
"""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


a					:	Union[str, 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 		a_						( UpperCamelCase_	):







     def __init__(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       int = 1_01     )		->Optional[Any]:






         '''simple docstring'''






         _UpperCAmelCase          =   length







     def __len__(			self			:       Dict     )		->Dict:






         '''simple docstring'''






         return self.length







     def __getitem__(			self			:       int     ,					__UpperCamelCase			:       str     )		->Optional[int]:






         '''simple docstring'''






         return i







 class 		a_						:







     def __call__(			self			:       Dict     ,					__UpperCamelCase			:       List[Any]     )		->List[Any]:






         '''simple docstring'''






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







 class 		a_						( nn.Module	):







     def __init__(			self			:       List[str]     )		->List[Any]:






         '''simple docstring'''






         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         _UpperCAmelCase          =   nn.Linear(1_20     ,					80     )







     def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Any=None     )		->Tuple:






         '''simple docstring'''






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




class 		a_						( UpperCamelCase_	):







    @require_torch_neuroncore
    def      _snake_case					(			self			:       Optional[int]     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
        _UpperCAmelCase          =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase          =   f"""--output_dir {output_dir}""".split()
        _UpperCAmelCase          =   ['''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 		a_						( UpperCamelCase_	):







    @require_torch_multi_gpu
    def      _snake_case					(			self			:       str     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   f"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
        _UpperCAmelCase          =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase          =   f"""--output_dir {output_dir}""".split()
        _UpperCAmelCase          =   ['''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

 a					:	Any							=		HfArgumentParser((TrainingArguments,))
 a					:	Tuple							=		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 [1_0_1, 4_0, 7]:
  a					:	List[str]							=		DummyDataset(dataset_length)


  def  _UpperCamelCase	(						_A      )    ->  str:
      _UpperCAmelCase          =   list(range(len(_lowercase      )      )      )
      _UpperCAmelCase          =   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}

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

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

  a					:	Any							=		2

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

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







  a					:	List[str]							=		None

"""simple docstring"""



import re

from filelock import FileLock


try:
 import nltk

 a					:	str							=		True
except (ImportError, ModuleNotFoundError):
 a					:	List[str]							=		False

if NLTK_AVAILABLE:
 with FileLock('''.lock''') as lock:
  nltk.download('''punkt''', quiet=True)


def  _UpperCamelCase	(						_A      )    ->  str:





    """simple docstring"""

    re.sub("""<n>"""					,      """"""					,      _A      )  # remove pegasus newline char
    assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
    return "\n".join(nltk.sent_tokenize(_A      )      )