


"""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      		=			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 					__magic_name__		(			__UpperCAmelCase     ):






        def __init__(   self  :    Optional[int]  ,     snake_case__  :    int = 1_0_1				):

               '''simple docstring'''






               lowercase		:Tuple       							=				length






        def __len__(   self  :    str				):

               '''simple docstring'''






               return self.length







        def __getitem__(   self  :    Dict  ,     snake_case__  :    Union[str, Any]				):

               '''simple docstring'''






               return i







 class 					__magic_name__		:






        def __call__(   self  :    List[str]  ,     snake_case__  :    List[str]				):

               '''simple docstring'''






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







 class 					__magic_name__		(			nn.Module     ):






        def __init__(   self  :    List[Any]				):

               '''simple docstring'''






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







        def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    Tuple  ,     snake_case__  :    List[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 					__magic_name__		(			__UpperCAmelCase     ):






       @require_torch_neuroncore
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
              lowercase		:Optional[Any]       							=				self.get_auto_remove_tmp_dir()
              lowercase		:Tuple       							=				f"""--output_dir {output_dir}""".split()
              lowercase		:Optional[int]       							=				['''torchrun'''] + distributed_args + args
              execute_subprocess_async(snake_case__  ,     env=self.get_env()				)
              # successful return here == success - any errors would have caused an error in the sub-call







class 					__magic_name__		(			__UpperCAmelCase     ):






       @require_torch_multi_gpu
       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:int       							=				f"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
              lowercase		:List[str]       							=				self.get_auto_remove_tmp_dir()
              lowercase		:Optional[Any]       							=				f"""--output_dir {output_dir}""".split()
              lowercase		:Dict       							=				['''torchrun'''] + distributed_args + args
              execute_subprocess_async(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       lowerCamelCase					(a_						:EvalPrediction)     ->							Dict:
         lowercase		:Optional[int]       							=				list(range(len(a_)))
         lowercase		:int       							=				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"""



def       lowerCamelCase					(a_						:int)     ->							str:
       if number > 0:
              raise ValueError('''input must be a negative integer''')
       lowercase		:Any       							=				len(bin(a_)[3:])
       lowercase		:Any       							=				bin(abs(a_) - (1 << binary_number_length))[3:]
       lowercase		:Tuple       							=				(
           (
               '''1'''
               + '''0''' * (binary_number_length - len(a_))
               + twos_complement_number
           )
           if number < 0
           else '''0'''
       )
       return "0b" + twos_complement_number


if __name__ == "__main__":
 import doctest

 doctest.testmod()

