import json
import multiprocessing
import os
import time

import ConfigSpace
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from ConfigSpace.read_and_write import json as csj
from md_discovery import tmp_discover
from settings import er_output_dir, similarity_threshold, target_attr, hpo_output_dir


def fuck(i):
    i = i * i + 1


def test1():
    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())


def test2():
    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)


def test3():
    dic = {'description': 0, 'id': 1, 'manufacturer': 0, 'name': 0.9309734582901001, 'price': 0.912541675567627}
    ll = list(dic.values())
    ten = torch.Tensor(ll)
    t = ten.unsqueeze(1)
    t = t.unsqueeze(2)
    y = t.repeat(1, 742, 742)
    print(ten)
    print(y)
    print(torch.isfinite(ten))
    print(torch.count_nonzero(y).item())


def test4():
    one_bool_tensor = torch.ones((3, 3, 3), dtype=torch.bool)
    print(torch.count_nonzero(one_bool_tensor).item())


def test5():
    ten1 = torch.tensor([[1, 2, 3],
                         [7, 8, 9]])
    ten2 = torch.tensor([[4, 5, 6],
                         [11, 12, 15]])
    result = ten1 * ten2
    r = torch.sum(result, 1)
    print('\n')
    print(result)
    print(r)


def test6():
    table_tensor = torch.tensor([[[1., 2., 3.],
                                  [4., 5., 6.],
                                  [7., 8., 9.]],
                                 [[1., 2., 3.],
                                  [4., 5., 6.],
                                  [7., 8., 9.]]])
    t = torch.tensor([[1., 2., 3.],
                      [4., 5., 6.]])
    norm1 = torch.nn.functional.normalize(table_tensor, dim=1)
    norm2 = torch.nn.functional.normalize(table_tensor, dim=2)
    print('\n')
    print(norm1)
    print(norm2)
    print(t.shape)


def test7():
    iterations = 1
    filename_list = os.listdir(er_output_dir)
    if len(filename_list) > 0:
        for _ in filename_list:
            if _.startswith('eval_result'):
                iterations = int(_[12:13]) + 1
    print(iterations)


def test8():
    cum = np.load(hpo_output_dir + 'incumbent.npy')
    with open(hpo_output_dir + "configspace.json", 'r') as load_f:
        dict_configspace = json.load(load_f)
    str_configspace = json.dumps(dict_configspace)
    configspace = csj.read(str_configspace)
    config = ConfigSpace.Configuration(configspace, vector=cum)
    print(cum)


if __name__ == '__main__':
    start = time.time()
    t_single_tuple_path = er_output_dir + "t_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.pairs_inference(t_single_tuple_path, similarity_threshold, target_attr)
    print(time.time() - start)