




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


UpperCAmelCase     :     Tuple							=	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     __lowerCAmelCase	(   UpperCamelCase__):







           def __init__(   self		,				lowerCAmelCase__ = 1_0_1							)       ->						Tuple:



             '''simple docstring'''




             a__     : Optional[Any]  =length







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



             '''simple docstring'''




             return self.length







           def __getitem__(   self		,				lowerCAmelCase__							)       ->						int:



             '''simple docstring'''




             return i







     class     __lowerCAmelCase	:







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



             '''simple docstring'''




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







     class     __lowerCAmelCase	(   nn.Module):







           def __init__(   self							)       ->						List[str]:



             '''simple docstring'''




             super().__init__()
             # Add some (unused) params otherwise DDP will complain.
             a__     : int  =nn.Linear(1_2_0		,				8_0							)







           def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__=None							)       ->						Optional[int]:



             '''simple docstring'''




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







class     __lowerCAmelCase	(   UpperCamelCase__):







      @require_torch_neuroncore
      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Any  =F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
        a__     : str  =self.get_auto_remove_tmp_dir()
        a__     : Optional[Any]  =F'''--output_dir {output_dir}'''.split()
        a__     : Tuple  =["torchrun"] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__		,				env=self.get_env()							)
        # successful return here == success - any errors would have caused an error in the sub-call







class     __lowerCAmelCase	(   UpperCamelCase__):







      @require_torch_multi_gpu
      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : int  =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__     : Union[str, Any]  =self.get_auto_remove_tmp_dir()
        a__     : List[Any]  =F'''--output_dir {output_dir}'''.split()
        a__     : Optional[int]  =["torchrun"] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__		,				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

     UpperCAmelCase     :     Optional[int]							=	HfArgumentParser((TrainingArguments,))
     UpperCAmelCase     :     int							=	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]:
          UpperCAmelCase     :     int							=	DummyDataset(dataset_length)
          def 					_A	(		SCREAMING_SNAKE_CASE					:       EvalPrediction						):





            """simple docstring"""



            a__     : Dict  =list(range(len(SCREAMING_SNAKE_CASE						)						)						)
            a__     : Tuple  =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}

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

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

          UpperCAmelCase     :     Tuple							=	2

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

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





          UpperCAmelCase     :     Any							=	None













import os
def 					_A	(		):





  """simple docstring"""



  a__     : Optional[int]  =os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE						)	,	"num.txt"						)
  with open(SCREAMING_SNAKE_CASE						) as file_hand:
    return str(sum(int(SCREAMING_SNAKE_CASE						) for line in file_hand						)						)[:10]


if __name__ == "__main__":
     print(solution())






