



'''simple docstring'''






import logging
import random

import ray

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


A_							: int						 =	logging.getLogger(__name__)



class 							lowercase       :


 """simple docstring"""


 def __init__(							self							)		->   Dict:
        _UpperCAmelCase  :						Optional[int]			     = False







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_							)		->   Optional[Any]:
        if not self.initialized:
               _UpperCAmelCase  :						Optional[int]			     = RagRetriever(
                   a_  ,question_encoder_tokenizer=a_  ,generator_tokenizer=a_  ,index=a_  ,init_retrieval=a_  ,)
               _UpperCAmelCase  :						Dict			     = True







 def _snake_case (							self							)		->   Any:
        self.retriever.index.init_index()







 def _snake_case (							self  ,a_  ,a_							)		->   int:
        _UpperCAmelCase     ,_UpperCAmelCase  :						int			     = self.retriever._main_retrieve(a_  ,a_							)
        return doc_ids, retrieved_doc_embeds







class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_  ,a_  ,a_  ,a_=None							)		->   Union[str, Any]:
        if index is not None and index.is_initialized() and len(a_							) > 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__(
            a_  ,question_encoder_tokenizer=a_  ,generator_tokenizer=a_  ,index=a_  ,init_retrieval=a_  ,)
        _UpperCAmelCase  :						Union[str, Any]			     = retrieval_workers
        if len(self.retrieval_workers							) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(a_  ,a_  ,a_  ,a_							)
                       for worker in self.retrieval_workers
                   ]							)







 def _snake_case (							self							)		->   Optional[int]:
        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 _snake_case (							self  ,a_  ,a_							)		->   int:
        if len(self.retrieval_workers							) > 0:
               # Select a random retrieval actor.
               _UpperCAmelCase  :						Union[str, Any]			     = self.retrieval_workers[random.randint(0  ,len(self.retrieval_workers							) - 1							)]
               _UpperCAmelCase     ,_UpperCAmelCase  :						int			     = ray.get(random_worker.retrieve.remote(a_  ,a_							)							)
        else:
               _UpperCAmelCase     ,_UpperCAmelCase  :						str			     = self._main_retrieve(a_  ,a_							)
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(a_							)







 @classmethod
 def _snake_case (							cls  ,a_  ,a_=None  ,**a_							)		->   Any:
        return super(a_  ,cls							).get_tokenizers(a_  ,a_  ,**a_							)







 @classmethod
 def _snake_case (							cls  ,a_  ,a_  ,a_=None  ,**a_							)		->   str:
        _UpperCAmelCase  :						Tuple			     = kwargs.pop("""config"""  ,a_							) or RagConfig.from_pretrained(a_  ,**a_							)
        _UpperCAmelCase  :						int			     = RagTokenizer.from_pretrained(a_  ,config=a_							)
        _UpperCAmelCase  :						Dict			     = rag_tokenizer.question_encoder
        _UpperCAmelCase  :						List[str]			     = rag_tokenizer.generator
        if indexed_dataset is not None:
               _UpperCAmelCase  :						Union[str, Any]			     = """custom"""
               _UpperCAmelCase  :						Dict			     = CustomHFIndex(config.retrieval_vector_size  ,a_							)
        else:
               _UpperCAmelCase  :						List[Any]			     = cls._build_index(a_							)
        return cls(
            a_  ,question_encoder_tokenizer=a_  ,generator_tokenizer=a_  ,retrieval_workers=a_  ,index=a_  ,)








'''simple docstring'''






from __future__ import annotations

import typing
from collections import Counter




def     snake_case_					(     lowerCAmelCase_		)->		typing.Counter[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						typing.Counter[int]			     = Counter()
       for base in range(1       ,   max_perimeter + 1		):
              for perpendicular in range(lowerCAmelCase_       ,   max_perimeter + 1		):
                     _UpperCAmelCase  :						List[str]			     = (base * base + perpendicular * perpendicular) ** 0.5
                     if hypotenuse == int(lowerCAmelCase_		):
                            _UpperCAmelCase  :						Optional[Any]			     = int(base + perpendicular + hypotenuse		)
                            if perimeter > max_perimeter:
                                   continue
                            triplets[perimeter] += 1
       return triplets




def     snake_case_					(     lowerCAmelCase_ = 1000		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = pythagorean_triple(lowerCAmelCase_		)
       return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
   print(f"""Perimeter {solution()} has maximum solutions""")



