



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	(   )	->     str:
   UpperCamelCase    :  Dict							=	os.getenv('SM_HP_MP_PARAMETERS'       ,     '{}'	)
   try:
      # Parse it and check the field "partitions" is included, it is required for model parallel.
      UpperCamelCase    :  Optional[int]							=	json.loads(snake_case__	)
      if "partitions" not in smp_options:
         return False
   except json.JSONDecodeError:
      return False

   # Get the sagemaker specific framework parameters from mpi_options variable.
   UpperCamelCase    :  Optional[int]							=	os.getenv('SM_FRAMEWORK_PARAMS'       ,     '{}'	)
   try:
      # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
      UpperCamelCase    :  Any							=	json.loads(snake_case__	)
      if not mpi_options.get('sagemaker_mpi_enabled'       ,     snake_case__	):
         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_		(  snake_case_							):
       UpperCAmelCase__				:					str        =  field(
           default=""       ,    metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}       ,    )
       def 					snake_case_       (			self       )     ->		List[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 					snake_case_       (			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:
             UpperCamelCase    :  Optional[Any]							=	torch.device('cpu'       )
             UpperCamelCase    :  List[Any]							=	0
          elif is_sagemaker_model_parallel_available():
             UpperCamelCase    :  Tuple							=	smp.local_rank()
             UpperCamelCase    :  int							=	torch.device('cuda',			UpperCamelCase__       )
             UpperCamelCase    :  Dict							=	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    :  Any							=	int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'       )       )
             UpperCamelCase    :  Optional[Any]							=	torch.device('cuda',			self.local_rank       )
             UpperCamelCase    :  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
             UpperCamelCase    :  List[str]							=	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    :  Optional[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       )
             UpperCamelCase    :  Any							=	torch.device('cuda',			self.local_rank       )
             UpperCamelCase    :  Dict							=	1

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

          return device
       @property
       def 					snake_case_       (			self       )     ->		Optional[int]:




          if is_sagemaker_model_parallel_available():
             return smp.dp_size()

          return super().world_size
       @property
       def 					snake_case_       (			self       )     ->		List[str]:
          return not is_sagemaker_model_parallel_available()






       @property
       def 					snake_case_       (			self       )     ->		str:
          return False







"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Union[str, Any]						,							lowercase  :  List[str]						,							lowercase  :  Tuple						,							lowercase  :  List[Any]						,							lowercase  :  int )	->						List[Any]:


    """simple docstring"""





    if index == r:
        for j in range(lowercase ):
            print(data[j]						,							end=" " )
        print(" " )
        return
    #  When no more elements are there to put in data[]
    if i >= n:
        return
    # current is included, put next at next location
    snake_case    :      Union[str, Any]      					=    arr[i]
    combination_util(lowercase						,							lowercase						,							lowercase						,							index + 1						,							lowercase						,							i + 1 )
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							i + 1 )
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int] )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Any      					=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(lowercase						,							lowercase						,							lowercase						,							0						,							lowercase						,							0 )


if __name__ == "__main__":
  # Driver code to check the function above
  __snake_case   			=    [10, 20, 30, 40, 50]
  print_combination(arr, len(arr), 3)
  # This code is contributed by Ambuj sahu





