



'''simple docstring'''





from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowercase_		       =  logging.get_logger(__name__)


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset

  from transformers import Trainer






  class __A			(  A						):



   '''simple docstring'''







   def __init__(self ,   A = 101       )    ->     Optional[Any]:


        """simple docstring"""



        _a      =  length



   def __len__(self       )    ->     str:


        """simple docstring"""



        return self.length



   def __getitem__(self ,   A       )    ->     int:


        """simple docstring"""



        return i






  class __A			:



   '''simple docstring'''







   def __call__(self ,   A       )    ->     Tuple:


        """simple docstring"""



        return {"input_ids": torch.tensor(A       ), "labels": torch.tensor(A       )}






  class __A			(  nn.Module						):



   '''simple docstring'''







   def __init__(self       )    ->     Optional[Any]:


        """simple docstring"""



        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        _a      =  nn.Linear(120 ,   80       )



   def 					a__  (self ,   A ,   A=None       )    ->     Any:


        """simple docstring"""



        if labels is not None:
             return torch.tensor(0.0 ,   device=input_ids.device       ), input_ids
        else:
             return input_ids






class __A			(  A						):



 '''simple docstring'''







 @require_torch_neuroncore
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  f'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
      _a      =  self.get_auto_remove_tmp_dir()
      _a      =  f'''--output_dir {output_dir}'''.split()
      _a      =  ['''torchrun'''] + distributed_args + args
      execute_subprocess_async(A ,   env=self.get_env()       )
      # successful return here == success - any errors would have caused an error in the sub-call






class __A			(  A						):



 '''simple docstring'''







 @require_torch_multi_gpu
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  f'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
      _a      =  self.get_auto_remove_tmp_dir()
      _a      =  f'''--output_dir {output_dir}'''.split()
      _a      =  ['''torchrun'''] + distributed_args + args
      execute_subprocess_async(A ,   env=self.get_env()       )
      # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
  # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
  #
  # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

  lowercase_		       =  HfArgumentParser((TrainingArguments,))
  lowercase_		       =  parser.parse_args_into_dataclasses()[0]

  logger.warning(
      F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
      F"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
  )

  # Essentially, what we want to verify in the distributed case is that we get all samples back,
  # in the right order. (this is crucial for prediction for instance)
  for dataset_length in [101, 40, 7]:
    lowercase_		       =  DummyDataset(dataset_length)




    def lowerCAmelCase  (__A):



         """simple docstring"""



         _a      =  list(range(len(__A)))
         _a      =  p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         if not success and training_args.local_rank == 0:
              logger.warning(
                  '''Predictions and/or labels do not match expected results:\n  - predictions: '''
                  F'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}''')
         return {"success": success}

    lowercase_		       =  Trainer(
        model=DummyModel(),
        args=training_args,
        data_collator=DummyDataCollator(),
        eval_dataset=dataset,
        compute_metrics=compute_metrics,
    )
    lowercase_		       =  trainer.evaluate()
    logger.info(metrics)
    if metrics["eval_success"] is not True:
      logger.error(metrics)
      exit(1)

    lowercase_		       =  trainer.predict(dataset)
    logger.info(p.metrics)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics)
      exit(1)

    lowercase_		       =  2

    lowercase_		       =  trainer.evaluate()
    logger.info(metrics)
    if metrics["eval_success"] is not True:
      logger.error(metrics)
      exit(1)

    lowercase_		       =  trainer.predict(dataset)
    logger.info(p.metrics)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics)
      exit(1)
    lowercase_		       =  None








'''simple docstring'''





import random




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  num - 1
     _a      =  0

     while s % 2 == 0:
          _a      =  s // 2
          t += 1

     for _ in range(5):
          _a      =  random.randrange(2  ,	num - 1)
          _a      =  pow(__A  ,	__A  ,	__A)
          if v != 1:
               _a      =  0
               while v != (num - 1):
                    if i == t - 1:
                         return False
                    else:
                         _a      =  i + 1
                         _a      =  (v**2) % num
     return True




def lowerCAmelCase  (__A):



     """simple docstring"""



     if num < 2:
          return False

     _a      =  [
         2,
         3,
         5,
         7,
         11,
         13,
         17,
         19,
         23,
         29,
         31,
         37,
         41,
         43,
         47,
         53,
         59,
         61,
         67,
         71,
         73,
         79,
         83,
         89,
         97,
         101,
         103,
         107,
         109,
         113,
         127,
         131,
         137,
         139,
         149,
         151,
         157,
         163,
         167,
         173,
         179,
         181,
         191,
         193,
         197,
         199,
         211,
         223,
         227,
         229,
         233,
         239,
         241,
         251,
         257,
         263,
         269,
         271,
         277,
         281,
         283,
         293,
         307,
         311,
         313,
         317,
         331,
         337,
         347,
         349,
         353,
         359,
         367,
         373,
         379,
         383,
         389,
         397,
         401,
         409,
         419,
         421,
         431,
         433,
         439,
         443,
         449,
         457,
         461,
         463,
         467,
         479,
         487,
         491,
         499,
         503,
         509,
         521,
         523,
         541,
         547,
         557,
         563,
         569,
         571,
         577,
         587,
         593,
         599,
         601,
         607,
         613,
         617,
         619,
         631,
         641,
         643,
         647,
         653,
         659,
         661,
         673,
         677,
         683,
         691,
         701,
         709,
         719,
         727,
         733,
         739,
         743,
         751,
         757,
         761,
         769,
         773,
         787,
         797,
         809,
         811,
         821,
         823,
         827,
         829,
         839,
         853,
         857,
         859,
         863,
         877,
         881,
         883,
         887,
         907,
         911,
         919,
         929,
         937,
         941,
         947,
         953,
         967,
         971,
         977,
         983,
         991,
         997,
     ]

     if num in low_primes:
          return True

     for prime in low_primes:
          if (num % prime) == 0:
               return False

     return rabin_miller(__A)




def lowerCAmelCase  (__A = 1_024):



     """simple docstring"""



     while True:
          _a      =  random.randrange(2 ** (keysize - 1)  ,	2 ** (keysize))
          if is_prime_low_num(__A):
               return num


if __name__ == "__main__":
  lowercase_		       =  generate_large_prime()
  print(("Prime number:", num))
  print(("is_prime_low_num:", is_prime_low_num(num)))



