





import os
from datetime import datetime as dt

from github import Github


SCREAMING_SNAKE_CASE_		     = [
    """good first issue""",
    """feature request""",
    """wip""",
]



def      __lowercase     (   )				->     List[str]:


      '''simple docstring'''


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

      for issue in open_issues:
            SCREAMING_SNAKE_CASE	=		sorted([comment for comment in issue.get_comments()]				,		key=lambda _SCREAMING_SNAKE_CASE			: i.created_at				,		reverse=_lowerCamelCase			)
            SCREAMING_SNAKE_CASE	=		comments[0] if len(_lowerCamelCase			) > 0 else None
            SCREAMING_SNAKE_CASE	=		dt.utcnow()
            SCREAMING_SNAKE_CASE	=		(current_time - issue.updated_at).days
            SCREAMING_SNAKE_CASE	=		(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()









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


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		     = {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			str				=    "pegasus"
     __snake_case					:			List[Any]				=    ["past_key_values"]
     __snake_case					:			Any				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






     def __init__(							self		:					Dict		,lowerCamelCase__		:					List[str]=50265		,lowerCamelCase__		:					int=1024		,lowerCamelCase__		:					Optional[Any]=12		,lowerCamelCase__		:					Tuple=4096		,lowerCamelCase__		:					Dict=16		,lowerCamelCase__		:					Optional[Any]=12		,lowerCamelCase__		:					str=4096		,lowerCamelCase__		:					Dict=16		,lowerCamelCase__		:					Tuple=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					str=True		,lowerCamelCase__		:					Any=True		,lowerCamelCase__		:					List[Any]="gelu"		,lowerCamelCase__		:					Tuple=1024		,lowerCamelCase__		:					Optional[int]=0.1		,lowerCamelCase__		:					Tuple=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Optional[int]=0.02		,lowerCamelCase__		:					Optional[int]=0		,lowerCamelCase__		:					List[Any]=False		,lowerCamelCase__		:					Union[str, Any]=0		,lowerCamelCase__		:					Optional[int]=1		,lowerCamelCase__		:					Any=1		,**lowerCamelCase__		:					Dict		,)  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		vocab_size
           SCREAMING_SNAKE_CASE	=		max_position_embeddings
           SCREAMING_SNAKE_CASE	=		d_model
           SCREAMING_SNAKE_CASE	=		encoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		encoder_layers
           SCREAMING_SNAKE_CASE	=		encoder_attention_heads
           SCREAMING_SNAKE_CASE	=		decoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		decoder_layers
           SCREAMING_SNAKE_CASE	=		decoder_attention_heads
           SCREAMING_SNAKE_CASE	=		dropout
           SCREAMING_SNAKE_CASE	=		attention_dropout
           SCREAMING_SNAKE_CASE	=		activation_dropout
           SCREAMING_SNAKE_CASE	=		activation_function
           SCREAMING_SNAKE_CASE	=		init_std
           SCREAMING_SNAKE_CASE	=		encoder_layerdrop
           SCREAMING_SNAKE_CASE	=		decoder_layerdrop
           SCREAMING_SNAKE_CASE	=		use_cache
           SCREAMING_SNAKE_CASE	=		encoder_layers
           SCREAMING_SNAKE_CASE	=		scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=lowerCamelCase__		,eos_token_id=lowerCamelCase__		,is_encoder_decoder=lowerCamelCase__		,decoder_start_token_id=lowerCamelCase__		,forced_eos_token_id=lowerCamelCase__		,**lowerCamelCase__		,)






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	int:




           '''simple docstring'''


           return self.d_model


