




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__				:      Union[str, Any]	   = 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__	( UpperCamelCase__				):







        def __init__(     self  ,		A = 101			)  ->   int:



             '''simple docstring'''



             a     =				length







        def __len__(     self			)  ->   Union[str, Any]:



             '''simple docstring'''



             return self.length







        def __getitem__(     self  ,		A			)  ->   int:



             '''simple docstring'''



             return i







   class       a__	:







        def __call__(     self  ,		A			)  ->   str:



             '''simple docstring'''



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







   class       a__	( nn.Module				):







        def __init__(     self			)  ->   Union[str, Any]:



             '''simple docstring'''



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







        def       lowerCAmelCase_    (     self  ,		A  ,		A=None			)  ->   Tuple:



             '''simple docstring'''



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




class       a__	( UpperCamelCase__				):







     @require_torch_neuroncore
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''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__	( UpperCamelCase__				):







     @require_torch_multi_gpu
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''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__				:      Optional[Any]	   = HfArgumentParser((TrainingArguments,))
   lowercase__				:      Tuple	   = 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__				:      int	   = DummyDataset(dataset_length)

      def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Dict:
           a     =				list(range(len(__UpperCamelCase)))
           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__				:      Union[str, Any]	   = Trainer(
          model=DummyModel(),
          args=training_args,
          data_collator=DummyDataCollator(),
          eval_dataset=dataset,
          compute_metrics=compute_metrics,
      )
      lowercase__				:      List[Any]	   = trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

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

      lowercase__				:      Any	   = 2

      lowercase__				:      Union[str, Any]	   = trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

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





      lowercase__				:      str	   = None







import math

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5		, int(math.sqrt(__UpperCamelCase) + 1)		, 6):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 0.1)      ->					int:
     a     =				3
     a     =				3

     while primes / (2 * j - 1) >= ratio:
          for i in range(j * j + j + 1		, (j + 2) * (j + 2)		, j + 1):
               primes += is_prime(__UpperCamelCase)
          j += 2
     return j


if __name__ == "__main__":
   import doctest

   doctest.testmod()

