import torch
import numpy as np
import sklearn
import datetime
import bcolz
import os
import time

from sklearn.model_selection import KFold
from sklearn.decomposition import PCA
from scipy import interpolate


def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca=0):
    assert (embeddings1.shape[0] == embeddings2.shape[0])
    assert (embeddings1.shape[1] == embeddings2.shape[1])
    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
    nrof_thresholds = len(thresholds)
    k_fold = KFold(n_splits=nrof_folds, shuffle=False)

    tprs = np.zeros((nrof_folds, nrof_thresholds))
    fprs = np.zeros((nrof_folds, nrof_thresholds))
    accuracy = np.zeros((nrof_folds))
    best_thresholds = np.zeros((nrof_folds))
    indices = np.arange(nrof_pairs)
    # print('pca', pca)

    if pca == 0:
        diff = np.subtract(embeddings1, embeddings2)
        dist = np.sum(np.square(diff), 1)

    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
        # print('train_set', train_set)
        # print('test_set', test_set)
        if pca > 0:
            print('doing pca on', fold_idx)
            embed1_train = embeddings1[train_set]
            embed2_train = embeddings2[train_set]
            _embed_train = np.concatenate((embed1_train, embed2_train), axis=0)
            # print(_embed_train.shape)
            pca_model = PCA(n_components=pca)
            pca_model.fit(_embed_train)
            embed1 = pca_model.transform(embeddings1)
            embed2 = pca_model.transform(embeddings2)
            embed1 = sklearn.preprocessing.normalize(embed1)
            embed2 = sklearn.preprocessing.normalize(embed2)
            # print(embed1.shape, embed2.shape)
            diff = np.subtract(embed1, embed2)
            dist = np.sum(np.square(diff), 1)

        # Find the best threshold for the fold
        acc_train = np.zeros((nrof_thresholds))
        for threshold_idx, threshold in enumerate(thresholds):
            _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])
        best_threshold_index = np.argmax(acc_train)
#         print('best_threshold_index', best_threshold_index, acc_train[best_threshold_index])
        best_thresholds[fold_idx] = thresholds[best_threshold_index]
        for threshold_idx, threshold in enumerate(thresholds):
            tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(threshold,
                                                                                                 dist[test_set],
                                                                                                 actual_issame[
                                                                                                     test_set])
        _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set],
                                                      actual_issame[test_set])

    tpr = np.mean(tprs, 0)
    fpr = np.mean(fprs, 0)
    return tpr, fpr, accuracy, best_thresholds


def calculate_accuracy(threshold, dist, actual_issame):
    predict_issame = np.less(dist, threshold)
    tp = np.sum(np.logical_and(predict_issame, actual_issame))
    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
    tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))

    tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)
    fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)
    acc = float(tp + tn) / dist.size
    return tpr, fpr, acc


def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
    '''
    Copy from [insightface](https://github.com/deepinsight/insightface)
    :param thresholds:
    :param embeddings1:
    :param embeddings2:
    :param actual_issame:
    :param far_target:
    :param nrof_folds:
    :return:
    '''
    assert (embeddings1.shape[0] == embeddings2.shape[0])
    assert (embeddings1.shape[1] == embeddings2.shape[1])
    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
    nrof_thresholds = len(thresholds)
    k_fold = KFold(n_splits=nrof_folds, shuffle=False)

    val = np.zeros(nrof_folds)
    far = np.zeros(nrof_folds)

    diff = np.subtract(embeddings1, embeddings2)
    dist = np.sum(np.square(diff), 1)
    indices = np.arange(nrof_pairs)

    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):

        # Find the threshold that gives FAR = far_target
        far_train = np.zeros(nrof_thresholds)
        for threshold_idx, threshold in enumerate(thresholds):
            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
        if np.max(far_train) >= far_target:
            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
            threshold = f(far_target)
        else:
            threshold = 0.0

        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])

    val_mean = np.mean(val)
    far_mean = np.mean(far)
    val_std = np.std(val)
    return val_mean, val_std, far_mean


def calculate_val_far(threshold, dist, actual_issame):
    predict_issame = np.less(dist, threshold)
    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
    n_same = np.sum(actual_issame)
    n_diff = np.sum(np.logical_not(actual_issame))
    val = float(true_accept) / float(n_same)
    far = float(false_accept) / float(n_diff)
    return val, far


def eval(embeddings, actual_issame, nrof_folds=10, pca=0):
    # Calculate evaluation metrics
    thresholds = np.arange(0, 4, 0.01)
    embeddings1 = embeddings[0::2]
    embeddings2 = embeddings[1::2]
    tpr, fpr, accuracy, best_thresholds = calculate_roc(thresholds, embeddings1, embeddings2,
                                       np.asarray(actual_issame), nrof_folds=nrof_folds, pca=pca)
#     thresholds = np.arange(0, 4, 0.001)
#     val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,
#                                       np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
#     return tpr, fpr, accuracy, best_thresholds, val, val_std, far
    return tpr, fpr, accuracy, best_thresholds


def get_val_pair(path, name):
    carray = bcolz.carray(rootdir = os.path.join(path, name), mode='r')
    issame = np.load(os.path.join(path,'lfw_list.npy'))
    return carray, issame


def evaluate(model, carray, issame, nrof_folds = 5, tta = False):
    idx = 0
    embeddings = np.zeros([len(carray), 512])
    model.eval()

    print("\n======Evaluating Model ...======")
    evaluate_start = time.time()

    with torch.no_grad():
        while (idx + 10) <= (len(carray)):
            batch = torch.tensor(carray[idx:idx + 10])
            if tta:
                fliped = hflip_batch(batch)
                emb_batch = model(batch.to(torch.device("cuda:0"))) + model(fliped.to(torch.device("cuda:0")))
                embeddings[idx:idx + 10] = l2_norm(emb_batch)
            else:
                emb = model(batch.to(torch.device("cuda:0"))).cpu()
                embeddings[idx:idx + 10] = emb
            idx += 10
        if idx < len(carray):
            batch = torch.tensor(carray[idx:])            
            if tta:
                fliped = hflip_batch(batch)
                emb_batch = model(batch.to(torch.device("cuda:0"))) + model(fliped.to(torch.device("cuda:0")))
                embeddings[idx:] = l2_norm(emb_batch)
            else:
                embeddings[idx:] = model(batch.to(torch.device("cuda:0"))).cpu()
    tpr, fpr, accuracy, best_thresholds = eval(embeddings, issame, nrof_folds)

    print('Evaluating time: %.3fs' % (time.time() - evaluate_start))

    return accuracy.mean(), accuracy.std(),best_thresholds.mean()