





'''simple docstring'''







import os
from datetime import datetime as dt

from github import Github


lowerCAmelCase_					:    str      	=			[
    'good first issue',
    'feature request',
    'wip',
]






def  _lowerCamelCase				( )	->	int:
				_a								= Github(os.environ["GITHUB_TOKEN"]						)
				_a								= g.get_repo("huggingface/accelerate"						)
				_a								= repo.get_issues(state="open"						)

				for issue in open_issues:
								_a								= sorted([comment for comment in issue.get_comments()]    ,							key=lambda lowercase						: i.created_at    ,							reverse=lowercase						)
								_a								= comments[0] if len(lowercase						) > 0 else None
								_a								= dt.utcnow()
								_a								= (current_time - issue.updated_at).days
								_a								= (current_time - issue.created_at).days
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and days_since_updated > 7
								    and days_since_creation >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()						)
								):
												# Close issue since it has been 7 days of inactivity since bot mention.
												issue.edit(state="closed"						)
								elif (
								    days_since_updated > 23
								    and days_since_creation >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()						)
								):
												# Add stale comment
												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/accelerate/blob/main/CONTRIBUTING.md) "
												    "are likely to be ignored."						)


if __name__ == "__main__":
					main()







'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    int      	=			{
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='gpt_bigcode'
				__a            =['past_key_values']
				__a            ={
				    'hidden_size': 'n_embd',
				    'max_position_embeddings': 'n_positions',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}




				def __init__(  self     :					Optional[Any]					,				__a     :					Tuple=5_02_57					,				__a     :					str=10_24					,				__a     :					Dict=7_68					,				__a     :					Tuple=12					,				__a     :					str=12					,				__a     :					Optional[int]=None					,				__a     :					Dict="gelu_pytorch_tanh"					,				__a     :					Tuple=0.1					,				__a     :					Tuple=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					Tuple=1e-5					,				__a     :					str=0.02					,				__a     :					Dict=True					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[int]=5_02_56					,				__a     :					Optional[int]=5_02_56					,				__a     :					Union[str, Any]=True					,				__a     :					Dict=True					,				__a     :					Union[str, Any]=True					,				**__a     :					List[Any]					,				):
								_a								= vocab_size
								_a								= n_positions
								_a								= n_embd
								_a								= n_layer
								_a								= n_head
								_a								= n_inner
								_a								= activation_function
								_a								= resid_pdrop
								_a								= embd_pdrop
								_a								= attn_pdrop
								_a								= layer_norm_epsilon
								_a								= initializer_range
								_a								= scale_attn_weights
								_a								= use_cache
								_a								= attention_softmax_in_fpaa
								_a								= scale_attention_softmax_in_fpaa
								_a								= multi_query

								_a								= bos_token_id
								_a								= eos_token_id

								super().__init__(bos_token_id=__a					,				eos_token_id=__a					,				**__a					)
