

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=10_000			,							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=750			,							metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}				)
							_UpperCAmelCase    :  Optional[int]						      =      field(
							    default=16			,							metadata={'''help''': '''Number of gradient accumulation steps.'''}				)
							_UpperCAmelCase    :  Optional[bool]						      =      field(
							    default=SCREAMING_SNAKE_CASE			,							metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}				)
							_UpperCAmelCase    :  Optional[int]						      =      field(default=50_000			,							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_024			,							metadata={'''help''': '''Sequence lengths used for training.'''}				)
							_UpperCAmelCase    :  Optional[int]						      =      field(default=1			,							metadata={'''help''': '''Training seed.'''}				)
							_UpperCAmelCase    :  Optional[int]						      =      field(
							    default=1_024			,							metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}			,							)
							_UpperCAmelCase    :  Optional[str]						      =      field(
							    default=SCREAMING_SNAKE_CASE			,							metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}				)
							_UpperCAmelCase    :  Optional[bool]						      =      field(default=SCREAMING_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_024			,							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=SCREAMING_SNAKE_CASE			,							metadata={'''help''': '''Number of workers used for code evaluation.'''}				)
							_UpperCAmelCase    :  Optional[int]						      =      field(
							    default=SCREAMING_SNAKE_CASE			,							metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}			,							)
							_UpperCAmelCase    :  Optional[bool]						      =      field(
							    default=SCREAMING_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=256			,							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=10			,							metadata={'''help''': '''Number of generations to run in parallel.'''}				)
							_UpperCAmelCase    :  Optional[int]						      =      field(
							    default=200			,							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=SCREAMING_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=100_000			,							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_000			,							metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}				)
							_UpperCAmelCase    :  Optional[float]						      =      field(
							    default=100			,							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=SCREAMING_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=200_000			,							metadata={'''help''': '''Number of examples to train tokenizer on.'''}				)
							_UpperCAmelCase    :  Optional[int]						      =      field(
							    default=32_768			,							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=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE			,							metadata={'''help''': '''Push saved tokenizer to the hub.'''}				)








'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


lowerCamelCase							:   Any  						=	re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase							:   str  						=	re.compile(R'([a-z\d])([A-Z])')

lowerCamelCase							:   Optional[int]  						=	re.compile(R'(?<!_)_(?!_)')
lowerCamelCase							:   List[Any]  						=	re.compile(R'(_{2,})')

lowerCamelCase							:   str  						=	R'^\w+(\.\w+)*$'

lowerCamelCase							:   Dict  						=	R'<>:/\|?*'

def _SCREAMING_SNAKE_CASE      (A       )       ->       Any:






       """simple docstring"""







       lowercase__      			= _uppercase_uppercase_re.sub(R'''\1_\2'''  , A       )
       lowercase__      			= _lowercase_uppercase_re.sub(R'''\1_\2'''  , A       )
       return name.lower()

def _SCREAMING_SNAKE_CASE      (A       )       ->       Tuple:






       """simple docstring"""







       lowercase__      			= _single_underscore_re.split(A       )
       lowercase__      			= [_multiple_underscores_re.split(A       ) for n in name]
       return "".join(n.capitalize() for n in itertools.chain.from_iterable(A       ) if n != ''''''       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       Tuple:






       """simple docstring"""







       if os.path.basename(A       ) != name:
              raise ValueError(f"Should be a dataset name, not a path: {name}"       )
       return camelcase_to_snakecase(A       )

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Optional[Any]:






       """simple docstring"""







       if os.path.basename(A       ) != name:
              raise ValueError(f"Should be a dataset name, not a path: {name}"       )
       if not re.match(_split_re  , A       ):
              raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."       )
       return f"{filename_prefix_for_name(A       )}-{split}"

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A=None       )       ->       List[str]:






       """simple docstring"""







       lowercase__      			= filename_prefix_for_split(A  , A       )
       if filetype_suffix:
              prefix += f".{filetype_suffix}"
       lowercase__      			= os.path.join(A  , A       )
       return f"{filepath}*"

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A=None  , A=None       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= filename_prefix_for_split(A  , A       )
       lowercase__      			= os.path.join(A  , A       )

       if shard_lengths:
              lowercase__      			= len(A       )
              lowercase__      			= [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(A       )]
              if filetype_suffix:
                     lowercase__      			= [filename + f".{filetype_suffix}" for filename in filenames]
              return filenames
       else:
              lowercase__      			= prefix
              if filetype_suffix:
                     filename += f".{filetype_suffix}"
              return [filename]




