



"""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


_UpperCAmelCase     =			logging.get_logger(__name__)





def      __magic_name__				(							):
   # Get the sagemaker specific mp parameters from smp_options variable.
   SCREAMING_SNAKE_CASE_: List[str]     			=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_: List[Any]     			=json.loads(lowercase )
      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_: str     			=os.getenv("""SM_FRAMEWORK_PARAMS"""							,			"""{}""" )
   try:
      # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=json.loads(lowercase )
      if not mpi_options.get("""sagemaker_mpi_enabled"""							,			lowercase ):
         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 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				str     						=					field(
      default=''	,      metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}	,      )



  def 	lowerCamelCase__       (						self :					List[str]		)     ->					List[Any]:


     '''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 :					str		)     ->					"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:
        SCREAMING_SNAKE_CASE_: str     			=torch.device("""cpu"""		)
        SCREAMING_SNAKE_CASE_: List[str]     			=0
     elif is_sagemaker_model_parallel_available():
        SCREAMING_SNAKE_CASE_: Optional[int]     			=smp.local_rank()
        SCREAMING_SNAKE_CASE_: Tuple     			=torch.device("""cuda"""       ,		lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: 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		)
        SCREAMING_SNAKE_CASE_: str     			=int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""		)		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.device("""cuda"""       ,		self.local_rank		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=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_: Union[str, Any]     			=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_: 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		)
        SCREAMING_SNAKE_CASE_: Dict     			=torch.device("""cuda"""       ,		self.local_rank		)
        SCREAMING_SNAKE_CASE_: Tuple     			=1

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

     return device



  @property
  def 	lowerCamelCase__       (						self :					List[str]		)     ->					List[Any]:


     '''simple docstring'''




     if is_sagemaker_model_parallel_available():
        return smp.dp_size()

     return super().world_size



  @property
  def 	lowerCamelCase__       (						self :					str		)     ->					str:


     '''simple docstring'''




     return not is_sagemaker_model_parallel_available()





  @property
  def 	lowerCamelCase__       (						self :					Dict		)     ->					Optional[Any]:


     '''simple docstring'''




     return False








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   return str(lowercase ) == str(lowercase )[::-1]





def      __magic_name__				(							lowercase ):
   return int(lowercase ) + int(str(lowercase )[::-1] )





def      __magic_name__				(							lowercase = 1_0000 ):
   SCREAMING_SNAKE_CASE_: List[str]     			=[]
   for num in range(1							,			lowercase ):
      SCREAMING_SNAKE_CASE_: List[Any]     			=0
      SCREAMING_SNAKE_CASE_: int     			=num
      while iterations < 50:
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=sum_reverse(lowercase )
         iterations += 1
         if is_palindrome(lowercase ):
            break
      else:
         lychrel_nums.append(lowercase )
   return len(lowercase )


if __name__ == "__main__":
   print(f"""{solution() = }""")



