




"""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


A_      			=       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       lowercase(  __a				):







      '''simple docstring'''





      def __init__(       self:	Dict,   a_:	int = 101	):




        '''simple docstring'''



        _snake_case							:	Optional[Any]			=						length


      def __len__(       self:	Dict	):




        '''simple docstring'''



        return self.length


      def __getitem__(       self:	Optional[Any],   a_:	Union[str, Any]	):




        '''simple docstring'''



        return i







     class       lowercase:







      '''simple docstring'''





      def __call__(       self:	Tuple,   a_:	Optional[int]	):




        '''simple docstring'''



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







     class       lowercase(  nn.Module				):







      '''simple docstring'''





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




        '''simple docstring'''



        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        _snake_case							:	Optional[int]			=						nn.Linear(120,   80	)


      def 				UpperCamelCase_     (       self:	Optional[int],   a_:	Dict,   a_:	Optional[int]=None	):




        '''simple docstring'''



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






class       lowercase(  __a				):







 '''simple docstring'''





 @require_torch_neuroncore
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	int			=						f"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
   _snake_case							:	Union[str, Any]			=						self.get_auto_remove_tmp_dir()
   _snake_case							:	int			=						f"--output_dir {output_dir}".split()
   _snake_case							:	List[Any]			=						["""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       lowercase(  __a				):







 '''simple docstring'''





 @require_torch_multi_gpu
 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						f"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
   _snake_case							:	Tuple			=						self.get_auto_remove_tmp_dir()
   _snake_case							:	Tuple			=						f"--output_dir {output_dir}".split()
   _snake_case							:	Optional[int]			=						["""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

     A_      			=       HfArgumentParser((TrainingArguments,))
     A_      			=       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 [1_01, 40, 7]:
          A_      			=       DummyDataset(dataset_length)


          def       UpperCAmelCase__ (snake_case__							:      EvalPrediction					):
            """simple docstring"""

            _snake_case							:	Tuple			=						list(range(len(snake_case__					)					)					)
            _snake_case							:	Union[str, Any]			=						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}

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

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

          A_      			=       2

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

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






          A_      			=       None








"""simple docstring"""


from abc import ABC, abstractmethod
from argparse import ArgumentParser






class       lowercase(  __a				):







 '''simple docstring'''





 @staticmethod
 @abstractmethod
 def 				UpperCamelCase_     (       a_:	ArgumentParser	):




   '''simple docstring'''



   raise NotImplementedError()


 @abstractmethod
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   raise NotImplementedError()


