
'''simple docstring'''






import logging
import random

import ray

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


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

class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      False



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        if not self.initialized:
               lowercase  :		Any						=      RagRetriever(
                   A   ,		question_encoder_tokenizer=A   ,		generator_tokenizer=A   ,		index=A   ,		init_retrieval=A   ,		)
               lowercase  :		Tuple						=      True



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.retriever.index.init_index()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        del self.retriever
        lowercase  :		Dict						=      False





 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		int						=      self.retriever._main_retrieve(A   ,		A )
        lowercase  :		Tuple						=      self.retriever.index.get_doc_dicts(A )
        return doc_ids, retrieved_doc_embeds, doc_dicts






class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 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   ,		)

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



 def    SCREAMING_SNAKE_CASE(  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    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''simple docstring'''




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



 @classmethod
 def    SCREAMING_SNAKE_CASE(  cls   ,		A   ,		A=None   ,		**A ):






        '''simple docstring'''




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



 @classmethod
 def    SCREAMING_SNAKE_CASE(  cls   ,		A   ,		A   ,		A=None   ,		**A ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      kwargs.pop('''config'''   ,		A ) or RagConfig.from_pretrained(A   ,		**A )
        lowercase  :		Dict						=      RagTokenizer.from_pretrained(A   ,		config=A )
        lowercase  :		Optional[int]						=      rag_tokenizer.question_encoder
        lowercase  :		Optional[int]						=      rag_tokenizer.generator

        if indexed_dataset is not None:
               lowercase  :		Optional[Any]						=      '''custom'''
               lowercase  :		List[str]						=      CustomHFIndex(config.retrieval_vector_size   ,		A )
        else:
               lowercase  :		Any						=      cls._build_index(A )

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





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        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
        lowercase  :		Optional[Any]						=      self._build_index(self.config )

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










'''simple docstring'''






def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->list[tuple[int, int, int]]:






       '''simple docstring'''




       lowercase  :		Dict						=      sorted(lowerCamelCase   ,				key=lambda lowerCamelCase      : edge[2]      )

       lowercase  :		str						=      list(range(lowerCamelCase      )      )

       def find_parent(lowerCamelCase      ):
              if i != parent[i]:
                     lowercase  :		Optional[int]						=      find_parent(parent[i]      )
              return parent[i]

       lowercase  :		Union[str, Any]						=      0
       lowercase  :		Any						=      []

       for edge in edges:
              lowercase  :		Optional[Any]						=      find_parent(edge[0]      )
              lowercase  :		Any						=      find_parent(edge[1]      )
              if parent_a != parent_b:
                     minimum_spanning_tree_cost += edge[2]
                     minimum_spanning_tree.append(lowerCamelCase      )
                     lowercase  :		Optional[int]						=      parent_b

       return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
      lowercase	,	lowercase	:      List[Any]				=       list(map(int, input().strip().split()))
      lowercase	:      List[Any]				=       []

      for _ in range(num_edges):
            lowercase	,	lowercase	,	lowercase	:      Optional[Any]				=       (int(x) for x in input().strip().split())
            edges.append((nodea, nodea, cost))

      kruskal(num_nodes, edges)




