
import math

import tensorflow as tf
from packaging import version






def 						__magic_name__			(   __a      :	List[str]						):



     '''simple docstring'''




     UpperCamelCase__				=    tf.convert_to_tensor(__a						)
     UpperCamelCase__				=    0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)	,		x.dtype						)						))

     return x * cdf






def 						__magic_name__			(   __a      :	List[Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    tf.convert_to_tensor(__a						)
     UpperCamelCase__				=    tf.cast(math.pi	,		x.dtype						)
     UpperCamelCase__				=    tf.cast(0.044_715	,		x.dtype						)
     UpperCamelCase__				=    0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(__a	,		3						))						))

     return x * cdf






def 						__magic_name__			(   __a      :	Union[str, Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    tf.convert_to_tensor(__a						)

     return x * tf.tanh(tf.math.softplus(__a						)						)






def 						__magic_name__			(   __a      :	Tuple						):



     '''simple docstring'''




     UpperCamelCase__				=    tf.convert_to_tensor(__a						)
     UpperCamelCase__				=    tf.cast(0.044_715	,		x.dtype						)
     UpperCamelCase__				=    tf.cast(0.7_978_845_608	,		x.dtype						)

     return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)						))






def 						__magic_name__			(   __a      :	Tuple						):



     '''simple docstring'''




     UpperCamelCase__				=    tf.convert_to_tensor(__a						)
     UpperCamelCase__				=    tf.cast(1.702	,		x.dtype						)
     return x * tf.math.sigmoid(coeff * x						)






def 						__magic_name__			(   __a      :	List[Any]						):



     '''simple docstring'''




     return tf.clip_by_value(_gelu(__a						)	,		-10	,		10						)






def 						__magic_name__			(   __a      :	List[str]	,		__a      :	Optional[Any]=-1						):



     '''simple docstring'''




     UpperCamelCase__		,	UpperCamelCase__				=    tf.split(__a	,		2	,		axis=__a						)
     return a * tf.math.sigmoid(__a						)


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):






    def 						__magic_name__			(   __a      :	Tuple						):



         '''simple docstring'''




         return tf.keras.activations.gelu(__a	,		approximate=__a						)

    lowerCamelCase_						     =					tf.keras.activations.gelu




    lowerCamelCase_						     =					approximate_gelu_wrap
else:
    lowerCamelCase_						     =					_gelu
    lowerCamelCase_						     =					_gelu_new


lowerCamelCase_						     =					{
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}






def 						__magic_name__			(   __a      :	str						):



     '''simple docstring'''




     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"						)







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()





