'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional





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





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





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





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





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=field(
       default='codeparrot/codeparrot' ,				metadata={'help': 'Name or path to the tokenizer.'} )
   lowerCamelCase_    					=field(
       default='codeparrot/codeparrot-clean-train' ,				metadata={'help': 'Name or path to the dataset to pretokenize.'} )
   lowerCamelCase_    					=field(
       default='tokenized-codeparrot-train' ,				metadata={'help': 'Repo name of the pretokenized data.'} )
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Number of workers used for code evaluation.'} )





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=field(
       default='gpt2-large' ,				metadata={'help': 'Configuration to use for model initialization.'} )
   lowerCamelCase_    					=field(
       default='codeparrot/codeparrot' ,				metadata={'help': 'Tokenizer attached to model.'} )
   lowerCamelCase_    					=field(default='codeparrot' ,				metadata={'help': 'Name of the created model.'} )
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Push saved tokenizer to the hub.'} )







'''simple docstring'''

def __a (	__lowerCamelCase			:						int = 600_851_475_143				)	->							int:
      '''simple docstring'''


      try:
            lowercase_     =      int(__lowerCamelCase				)
      except (TypeError, ValueError):
            raise TypeError("Parameter n must be int or castable to int."				)
      if n <= 0:
            raise ValueError("Parameter n must be greater than or equal to one."				)
      lowercase_     =      1
      lowercase_     =      2
      while i * i <= n:
            while n % i == 0:
                  lowercase_     =      i
                  n //= i
            i += 1
      if n > 1:
            lowercase_     =      n
      return int(__lowerCamelCase				)


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






