#!/usr/bin/env python
# -*- coding: utf-8 -*-

import operator
import pickle
import numpy as np
import pandas as pd

s2_label_dict = {
    '0': 0,
    '1': 1,
    '2': 2,
    '3': 3,
    '4': 4,
    '5': 5,
    '6': 6,
    '7': 7,
    '8': 8,
    '9': 9,
    'a': 10,
    'b': 11,
    'c': 12,
    'd': 13,
    'e': 14,
    'f': 15
}
s2_label_decode_dict = {v: k for k, v in s2_label_dict.items()}

s2_weights = [0.025, 0.025, 0.025,
              0.025, 0.025, 0.025,
              0.025, 0.025, 0.025,
              0.0325, 0.0325, 0.0325,
              0.035, 0.035, 0.035,
              0.0375, 0.0375, 0.0375,
              0.04, 0.04, 0.04,
              0.0425, 0.0425, 0.0425,
              0.045, 0.045, 0.0475,
              0.025, 0.025, 0.025,
              0.0, 0.0, 0.0]

def generate_s2_index(s2_label):
    result = [0 for _ in range(33)]
    for i, char_ in enumerate(s2_label):
        result[i] = s2_label_dict[char_]
    return result


def decode_s2(x):
    result = []
    for i in x:
        result.append(s2_label_decode_dict[i])
    return ''.join(result)


def sample_csv2pkl(csv_path, pkl_path):
    # df = pd.read_csv('/Users/liujianlin/odps_clt_release_64/bin/addr6node_small1.csv', sep='^', encoding="utf_8_sig")
    df = pd.read_csv(csv_path, sep='^', encoding="utf_8_sig")
    # print(df)
    data = []
    for index, row in df.iterrows():
        node_s = []
        label = []
        node1 = [row['node_t1'], row['poi_address_mask1'], row['node1'], generate_s2_index(row['node1'])]
        node2 = [row['node_t2'], row['poi_address_mask2'], row['node2'], generate_s2_index(row['node2'])]
        node3 = [row['node_t3'], row['poi_address_mask3'], row['node3'], generate_s2_index(row['node3'])]
        node4 = [row['node_t4'], row['poi_address_mask4'], row['node4'], generate_s2_index(row['node4'])]
        node5 = [row['node_t5'], row['poi_address_mask5'], row['node5'], generate_s2_index(row['node5'])]
        node6 = [row['node_t6'], row['poi_address_mask6'], row['node6'], generate_s2_index(row['node6'])]
        label.extend(node1[3])
        label.extend(node2[3])
        label.extend(node3[3])
        label.extend(node4[3])
        label.extend(node5[3])
        label.extend(node6[3])
        node1.append(label)
        node2.append(label)
        node3.append(label)
        node4.append(label)
        node5.append(label)
        node6.append(label)
        node_s.append(node1)
        node_s.append(node2)
        node_s.append(node3)
        node_s.append(node4)
        node_s.append(node5)
        node_s.append(node6)
        data.append(node_s)
    # print(data)

    with open(pkl_path,'wb') as f:
        pickle.dump(data,f)


def calculate_multi_s2_acc(predicted_s2, y):
    acc_cnt = np.array([0, 0, 0, 0, 0, 0, 0])
    y = y.view(-1, 33).tolist()
    predicted = predicted_s2.view(-1, 33).tolist()
    # print(y.shape, predicted.shape)
    for index, s2 in enumerate(y):
        for c, i in enumerate(range(12, 33, 3)):
            y_l10 = y[index][12:i+3]
            p_l10 = predicted[index][12:i+3]
            # print(y_l10, p_l10, operator.eq(y_l10, p_l10))
            if operator.eq(y_l10, p_l10):
                acc_cnt[c] += 1
        # print('==='*20)
    # print(acc_cnt)
    return acc_cnt

def calculate_multi_s2_acc_batch(predicted_s2, y, sequence_len = 6):
    acc_cnt = np.array([0, 0, 0, 0, 0, 0, 0])
    y = y.view(-1, sequence_len, 33).tolist()
    predicted = predicted_s2.view(-1, sequence_len, 33).tolist()
    # print(y.shape, predicted.shape)
    batch_size = len(y)
    for batch_i in range(batch_size):
        for index, s2 in enumerate(y[batch_i]):
            for c, i in enumerate(range(12, 33, 3)):
                y_l10 = y[batch_i][index][12:i+3]
                p_l10 = predicted[batch_i][index][12:i+3]
                # print(y_l10, p_l10, operator.eq(y_l10, p_l10))
                if operator.eq(y_l10, p_l10):
                    acc_cnt[c] += 1
        # print('==='*20)
    # print(acc_cnt)
    return acc_cnt



def calculate_alias_acc(predicted, y):
    tp, fp, fn, tn = 0, 0, 0, 0
    acc = 0
    for index, label in enumerate(y):
        if int(label) == int(predicted[index]):
            acc += 1
        if int(label) == 1:
            fn += 1
            if int(predicted[index]) == 1:
                tp += 1
    if fn == 0:
        precision = 0
    else:
        precision = tp / fn * 100
    return tp, fn, acc


def calculate_aoi_acc(predicted, y):
    tp, fp, fn, tn = 0, 0, 0, 0
    acc = 0
    for index, label in enumerate(y):
        if int(label) == int(predicted[index]):
            acc += 1
        if int(label) == 0:
            fn += 1
            if int(predicted[index]) == 0:
                tp += 1
    if fn == 0:
        precision = 0
    else:
        precision = tp / fn * 100
    return tp, fn, acc