



import logging
import random

import ray

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


a_						:     List[str]	     =		logging.getLogger(__name__)





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		False







     def 						_a      (self				,			__a				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



           self.retriever.index.init_index()







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase			,							lowerCamelCase								=		self.retriever._main_retrieve(__a				,			__a    )
           return doc_ids, retrieved_doc_embeds





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def __init__(self				,			__a				,			__a				,			__a				,			__a				,			__a=None    ):






           '''simple docstring'''



           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				,			)
           lowerCamelCase								=		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 						_a      (self    ):






           '''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 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



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







     @classmethod
     def 						_a      (cls				,			__a				,			__a=None				,			**__a    ):






           '''simple docstring'''



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







     @classmethod
     def 						_a      (cls				,			__a				,			__a				,			__a=None				,			**__a    ):






           '''simple docstring'''



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




from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			42
     _A       =			42





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[[] for _ in range(__a    )]
           lowerCamelCase								=		size







     def __getitem__(self				,			__a    ):






           '''simple docstring'''



           return iter(self._graph[vertex]    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self._size







     def 						_a      (self				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           if weight not in (0, 1):
                 raise ValueError("Edge weight must be either 0 or 1."    )

           if to_vertex < 0 or to_vertex >= self.size:
                 raise ValueError("Vertex indexes must be in [0; size)."    )

           self._graph[from_vertex].append(Edge(__a				,			__a    )    )







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		deque([start_vertex]    )
           lowerCamelCase								=		[None] * self.size
           lowerCamelCase								=		0

           while queue:
                 lowerCamelCase								=		queue.popleft()
                 lowerCamelCase								=		distances[current_vertex]
                 if current_distance is None:
                       continue

                 for edge in self[current_vertex]:
                       lowerCamelCase								=		current_distance + edge.weight
                       lowerCamelCase								=		distances[edge.destination_vertex]
                       if (
                           isinstance(__a				,			__a    )
                           and new_distance >= dest_vertex_distance
                       ):
                             continue
                       lowerCamelCase								=		new_distance
                       if edge.weight == 0:
                             queue.appendleft(edge.destination_vertex    )
                       else:
                             queue.append(edge.destination_vertex    )

           if distances[finish_vertex] is None:
                 raise ValueError("No path from start_vertex to finish_vertex."    )

           return distances[finish_vertex]


if __name__ == "__main__":
     import doctest

     doctest.testmod()