




'''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


_A:					List[Any]							=				logging.get_logger(__name__)







def _lowerCAmelCase     (      )->     Dict:
 __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 UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Union[str, Any]						   =       field(
        default=""""""					,			metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}					,			)



    def 					__lowerCamelCase						(  self					):
     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 					__lowerCamelCase						(  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'     ,			__UpperCamelCase					)
      __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(__UpperCamelCase					)

     return device



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

     return super().world_size



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




    @property
    def 					__lowerCamelCase						(  self					):
     return False












'''simple docstring'''




from string import ascii_uppercase

_A:					Union[str, Any]							=				{char: i for i, char in enumerate(ascii_uppercase)}
_A:					str							=				dict(enumerate(ascii_uppercase))







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   len(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 while True:
  if x == i:
   __UpperCAmelCase					=   0
  if len(_lowerCAmelCase    ) == len(_lowerCAmelCase    ):
   break
  key += key[i]
  i += 1
 return key







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in message:
  if letter == " ":
   cipher_text += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] - dicta[key_new[i]]) % 26
   i += 1
   cipher_text += dicta[x]
 return cipher_text







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in cipher_text:
  if letter == " ":
   or_txt += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] + dicta[key_new[i]] + 26) % 26
   i += 1
   or_txt += dicta[x]
 return or_txt







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   'THE GERMAN ATTACK'
 __UpperCAmelCase					=   'SECRET'
 __UpperCAmelCase					=   generate_key(_lowerCAmelCase       ,    _lowerCAmelCase    )
 __UpperCAmelCase					=   cipher_text(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print(F'Encrypted Text = {s}'    )
 print(F'Original Text = {original_text(_lowerCAmelCase       ,    _lowerCAmelCase    )}'    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





