


import logging
import random

import ray

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


lowerCAmelCase__      		= logging.getLogger(__name__)







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self)			->				List[Any]:
            _A      :       int				    = False


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				int:
            if not self.initialized:
                   _A      :       Tuple				    = RagRetriever(
                       __lowerCamelCase ,						question_encoder_tokenizer=__lowerCamelCase ,						generator_tokenizer=__lowerCamelCase ,						index=__lowerCamelCase ,						init_retrieval=__lowerCamelCase ,						)
                   _A      :       Dict				    = True


     def  _lowerCamelCase (						self)			->				Optional[int]:
            self.retriever.index.init_index()


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[str]:
            _A    ,			_A      :       Any				    = self.retriever._main_retrieve(__lowerCamelCase ,						__lowerCamelCase)
            return doc_ids, retrieved_doc_embeds







class      lowerCAmelCase__       (				UpperCamelCase__):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=None)			->				Optional[int]:
            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 ,						)
            _A      :       Tuple				    = 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  _lowerCamelCase (						self)			->				Dict:
            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  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				int:
            if len(self.retrieval_workers) > 0:
                   # Select a random retrieval actor.
                   _A      :       Tuple				    = self.retrieval_workers[random.randint(0 ,						len(self.retrieval_workers) - 1)]
                   _A    ,			_A      :       str				    = ray.get(random_worker.retrieve.remote(__lowerCamelCase ,						__lowerCamelCase))
            else:
                   _A    ,			_A      :       Optional[int]				    = self._main_retrieve(__lowerCamelCase ,						__lowerCamelCase)
            return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCamelCase)


     @classmethod
     def  _lowerCamelCase (						cls ,						__lowerCamelCase ,						__lowerCamelCase=None ,						**__lowerCamelCase)			->				Union[str, Any]:
            return super(__lowerCamelCase ,						cls).get_tokenizers(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)


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







a__:      Dict									=		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

a__:      str									=		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[]

       for neighbour in graph[vert]:
              if not visited[neighbour]:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       order.append(UpperCamelCase__  )

       return order


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[vert]

       for neighbour in reversed_graph[vert]:
              if not visited[neighbour]:
                     component += find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       return component


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]]  )->list[list[int]]:
       A__											=						len(UpperCamelCase__  ) * [False]
       A__											=						{vert: [] for vert in range(len(UpperCamelCase__  )  )}

       for vert, neighbours in graph.items():
              for neighbour in neighbours:
                     reversed_graph[neighbour].append(UpperCamelCase__  )

       A__											=						[]
       for i, was_visited in enumerate(UpperCamelCase__  ):
              if not was_visited:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       A__											=						[]
       A__											=						len(UpperCamelCase__  ) * [False]

       for i in range(len(UpperCamelCase__  )  ):
              A__											=						order[len(UpperCamelCase__  ) - i - 1]
              if not visited[vert]:
                     A__											=						find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
                     components_list.append(UpperCamelCase__  )

       return components_list





