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






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    np.ndarray:
    __lowerCamelCase		=  XGBRegressor(verbosity=0				,  random_state=42				)
    xgb.fit(UpperCamelCase__				,  UpperCamelCase__				)
    # Predict target for test data
    __lowerCamelCase		=  xgb.predict(UpperCamelCase__				)
    __lowerCamelCase		=  predictions.reshape(len(UpperCamelCase__				)				,  1				)
    return predictions






def __lowerCAmelCase (							)	->    None:
    __lowerCamelCase		=  fetch_california_housing()
    __lowerCamelCase	,  __lowerCamelCase		=  data_handling(UpperCamelCase__				)
    __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase		=  train_test_split(
        UpperCamelCase__				,  UpperCamelCase__				,  test_size=0.2_5				,  random_state=1				)
    __lowerCamelCase		=  xgboost(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)
    # Error printing
    print(f"""Mean Absolute Error : {mean_absolute_error(UpperCamelCase__				,  UpperCamelCase__				)}"""				)
    print(f"""Mean Square Error  : {mean_squared_error(UpperCamelCase__				,  UpperCamelCase__				)}"""				)


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  main()




'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Union[str, Any]		=     '''speech_to_text_2'''
     lowerCamelCase				:	Any		=     ['''past_key_values''']
     lowerCamelCase				:	Optional[Any]		=     {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}





     def __init__( self       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[Any]=1_0_0_0_0    ,      UpperCAmelCase__       :      int=6    ,      UpperCAmelCase__       :      Optional[Any]=2_0_4_8    ,      UpperCAmelCase__       :      Union[str, Any]=4    ,      UpperCAmelCase__       :      List[Any]=0.0    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      str="relu"    ,      UpperCAmelCase__       :      Any=2_5_6    ,      UpperCAmelCase__       :      Optional[Any]=0.1    ,      UpperCAmelCase__       :      Any=0.0    ,      UpperCAmelCase__       :      Tuple=0.0    ,      UpperCAmelCase__       :      List[Any]=0.02    ,      UpperCAmelCase__       :      List[Any]=2    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      List[str]=1    ,      UpperCAmelCase__       :      Any=0    ,      UpperCAmelCase__       :      Dict=2    ,      UpperCAmelCase__       :      int=1_0_2_4    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Dict:
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			d_model
        lowerCAmelCase     =			decoder_ffn_dim
        lowerCAmelCase     =			decoder_layers
        lowerCAmelCase     =			decoder_attention_heads
        lowerCAmelCase     =			dropout
        lowerCAmelCase     =			attention_dropout
        lowerCAmelCase     =			activation_dropout
        lowerCAmelCase     =			activation_function
        lowerCAmelCase     =			init_std
        lowerCAmelCase     =			decoder_layerdrop
        lowerCAmelCase     =			use_cache
        lowerCAmelCase     =			decoder_layers
        lowerCAmelCase     =			scale_embedding  # scale factor will be sqrt(d_model) if True
        lowerCAmelCase     =			max_target_positions

        super().__init__(
            pad_token_id=UpperCAmelCase__    ,      bos_token_id=UpperCAmelCase__    ,      eos_token_id=UpperCAmelCase__    ,      decoder_start_token_id=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )





