


from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				str:
  return DownloadCommand(args.model							,   args.cache_dir							,   args.force							,   args.trust_remote_code				)


class 		__magic_name__						(lowercase_				):





     '''simple docstring'''



     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:Tuple   ):
       snake_case__						= parser.add_parser('''download'''   )
       download_parser.add_argument(
           '''--cache-dir'''     ,		type=_lowercase     ,		default=_lowercase     ,		help='''Path to location to store the models'''   )
       download_parser.add_argument(
           '''--force'''     ,		action='''store_true'''     ,		help='''Force the model to be download even if already in cache-dir'''   )
       download_parser.add_argument(
           '''--trust-remote-code'''     ,		action='''store_true'''     ,		help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''     ,		)
       download_parser.add_argument('''model'''     ,		type=_lowercase     ,		help='''Name of the model to download'''   )
       download_parser.set_defaults(func=_lowercase   )

     def __init__(					self:Optional[int]     ,		_a:Any     ,		_a:List[str]     ,		_a:Union[str, Any]     ,		_a:Union[str, Any]   ):
       snake_case__						= model
       snake_case__						= cache
       snake_case__						= force
       snake_case__						= trust_remote_code







     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       from ..models.auto import AutoModel, AutoTokenizer

       AutoModel.from_pretrained(
           self._model     ,		cache_dir=self._cache     ,		force_download=self._force     ,		trust_remote_code=self._trust_remote_code   )
       AutoTokenizer.from_pretrained(
           self._model     ,		cache_dir=self._cache     ,		force_download=self._force     ,		trust_remote_code=self._trust_remote_code   )




'''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


SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			logging.get_logger(__name__)




def 							_UpperCamelCase				(   )      -> List[Any]:
							# 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(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    snake_case     (      lowercase_		):






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



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



				@cached_property
				def   a__					(    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:
																		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',     _lowercase       )
																		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(_lowercase       )

											return device



				@property
				def   a__					(    self       )     ->							Optional[Any]:
											if is_sagemaker_model_parallel_available():
																		return smp.dp_size()

											return super().world_size



				@property
				def   a__					(    self       )     ->							Optional[Any]:
											return not is_sagemaker_model_parallel_available()





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





