






def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							snake_case_           =							index
							snake_case_           =							2 * index + 1
							snake_case_           =							2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														snake_case_           =							left_index

							if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
														snake_case_           =							right_index

							if largest != index:
														snake_case_      ,		snake_case_           =							unsorted[index], unsorted[largest]
														heapify(a_							,					a_							,					a_)





def 						__UpperCAmelCase      (					a_):
							snake_case_           =							len(a_)
							for i in range(n // 2 - 1							,					-1							,					-1):
														heapify(a_							,					a_							,					a_)
							for i in range(n - 1							,					0							,					-1):
														snake_case_      ,		snake_case_           =							unsorted[i], unsorted[0]
														heapify(a_							,					0							,					a_)
							return unsorted


if __name__ == "__main__":
	lowercase  							= input("Enter numbers separated by a comma:\n").strip()
	lowercase  							= [int(item) for item in user_input.split(",")]
	print(heap_sort(unsorted))













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 						__UpperCAmelCase      (					a_							,					a_=1_00							,					a_=" "):
							snake_case_           =							text.split(a_)
							return [character.join(text[i : i + n]).strip() for i in range(0							,					len(a_)							,					a_)]





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





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

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

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

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

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

							snake_case_           =							data_shrad.map(
							    partial(a_							,					ctx_encoder=a_							,					ctx_tokenizer=a_							,					device=a_)							,					batched=a_							,					batch_size=16							,					features=a_							,					)
							dataset.save_to_disk(a_)





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							[]

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

							snake_case_           =							concatenate_datasets(a_)
							faiss.omp_set_num_threads(96)

							snake_case_           =							faiss.IndexHNSWFlat(7_68							,					1_28							,					faiss.METRIC_INNER_PRODUCT)
							concat.add_faiss_index('embeddings'							,					custom_index=a_)
							concat.get_index('embeddings').save(
							    a_)  # since we load the index in to memory,we can directly update the index in the disk





