

'''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


lowercase__         =      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 	A_			(    lowerCamelCase__							):






      '''simple docstring'''



      def __init__(		self			:					str					,      lowercase_			:					int = 101	)						->     str:
       UpperCAmelCase	:     Dict						 =			length


      def __len__(		self			:					Optional[int]	)						->     Dict:
       return self.length




      def __getitem__(		self			:					List[str]					,      lowercase_			:					Union[str, Any]	)						->     Dict:
       return i




 class 	A_			:






      '''simple docstring'''



      def __call__(		self			:					str					,      lowercase_			:					Any	)						->     Optional[int]:
       return {"input_ids": torch.tensor(lowercase__	), "labels": torch.tensor(lowercase__	)}




 class 	A_			(    nn.Module							):






      '''simple docstring'''



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




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




class 	A_			(    lowerCamelCase__							):






     '''simple docstring'''



     @require_torch_neuroncore
     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     str:
      UpperCAmelCase	:     List[str]						 =			f"""--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        """.split()
      UpperCAmelCase	:     List[Any]						 =			self.get_auto_remove_tmp_dir()
      UpperCAmelCase	:     str						 =			f"""--output_dir {output_dir}""".split()
      UpperCAmelCase	:     int						 =			['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 	A_			(    lowerCamelCase__							):






     '''simple docstring'''



     @require_torch_multi_gpu
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Optional[int]:
      UpperCAmelCase	:     List[Any]						 =			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()
      UpperCAmelCase	:     Optional[Any]						 =			self.get_auto_remove_tmp_dir()
      UpperCAmelCase	:     Tuple						 =			f"""--output_dir {output_dir}""".split()
      UpperCAmelCase	:     Optional[Any]						 =			['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

 lowercase__         =      HfArgumentParser((TrainingArguments,))
 lowercase__         =      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]:
  lowercase__         =      DummyDataset(dataset_length)




  def 				UpperCamelCase(					UpperCAmelCase_     ):
   UpperCAmelCase	:     List[str]						 =			list(range(len(A__     )     )     )
   UpperCAmelCase	:     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}

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

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

  lowercase__         =      2

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

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




  lowercase__         =      None







'''simple docstring'''

def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if b == 0:
  return 1
 if (b % 2) == 0:
  return actual_power(UpperCAmelCase_  ,	int(b / 2     )     ) * actual_power(UpperCAmelCase_  ,	int(b / 2     )     )
 else:
  return a * actual_power(UpperCAmelCase_  ,	int(b / 2     )     ) * actual_power(UpperCAmelCase_  ,	int(b / 2     )     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if b < 0:
  return 1 / actual_power(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return actual_power(UpperCAmelCase_  ,	UpperCAmelCase_     )


if __name__ == "__main__":
 print(power(-2, -3))




