





"""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


lowerCAmelCase_			=							logging.get_logger(__name__)

def       __lowerCamelCase    (       )		->       Any:






       """simple docstring"""




       _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(SCREAMING_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     =     os.getenv('SM_FRAMEWORK_PARAMS','{}'							)
       try:
              # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
              _UpperCAmelCase     =     json.loads(SCREAMING_SNAKE_CASE							)
              if not mpi_options.get('sagemaker_mpi_enabled',SCREAMING_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							):
       lowerCAmelCase__						 =  field(
           default=""""""			,	metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}			,	)






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






       @cached_property
       def 							__A (   self     ):
              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'    ,  a__     )
                     _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(a__     )

              return device






       @property
       def 							__A (   self     ):
              if is_sagemaker_model_parallel_available():
                     return smp.dp_size()

              return super().world_size






       @property
       def 							__A (   self     ):
              return not is_sagemaker_model_parallel_available()






       @property
       def 							__A (   self     ):
              return False













"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       list:






       """simple docstring"""




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

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


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






