"""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


SCREAMING_SNAKE_CASE__:Optional[int]			=	logging.get_logger(__name__)



def 					_lowerCamelCase(					):
       # Get the sagemaker specific mp parameters from smp_options variable.
       __a     				=  os.getenv("SM_HP_MP_PARAMETERS" ,  "{}"							)
       try:
              # Parse it and check the field "partitions" is included, it is required for model parallel.
              __a     				=  json.loads(a							)
              if "partitions" not in smp_options:
                     return False
       except json.JSONDecodeError:
              return False

       # Get the sagemaker specific framework parameters from mpi_options variable.
       __a     				=  os.getenv("SM_FRAMEWORK_PARAMS" ,  "{}"							)
       try:
              # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
              __a     				=  json.loads(a							)
              if not mpi_options.get("sagemaker_mpi_enabled" ,  a							):
                     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 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		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."  ,						lowerCamelCase  ,						)


 @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:
               __a     				=  torch.device("cpu"		)
               __a     				=  0
        elif is_sagemaker_model_parallel_available():
               __a     				=  smp.local_rank()
               __a     				=  torch.device("cuda"  ,						lowerCamelCase		)
               __a     				=  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		)
               __a     				=  int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"		)		)
               __a     				=  torch.device("cuda"  ,						self.local_rank		)
               __a     				=  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
               __a     				=  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.
               __a     				=  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		)
               __a     				=  torch.device("cuda"  ,						self.local_rank		)
               __a     				=  1

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

        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"""



import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

SCREAMING_SNAKE_CASE__:List[str]			=	3



def 					_lowerCamelCase(					a							):
       print("Generating primitive root of p"							)
       while True:
              __a     				=  random.randrange(3 ,  a							)
              if pow(a ,  2 ,  a							) == 1:
                     continue
              if pow(a ,  a ,  a							) == 1:
                     continue
              return g



def 					_lowerCamelCase(					a							):
       print("Generating prime p..."							)
       __a     				=  rabin_miller.generate_large_prime(a							)  # select large prime number.
       __a     				=  primitive_root(a							)  # one primitive root on modulo p.
       __a     				=  random.randrange(3 ,  a							)  # private_key -> have to be greater than 2 for safety.
       __a     				=  cryptomath.find_mod_inverse(pow(a ,  a ,  a							) ,  a							)

       __a     				=  (key_size, e_a, e_a, p)
       __a     				=  (key_size, d)

       return public_key, private_key



def 					_lowerCamelCase(					a ,  a							):
       if os.path.exists(F"{name}_pubkey.txt"							) or os.path.exists(F"{name}_privkey.txt"							):
              print("\nWARNING:"							)
              print(
                  F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
                  "Use a different name or delete these files and re-run this program."							)
              sys.exit()

       __a				, __a     				=  generate_key(a							)
       print(F"\nWriting public key to file {name}_pubkey.txt..."							)
       with open(F"{name}_pubkey.txt" ,  "w"							) as fo:
              fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"							)

       print(F"Writing private key to file {name}_privkey.txt..."							)
       with open(F"{name}_privkey.txt" ,  "w"							) as fo:
              fo.write(F"{private_key[0]},{private_key[1]}"							)



def 					_lowerCamelCase(					):
       print("Making key files..."							)
       make_key_files("elgamal" ,  2_0_4_8							)
       print("Key files generation successful"							)


if __name__ == "__main__":
   main()
