import multiprocessing
import time
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm

from md_discovery.multi_process_infer_by_pairs import table_encode, inference_from_record_pairs
from md_discovery import tmp_discover
from settings import er_output_dir, similarity_threshold, target_attr, embedding_dict
def fuck(i):
    i = i*i+1


if __name__ == '__main__':
    start = time.time()
    tp_single_tuple_path = er_output_dir + "tp_single_tuple.csv"
    # tp_mds, tp_vio = inference_from_record_pairs(tp_single_tuple_path, similarity_threshold, target_attr)
    tp_mds, tp_vio = tmp_discover.inference_from_record_pairs(tp_single_tuple_path, similarity_threshold, target_attr)
    print(time.time()-start)

    # li = [[[6, 6, 2],
    #        [2, 4, 6],
    #        [2, 4, 7],
    #        [3, 6, 4]],
    #       [[6, 2, 7],
    #        [3, 2, 4],
    #        [5, 3, 5],
    #        [6, 2, 4]],
    #       [[7, 2, 2],
    #        [6, 3, 2],
    #        [6, 4, 3],
    #        [6, 5, 6]]]
    # tensor = torch.Tensor(li)
    # norm_tensor = torch.nn.functional.normalize(tensor, dim=2)
    # print(norm_tensor, '\n')
    # sim_ten = torch.matmul(norm_tensor, norm_tensor.transpose(1, 2))
    # print(sim_ten/2 + 0.5, '\n')
    # print(sim_ten.size())

    # multiprocessing.set_start_method("spawn")
    # manager = multiprocessing.Manager()
    # lock = manager.Lock()
    # pool = multiprocessing.Pool(16)
    # with manager:
    #     for _ in tqdm(range(0, 1000)):
    #         result = pool.apply_async(fuck, args=(_,))
    #         print(result)