


import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


a				:							str					=			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	(  snake_case_     ):

         def __init__(			self	,						a__ = 1_01					):






          '''simple docstring'''






          snake_case   : str	  =      length

         def __len__(			self					):






          '''simple docstring'''






          return self.length



         def __getitem__(			self	,						a__					):






          '''simple docstring'''






          return i







  class      lowerCamelCase	:

         def __call__(			self	,						a__					):






          '''simple docstring'''






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







  class      lowerCamelCase	(  nn.Module     ):

         def __init__(			self					):






          '''simple docstring'''






          super().__init__()
          # Add some (unused) params otherwise DDP will complain.
          snake_case   : Optional[Any]	  =      nn.Linear(1_20	,						80					)



         def 				UpperCAmelCase				(			self	,						a__	,						a__=None					):






          '''simple docstring'''






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






def    _UpperCAmelCase	(   )		->str:


 '''simple docstring'''
 snake_case   : Optional[Any]	  =      HfArgumentParser((TrainingArguments,)       )
 sys.argv += ["--output_dir", "./examples"]
 snake_case   : int	  =      parser.parse_args_into_dataclasses()[0]

 logger.warning(
     f'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
     f'''tpu_num_cores: {training_args.tpu_num_cores}''' ,     )

 # 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 [1001, 256, 15]:
  snake_case   : Any	  =      DummyDataset(__lowerCamelCase       )

  def compute_metrics(__lowerCamelCase       ) -> Dict:
   snake_case   : Any	  =      list(range(len(__lowerCamelCase       )       )       )
   snake_case   : Optional[int]	  =      p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
   return {"success": success}

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

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

  snake_case   : Union[str, Any]	  =      2

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

  snake_case   : Union[str, Any]	  =      trainer.predict(__lowerCamelCase       )
  logger.info(p.metrics       )
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics       )
   exit(1       )

  snake_case   : Dict	  =      None

 logger.info('''🔥 All distributed tests successful'''       )






def    _UpperCAmelCase	(   __lowerCamelCase       )		->Any:


 '''simple docstring'''
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
  main()





from itertools import permutations






def    _UpperCAmelCase	(   )		->str:


 '''simple docstring'''
 snake_case   : Any	  =      list(map(''''''.join ,     permutations('''0123456789'''       )       )       )
 return result[999999]


if __name__ == "__main__":
  print(solution())






