






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


lowerCamelCase			:					Optional[int]         =	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 							_UpperCamelCase			(a_			):


       def __init__(	self					,						__UpperCamelCase = 1_0_1	)->	Optional[Any]:
            __lowerCAmelCase								=					length


       def __len__(	self	)->	Dict:
            return self.length


       def __getitem__(	self					,						__UpperCamelCase	)->	int:
            return i



 class 							_UpperCamelCase			:


       def __call__(	self					,						__UpperCamelCase	)->	str:
            return {"input_ids": torch.tensor(__UpperCamelCase	), "labels": torch.tensor(__UpperCamelCase	)}



 class 							_UpperCamelCase			(nn.Module			):


       def __init__(	self	)->	Optional[int]:
            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            __lowerCAmelCase								=					nn.Linear(1_2_0					,						8_0	)


       def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase=None	)->	Any:
            if labels is not None:
                 return torch.tensor(0.0					,						device=input_ids.device	), input_ids
            else:
                 return input_ids



class 							_UpperCamelCase			(a_			):


      @require_torch_neuroncore
      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
           __lowerCAmelCase								=					self.get_auto_remove_tmp_dir()
           __lowerCAmelCase								=					F"""--output_dir {output_dir}""".split()
           __lowerCAmelCase								=					["torchrun"] + distributed_args + args
           execute_subprocess_async(__UpperCamelCase					,						env=self.get_env()	)
           # successful return here == success - any errors would have caused an error in the sub-call



class 							_UpperCamelCase			(a_			):


      @require_torch_multi_gpu
      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
           __lowerCAmelCase								=					self.get_auto_remove_tmp_dir()
           __lowerCAmelCase								=					F"""--output_dir {output_dir}""".split()
           __lowerCAmelCase								=					["torchrun"] + distributed_args + args
           execute_subprocess_async(__UpperCamelCase					,						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

 lowerCamelCase			:					List[Any]         =	HfArgumentParser((TrainingArguments,))
 lowerCamelCase			:					Union[str, 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]:
  lowerCamelCase			:					str         =	DummyDataset(dataset_length)





  def       __lowerCAmelCase      (						__snake_case       ):
       __lowerCAmelCase								=					list(range(len(__snake_case       )       )       )
       __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}

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

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

  lowerCamelCase			:					Dict         =	2

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

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



  lowerCamelCase			:					Tuple         =	None














from __future__ import annotations





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					list(range(len(__snake_case       )       )       )
     __lowerCAmelCase								=					[v / w for v, w in zip(__snake_case					, __snake_case       )]
     index.sort(key=lambda __snake_case       : ratio[i]					, reverse=__snake_case       )

     __lowerCAmelCase								=					0
     __lowerCAmelCase								=					[0] * len(__snake_case       )
     for i in index:
          if weight[i] <= capacity:
               __lowerCAmelCase								=					1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __lowerCAmelCase								=					capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()






