"""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


SCREAMING_SNAKE_CASE__:Optional[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 				snake_case__     (   lowerCamelCase__   ):


    def __init__(				self  ,						lowerCamelCase = 101		):
           __a     				=  length


    def __len__(				self		):
           return self.length






    def __getitem__(				self  ,						lowerCamelCase		):
           return i






   class 				snake_case__     :


    def __call__(				self  ,						lowerCamelCase		):
           return {"input_ids": torch.tensor(lowercase__		), "labels": torch.tensor(lowercase__		)}






   class 				snake_case__     (   nn.Module   ):


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






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






class 				snake_case__     (   lowerCamelCase__   ):


 @require_torch_neuroncore
 def 		a__ (				self		):
        __a     				=  F"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
        __a     				=  self.get_auto_remove_tmp_dir()
        __a     				=  F"--output_dir {output_dir}".split()
        __a     				=  ["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 				snake_case__     (   lowerCamelCase__   ):


 @require_torch_multi_gpu
 def 		a__ (				self		):
        __a     				=  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()
        __a     				=  self.get_auto_remove_tmp_dir()
        __a     				=  F"--output_dir {output_dir}".split()
        __a     				=  ["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

   SCREAMING_SNAKE_CASE__:Tuple			=	HfArgumentParser((TrainingArguments,))
   SCREAMING_SNAKE_CASE__:Optional[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]:
      SCREAMING_SNAKE_CASE__:str			=	DummyDataset(dataset_length)



      def 					_lowerCamelCase(					a							):
             __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}

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

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

      SCREAMING_SNAKE_CASE__:Union[str, Any]			=	2

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

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




      SCREAMING_SNAKE_CASE__:Dict			=	None

"""simple docstring"""



from ..utils import DummyObject, requires_backends






class 				snake_case__     (   metaclass=snake_case_   ):
 _snake_case   :			Union[str, Any]           =		["""onnx"""]


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(self  ,						["onnx"]		)


 @classmethod
 def 		a__ (				cls  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(cls  ,						["onnx"]		)






 @classmethod
 def 		a__ (				cls  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(cls  ,						["onnx"]		)
