





import os
from datetime import datetime as dt

from github import Github


SCREAMING_SNAKE_CASE_						       =	[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]



def        __lowercase       (				)			->       Dict:


      '''simple docstring'''


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

      for issue in open_issues:
            SCREAMING_SNAKE_CASE		=						sorted(issue.get_comments()			,						key=lambda _SCREAMING_SNAKE_CASE     : i.created_at			,						reverse=_SCREAMING_SNAKE_CASE     )
            SCREAMING_SNAKE_CASE		=						comments[0] if len(_SCREAMING_SNAKE_CASE     ) > 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()









import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


SCREAMING_SNAKE_CASE_						       =	logging.getLogger(__name__)

class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   str	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Tuple	)					->		Optional[Any]:




           '''simple docstring'''


           if not self.initialized:
                 SCREAMING_SNAKE_CASE		=						RagRetriever(
                     lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,index=lowerCamelCase__						,init_retrieval=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						True






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           self.retriever.index.init_index()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[str]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.retriever._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return doc_ids, retrieved_doc_embeds

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Dict=None	)					->		Any:




           '''simple docstring'''


           if index is not None and index.is_initialized() and len(lowerCamelCase__	) > 0:
                 raise ValueError(
                     """When using Ray for distributed fine-tuning, """
                     """you'll need to provide the paths instead, """
                     """as the dataset and the index are loaded """
                     """separately. More info in examples/rag/use_own_knowledge_dataset.py """	)
           super().__init__(
               lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,index=lowerCamelCase__						,init_retrieval=lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						retrieval_workers
           if len(self.retrieval_workers	) > 0:
                 ray.get(
                     [
                         worker.create_rag_retriever.remote(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)
                         for worker in self.retrieval_workers
                     ]	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Union[str, Any]:




           '''simple docstring'''


           logger.info("""initializing retrieval"""	)

           if len(self.retrieval_workers	) > 0:
                 ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]	)
           else:
                 # Non-distributed training. Load index into this same process.
                 self.index.init_index()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   int	)					->		Dict:




           '''simple docstring'''


           if len(self.retrieval_workers	) > 0:
                 # Select a random retrieval actor.
                 SCREAMING_SNAKE_CASE		=						self.retrieval_workers[random.randint(0						,len(self.retrieval_workers	) - 1	)]
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						ray.get(random_worker.retrieve.remote(lowerCamelCase__						,lowerCamelCase__	)	)
           else:
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase__	)






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Optional[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Union[str, Any]=None						,**lowerCamelCase__						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


           return super(lowerCamelCase__						,cls	).get_tokenizers(lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__	)






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Any						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[Any]=None						,**lowerCamelCase__						:   Any	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						kwargs.pop("""config"""						,lowerCamelCase__	) or RagConfig.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						RagTokenizer.from_pretrained(lowerCamelCase__						,config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rag_tokenizer.question_encoder
           SCREAMING_SNAKE_CASE		=						rag_tokenizer.generator
           if indexed_dataset is not None:
                 SCREAMING_SNAKE_CASE		=						"""custom"""
                 SCREAMING_SNAKE_CASE		=						CustomHFIndex(config.retrieval_vector_size						,lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						cls._build_index(lowerCamelCase__	)
           return cls(
               lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,retrieval_workers=lowerCamelCase__						,index=lowerCamelCase__						,)


