
from dataclasses import dataclass, field
from typing import Optional



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot/codeparrot'	,    metadata={'help': 'Model name or path of model to be trained.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='./'	,    metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot/codeparrot-clean-train'	,    metadata={'help': 'Name or path of training dataset.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot/codeparrot-clean-valid'	,    metadata={'help': 'Name or path of validation dataset.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=2	,    metadata={'help': 'Batch size for training.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=2	,    metadata={'help': 'Batch size for evaluation.'}				)
 __lowerCAmelCase	:  Optional[float]	   =      field(default=0.1	,    metadata={'help': 'Value of weight decay.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=10_000	,    metadata={'help': 'Size of buffer used to shuffle streaming dataset.'}				)
 __lowerCAmelCase	:  Optional[float]	   =      field(default=2e-4	,    metadata={'help': 'Learning rate fo training.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(default='cosine'	,    metadata={'help': 'Learning rate.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=750	,    metadata={'help': 'Number of warmup steps in the learning rate schedule.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=16	,    metadata={'help': 'Number of gradient accumulation steps.'}				)
 __lowerCAmelCase	:  Optional[bool]	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=50_000	,    metadata={'help': 'Maximum number of training steps.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=-1	,    metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=1_024	,    metadata={'help': 'Sequence lengths used for training.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=1	,    metadata={'help': 'Training seed.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=1_024	,    metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'}	,    )
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'States path if the training should continue from a checkpoint folder.'}				)
 __lowerCAmelCase	:  Optional[bool]	   =      field(default=UpperCAmelCase_	,    metadata={'help': 'If True the data is pretokenized.'}				)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot/codeparrot'	,    metadata={'help': 'Model name or path of model to be evaluated.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot/codeparrot-clean-valid'	,    metadata={'help': 'Name or path of validation dataset.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=2	,    metadata={'help': 'Batch size used for evaluation.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=-1	,    metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=1_024	,    metadata={'help': 'Length of sequences to be evaluated.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=1	,    metadata={'help': 'Random seed used for evaluation.'}				)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot/codeparrot'	,    metadata={'help': 'Model name or path of model to be evaluated.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=UpperCAmelCase_	,    metadata={'help': 'Number of workers used for code evaluation.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}	,    )
 __lowerCAmelCase	:  Optional[bool]	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'Sample from the language model\'s output distribution.'}				)
 __lowerCAmelCase	:  Optional[float]	   =      field(default=0.2	,    metadata={'help': 'Sampling temperature used for generation.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=256	,    metadata={'help': 'Maximum number of newly generated tokens.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=0	,    metadata={'help': 'Top-k parameter used for generation.'}				)
 __lowerCAmelCase	:  Optional[float]	   =      field(default=0.95	,    metadata={'help': 'Top-p parameter used for nucleus sampling.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=10	,    metadata={'help': 'Number of generations to run in parallel.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=200	,    metadata={'help': 'Number of completions to generate for each sample.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=1	,    metadata={'help': 'Random seed used for evaluation.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='eval_results.json'	,    metadata={'help': 'Random seed used for evaluation.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='0'	,    metadata={'help': 'Allow `code_eval` to execute Python code on machine'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=-1	,    metadata={
         'help': (
             'Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'
             ' number corresponds to which GPU device id to run on.'
         )
     }	,    )



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=UpperCAmelCase_	,    metadata={
         'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
     }	,    )
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='transformersbook/codeparrot'	,    metadata={'help': 'Folder or name of dataset to process.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot-clean'	,    metadata={'help': 'Folder to save processed processed dataset.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=100_000	,    metadata={'help': 'Number of files to save per JSON output file.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(default='content'	,    metadata={'help': 'Column containing text data to process.'}				)
 __lowerCAmelCase	:  Optional[float]	   =      field(
     default=1_000	,    metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}				)
 __lowerCAmelCase	:  Optional[float]	   =      field(
     default=100	,    metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}				)
 __lowerCAmelCase	:  Optional[float]	   =      field(
     default=0.25	,    metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}				)
 __lowerCAmelCase	:  Optional[float]	   =      field(
     default=1.5	,    metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}				)
 __lowerCAmelCase	:  Optional[float]	   =      field(
     default=0.7	,    metadata={'help': 'Probability for filtering config, test and uncommon files.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot/codeparrot'	,    metadata={'help': 'Name or path to the tokenizer.'}	,    )
 __lowerCAmelCase	:  Optional[bool]	   =      field(
     default=UpperCAmelCase_	,    metadata={'help': 'If True, near-duplicate samples are removed.'}				)
 __lowerCAmelCase	:  Optional[float]	   =      field(
     default=0.85	,    metadata={'help': 'Jaccard threshold for near-duplicate samples.'}				)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='gpt2'	,    metadata={'help': 'Base tokenizer to build new tokenizer from.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='transformersbook/codeparrot-train'	,    metadata={'help': 'Dataset to train tokenizer on.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(default='content'	,    metadata={'help': 'Column containing text data to process.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=200_000	,    metadata={'help': 'Number of examples to train tokenizer on.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(
     default=32_768	,    metadata={'help': 'Number of examples to train the tokenizer on.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(default='codeparrot'	,    metadata={'help': 'Name of new tokenizer.'}				)
 __lowerCAmelCase	:  Optional[bool]	   =      field(default=UpperCAmelCase_	,    metadata={'help': 'Push saved tokenizer to the hub.'}				)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot/codeparrot'	,    metadata={'help': 'Name or path to the tokenizer.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot/codeparrot-clean-train'	,    metadata={'help': 'Name or path to the dataset to pretokenize.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='tokenized-codeparrot-train'	,    metadata={'help': 'Repo name of the pretokenized data.'}				)
 __lowerCAmelCase	:  Optional[int]	   =      field(default=UpperCAmelCase_	,    metadata={'help': 'Number of workers used for code evaluation.'}				)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='gpt2-large'	,    metadata={'help': 'Configuration to use for model initialization.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(
     default='codeparrot/codeparrot'	,    metadata={'help': 'Tokenizer attached to model.'}				)
 __lowerCAmelCase	:  Optional[str]	   =      field(default='codeparrot'	,    metadata={'help': 'Name of the created model.'}				)
 __lowerCAmelCase	:  Optional[bool]	   =      field(default=UpperCAmelCase_	,    metadata={'help': 'Push saved tokenizer to the hub.'}				)








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())






