






'''simple docstring'''
import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase=1_0_0					,  _UpperCAmelCase=" " )				->				str:






					"""simple docstring"""







					a_									=   text.split(_UpperCAmelCase )
					return [character.join(text[i : i + n] ).strip() for i in range(0					,  len(_UpperCAmelCase )					,  _UpperCAmelCase )]

def 							a	(   _UpperCAmelCase )				->				Any:






					"""simple docstring"""







					a_							,					a_									=   [], []
					for title, text in zip(documents['title']					,  documents['text'] ):
										if text is not None:
															for passage in split_text(_UpperCAmelCase ):
																				titles.append(title if title is not None else '' )
																				texts.append(_UpperCAmelCase )
					return {"title": titles, "text": texts}

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				int:






					"""simple docstring"""







					a_									=   load_dataset(
					    'csv'					,  data_files=[csv_path]					,  split='train'					,  delimiter='\t'					,  column_names=['title', 'text'] )
					a_									=   kb_dataset.map(
					    _UpperCAmelCase					,  batched=_UpperCAmelCase					,  num_proc=1 )  # if you want you can load already splitted csv.
					a_									=   [kb_dataset.shard(_UpperCAmelCase					,  _UpperCAmelCase					,  contiguous=_UpperCAmelCase ) for i in range(_UpperCAmelCase )]
					a_									=   kb_list[process_num]

					a_									=   'data_' + str(_UpperCAmelCase )
					a_									=   os.path.join(_UpperCAmelCase					,  _UpperCAmelCase )

					a_									=   DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base' )
					a_									=   ctx_encoder.to(device=_UpperCAmelCase )

					def embed(
					    _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase ) -> dict:
										a_									=   ctx_tokenizer(
										    documents['title']					,  documents['text']					,  truncation=_UpperCAmelCase					,  padding='longest'					,  return_tensors='pt' )['input_ids']
										a_									=   ctx_encoder(input_ids.to(device=_UpperCAmelCase )					,  return_dict=_UpperCAmelCase ).pooler_output
										return {"embeddings": embeddings.detach().cpu().numpy()}

					a_									=   Features(
					    {'text': Value('string' ), 'title': Value('string' ), 'embeddings': Sequence(Value('float32' ) )} )  # optional, save as float32 instead of float64 to save space

					a_									=   data_shrad.map(
					    partial(_UpperCAmelCase					,  ctx_encoder=_UpperCAmelCase					,  ctx_tokenizer=_UpperCAmelCase					,  device=_UpperCAmelCase )					,  batched=_UpperCAmelCase					,  batch_size=1_6					,  features=_UpperCAmelCase					,  )
					dataset.save_to_disk(_UpperCAmelCase )





def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[int]:






					"""simple docstring"""







					a_									=   []

					for shard_address in glob(str(_UpperCAmelCase ) + '/*/' ):
										data_shard_list.append(load_from_disk(_UpperCAmelCase ) )

					a_									=   concatenate_datasets(_UpperCAmelCase )
					faiss.omp_set_num_threads(9_6 )

					a_									=   faiss.IndexHNSWFlat(7_6_8					,  1_2_8					,  faiss.METRIC_INNER_PRODUCT )
					concat.add_faiss_index('embeddings'					,  custom_index=_UpperCAmelCase )
					concat.get_index('embeddings' ).save(
					    _UpperCAmelCase )  # since we load the index in to memory,we can directly update the index in the disk









'''simple docstring'''
class    _snake_case   :

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__     )      ->	None:
										a_									=   data
										a_									=   42
										a_									=   42



def 							a	(   _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_									=   0
					a_									=   x

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_							,					a_									=   find_set(_UpperCAmelCase ), find_set(_UpperCAmelCase )
					if x == y:
										return

					elif x.rank > y.rank:
										a_									=   x
					else:
										a_									=   y
										if x.rank == y.rank:
															y.rank += 1

def 							a	(   _UpperCAmelCase )				->				Node:






					"""simple docstring"""







					if x != x.parent:
										a_									=   find_set(x.parent )
					return x.parent

def 							a	(   _UpperCAmelCase )				->				set:






					"""simple docstring"""







					a_									=   ({0, 1, 2}, {3, 4, 5})
					for s in sets:
										if node.data in s:
															return s
					a_									=   F'''{node.data} is not in {sets}'''
					raise ValueError(_UpperCAmelCase )

def 							a	(   )				->				None:






					"""simple docstring"""







					a_									=   [Node(_UpperCAmelCase ) for i in range(6 )]
					for v in vertex:
										make_set(_UpperCAmelCase )

					union_set(vertex[0]					,  vertex[1] )
					union_set(vertex[1]					,  vertex[2] )
					union_set(vertex[3]					,  vertex[4] )
					union_set(vertex[3]					,  vertex[5] )

					for nodea in vertex:
										for nodea in vertex:
															if find_python_set(_UpperCAmelCase ).isdisjoint(find_python_set(_UpperCAmelCase ) ):
																				assert find_set(_UpperCAmelCase ) != find_set(_UpperCAmelCase )
															else:
																				assert find_set(_UpperCAmelCase ) == find_set(_UpperCAmelCase )


if __name__ == "__main__":
						test_disjoint_set()

