




from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 							UpperCamelCase_       :

    '''simple docstring'''






    UpperCAmelCase__				    = field(
        default='''codeparrot/codeparrot'''						,   metadata={'''help''': '''Model name or path of model to be trained.'''}	)
    UpperCAmelCase__				    = field(
        default='''./'''						,   metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}	)
    UpperCAmelCase__				    = field(
        default='''codeparrot/codeparrot-clean-train'''						,   metadata={'''help''': '''Name or path of training dataset.'''}	)
    UpperCAmelCase__				    = field(
        default='''codeparrot/codeparrot-clean-valid'''						,   metadata={'''help''': '''Name or path of validation dataset.'''}	)
    UpperCAmelCase__				    = field(default=2						,   metadata={'''help''': '''Batch size for training.'''}	)
    UpperCAmelCase__				    = field(default=2						,   metadata={'''help''': '''Batch size for evaluation.'''}	)
    UpperCAmelCase__				    = field(default=0.1						,   metadata={'''help''': '''Value of weight decay.'''}	)
    UpperCAmelCase__				    = field(
        default=1_0000						,   metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}	)
    UpperCAmelCase__				    = field(default=2E-4						,   metadata={'''help''': '''Learning rate fo training.'''}	)
    UpperCAmelCase__				    = field(default='''cosine'''						,   metadata={'''help''': '''Learning rate.'''}	)
    UpperCAmelCase__				    = field(
        default=750						,   metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}	)
    UpperCAmelCase__				    = field(
        default=16						,   metadata={'''help''': '''Number of gradient accumulation steps.'''}	)
    UpperCAmelCase__				    = field(
        default=a__						,   metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}	)
    UpperCAmelCase__				    = field(default=5_0000						,   metadata={'''help''': '''Maximum number of training steps.'''}	)
    UpperCAmelCase__				    = field(
        default=-1						,   metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}	)
    UpperCAmelCase__				    = field(default=1024						,   metadata={'''help''': '''Sequence lengths used for training.'''}	)
    UpperCAmelCase__				    = field(default=1						,   metadata={'''help''': '''Training seed.'''}	)
    UpperCAmelCase__				    = field(
        default=1024						,   metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}						,   )
    UpperCAmelCase__				    = field(
        default=a__						,   metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}	)
    UpperCAmelCase__				    = field(default=a__						,   metadata={'''help''': '''If True the data is pretokenized.'''}	)


@dataclass
class 							UpperCamelCase_       :

    '''simple docstring'''






    UpperCAmelCase__				    = field(
        default='''codeparrot/codeparrot'''						,   metadata={'''help''': '''Model name or path of model to be evaluated.'''}	)
    UpperCAmelCase__				    = field(
        default='''codeparrot/codeparrot-clean-valid'''						,   metadata={'''help''': '''Name or path of validation dataset.'''}	)
    UpperCAmelCase__				    = field(default=2						,   metadata={'''help''': '''Batch size used for evaluation.'''}	)
    UpperCAmelCase__				    = field(
        default=-1						,   metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}	)
    UpperCAmelCase__				    = field(default=1024						,   metadata={'''help''': '''Length of sequences to be evaluated.'''}	)
    UpperCAmelCase__				    = field(default=1						,   metadata={'''help''': '''Random seed used for evaluation.'''}	)


@dataclass
class 							UpperCamelCase_       :

    '''simple docstring'''






    UpperCAmelCase__				    = field(
        default='''codeparrot/codeparrot'''						,   metadata={'''help''': '''Model name or path of model to be evaluated.'''}	)
    UpperCAmelCase__				    = field(default=a__						,   metadata={'''help''': '''Number of workers used for code evaluation.'''}	)
    UpperCAmelCase__				    = field(
        default=a__						,   metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}						,   )
    UpperCAmelCase__				    = field(
        default=a__						,   metadata={'''help''': '''Sample from the language model\'s output distribution.'''}	)
    UpperCAmelCase__				    = field(default=0.2						,   metadata={'''help''': '''Sampling temperature used for generation.'''}	)
    UpperCAmelCase__				    = field(default=256						,   metadata={'''help''': '''Maximum number of newly generated tokens.'''}	)
    UpperCAmelCase__				    = field(default=0						,   metadata={'''help''': '''Top-k parameter used for generation.'''}	)
    UpperCAmelCase__				    = field(default=0.9_5						,   metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}	)
    UpperCAmelCase__				    = field(default=10						,   metadata={'''help''': '''Number of generations to run in parallel.'''}	)
    UpperCAmelCase__				    = field(
        default=200						,   metadata={'''help''': '''Number of completions to generate for each sample.'''}	)
    UpperCAmelCase__				    = field(default=1						,   metadata={'''help''': '''Random seed used for evaluation.'''}	)
    UpperCAmelCase__				    = field(
        default='''eval_results.json'''						,   metadata={'''help''': '''Random seed used for evaluation.'''}	)
    UpperCAmelCase__				    = field(
        default='''0'''						,   metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}	)
    UpperCAmelCase__				    = 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'''






    UpperCAmelCase__				    = field(
        default=a__						,   metadata={
            '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
        }						,   )
    UpperCAmelCase__				    = field(
        default='''transformersbook/codeparrot'''						,   metadata={'''help''': '''Folder or name of dataset to process.'''}	)
    UpperCAmelCase__				    = field(
        default='''codeparrot-clean'''						,   metadata={'''help''': '''Folder to save processed processed dataset.'''}	)
    UpperCAmelCase__				    = field(
        default=10_0000						,   metadata={'''help''': '''Number of files to save per JSON output file.'''}	)
    UpperCAmelCase__				    = field(default='''content'''						,   metadata={'''help''': '''Column containing text data to process.'''}	)
    UpperCAmelCase__				    = field(
        default=1000						,   metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}	)
    UpperCAmelCase__				    = field(
        default=100						,   metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}	)
    UpperCAmelCase__				    = field(
        default=0.2_5						,   metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}	)
    UpperCAmelCase__				    = field(
        default=1.5						,   metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}	)
    UpperCAmelCase__				    = field(
        default=0.7						,   metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}	)
    UpperCAmelCase__				    = field(
        default='''codeparrot/codeparrot'''						,   metadata={'''help''': '''Name or path to the tokenizer.'''}						,   )
    UpperCAmelCase__				    = field(
        default=a__						,   metadata={'''help''': '''If True, near-duplicate samples are removed.'''}	)
    UpperCAmelCase__				    = field(
        default=0.8_5						,   metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}	)


@dataclass
class 							UpperCamelCase_       :

    '''simple docstring'''






    UpperCAmelCase__				    = field(
        default='''gpt2'''						,   metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}	)
    UpperCAmelCase__				    = field(
        default='''transformersbook/codeparrot-train'''						,   metadata={'''help''': '''Dataset to train tokenizer on.'''}	)
    UpperCAmelCase__				    = field(default='''content'''						,   metadata={'''help''': '''Column containing text data to process.'''}	)
    UpperCAmelCase__				    = field(default=20_0000						,   metadata={'''help''': '''Number of examples to train tokenizer on.'''}	)
    UpperCAmelCase__				    = field(
        default=3_2768						,   metadata={'''help''': '''Number of examples to train the tokenizer on.'''}	)
    UpperCAmelCase__				    = field(default='''codeparrot'''						,   metadata={'''help''': '''Name of new tokenizer.'''}	)
    UpperCAmelCase__				    = field(default=a__						,   metadata={'''help''': '''Push saved tokenizer to the hub.'''}	)


@dataclass
class 							UpperCamelCase_       :

    '''simple docstring'''






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


@dataclass
class 							UpperCamelCase_       :

    '''simple docstring'''






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









'''simple docstring'''




from __future__ import annotations






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	tuple[float, list[float]]:
     __A		:					int						=			list(range(len(__snake_case     )     )     )
     __A		:					Optional[Any]						=			[v / w for v, w in zip(__snake_case					,				__snake_case     )]
     index.sort(key=lambda __snake_case     : ratio[i]					,				reverse=__snake_case     )

     __A		:					float						=			0
     __A		:					list[float]						=			[0] * len(__snake_case     )
     for i in index:
          if weight[i] <= capacity:
               __A		:					Optional[int]						=			1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __A		:					List[Any]						=			capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()