





from dataclasses import dataclass, field
from typing import Optional



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Model name or path of model to be trained.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='./' ,     metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot-clean-train' ,     metadata={'help': 'Name or path of training dataset.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot-clean-valid' ,     metadata={'help': 'Name or path of validation dataset.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=2 ,     metadata={'help': 'Batch size for training.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=2 ,     metadata={'help': 'Batch size for evaluation.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(default=0.1 ,     metadata={'help': 'Value of weight decay.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=1_0_0_0_0 ,     metadata={'help': 'Size of buffer used to shuffle streaming dataset.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(default=2e-4 ,     metadata={'help': 'Learning rate fo training.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(default='cosine' ,     metadata={'help': 'Learning rate.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=7_5_0 ,     metadata={'help': 'Number of warmup steps in the learning rate schedule.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=1_6 ,     metadata={'help': 'Number of gradient accumulation steps.'}    )
      _UpperCAmelCase			:Optional[bool]							=     field(
          default=__snake_case ,     metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=5_0_0_0_0 ,     metadata={'help': 'Maximum number of training steps.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=-1 ,     metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1_0_2_4 ,     metadata={'help': 'Sequence lengths used for training.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1 ,     metadata={'help': 'Training seed.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=1_0_2_4 ,     metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'} ,     )
      _UpperCAmelCase			:Optional[str]							=     field(
          default=__snake_case ,     metadata={'help': 'States path if the training should continue from a checkpoint folder.'}    )
      _UpperCAmelCase			:Optional[bool]							=     field(default=__snake_case ,     metadata={'help': 'If True the data is pretokenized.'}    )



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Model name or path of model to be evaluated.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot-clean-valid' ,     metadata={'help': 'Name or path of validation dataset.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=2 ,     metadata={'help': 'Batch size used for evaluation.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=-1 ,     metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1_0_2_4 ,     metadata={'help': 'Length of sequences to be evaluated.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1 ,     metadata={'help': 'Random seed used for evaluation.'}    )



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Model name or path of model to be evaluated.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=__snake_case ,     metadata={'help': 'Number of workers used for code evaluation.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=__snake_case ,     metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'} ,     )
      _UpperCAmelCase			:Optional[bool]							=     field(
          default=__snake_case ,     metadata={'help': 'Sample from the language model\'s output distribution.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(default=0.2 ,     metadata={'help': 'Sampling temperature used for generation.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=2_5_6 ,     metadata={'help': 'Maximum number of newly generated tokens.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=0 ,     metadata={'help': 'Top-k parameter used for generation.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(default=0.95 ,     metadata={'help': 'Top-p parameter used for nucleus sampling.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1_0 ,     metadata={'help': 'Number of generations to run in parallel.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=2_0_0 ,     metadata={'help': 'Number of completions to generate for each sample.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1 ,     metadata={'help': 'Random seed used for evaluation.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='eval_results.json' ,     metadata={'help': 'Random seed used for evaluation.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='0' ,     metadata={'help': 'Allow `code_eval` to execute Python code on machine'}    )
      _UpperCAmelCase			: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       A__     :
      _UpperCAmelCase			:Optional[int]							=     field(
          default=__snake_case ,     metadata={
              'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
          } ,     )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='transformersbook/codeparrot' ,     metadata={'help': 'Folder or name of dataset to process.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot-clean' ,     metadata={'help': 'Folder to save processed processed dataset.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=1_0_0_0_0_0 ,     metadata={'help': 'Number of files to save per JSON output file.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(default='content' ,     metadata={'help': 'Column containing text data to process.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=1_0_0_0 ,     metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=1_0_0 ,     metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=0.25 ,     metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=1.5 ,     metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=0.7 ,     metadata={'help': 'Probability for filtering config, test and uncommon files.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Name or path to the tokenizer.'} ,     )
      _UpperCAmelCase			:Optional[bool]							=     field(
          default=__snake_case ,     metadata={'help': 'If True, near-duplicate samples are removed.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=0.85 ,     metadata={'help': 'Jaccard threshold for near-duplicate samples.'}    )



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[str]							=     field(
          default='gpt2' ,     metadata={'help': 'Base tokenizer to build new tokenizer from.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='transformersbook/codeparrot-train' ,     metadata={'help': 'Dataset to train tokenizer on.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(default='content' ,     metadata={'help': 'Column containing text data to process.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=2_0_0_0_0_0 ,     metadata={'help': 'Number of examples to train tokenizer on.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=3_2_7_6_8 ,     metadata={'help': 'Number of examples to train the tokenizer on.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(default='codeparrot' ,     metadata={'help': 'Name of new tokenizer.'}    )
      _UpperCAmelCase			:Optional[bool]							=     field(default=__snake_case ,     metadata={'help': 'Push saved tokenizer to the hub.'}    )



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



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







from __future__ import annotations



def 	A_       (  _lowerCAmelCase  )		->				list[int]:
 UpperCamelCase			:      Optional[Any] 			=     [True] * limit
 UpperCamelCase			:      Optional[Any] 			=     False
 UpperCamelCase			:      List[str] 			=     False
 UpperCamelCase			:      Tuple 			=     True

 for i in range(3							,     int(limit**0.5 + 1  )							,     2  ):
  UpperCamelCase			:      Optional[Any] 			=     i * 2
  while index < limit:
   UpperCamelCase			:      int 			=     False
   UpperCamelCase			:      Optional[int] 			=     index + i

 UpperCamelCase			:      Any 			=     [2]

 for i in range(3							,     _lowerCAmelCase							,     2  ):
  if is_prime[i]:
   primes.append(_lowerCAmelCase  )

 return primes



def 	A_       (  _lowerCAmelCase = 100_0000  )		->				int:
 UpperCamelCase			:      Union[str, Any] 			=     prime_sieve(_lowerCAmelCase  )
 UpperCamelCase			:      List[str] 			=     0
 UpperCamelCase			:      Union[str, Any] 			=     0

 for i in range(len(_lowerCAmelCase  )  ):
  for j in range(i + length							,     len(_lowerCAmelCase  )  ):
   UpperCamelCase			:      Dict 			=     sum(primes[i:j]  )
   if sol >= ceiling:
    break

   if sol in primes:
    UpperCamelCase			:      int 			=     j - i
    UpperCamelCase			:      Dict 			=     sol

 return largest


if __name__ == "__main__":
    print(f"""{solution() = }""")
