"""simple docstring"""





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
       import tensorflow as tf


_UpperCamelCase     :		Dict											=					logging.get_logger(__name__)


@dataclass
class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					Tuple										=						[
          "no_inference",
          "no_cuda",
          "no_tpu",
          "no_speed",
          "no_memory",
          "no_env_print",
          "no_multi_process",
      ]


      def __init__(     self	,    **a							) ->      Tuple:
             for deprecated_arg in self.deprecated_args:
                    if deprecated_arg in kwargs:
                           lowercase__    :	Dict     					=			deprecated_arg[3:]
                           lowercase__    :	Tuple     					=			not kwargs.pop(a							)
                           logger.warning(
                               f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
                               f""" {positive_arg}={kwargs[positive_arg]}"""							)
             lowercase__    :	Optional[int]     					=			kwargs.pop('tpu_name'	,    self.tpu_name							)
             lowercase__    :	List[Any]     					=			kwargs.pop('device_idx'	,    self.device_idx							)
             lowercase__    :	Optional[int]     					=			kwargs.pop('eager_mode'	,    self.eager_mode							)
             lowercase__    :	Any     					=			kwargs.pop('use_xla'	,    self.use_xla							)
             super().__init__(**a							)

      lowerCamelCase__	:					str										=						field(
          default=_a				,							metadata={"help": "Name of TPU"}				,							)
      lowerCamelCase__	:					int										=						field(
          default=0				,							metadata={"help": "CPU / GPU device index. Defaults to 0."}				,							)
      lowerCamelCase__	:					bool										=						field(default=_a				,							metadata={"help": "Benchmark models in eager model."})
      lowerCamelCase__	:					bool										=						field(
          default=_a				,							metadata={
              "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
          }				,							)


      @cached_property
      def 	_UpperCAmelCase (     self							) ->      Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
             requires_backends(self	,    ['tf']							)
             lowercase__    :	Any     					=			None
             if self.tpu:
                    try:
                           if self.tpu_name:
                                  lowercase__    :	List[str]     					=			tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name							)
                           else:
                                  lowercase__    :	List[str]     					=			tf.distribute.cluster_resolver.TPUClusterResolver()
                    except ValueError:
                           lowercase__    :	List[str]     					=			None
             return tpu


      @cached_property
      def 	_UpperCAmelCase (     self							) ->      Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
             requires_backends(self	,    ['tf']							)
             if self.is_tpu:
                    tf.config.experimental_connect_to_cluster(self._setup_tpu							)
                    tf.tpu.experimental.initialize_tpu_system(self._setup_tpu							)

                    lowercase__    :	List[Any]     					=			tf.distribute.TPUStrategy(self._setup_tpu							)
             else:
                    # currently no multi gpu is allowed
                    if self.is_gpu:
                           # TODO: Currently only single GPU is supported
                           tf.config.set_visible_devices(self.gpu_list[self.device_idx]	,    'GPU'							)
                           lowercase__    :	int     					=			tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""							)
                    else:
                           tf.config.set_visible_devices([]	,    'GPU'							)  # disable GPU
                           lowercase__    :	str     					=			tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""							)

             return strategy


      @property
      def 	_UpperCAmelCase (     self							) ->      bool:
             requires_backends(self	,    ['tf']							)
             return self._setup_tpu is not None


      @property
      def 	_UpperCAmelCase (     self							) ->      "tf.distribute.Strategy":
             requires_backends(self	,    ['tf']							)
             return self._setup_strategy


      @property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             requires_backends(self	,    ['tf']							)
             return tf.config.list_physical_devices('GPU'							)


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             requires_backends(self	,    ['tf']							)
             if self.cuda:
                    return len(self.gpu_list							)
             return 0


      @property
      def 	_UpperCAmelCase (     self							) ->      bool:
             return self.n_gpu > 0


"""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        a_  (   _lowerCAmelCase						:				dict  ):





       '''simple docstring'''

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





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			XGBRegressor(verbosity=0     ,				random_state=42  )
       xgb.fit(_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Predict target for test data
       lowercase__    :	str     					=			xgb.predict(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			predictions.reshape(len(_lowerCAmelCase  )     ,				1  )
       return predictions





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			fetch_california_housing()
       lowercase__					,   lowercase__    :	str     					=			data_handling(_lowerCAmelCase  )
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			train_test_split(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				test_size=0.2_5     ,				random_state=1  )
       lowercase__    :	Any     					=			xgboost(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Error printing
       print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )
       print(f"""Mean Square Error  : {mean_squared_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

