




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								=	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__       ):




         """simple docstring"""

         def __init__(			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	int = 101  ) ->			List[str]:
              lowerCAmelCase__            =					length


         def __len__(			self	:	Tuple  ) ->			Union[str, Any]:
              return self.length


         def __getitem__(			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			int:
              return i






     class 					__lowerCamelCase			:




         """simple docstring"""

         def __call__(			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	List[str]  ) ->			int:
              return {"input_ids": torch.tensor(SCREAMING_SNAKE_CASE__  ), "labels": torch.tensor(SCREAMING_SNAKE_CASE__  )}






     class 					__lowerCamelCase			(       nn.Module       ):




         """simple docstring"""

         def __init__(			self	:	Optional[int]  ) ->			Any:
              super().__init__()
              # Add some (unused) params otherwise DDP will complain.
              lowerCAmelCase__            =					nn.Linear(120  ,      80  )


         def   a (			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	List[str]=None  ) ->			Optional[Any]:
              if labels is not None:
                   return torch.tensor(0.0  ,      device=input_ids.device  ), input_ids
              else:
                   return input_ids





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

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






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    @require_torch_multi_gpu
    def   a (			self	:	Dict  ) ->			List[str]:
         lowerCAmelCase__            =					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()
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					f'--output_dir {output_dir}'.split()
         lowerCAmelCase__            =					["torchrun"] + distributed_args + args
         execute_subprocess_async(SCREAMING_SNAKE_CASE__  ,      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								=	HfArgumentParser((TrainingArguments,))
     UpperCamelCase								=	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								=	DummyDataset(dataset_length)



          def 				_A  (		lowerCAmelCase_	:   EvalPrediction     ):
               """simple docstring"""

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

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

          UpperCamelCase								=	2

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

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






          UpperCamelCase								=	None





"""simple docstring"""






import operator




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = None	)						->				list:
  _snake_case   	=      operator.lt if reverse else operator.gt
  _snake_case   	=      solution or []

  if not arr:
    return solution

  _snake_case   	=      [arr.pop(0	)]
  for i, item in enumerate(lowerCAmelCase_	):
    if _operator(lowerCAmelCase_  ,						sublist[-1]	):
      sublist.append(lowerCAmelCase_	)
      arr.pop(lowerCAmelCase_	)

    #  merging sublist into solution list
  if not solution:
    solution.extend(lowerCAmelCase_	)
  else:
    while sublist:
      _snake_case   	=      sublist.pop(0	)
      for i, xx in enumerate(lowerCAmelCase_	):
        if not _operator(lowerCAmelCase_  ,						lowerCAmelCase_	):
          solution.insert(lowerCAmelCase_  ,						lowerCAmelCase_	)
          break
      else:
        solution.append(lowerCAmelCase_	)

  strand_sort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
  return solution


if __name__ == "__main__":
     assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
     assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]





