





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


_lowerCamelCase 					=      logging.get_logger(__name__)






def 					SCREAMING_SNAKE_CASE__				(							):
		# Get the sagemaker specific mp parameters from smp_options variable.
		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(UpperCamelCase__     )
				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(UpperCamelCase__     )
				if not mpi_options.get("""sagemaker_mpi_enabled"""       , UpperCamelCase__     ):
						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__     ):
							lowerCamelCase_							     =       field(
							    default=""					,	metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}					,	)


							def   _snake_case	(				self     :Any		) -> Optional[int]:



									"""simple docstring"""



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


							@cached_property
							def   _snake_case	(				self     :List[Any]		) -> "torch.device":



									"""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:
											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"""				,					__A		)
											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(__A		)

									return device


							@property
							def   _snake_case	(				self     :Tuple		) -> int:



									"""simple docstring"""



									if is_sagemaker_model_parallel_available():
											return smp.dp_size()

									return super().world_size


							@property
							def   _snake_case	(				self     :int		) -> int:



									"""simple docstring"""



									return not is_sagemaker_model_parallel_available()


							@property
							def   _snake_case	(				self     :Any		) -> Any:



									"""simple docstring"""



									return False






import copy

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


_lowerCamelCase 					=      logging.get_logger(__name__)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "encoder-decoder"
							lowerCamelCase_							     =       True


							def __init__(				self     :Optional[int]				,					**__A     :str		) -> int:



									"""simple docstring"""



									super().__init__(**__A		)
									assert (
									    "encoder" in kwargs and "decoder" in kwargs
									), "Config has to be initialized with encoder and decoder config"
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""encoder"""		)
									SCREAMING_SNAKE_CASE__								=   encoder_config.pop("""model_type"""		)
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""decoder"""		)
									SCREAMING_SNAKE_CASE__								=   decoder_config.pop("""model_type"""		)

									from ..auto.configuration_auto import AutoConfig

									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   True


							@classmethod
							def   _snake_case	(				cls     :str				,					__A     :PretrainedConfig				,					__A     :PretrainedConfig				,					**__A     :List[str]		) -> PretrainedConfig:



									"""simple docstring"""



									logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""		)
									SCREAMING_SNAKE_CASE__								=   True
									SCREAMING_SNAKE_CASE__								=   True

									return cls(encoder=encoder_config.to_dict()				,					decoder=decoder_config.to_dict()				,					**__A		)


							def   _snake_case	(				self     :str		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__								=   self.encoder.to_dict()
									SCREAMING_SNAKE_CASE__								=   self.decoder.to_dict()
									SCREAMING_SNAKE_CASE__								=   self.__class__.model_type
									return output