




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      Dict  =   ['FNetTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      Tuple  =   ['FNetTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      str  =   [
           'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
           'FNetForMaskedLM',
           'FNetForMultipleChoice',
           'FNetForNextSentencePrediction',
           'FNetForPreTraining',
           'FNetForQuestionAnswering',
           'FNetForSequenceClassification',
           'FNetForTokenClassification',
           'FNetLayer',
           'FNetModel',
           'FNetPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_fnet import FNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_fnet_fast import FNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_fnet import (
                  FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FNetForMaskedLM,
                  FNetForMultipleChoice,
                  FNetForNextSentencePrediction,
                  FNetForPreTraining,
                  FNetForQuestionAnswering,
                  FNetForSequenceClassification,
                  FNetForTokenClassification,
                  FNetLayer,
                  FNetModel,
                  FNetPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__       :      Optional[int]  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












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      (      _SCREAMING_SNAKE_CASE    )	-> tuple:
       return (data["data"], data["target"])

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

def      A      (      )	-> None:
       lowerCamelCase   :      Dict			 =					fetch_california_housing()
       lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					data_handling(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					train_test_split(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,test_size=0.25 ,random_state=1    )
       lowerCamelCase   :      Any			 =					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()






