





import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


UpperCAmelCase_					=					logging.get_logger(__name__)




def 						lowerCAmelCase_       (    ) ->      Any:
    UpperCamelCase__				:							str			  = os.getenv('''SM_HP_MP_PARAMETERS'''						,  '''{}''' )
    try:
        # Parse it and check the field "partitions" is included, it is required for model parallel.
        UpperCamelCase__				:							str			  = json.loads(_lowercase )
        if "partitions" not in smp_options:
            return False
    except json.JSONDecodeError:
        return False

    # Get the sagemaker specific framework parameters from mpi_options variable.
    UpperCamelCase__				:							Dict			  = os.getenv('''SM_FRAMEWORK_PARAMS'''						,  '''{}''' )
    try:
        # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
        UpperCamelCase__				:							Union[str, Any]			  = json.loads(_lowercase )
        if not mpi_options.get('''sagemaker_mpi_enabled'''						,  _lowercase ):
            return False
    except json.JSONDecodeError:
        return False
    # Lastly, check if the `smdistributed` module is present.
    return importlib.util.find_spec('''smdistributed''' ) is not None


if is_sagemaker_model_parallel_available():
  import smdistributed.modelparallel.torch as smp

  smp.init()






@dataclass
class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				int  						=						field(
       default=""     ,							metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}     ,							)




   def  UpperCamelCase__    (       self				)					-> int:






       """simple docstring"""
       super().__post_init__()
       warnings.warn(
           '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
           '''`TrainingArguments` instead.''',						lowerCamelCase_,						)




   @cached_property
   def  UpperCamelCase__    (       self				)					-> Optional[int]:






       """simple docstring"""
       logger.info('''PyTorch: setting up devices'''				)
       if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
           logger.warning(
               '''torch.distributed process group is initialized, but local_rank == -1. '''
               '''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'''				)
       if self.no_cuda:
           UpperCamelCase__				:							Tuple			  = torch.device('''cpu'''				)
           UpperCamelCase__				:							Optional[int]			  = 0
       elif is_sagemaker_model_parallel_available():
           UpperCamelCase__				:							Dict			  = smp.local_rank()
           UpperCamelCase__				:							List[str]			  = torch.device('''cuda''',						lowerCamelCase_				)
           UpperCamelCase__				:							Optional[Any]			  = 1
       elif is_sagemaker_dp_enabled():
           import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

           torch.distributed.init_process_group(backend='''smddp''',						timeout=self.ddp_timeout_delta				)
           UpperCamelCase__				:							Dict			  = int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''				)				)
           UpperCamelCase__				:							Optional[Any]			  = torch.device('''cuda''',						self.local_rank				)
           UpperCamelCase__				:							List[Any]			  = 1
       elif self.local_rank == -1:
           # if n_gpu is > 1 we'll use nn.DataParallel.
           # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
           # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
           # trigger an error that a device index is missing. Index 0 takes into account the
           # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
           # will use the first GPU in that env, i.e. GPU#1
           UpperCamelCase__				:							int			  = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''				)
           # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
           # the default value.
           UpperCamelCase__				:							int			  = torch.cuda.device_count()
       else:
           # Here, we'll use torch.distributed.
           # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
           if not torch.distributed.is_initialized():
               torch.distributed.init_process_group(backend='''nccl''',						timeout=self.ddp_timeout_delta				)
           UpperCamelCase__				:							Union[str, Any]			  = torch.device('''cuda''',						self.local_rank				)
           UpperCamelCase__				:							Dict			  = 1

       if device.type == "cuda":
           torch.cuda.set_device(lowerCamelCase_				)

       return device




   @property
   def  UpperCamelCase__    (       self				)					-> str:






       """simple docstring"""
       if is_sagemaker_model_parallel_available():
           return smp.dp_size()

       return super().world_size




   @property
   def  UpperCamelCase__    (       self				)					-> Optional[int]:






       """simple docstring"""
       return not is_sagemaker_model_parallel_available()




   @property
   def  UpperCamelCase__    (       self				)					-> int:






       """simple docstring"""
       return False








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       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict			)   ->							tuple:




		'''simple docstring'''
		return (data["data"], data["target"])


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray			)   ->							np.ndarray:




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


def       SCREAMING_SNAKE_CASE__  (       )   ->							None:




		'''simple docstring'''
		lowercase__					:      List[str]      =		fetch_california_housing()
		lowercase__   ,				lowercase__					:      Optional[int]      =		data_handling(_lowercase			)
		lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Optional[Any]      =		train_test_split(
		    _lowercase   ,    _lowercase   ,    test_size=0.25   ,    random_state=1			)
		lowercase__					:      Any      =		xgboost(_lowercase   ,    _lowercase   ,    _lowercase			)
		# Error printing
		print(f"""Mean Absolute Error : {mean_absolute_error(_lowercase   ,    _lowercase			)}"""			)
		print(f"""Mean Square Error  : {mean_squared_error(_lowercase   ,    _lowercase			)}"""			)


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()


