

"""simple docstring"""


from dataclasses import dataclass, field
from typing import Optional

@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Model name or path of model to be trained."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""./"""   ,	metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot-clean-train"""   ,	metadata={"""help""": """Name or path of training dataset."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot-clean-valid"""   ,	metadata={"""help""": """Name or path of validation dataset."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=2   ,	metadata={"""help""": """Batch size for training."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=2   ,	metadata={"""help""": """Batch size for evaluation."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(default=0.1   ,	metadata={"""help""": """Value of weight decay."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=1_00_00   ,	metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(default=2E-4   ,	metadata={"""help""": """Learning rate fo training."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default="""cosine"""   ,	metadata={"""help""": """Learning rate."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=7_50   ,	metadata={"""help""": """Number of warmup steps in the learning rate schedule."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=16   ,	metadata={"""help""": """Number of gradient accumulation steps."""})
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=5_00_00   ,	metadata={"""help""": """Maximum number of training steps."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=-1   ,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=10_24   ,	metadata={"""help""": """Sequence lengths used for training."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=1   ,	metadata={"""help""": """Training seed."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=10_24   ,	metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """States path if the training should continue from a checkpoint folder."""})
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """If True the data is pretokenized."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Model name or path of model to be evaluated."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot-clean-valid"""   ,	metadata={"""help""": """Name or path of validation dataset."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=2   ,	metadata={"""help""": """Batch size used for evaluation."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=-1   ,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=10_24   ,	metadata={"""help""": """Length of sequences to be evaluated."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=1   ,	metadata={"""help""": """Random seed used for evaluation."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Model name or path of model to be evaluated."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Number of workers used for code evaluation."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """Sample from the language model's output distribution."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(default=0.2   ,	metadata={"""help""": """Sampling temperature used for generation."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=2_56   ,	metadata={"""help""": """Maximum number of newly generated tokens."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=0   ,	metadata={"""help""": """Top-k parameter used for generation."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(default=0.95   ,	metadata={"""help""": """Top-p parameter used for nucleus sampling."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=10   ,	metadata={"""help""": """Number of generations to run in parallel."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=2_00   ,	metadata={"""help""": """Number of completions to generate for each sample."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=1   ,	metadata={"""help""": """Random seed used for evaluation."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""eval_results.json"""   ,	metadata={"""help""": """Random seed used for evaluation."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""0"""   ,	metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""})
     _SCREAMING_SNAKE_CASE						: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							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=UpperCamelCase_   ,	metadata={
             """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
         }   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""transformersbook/codeparrot"""   ,	metadata={"""help""": """Folder or name of dataset to process."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot-clean"""   ,	metadata={"""help""": """Folder to save processed processed dataset."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=10_00_00   ,	metadata={"""help""": """Number of files to save per JSON output file."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default="""content"""   ,	metadata={"""help""": """Column containing text data to process."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=10_00   ,	metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=1_00   ,	metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=0.25   ,	metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=1.5   ,	metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=0.7   ,	metadata={"""help""": """Probability for filtering config, test and uncommon files."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Name or path to the tokenizer."""}   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """If True, near-duplicate samples are removed."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=0.85   ,	metadata={"""help""": """Jaccard threshold for near-duplicate samples."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""gpt2"""   ,	metadata={"""help""": """Base tokenizer to build new tokenizer from."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""transformersbook/codeparrot-train"""   ,	metadata={"""help""": """Dataset to train tokenizer on."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default="""content"""   ,	metadata={"""help""": """Column containing text data to process."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=20_00_00   ,	metadata={"""help""": """Number of examples to train tokenizer on."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=3_27_68   ,	metadata={"""help""": """Number of examples to train the tokenizer on."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default="""codeparrot"""   ,	metadata={"""help""": """Name of new tokenizer."""})
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Push saved tokenizer to the hub."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Name or path to the tokenizer."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot-clean-train"""   ,	metadata={"""help""": """Name or path to the dataset to pretokenize."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""tokenized-codeparrot-train"""   ,	metadata={"""help""": """Repo name of the pretokenized data."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Number of workers used for code evaluation."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""gpt2-large"""   ,	metadata={"""help""": """Configuration to use for model initialization."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Tokenizer attached to model."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default="""codeparrot"""   ,	metadata={"""help""": """Name of the created model."""})
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Push saved tokenizer to the hub."""})







"""simple docstring"""


from math import sqrt




def  _lowercase					(    __lowerCAmelCase							)       ->				bool:

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5  ,					int(sqrt(__lowerCAmelCase							) + 1							)  ,					6							):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True




def  _lowercase					(    __lowerCAmelCase = 1_0001							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	0
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	1
      while count != nth and number < 3:
            number += 1
            if is_prime(__lowerCAmelCase							):
                  count += 1
      while count != nth:
            number += 2
            if is_prime(__lowerCAmelCase							):
                  count += 1
      return number


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




