

'''simple docstring'''





from __future__ import annotations

from typing import Any





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		str		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=num_of_nodes
										lowercase :							list[list[int]]    		=[]
										lowercase :							dict[int, int]    		={}







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										self.m_edges.append([u_node, v_node, weight]   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''

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







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''

										if self.m_component[u_node] != u_node:
																for k in self.m_component:
																						lowercase :							List[Any]    		=self.find_component(UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		list[int]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''

										if component_size[u_node] <= component_size[v_node]:
																lowercase :							List[str]    		=v_node
																component_size[v_node] += component_size[u_node]
																self.set_component(UpperCAmelCase__   )

										elif component_size[u_node] >= component_size[v_node]:
																lowercase :							Optional[Any]    		=self.find_component(UpperCAmelCase__   )
																component_size[u_node] += component_size[v_node]
																self.set_component(UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							int    		=[]
										lowercase :							List[Any]    		=0

										lowercase :							list[Any]    		=[-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 :							Any    		=self.m_num_of_nodes

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

																						lowercase :							int    		=self.m_component[u]
																						lowercase :							List[Any]    		=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 :							Tuple    		=[u, v, w]

																for edge in minimum_weight_edge:
																						if isinstance(UpperCAmelCase__		,					UpperCAmelCase__   ):
																												lowercase		,	lowercase		,	lowercase :							Any    		=edge

																												lowercase :							Tuple    		=self.m_component[u]
																												lowercase :							Tuple    		=self.m_component[v]

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

																lowercase :							List[str]    		=[-1] * self.m_num_of_nodes
										print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''   )






def 				_lowerCAmelCase			(   )				->  None:
						pass


if __name__ == "__main__":
							import doctest

							doctest.testmod()








'''simple docstring'''





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


UpperCamelCase_    						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_    						=       """cuda""" if torch.cuda.is_available() else """cpu"""






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     Tuple=100   , __magic_name__						:     Optional[int]=" "						)				->  List[str]:
						lowercase :							List[Any]    		=text.split(__magic_name__						)
						return [character.join(text[i : i + n]						).strip() for i in range(0   , len(__magic_name__						)   , __magic_name__						)]






def 				_lowerCAmelCase			(   __magic_name__						:     dict						)				->  dict:
						lowercase		,	lowercase :							int    		=[], []
						for title, text in zip(documents['''title''']   , documents['''text''']						):
												if text is not None:
																		for passage in split_text(__magic_name__						):
																								titles.append(title if title is not None else ''''''						)
																								texts.append(__magic_name__						)
						return {"title": titles, "text": texts}






def 				_lowerCAmelCase			(   __magic_name__						:     dict   , __magic_name__						:     DPRContextEncoder   , __magic_name__						:     DPRContextEncoderTokenizerFast						)				->  dict:
						lowercase :							Dict    		=ctx_tokenizer(
						    documents['''title''']   , documents['''text''']   , truncation=__magic_name__   , padding='''longest'''   , return_tensors='''pt'''						)['''input_ids''']
						lowercase :							Optional[int]    		=ctx_encoder(input_ids.to(device=__magic_name__						)   , return_dict=__magic_name__						).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}






def 				_lowerCAmelCase			(   __magic_name__						:     "RagExampleArguments"   , __magic_name__						:     "ProcessingArguments"   , __magic_name__						:     "IndexHnswArguments"   , )				->  str:
						######################################
						logger.info('''Step 1 - Create the dataset'''						)
						######################################

						# The dataset needed for RAG must have three columns:
						# - title (string): title of the document
						# - text (string): text of a passage of the document
						# - embeddings (array of dimension d): DPR representation of the passage
						# Let's say you have documents in tab-separated csv files with columns "title" and "text"
						assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

						# You can load a Dataset object this way
						lowercase :							Tuple    		=load_dataset(
						    '''csv'''   , data_files=[rag_example_args.csv_path]   , split='''train'''   , delimiter='''\t'''   , column_names=['''title''', '''text''']						)

						# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

						# Then split the documents into passages of 100 words
						lowercase :							Optional[int]    		=dataset.map(__magic_name__   , batched=__magic_name__   , num_proc=processing_args.num_proc						)

						# And compute the embeddings
						lowercase :							Any    		=DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__magic_name__						)
						lowercase :							Any    		=DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
						lowercase :							Optional[int]    		=Features(
						    {'''text''': Value('''string'''						), '''title''': Value('''string'''						), '''embeddings''': Sequence(Value('''float32'''						)						)}						)  # optional, save as float32 instead of float64 to save space
						lowercase :							Optional[Any]    		=dataset.map(
						    partial(__magic_name__   , ctx_encoder=__magic_name__   , ctx_tokenizer=__magic_name__						)   , batched=__magic_name__   , batch_size=processing_args.batch_size   , features=__magic_name__   , )

						# And finally save your dataset
						lowercase :							Optional[Any]    		=os.path.join(rag_example_args.output_dir   , '''my_knowledge_dataset'''						)
						dataset.save_to_disk(__magic_name__						)
						# from datasets import load_from_disk
						# dataset = load_from_disk(passages_path)  # to reload the dataset

						######################################
						logger.info('''Step 2 - Index the dataset'''						)
						######################################

						# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
						lowercase :							Union[str, Any]    		=faiss.IndexHNSWFlat(index_hnsw_args.d   , index_hnsw_args.m   , faiss.METRIC_INNER_PRODUCT						)
						dataset.add_faiss_index('''embeddings'''   , custom_index=__magic_name__						)

						# And save the index
						lowercase :							Dict    		=os.path.join(rag_example_args.output_dir   , '''my_knowledge_dataset_hnsw_index.faiss'''						)
						dataset.get_index('''embeddings'''						).save(__magic_name__						)
						# dataset.load_faiss_index("embeddings", index_path)  # to reload the index





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=str(Path(lowercase__      ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'      )						,							metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}						,							)
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}						,							)
				lowerCamelCase_		       =    field(
				    default='facebook/rag-sequence-nq'						,							metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}						,							)
				lowerCamelCase_		       =    field(
				    default='facebook/dpr-ctx_encoder-multiset-base'						,							metadata={
				        'help': (
				            'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
				            ' \'facebook/dpr-ctx_encoder-multiset-base\''
				        )
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=str(Path(lowercase__      ).parent / 'test_run' / 'dummy-kb'      )						,							metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={
				        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=16						,							metadata={
				        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
				    }						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=7_68						,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						,							)
				lowerCamelCase_		       =    field(
				    default=1_28						,							metadata={
				        'help': (
				            'The number of bi-directional links created for every new element during the HNSW index construction.'
				        )
				    }						,							)


if __name__ == "__main__":
							logging.basicConfig(level=logging.WARNING)
							logger.setLevel(logging.INFO)

							UpperCamelCase_    						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							UpperCamelCase_						,   UpperCamelCase_						,   UpperCamelCase_    						=       parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														UpperCamelCase_    						=       rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)





