


"""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


UpperCAmelCase						:		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    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

           def __init__(				self			:				List[Any] ,  lowerCAmelCase_			:				int = 1_0_1):







             """simple docstring"""







             lowercase_								= length

           def __len__(				self			:				List[Any]):







             """simple docstring"""







             return self.length




           def __getitem__(				self			:				Dict ,  lowerCAmelCase_			:				List[str]):







             """simple docstring"""







             return i







       class    SCREAMING_SNAKE_CASE__ :

           def __call__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Union[str, Any]):







             """simple docstring"""







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







       class    SCREAMING_SNAKE_CASE__ (							nn.Module   ):

           def __init__(				self			:				Any):







             """simple docstring"""







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




           def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Any=None):







             """simple docstring"""







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


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    @require_torch_neuroncore
    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







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







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    @require_torch_multi_gpu
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      lowercase_								= 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()
      lowercase_								= self.get_auto_remove_tmp_dir()
      lowercase_								= F'''--output_dir {output_dir}'''.split()
      lowercase_								= ["""torchrun"""] + distributed_args + args
      execute_subprocess_async(lowercase_ ,  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[Any]			= HfArgumentParser((TrainingArguments,))
       UpperCAmelCase						:		List[Any]			= 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Dict:







                '''simple docstring'''


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

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

              UpperCAmelCase						:		List[str]			= 2

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

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


              UpperCAmelCase						:		Tuple			= None






"""simple docstring"""
from ..utils import DummyObject, requires_backends


class    SCREAMING_SNAKE_CASE__ (							metaclass=__UpperCAmelCase   ):
    lowercase__      =   ["keras_nlp"]

    def __init__(				self			:				Dict ,  *lowerCAmelCase_			:				List[str] ,  **lowerCAmelCase_			:				Tuple):







      """simple docstring"""







      requires_backends(self ,  ["""keras_nlp"""])

