


"""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


a__				:							Union[str, Any]						=     logging.get_logger(__name__)




def    UpperCAmelCase__       ():




					'''simple docstring'''


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

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

	"""simple docstring"""





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




	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	Any:
						super().__post_init__()
						warnings.warn(
						    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
						    "`TrainingArguments` instead."			,			UpperCAmelCase__			,			)




	@cached_property
	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	"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:
											__SCREAMING_SNAKE_CASE						   =				torch.device("cpu"   )
											__SCREAMING_SNAKE_CASE						   =				0
						elif is_sagemaker_model_parallel_available():
											__SCREAMING_SNAKE_CASE						   =				smp.local_rank()
											__SCREAMING_SNAKE_CASE						   =				torch.device("cuda"			,			UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				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   )
											__SCREAMING_SNAKE_CASE						   =				int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"   )   )
											__SCREAMING_SNAKE_CASE						   =				torch.device("cuda"			,			self.local_rank   )
											__SCREAMING_SNAKE_CASE						   =				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
											__SCREAMING_SNAKE_CASE						   =				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.
											__SCREAMING_SNAKE_CASE						   =				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   )
											__SCREAMING_SNAKE_CASE						   =				torch.device("cuda"			,			self.local_rank   )
											__SCREAMING_SNAKE_CASE						   =				1

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

						return device




	@property
	def   UpperCAmelCase_      ( self			:				Dict   )       ->	Any:
						if is_sagemaker_model_parallel_available():
											return smp.dp_size()

						return super().world_size




	@property
	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	List[Any]:
						return not is_sagemaker_model_parallel_available()




	@property
	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	int:
						return False


"""simple docstring"""




def lowercase_  (   _snake_case   ,_snake_case						):

							if not (isinstance(_snake_case   ,_snake_case						) and isinstance(_snake_case   ,_snake_case						)):
														raise ValueError("""longest_common_substring() takes two strings for inputs"""						)

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   len(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       int           =   len(_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   [[0] * (texta_length + 1) for _ in range(texta_length + 1						)]
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   0
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   0

							for i in range(1   ,texta_length + 1						):
														for j in range(1   ,texta_length + 1						):
																					if texta[i - 1] == texta[j - 1]:
																												SCREAMING_SNAKE_CASE__						:       int           =   1 + dp[i - 1][j - 1]
																												if dp[i][j] > ans_length:
																																			SCREAMING_SNAKE_CASE__						:       List[Any]           =   i
																																			SCREAMING_SNAKE_CASE__						:       List[str]           =   dp[i][j]

							return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
					import doctest

					doctest.testmod()




