import threading

import numpy as np

import cv2

cv2.setNumThreads(0)

cv2.ocl.setUseOpenCL(False)

from sklearn.model_selection import KFold

from config import Config

import argparse

import json

import pandas as pd

def get_folds(data, num):

    kf = KFold(n_splits=num, shuffle=True, random_state=42)

    kf.get_n_splits(data)

    return kf.split(data)

def get_csv_folds(ds, fn, holdout=False):

    df = pd.read_csv(fn)

    # df = df[df['source'] != 'janowczyk']

    df = df[['img_id', 'fold', 'cluster', 'source']]

    folds = []

    polosa_id = '193ffaa5272d5c421ae02130a64d98ad120ec70e4ed97a72cdcd4801ce93b066'

    galaxy_ids = ['538b7673d507014d83af238876e03617396b70fe27f525f8205a4a96900fbb8e', 'a102535b0e88374bea4a1cfd9ee7cb3822ff54f4ab2a9845d428ec22f9ee2288', 'cb4df20a83b2f38b394c67f1d9d4aef29f9794d5345da3576318374ec3a11490', 'f29fd9c52e04403cd2c7d43b6fe2479292e53b2f61969d25256d2d2aca7c6a81']

    all_folds_ids = galaxy_ids + [polosa_id]

    for it in set(df['fold']):

        toadd = (df['fold'] != it) | (df['img_id'].isin(all_folds_ids)) | (df['source'] == 'wikimedia')

        val = df[(df['fold'] == it)]['img_id'].values.tolist()

        train_groups = df[toadd]['cluster'].values

        train_ids = df[toadd]['img_id'].values

        train = []

        for i in range(len(train_ids)):

            rep = 1

            if train_groups[i] in ['b', 'd', 'e', 'm']:

                rep = 4

            elif train_groups[i] in ['c', 'n']:

                rep = 3

            if train_ids[i] == polosa_id:

                rep = 5

            train.extend([train_ids[i]] * rep)

        folds.append((ds.get_indexes_by_names(train), ds.get_indexes_by_names(val)))

    return folds

def get_config():

    parser = argparse.ArgumentParser()

    parser.add_argument('config_path')

    parser.add_argument('--fold', dest='fold', default=None,

                        help='fold to process')

    args = parser.parse_args()

    with open(args.config_path, 'r') as f:

        config = json.load(f)

        print(config)

    config['fold'] = args.fold

    return Config(**config)

def update_config(config, **kwargs):

    d = config._asdict()

    d.update(**kwargs)

    print(d)

    return Config(**d)