
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase_						     =					{
    'configuration_pix2struct': [
        'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Pix2StructConfig',
        'Pix2StructTextConfig',
        'Pix2StructVisionConfig',
    ],
    'processing_pix2struct': ['Pix2StructProcessor'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					['Pix2StructImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Pix2StructPreTrainedModel',
        'Pix2StructForConditionalGeneration',
        'Pix2StructVisionModel',
        'Pix2StructTextModel',
    ]

if TYPE_CHECKING:
    from .configuration_pixastruct import (
        PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        PixaStructConfig,
        PixaStructTextConfig,
        PixaStructVisionConfig,
    )
    from .processing_pixastruct import PixaStructProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_pixastruct import PixaStructImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_pixastruct import (
            PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
            PixaStructForConditionalGeneration,
            PixaStructPreTrainedModel,
            PixaStructTextModel,
            PixaStructVisionModel,
        )

else:
    import sys

    lowerCamelCase_						     =					_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 						__magic_name__			(   __a      :	dict						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	np.ndarray	,		__a      :	np.ndarray	,		__a      :	np.ndarray						):



     '''simple docstring'''




     UpperCamelCase__				=    XGBRegressor(verbosity=0	,		random_state=42						)
     xgb.fit(__a	,		__a						)
     # Predict target for test data
     UpperCamelCase__				=    xgb.predict(__a						)
     UpperCamelCase__				=    predictions.reshape(len(__a						)	,		1						)
     return predictions






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    fetch_california_housing()
     UpperCamelCase__		,	UpperCamelCase__				=    data_handling(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    train_test_split(
         __a	,		__a	,		test_size=0.25	,		random_state=1						)
     UpperCamelCase__				=    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()





