


import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
__UpperCamelCase    		=       [
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]
def        UpperCamelCase_(				_A   :str  )->		Union[str, Any]:
			# Test all the extensions added in the setup
			for file in FILES_TO_FIND:
						if not (transformers_path / file).exists():
									return False
			return True


if __name__ == "__main__":
						__UpperCamelCase    		=       argparse.ArgumentParser()
						parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
						__UpperCamelCase    		=       parser.parse_args()
						if args.check_lib:
												__UpperCamelCase    		=       importlib.import_module('transformers')
												__UpperCamelCase    		=       Path(transformers_module.__file__).parent
						else:
												__UpperCamelCase    		=       Path.cwd() / 'build/lib/transformers'
						if not test_custom_files_are_present(transformers_path):
												raise ValueError('The built release does not contain the custom files. Fix this before going further!')






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        UpperCamelCase_(				)->		Any:
			# Get the sagemaker specific mp parameters from smp_options variable.
			UpperCamelCase__					=       os.getenv("SM_HP_MP_PARAMETERS"				,     "{}"  )
			try:
						# Parse it and check the field "partitions" is included, it is required for model parallel.
						UpperCamelCase__					=       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.
			UpperCamelCase__					=       os.getenv("SM_FRAMEWORK_PARAMS"				,     "{}"  )
			try:
						# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
						UpperCamelCase__					=       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        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






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




			def 				snake_case__				(							self     ):

						'''simple docstring'''


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




			@cached_property
			def 				snake_case__				(							self     ):

						'''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:
									UpperCamelCase__					=       torch.device("cpu"     )
									UpperCamelCase__					=       0
						elif is_sagemaker_model_parallel_available():
									UpperCamelCase__					=       smp.local_rank()
									UpperCamelCase__					=       torch.device("cuda"      ,						snake_case     )
									UpperCamelCase__					=       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     )
									UpperCamelCase__					=       int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"     )     )
									UpperCamelCase__					=       torch.device("cuda"      ,						self.local_rank     )
									UpperCamelCase__					=       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
									UpperCamelCase__					=       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.
									UpperCamelCase__					=       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     )
									UpperCamelCase__					=       torch.device("cuda"      ,						self.local_rank     )
									UpperCamelCase__					=       1

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

						return device




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if is_sagemaker_model_parallel_available():
									return smp.dp_size()

						return super().world_size




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return not is_sagemaker_model_parallel_available()







			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return False


