



import logging
import random

import ray

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


snake_case__					:     List[Any]    		=   logging.getLogger(__name__)

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			False



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							Dict							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							self.retriever.index.init_index()



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							List[Any]							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



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

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							Union[str, Any]							,      lowerCamelCase	:							Dict							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							List[Any]							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Tuple=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							,      )
							__lowercase		       =			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     _snake_case	(	self	:							Tuple	):






							'''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     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							str							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



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



						@classmethod
						def     _snake_case	(	cls	:							Optional[Any]							,      lowerCamelCase	:							List[Any]							,      lowerCamelCase	:							Optional[Any]=None							,      **lowerCamelCase	:							int	):






							'''simple docstring'''



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



						@classmethod
						def     _snake_case	(	cls	:							Optional[int]							,      lowerCamelCase	:							str							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							str=None							,      **lowerCamelCase	:							Tuple	):






							'''simple docstring'''



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








from __future__ import annotations

from typing import Any

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							Union[str, Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			num_of_nodes
							__lowercase		       =			[]
							__lowercase		       =			{}



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							self.m_edges.append([u_node, v_node, weight]	)



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if self.m_component[u_node] == u_node:
								return u_node
							return self.find_component(self.m_component[u_node]	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if self.m_component[u_node] != u_node:
								for k in self.m_component:
									__lowercase		       =			self.find_component(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							list[int]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if component_size[u_node] <= component_size[v_node]:
								__lowercase		       =			v_node
								component_size[v_node] += component_size[u_node]
								self.set_component(lowerCamelCase	)

							elif component_size[u_node] >= component_size[v_node]:
								__lowercase		       =			self.find_component(lowerCamelCase	)
								component_size[u_node] += component_size[v_node]
								self.set_component(lowerCamelCase	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase		       =			[]
							__lowercase		       =			0

							__lowercase		       =			[-1] * self.m_num_of_nodes

							# A list of components (initialized to all of the nodes)
							for node in range(self.m_num_of_nodes	):
								self.m_component.update({node: node}	)
								component_size.append(1	)

							__lowercase		       =			self.m_num_of_nodes

							while num_of_components > 1:
								for edge in self.m_edges:
									__lowercase       ,    __lowercase       ,    __lowercase		       =			edge

									__lowercase		       =			self.m_component[u]
									__lowercase		       =			self.m_component[v]

									if u_component != v_component:

										for component in (u_component, v_component):
											if (
											    minimum_weight_edge[component] == -1
											    or minimum_weight_edge[component][2] > w
											):
												__lowercase		       =			[u, v, w]

								for edge in minimum_weight_edge:
									if isinstance(lowerCamelCase							,      lowerCamelCase	):
										__lowercase       ,    __lowercase       ,    __lowercase		       =			edge

										__lowercase		       =			self.m_component[u]
										__lowercase		       =			self.m_component[v]

										if u_component != v_component:
											mst_weight += w
											self.union(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
											print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""	)
											num_of_components -= 1

								__lowercase		       =			[-1] * self.m_num_of_nodes
							print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""	)



def    snake_case_				(    ):
	pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()



