



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


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






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  os.getenv('''SM_HP_MP_PARAMETERS''' ,'''{}''')
				try:
								# Parse it and check the field "partitions" is included, it is required for model parallel.
								lowerCAmelCase__  :       Tuple				=  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.
				lowerCAmelCase__  :       List[str]				=  os.getenv('''SM_FRAMEWORK_PARAMS''' ,'''{}''')
				try:
								# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
								lowerCAmelCase__  :       List[str]				=  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  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



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



		def 	lowerCAmelCase__   (self				)  ->    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.'''	,__lowerCamelCase	,)



		@cached_property
		def 	lowerCAmelCase__   (self				)  ->    "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:
										lowerCAmelCase__  :       Optional[Any]				=  torch.device('''cpu'''				)
										lowerCAmelCase__  :       List[str]				=  0
						elif is_sagemaker_model_parallel_available():
										lowerCAmelCase__  :       Optional[int]				=  smp.local_rank()
										lowerCAmelCase__  :       List[Any]				=  torch.device('''cuda'''	,__lowerCamelCase				)
										lowerCAmelCase__  :       List[str]				=  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__  :       Optional[int]				=  int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''				)				)
										lowerCAmelCase__  :       Union[str, Any]				=  torch.device('''cuda'''	,self.local_rank				)
										lowerCAmelCase__  :       str				=  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__  :       Optional[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.
										lowerCAmelCase__  :       Union[str, Any]				=  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__  :       List[Any]				=  torch.device('''cuda'''	,self.local_rank				)
										lowerCAmelCase__  :       int				=  1

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

						return device



		@property
		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						if is_sagemaker_model_parallel_available():
										return smp.dp_size()

						return super().world_size



		@property
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						return not is_sagemaker_model_parallel_available()



		@property
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						return False






import copy

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


__snake_case       :	Any            =logging.get_logger(__name__)



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""encoder-decoder"""
		snake_case_			=True



		def __init__(self	,**__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						assert (
						    "encoder" in kwargs and "decoder" in kwargs
						), "Config has to be initialized with encoder and decoder config"
						lowerCAmelCase__  :       Optional[Any]				=  kwargs.pop('''encoder'''				)
						lowerCAmelCase__  :       Any				=  encoder_config.pop('''model_type'''				)
						lowerCAmelCase__  :       str				=  kwargs.pop('''decoder'''				)
						lowerCAmelCase__  :       Tuple				=  decoder_config.pop('''model_type'''				)

						from ..auto.configuration_auto import AutoConfig

						lowerCAmelCase__  :       Tuple				=  AutoConfig.for_model(__lowerCamelCase	,**__lowerCamelCase				)
						lowerCAmelCase__  :       Any				=  AutoConfig.for_model(__lowerCamelCase	,**__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  True



		@classmethod
		def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase				)  ->    PretrainedConfig:

						"""simple docstring"""






						logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''				)
						lowerCAmelCase__  :       Optional[int]				=  True
						lowerCAmelCase__  :       List[str]				=  True

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



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  copy.deepcopy(self.__dict__				)
						lowerCAmelCase__  :       Optional[Any]				=  self.encoder.to_dict()
						lowerCAmelCase__  :       str				=  self.decoder.to_dict()
						lowerCAmelCase__  :       Optional[int]				=  self.__class__.model_type
						return output

