

'''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					:			Tuple							=    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 							lowercase_						(		UpperCAmelCase_			):

    '''simple docstring'''





    def __init__(  self ,			a_ = 1_0_1  )				->					Any:


      """simple docstring"""
      UpperCAmelCase    				=						length
    def __len__(  self  )				->					Optional[Any]:


      """simple docstring"""
      return self.length





    def __getitem__(  self ,			a_  )				->					str:


      """simple docstring"""
      return i




  class 							lowercase_						:

    '''simple docstring'''





    def __call__(  self ,			a_  )				->					List[str]:


      """simple docstring"""
      return {"input_ids": torch.tensor(SCREAMING_SNAKE_CASE_  ), "labels": torch.tensor(SCREAMING_SNAKE_CASE_  )}




  class 							lowercase_						(		nn.Module			):

    '''simple docstring'''





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


      """simple docstring"""
      super().__init__()
      # Add some (unused) params otherwise DDP will complain.
      UpperCAmelCase    				=						nn.Linear(1_2_0 ,			8_0  )





    def     snake_case_      (  self ,			a_ ,			a_=None  )				->					Optional[int]:


      """simple docstring"""




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



class 							lowercase_						(		UpperCAmelCase_			):

  '''simple docstring'''





  @require_torch_neuroncore
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						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    				=						self.get_auto_remove_tmp_dir()
    UpperCAmelCase    				=						F'''--output_dir {output_dir}'''.split()
    UpperCAmelCase    				=						["""torchrun"""] + distributed_args + args
    execute_subprocess_async(SCREAMING_SNAKE_CASE_ ,			env=self.get_env()  )
    # successful return here == success - any errors would have caused an error in the sub-call




class 							lowercase_						(		UpperCAmelCase_			):

  '''simple docstring'''





  @require_torch_multi_gpu
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						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    				=						self.get_auto_remove_tmp_dir()
    UpperCAmelCase    				=						F'''--output_dir {output_dir}'''.split()
    UpperCAmelCase    				=						["""torchrun"""] + distributed_args + args
    execute_subprocess_async(SCREAMING_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

  _a					:			List[Any]							=    HfArgumentParser((TrainingArguments,))
  _a					:			Optional[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]:
    _a					:			Tuple							=    DummyDataset(dataset_length)


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

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

    _a					:			str							=    2

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

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



    _a					:			List[Any]							=    None




lowerCamelCase__     :		dict[tuple[int, int, int], int]        = {}

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  if late == 3 or absent == 2:
    return 0

  # if we have no days left, and have not failed any other rules,
  # we have a prize string
  if days == 0:
    return 1

  # No easy solution, so now we need to do the recursive calculation

  # First, check if the combination is already in the cache, and
  # if yes, return the stored value from there since we already
  # know the number of possible prize strings from this point on
  lowercase__	: Tuple     		=   (days, absent, late)
  if key in cache:
    return cache[key]

  # now we calculate the three possible ways that can unfold from
  # this point on, depending on our attendance today

  # 1) if we are late (but not absent), the "absent" counter stays as
  # it is, but the "late" counter increases by one
  lowercase__	: Union[str, Any]     		=   _calculate(days - 1			,			lowercase_			,			late + 1 )

  # 2) if we are absent, the "absent" counter increases by 1, and the
  # "late" counter resets to 0
  lowercase__	: List[str]     		=   _calculate(days - 1			,			absent + 1			,			0 )

  # 3) if we are on time, this resets the "late" counter and keeps the
  # absent counter
  lowercase__	: Dict     		=   _calculate(days - 1			,			lowercase_			,			0 )

  lowercase__	: List[str]     		=   state_late + state_absent + state_ontime

  lowercase__	: List[Any]     		=   prizestrings
  return prizestrings

def 		UpperCamelCase						(   lowercase_ = 30 )		->						int:


  '''simple docstring'''






  return _calculate(lowercase_			,			absent=0			,			late=0 )


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






