


'''simple docstring'''






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    _snake_case  (	)		->       Union[str, Any]:
							# Get the sagemaker specific mp parameters from smp_options variable.
							lowerCAmelCase__			  =      os.getenv('''SM_HP_MP_PARAMETERS'''					,       '''{}'''			)
							try:
														# Parse it and check the field "partitions" is included, it is required for model parallel.
														lowerCAmelCase__			  =      json.loads(A			)
														if "partitions" not in smp_options:
																					return False
							except json.JSONDecodeError:
														return False

							# Get the sagemaker specific framework parameters from mpi_options variable.
							lowerCAmelCase__			  =      os.getenv('''SM_FRAMEWORK_PARAMS'''					,       '''{}'''			)
							try:
														# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
														lowerCAmelCase__			  =      json.loads(A			)
														if not mpi_options.get('''sagemaker_mpi_enabled'''					,       A			):
																					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 						a__							(				a__    ):





							'''simple docstring'''






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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				"torch.device":
														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:
																					lowerCAmelCase__			  =      torch.device('''cpu'''  )
																					lowerCAmelCase__			  =      0
														elif is_sagemaker_model_parallel_available():
																					lowerCAmelCase__			  =      smp.local_rank()
																					lowerCAmelCase__			  =      torch.device('''cuda'''   ,       lowerCamelCase_  )
																					lowerCAmelCase__			  =      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  )
																					lowerCAmelCase__			  =      int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''  )  )
																					lowerCAmelCase__			  =      torch.device('''cuda'''   ,       self.local_rank  )
																					lowerCAmelCase__			  =      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
																					lowerCAmelCase__			  =      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.
																					lowerCAmelCase__			  =      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  )
																					lowerCAmelCase__			  =      torch.device('''cuda'''   ,       self.local_rank  )
																					lowerCAmelCase__			  =      1

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

														return device







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														if is_sagemaker_model_parallel_available():
																					return smp.dp_size()

														return super().world_size







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														return not is_sagemaker_model_parallel_available()







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														return False



'''simple docstring'''






from __future__ import annotations

from random import choice


def    _snake_case  (	A			)		->       int:
							return choice(A			)


def    _snake_case  (	A					,       A			)		->       int:
							lowerCAmelCase__			  =      random_pivot(A			)

							# partition based on pivot
							# linear time
							lowerCAmelCase__			  =      [e for e in lst if e < pivot]
							lowerCAmelCase__			  =      [e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(A			) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(A			) < k - 1:
														return kth_number(A					,       k - len(A			) - 1			)
							# pivot is in elements smaller than k
							else:
														return kth_number(A					,       A			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()