A       )}"""       )
  print(f"""Mean Square Error  : {mean_squared_error(_A				,    _A       )}"""       )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()