



"""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 ):
   SCREAMING_SNAKE_CASE_: int     			=False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      SCREAMING_SNAKE_CASE_: Tuple     			=True
      for i in range(0							,			len(lowercase ) - 1							,			2 ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=input_list[i + 1], input_list[i]
            # swapping if elements not in order
            SCREAMING_SNAKE_CASE_: Tuple     			=False

      for i in range(1							,			len(lowercase ) - 1							,			2 ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=input_list[i + 1], input_list[i]
            # swapping if elements not in order
            SCREAMING_SNAKE_CASE_: str     			=False
   return input_list


if __name__ == "__main__":
   print("""Enter list to be sorted""")
   _UpperCAmelCase     =			[int(x) for x in input().split()]
   # inputing elements of the list in one line
   _UpperCAmelCase     =			odd_even_sort(input_list)
   print("""The sorted list is""")
   print(sorted_list)



