

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


lowercase_									=   logging.get_logger(__name__)
def      a      (			)    ->      str:





      """simple docstring"""



      _lowercase	       =os.getenv('SM_HP_MP_PARAMETERS'      ,   '{}'     )
      try:
            # Parse it and check the field "partitions" is included, it is required for model parallel.
            _lowercase	       =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.
      _lowercase	       =os.getenv('SM_FRAMEWORK_PARAMS'      ,   '{}'     )
      try:
            # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
            _lowercase	       =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     (					__snake_case							):
  _a									=  field(
      default=""""""			,			metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}			,			)




  def   A__     (				self					)  -> str:
        '''simple docstring'''






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




  @cached_property
  def   A__     (				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:
              _lowercase	       =torch.device('cpu'					)
              _lowercase	       =0
        elif is_sagemaker_model_parallel_available():
              _lowercase	       =smp.local_rank()
              _lowercase	       =torch.device('cuda'							,   __SCREAMING_SNAKE_CASE					)
              _lowercase	       =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					)
              _lowercase	       =int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'					)					)
              _lowercase	       =torch.device('cuda'							,   self.local_rank					)
              _lowercase	       =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
              _lowercase	       =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.
              _lowercase	       =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					)
              _lowercase	       =torch.device('cuda'							,   self.local_rank					)
              _lowercase	       =1

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

        return device




  @property
  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        if is_sagemaker_model_parallel_available():
              return smp.dp_size()

        return super().world_size




  @property
  def   A__     (				self					)  -> str:
        '''simple docstring'''






        return not is_sagemaker_model_parallel_available()




  @property
  def   A__     (				self					)  -> int:
        '''simple docstring'''






        return False










"""simple docstring"""





from typing import Any






class      a						:






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						None






class      a						:






    def __init__(       self     :							Union[str, Any]					)     ->		List[Any]:
      lowerCamelCase_         =						None






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      lowerCamelCase_         =						self.head
      while temp is not None:
        print(temp.data       ,						end=' '					)
        lowerCamelCase_         =						temp.next
      print()






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Union[str, Any]:
      lowerCamelCase_         =						Node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.head
      lowerCamelCase_         =						new_node






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		Union[str, Any]:
      if node_data_a == node_data_a:
        return
      else:
        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        if node_a is None or node_a is None:
          return

        lowerCamelCase_     ,	lowerCamelCase_         =						node_a.data, node_a.data


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						LinkedList()
  for i in range(5, 0, -1):
    ll.push(i)

  ll.print_list()

  ll.swap_nodes(1, 4)
  print('''After swapping''')
  ll.print_list()




