




'''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 		__magic_name__    (	)		->					str:

					'''simple docstring'''

					snake_case_									=				os.getenv('''SM_HP_MP_PARAMETERS''',      '''{}'''	)
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										snake_case_									=				json.loads(__UpperCAmelCase	)
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					snake_case_									=				os.getenv('''SM_FRAMEWORK_PARAMS''',      '''{}'''	)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										snake_case_									=				json.loads(__UpperCAmelCase	)
										if not mpi_options.get('''sagemaker_mpi_enabled''',      __UpperCAmelCase	):
															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				(   _lowerCamelCase     ):
				snake_case_      =		field(
				    default=""   ,     metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}   ,     )


				def   A_ (		self    :					Optional[Any]					):
									super().__post_init__()
									warnings.warn(
									    '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
									    '''`TrainingArguments` instead.'''       ,				lowercase_       ,				)


				@cached_property
				def   A_ (		self    :					List[Any]					):
									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:
														snake_case_									=				torch.device('''cpu'''					)
														snake_case_									=				0
									elif is_sagemaker_model_parallel_available():
														snake_case_									=				smp.local_rank()
														snake_case_									=				torch.device('''cuda'''       ,				lowercase_					)
														snake_case_									=				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					)
														snake_case_									=				int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''					)					)
														snake_case_									=				torch.device('''cuda'''       ,				self.local_rank					)
														snake_case_									=				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
														snake_case_									=				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.
														snake_case_									=				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					)
														snake_case_									=				torch.device('''cuda'''       ,				self.local_rank					)
														snake_case_									=				1

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

									return device


				@property
				def   A_ (		self    :					Optional[int]					):
									if is_sagemaker_model_parallel_available():
														return smp.dp_size()

									return super().world_size


				@property
				def   A_ (		self    :					Dict					):
									return not is_sagemaker_model_parallel_available()


				@property
				def   A_ (		self    :					Optional[Any]					):
									return False



import math




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							__UpperCamelCase	:   List[Any]			   =					input("Enter message: ")
							__UpperCamelCase	:   Optional[int]			   =					int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
							__UpperCamelCase	:   str			   =					input("Encryption/Decryption [e/d]: ")

							if mode.lower().startswith("e"):
														__UpperCamelCase	:   List[str]			   =					encrypt_message(_lowerCamelCase						,  _lowerCamelCase)
							elif mode.lower().startswith("d"):
														__UpperCamelCase	:   Dict			   =					decrypt_message(_lowerCamelCase						,  _lowerCamelCase)

							# Append pipe symbol (vertical bar) to identify spaces at the end.
							print(F'Output:\n{text + "|"}')




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					[""] * key
							for col in range(_lowerCamelCase):
														__UpperCamelCase	:   Any			   =					col
														while pointer < len(_lowerCamelCase):
																					cipher_text[col] += message[pointer]
																					pointer += key
							return "".join(_lowerCamelCase)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					math.ceil(len(_lowerCamelCase) / key)
							__UpperCamelCase	:   Any			   =					key
							__UpperCamelCase	:   str			   =					(num_cols * num_rows) - len(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					[""] * num_cols
							__UpperCamelCase	:   Dict			   =					0
							__UpperCamelCase	:   int			   =					0

							for symbol in message:
														plain_text[col] += symbol
														col += 1

														if (
														    (col == num_cols)
														    or (col == num_cols - 1)
														    and (row >= num_rows - num_shaded_boxes)
														):
																					__UpperCamelCase	:   List[Any]			   =					0
																					row += 1

							return "".join(_lowerCamelCase)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()