





import logging
import random

import ray

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


a__      :     Optional[int]         =		logging.getLogger(__name__)





class UpperCAmelCase_				:


  def __init__(							self ):



       """simple docstring"""





       A_          =       False


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       self.retriever.index.init_index()




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       self.retriever._main_retrieve(lowerCamelCase_ ,lowerCamelCase_ )
       return doc_ids, retrieved_doc_embeds





class UpperCAmelCase_				( _UpperCamelCase ):


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



       """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_ ,)
       A_          =       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 						__UpperCAmelCase     (							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 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





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


  @classmethod
  def 						__UpperCAmelCase     (							cls ,__snake_case ,__snake_case=None ,**__snake_case ):



       """simple docstring"""





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




  @classmethod
  def 						__UpperCAmelCase     (							cls ,__snake_case ,__snake_case ,__snake_case=None ,**__snake_case ):



       """simple docstring"""





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










from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[float]   ,    _lowercase     : Tuple			)   ->							int:




		'''simple docstring'''
		print(f"""Vertex\tShortest Distance from vertex {src}"""			)
		for i, d in enumerate(_lowercase			):
				print(f"""{i}\t\t{d}"""			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : list[float]   ,    _lowercase     : int			)   ->							Any:




		'''simple docstring'''
		for j in range(_lowercase			):
				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		(graph[j][k] for k in ['src', 'dst', 'weight'])
				if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
						return True
		return False


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : int   ,    _lowercase     : int   ,    _lowercase     : int			)   ->							list[float]:




		'''simple docstring'''
		lowercase__					:      Dict      =		[float('inf'			)] * vertex_count
		lowercase__					:      Dict      =		0.0

		for _ in range(vertex_count - 1			):
				for j in range(_lowercase			):
						lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		(graph[j][k] for k in ['src', 'dst', 'weight'])

						if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
								lowercase__					:      str      =		distance[u] + w

		lowercase__					:      str      =		check_negative_cycle(_lowercase   ,    _lowercase   ,    _lowercase			)
		if negative_cycle_exists:
				raise Exception('Negative cycle found'			)

		return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Optional[int] = int(input("""Enter number of vertices: """).strip())
			__UpperCamelCase:	Union[str, Any] = int(input("""Enter number of edges: """).strip())

			__UpperCamelCase:	list[dict[str, int]] = [{} for _ in range(E)]

			for i in range(E):
						print("""Edge """, i + 1)
						__UpperCamelCase,	__UpperCamelCase,	__UpperCamelCase:	List[str] = (
						    int(x)
						    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
						)
						__UpperCamelCase:	List[Any] = {"""src""": src, """dst""": dest, """weight""": weight}

			__UpperCamelCase:	Optional[int] = int(input("""\nEnter shortest path source:""").strip())
			__UpperCamelCase:	Dict = bellman_ford(graph, V, E, source)
			print_distance(shortest_distance, 0)


