


from dataclasses import dataclass, field
from typing import Optional






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






@dataclass
class     UpperCamelCase:
					snake_case_		:				Optional[str]										=	field(
					    default="""codeparrot/codeparrot"""     ,							metadata={"""help""": """Model name or path of model to be evaluated."""}   )
					snake_case_		:				Optional[str]										=	field(
					    default="""codeparrot/codeparrot-clean-valid"""     ,							metadata={"""help""": """Name or path of validation dataset."""}   )
					snake_case_		:				Optional[int]										=	field(default=2     ,							metadata={"""help""": """Batch size used for evaluation."""}   )
					snake_case_		:				Optional[int]										=	field(
					    default=-1     ,							metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}   )
					snake_case_		:				Optional[int]										=	field(default=1024     ,							metadata={"""help""": """Length of sequences to be evaluated."""}   )
					snake_case_		:				Optional[int]										=	field(default=1     ,							metadata={"""help""": """Random seed used for evaluation."""}   )






@dataclass
class     UpperCamelCase:
					snake_case_		:				Optional[str]										=	field(
					    default="""codeparrot/codeparrot"""     ,							metadata={"""help""": """Model name or path of model to be evaluated."""}   )
					snake_case_		:				Optional[int]										=	field(default=_a     ,							metadata={"""help""": """Number of workers used for code evaluation."""}   )
					snake_case_		:				Optional[int]										=	field(
					    default=_a     ,							metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}     ,							)
					snake_case_		:				Optional[bool]										=	field(
					    default=_a     ,							metadata={"""help""": """Sample from the language model\'s output distribution."""}   )
					snake_case_		:				Optional[float]										=	field(default=0.2     ,							metadata={"""help""": """Sampling temperature used for generation."""}   )
					snake_case_		:				Optional[int]										=	field(default=256     ,							metadata={"""help""": """Maximum number of newly generated tokens."""}   )
					snake_case_		:				Optional[int]										=	field(default=0     ,							metadata={"""help""": """Top-k parameter used for generation."""}   )
					snake_case_		:				Optional[float]										=	field(default=0.95     ,							metadata={"""help""": """Top-p parameter used for nucleus sampling."""}   )
					snake_case_		:				Optional[int]										=	field(default=10     ,							metadata={"""help""": """Number of generations to run in parallel."""}   )
					snake_case_		:				Optional[int]										=	field(
					    default=200     ,							metadata={"""help""": """Number of completions to generate for each sample."""}   )
					snake_case_		:				Optional[int]										=	field(default=1     ,							metadata={"""help""": """Random seed used for evaluation."""}   )
					snake_case_		:				Optional[str]										=	field(
					    default="""eval_results.json"""     ,							metadata={"""help""": """Random seed used for evaluation."""}   )
					snake_case_		:				Optional[str]										=	field(
					    default="""0"""     ,							metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}   )
					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     UpperCamelCase:
					snake_case_		:				Optional[int]										=	field(
					    default=_a     ,							metadata={
					        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
					    }     ,							)
					snake_case_		:				Optional[str]										=	field(
					    default="""transformersbook/codeparrot"""     ,							metadata={"""help""": """Folder or name of dataset to process."""}   )
					snake_case_		:				Optional[str]										=	field(
					    default="""codeparrot-clean"""     ,							metadata={"""help""": """Folder to save processed processed dataset."""}   )
					snake_case_		:				Optional[int]										=	field(
					    default=10_0000     ,							metadata={"""help""": """Number of files to save per JSON output file."""}   )
					snake_case_		:				Optional[str]										=	field(default="""content"""     ,							metadata={"""help""": """Column containing text data to process."""}   )
					snake_case_		:				Optional[float]										=	field(
					    default=1000     ,							metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}   )
					snake_case_		:				Optional[float]										=	field(
					    default=100     ,							metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}   )
					snake_case_		:				Optional[float]										=	field(
					    default=0.25     ,							metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}   )
					snake_case_		:				Optional[float]										=	field(
					    default=1.5     ,							metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}   )
					snake_case_		:				Optional[float]										=	field(
					    default=0.7     ,							metadata={"""help""": """Probability for filtering config, test and uncommon files."""}   )
					snake_case_		:				Optional[str]										=	field(
					    default="""codeparrot/codeparrot"""     ,							metadata={"""help""": """Name or path to the tokenizer."""}     ,							)
					snake_case_		:				Optional[bool]										=	field(
					    default=_a     ,							metadata={"""help""": """If True, near-duplicate samples are removed."""}   )
					snake_case_		:				Optional[float]										=	field(
					    default=0.85     ,							metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}   )






@dataclass
class     UpperCamelCase:
					snake_case_		:				Optional[str]										=	field(
					    default="""gpt2"""     ,							metadata={"""help""": """Base tokenizer to build new tokenizer from."""}   )
					snake_case_		:				Optional[str]										=	field(
					    default="""transformersbook/codeparrot-train"""     ,							metadata={"""help""": """Dataset to train tokenizer on."""}   )
					snake_case_		:				Optional[str]										=	field(default="""content"""     ,							metadata={"""help""": """Column containing text data to process."""}   )
					snake_case_		:				Optional[int]										=	field(default=20_0000     ,							metadata={"""help""": """Number of examples to train tokenizer on."""}   )
					snake_case_		:				Optional[int]										=	field(
					    default=3_2768     ,							metadata={"""help""": """Number of examples to train the tokenizer on."""}   )
					snake_case_		:				Optional[str]										=	field(default="""codeparrot"""     ,							metadata={"""help""": """Name of new tokenizer."""}   )
					snake_case_		:				Optional[bool]										=	field(default=_a     ,							metadata={"""help""": """Push saved tokenizer to the hub."""}   )






@dataclass
class     UpperCamelCase:
					snake_case_		:				Optional[str]										=	field(
					    default="""codeparrot/codeparrot"""     ,							metadata={"""help""": """Name or path to the tokenizer."""}   )
					snake_case_		:				Optional[str]										=	field(
					    default="""codeparrot/codeparrot-clean-train"""     ,							metadata={"""help""": """Name or path to the dataset to pretokenize."""}   )
					snake_case_		:				Optional[str]										=	field(
					    default="""tokenized-codeparrot-train"""     ,							metadata={"""help""": """Repo name of the pretokenized data."""}   )
					snake_case_		:				Optional[int]										=	field(default=_a     ,							metadata={"""help""": """Number of workers used for code evaluation."""}   )






@dataclass
class     UpperCamelCase:
					snake_case_		:				Optional[str]										=	field(
					    default="""gpt2-large"""     ,							metadata={"""help""": """Configuration to use for model initialization."""}   )
					snake_case_		:				Optional[str]										=	field(
					    default="""codeparrot/codeparrot"""     ,							metadata={"""help""": """Tokenizer attached to model."""}   )
					snake_case_		:				Optional[str]										=	field(default="""codeparrot"""     ,							metadata={"""help""": """Name of the created model."""}   )
					snake_case_		:				Optional[bool]										=	field(default=_a     ,							metadata={"""help""": """Push saved tokenizer to the hub."""}   )









import os
from datetime import datetime as dt

from github import Github


A							:					Union[str, Any]     						=     [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]





def 	_lowerCAmelCase       (    )						->	str:


						'''simple docstring'''






						__snake_case				=		Github(os.environ["GITHUB_TOKEN"] )
						__snake_case				=		g.get_repo("huggingface/diffusers" )
						__snake_case				=		repo.get_issues(state="open" )

						for issue in open_issues:
												__snake_case				=		sorted(issue.get_comments()      ,	key=lambda _lowerCAmelCase : i.created_at      ,	reverse=_lowerCAmelCase )
												__snake_case				=		comments[0] if len(_lowerCAmelCase ) > 0 else None
												if (
												    last_comment is not None
												    and last_comment.user.login == "github-actions[bot]"
												    and (dt.utcnow() - issue.updated_at).days > 7
												    and (dt.utcnow() - issue.created_at).days >= 30
												    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
												):
																		# Closes the issue after 7 days of inactivity since the Stalebot notification.
																		issue.edit(state="closed" )
												elif (
												    "stale" in issue.get_labels()
												    and last_comment is not None
												    and last_comment.user.login != "github-actions[bot]"
												):
																		# Opens the issue if someone other than Stalebot commented.
																		issue.edit(state="open" )
																		issue.remove_from_labels("stale" )
												elif (
												    (dt.utcnow() - issue.updated_at).days > 23
												    and (dt.utcnow() - issue.created_at).days >= 30
												    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
												):
																		# Post a Stalebot notification after 23 days of inactivity.
																		issue.create_comment(
																		    "This issue has been automatically marked as stale because it has not had "
																		    "recent activity. If you think this still needs to be addressed "
																		    "please comment on this thread.\n\nPlease note that issues that do not follow the "
																		    "[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) "
																		    "are likely to be ignored." )
																		issue.add_to_labels("stale" )


if __name__ == "__main__":
	main()





