


'''simple docstring'''



from dataclasses import dataclass, field
from typing import Optional





@dataclass
class    UpperCAmelCase_  :





    """simple docstring"""






    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=1_0_0_0_0					,		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=7_5_0					,		metadata={'help': 'Number of warmup steps in the learning rate schedule.'}  )
    lowerCamelCase  :					Optional[int]							     =			field(
        default=1_6					,		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=5_0_0_0_0					,		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_0_2_4					,		metadata={'help': 'Sequence lengths used for training.'}  )
    lowerCamelCase  :					Optional[int]							     =			field(default=1					,		metadata={'help': 'Training seed.'}  )
    lowerCamelCase  :					Optional[int]							     =			field(
        default=1_0_2_4					,		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    UpperCAmelCase_  :





    """simple docstring"""






    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_0_2_4					,		metadata={'help': 'Length of sequences to be evaluated.'}  )
    lowerCamelCase  :					Optional[int]							     =			field(default=1					,		metadata={'help': 'Random seed used for evaluation.'}  )







@dataclass
class    UpperCAmelCase_  :





    """simple docstring"""






    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=2_5_6					,		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=1_0					,		metadata={'help': 'Number of generations to run in parallel.'}  )
    lowerCamelCase  :					Optional[int]							     =			field(
        default=2_0_0					,		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    UpperCAmelCase_  :





    """simple docstring"""






    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=1_0_0_0_0_0					,		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_0_0_0					,		metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}  )
    lowerCamelCase  :					Optional[float]							     =			field(
        default=1_0_0					,		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    UpperCAmelCase_  :





    """simple docstring"""






    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=2_0_0_0_0_0					,		metadata={'help': 'Number of examples to train tokenizer on.'}  )
    lowerCamelCase  :					Optional[int]							     =			field(
        default=3_2_7_6_8					,		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    UpperCAmelCase_  :





    """simple docstring"""






    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    UpperCAmelCase_  :





    """simple docstring"""






    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.'}  )










'''simple docstring'''



A__		:					Any												=  8.3_1_4_4_6_2  # Unit - J mol-1 K-1



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float							)     ->		float:
     if moles < 0 or kelvin < 0 or volume < 0:
          raise ValueError('Invalid inputs. Enter positive value.'							)
     return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float							)     ->		float:
     if moles < 0 or kelvin < 0 or pressure < 0:
          raise ValueError('Invalid inputs. Enter positive value.'							)
     return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
      from doctest import testmod

      testmod()






