


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 	a__						:

       def __init__( self   :  Any     ):
            a_     :  Tuple				      =     False

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  str     ):
            if not self.initialized:
                 a_     :  str				      =     RagRetriever(
                     lowercase_   ,				question_encoder_tokenizer=lowercase_   ,				generator_tokenizer=lowercase_   ,				index=lowercase_   ,				init_retrieval=lowercase_   ,				)
                 a_     :  List[str]				      =     True

       def     UpperCAmelCase( self   :  Optional[int]     ):
            self.retriever.index.init_index()

       def     UpperCAmelCase( self   :  Tuple     ):
            # delete the old self.retriever object before assigning the new index
            del self.retriever
            a_     :  Optional[int]				      =     False

       def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  List[Any]   ,				lowerCamelCase_   :  Optional[int]     ):
            a_     :  List[Any]				      =     self.retriever._main_retrieve(lowercase_   ,				lowercase_     )
            a_     :  str				      =     self.retriever.index.get_doc_dicts(lowercase_     )
            return doc_ids, retrieved_doc_embeds, doc_dicts


class 	a__						(    _UpperCAmelCase     ):

       def __init__( self   :  Union[str, Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  List[Any]   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  List[str]=None     ):
            if index is not None and index.is_initialized() and len(lowercase_     ) > 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__(
                lowercase_   ,				question_encoder_tokenizer=lowercase_   ,				generator_tokenizer=lowercase_   ,				index=lowercase_   ,				init_retrieval=lowercase_   ,				)

            a_     :  int				      =     retrieval_workers
            a_     :  int				      =     question_encoder_tokenizer
            a_     :  str				      =     generator_tokenizer
            if len(self.retrieval_workers     ) > 0:
                 ray.get(
                     [
                         worker.create_rag_retriever.remote(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_     )
                         for worker in self.retrieval_workers
                     ]     )

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

       @classmethod
       def     UpperCAmelCase( cls   :  List[str]   ,				lowerCamelCase_   :  List[Any]   ,				lowerCamelCase_   :  int=None   ,				**lowerCamelCase_   :  str     ):
            return super(lowercase_   ,				cls     ).get_tokenizers(lowercase_   ,				lowercase_   ,				**lowercase_     )

       @classmethod
       def     UpperCAmelCase( cls   :  Optional[int]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Any=None   ,				**lowerCamelCase_   :  Union[str, Any]     ):
            a_     :  Optional[int]				      =     kwargs.pop("""config"""   ,				lowercase_     ) or RagConfig.from_pretrained(lowercase_   ,				**lowercase_     )
            a_     :  int				      =     RagTokenizer.from_pretrained(lowercase_   ,				config=lowercase_     )
            a_     :  Any				      =     rag_tokenizer.question_encoder
            a_     :  List[Any]				      =     rag_tokenizer.generator

            if indexed_dataset is not None:
                 a_     :  Dict				      =     """custom"""
                 a_     :  Dict				      =     CustomHFIndex(config.retrieval_vector_size   ,				lowercase_     )
            else:
                 a_     :  Any				      =     cls._build_index(lowercase_     )

            return cls(
                lowercase_   ,				question_encoder_tokenizer=lowercase_   ,				generator_tokenizer=lowercase_   ,				retrieval_workers=lowercase_   ,				index=lowercase_   ,				)

       def     UpperCAmelCase( self   :  List[str]     ):
            logger.info("""re-loading the new dataset with embeddings"""     )
            # access from the training loop

            ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]     )

            # build the index object again
            a_     :  Optional[Any]				      =     self._build_index(self.config     )

            ray.get(
                [
                    worker.create_rag_retriever.remote(
                        self.config   ,				self.question_encoder_tokenizer   ,				self.generator_tokenizer   ,				lowercase_     )
                    for worker in self.retrieval_workers
                ]     )




'''simple docstring'''




from __future__ import annotations





class        __magic_name__		:







			def __init__(      self							:    List[str]							,     lowercase_							:    int       ):
										lowercase_   :     List[str]									=			vertices
										lowercase_   :     Optional[Any]									=			[[0] * vertices for _ in range(lowercase_       )]







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    list[int]       ):
										print("""Vertex \t Distance from Source"""       )
										for vertex in range(self.vertices       ):
																	print(lowercase_							,     """\t\t"""							,     distances_from_source[vertex]       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    list[int]							,     lowercase_							:    list[bool]       ):
										lowercase_   :     Dict									=			1E7
										lowercase_   :     int									=			0

										# Search not nearest vertex not in the shortest path tree
										for vertex in range(self.vertices       ):
																	if distances_from_source[vertex] < minimum and visited[vertex] is False:
																								lowercase_   :     Tuple									=			distances_from_source[vertex]
																								lowercase_   :     str									=			vertex
										return min_index







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    int       ):
										lowercase_   :     Optional[Any]									=			[int(1E7       )] * self.vertices  # distances from the source
										lowercase_   :     List[Any]									=			0
										lowercase_   :     Any									=			[False] * self.vertices

										for _ in range(self.vertices       ):
																	lowercase_   :     Tuple									=			self.minimum_distance(lowercase_							,     lowercase_       )
																	lowercase_   :     Union[str, Any]									=			True

																	# Update dist value of the adjacent vertices
																	# of the picked vertex only if the current
																	# distance is greater than new distance and
																	# the vertex in not in the shortest path tree
																	for v in range(self.vertices       ):
																								if (
																								    self.graph[u][v] > 0
																								    and visited[v] is False
																								    and distances[v] > distances[u] + self.graph[u][v]
																								):
																															lowercase_   :     Any									=			distances[u] + self.graph[u][v]

										self.print_solution(lowercase_       )


if __name__ == "__main__":
						_lowercase		:					Any													=		Graph(9)
						_lowercase		:					Any													=		[
						    [0, 4, 0, 0, 0, 0, 0, 8, 0],
						    [4, 0, 8, 0, 0, 0, 0, 11, 0],
						    [0, 8, 0, 7, 0, 4, 0, 0, 2],
						    [0, 0, 7, 0, 9, 14, 0, 0, 0],
						    [0, 0, 0, 9, 0, 10, 0, 0, 0],
						    [0, 0, 4, 14, 10, 0, 2, 0, 0],
						    [0, 0, 0, 0, 0, 2, 0, 1, 6],
						    [8, 11, 0, 0, 0, 0, 1, 0, 7],
						    [0, 0, 2, 0, 0, 0, 6, 7, 0],
						]
						graph.dijkstra(0)





