"""Helper for evaluation on the Labeled Faces in the Wild dataset
"""

# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.


import datetime
import os
import pickle


import numpy as np
import sklearn
import oneflow as flow

from scipy import interpolate
from sklearn.decomposition import PCA
from sklearn.model_selection import KFold
import cv2 as cv
import logging


class LFold:
    def __init__(self, n_splits=2, shuffle=False):
        self.n_splits = n_splits
        if self.n_splits > 1:
            self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle)

    def split(self, indices):
        if self.n_splits > 1:
            return self.k_fold.split(indices)
        else:
            return [(indices, indices)]


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 = LFold(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))
    indices = np.arange(nrof_pairs)

    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)):
        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)
            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)
            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)
        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


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
):
    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 = LFold(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))
    # print(true_accept, false_accept)
    # print(n_same, n_diff)
    val = float(true_accept) / float(n_same)
    far = float(false_accept) / float(n_diff)
    return val, far


def evaluate(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 = 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, val, val_std, far


def load_bin_cv(path, image_size):
    bins, issame_list = pickle.load(open(path, "rb"), encoding="bytes")
    data_list = []
    for flip in [0, 1]:
        data = flow.empty(len(issame_list) * 2, 3, image_size[0], image_size[1])
        data_list.append(data)
    for i in range(len(issame_list) * 2):
        _bin = bins[i]
        img_ori = cv.imdecode(_bin, cv.IMREAD_COLOR)[:, :, ::-1]

        for flip in [0, 1]:
            img = img_ori.copy()
            if flip == 1:
                img = cv.flip(img, 1)
            img = np.array(img).transpose((2, 0, 1))
            img = (img - 127.5) * 0.00784313725
            data_list[flip][i] = flow.tensor(img, dtype=flow.float)

        if i % 1000 == 0:
            logging.info("loading bin:%d", i)
    logging.info(data_list[0].shape)
    return data_list, issame_list


@flow.no_grad()
def test(data_set, backbone, batch_size, nfolds=10, is_consistent=False):
    logging.info("testing verification..")
    data_list = data_set[0]
    issame_list = data_set[1]
    embeddings_list = []
    time_consumed = 0.0
    if is_consistent:
        placement = flow.env.all_device_placement("cpu")
        sbp = flow.sbp.split(0)

    for i in range(len(data_list)):
        data = data_list[i]
        embeddings = None
        ba = 0
        while ba < data.shape[0]:
            bb = min(ba + batch_size, data.shape[0])
            count = bb - ba
            img = data[bb - batch_size : bb]
            time0 = datetime.datetime.now()
            with flow.no_grad():
                if is_consistent:
                    img = img.to_consistent(placement=placement, sbp=sbp)
                net_out = backbone(img.to("cuda"))

            if is_consistent:
                _embeddings = net_out.to_local().numpy()
            else:
                _embeddings = net_out.detach().numpy()
            time_now = datetime.datetime.now()
            diff = time_now - time0
            time_consumed += diff.total_seconds()
            if embeddings is None:
                embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
            embeddings[ba:bb, :] = _embeddings[(batch_size - count) :, :]
            ba = bb
        embeddings_list.append(embeddings)

    _xnorm = 0.0
    _xnorm_cnt = 0
    for embed in embeddings_list:
        for i in range(embed.shape[0]):
            _em = embed[i]
            _norm = np.linalg.norm(_em)
            _xnorm += _norm
            _xnorm_cnt += 1
    _xnorm /= _xnorm_cnt

    embeddings = embeddings_list[0].copy()
    embeddings = sklearn.preprocessing.normalize(embeddings)
    acc1 = 0.0
    std1 = 0.0
    embeddings = embeddings_list[0] + embeddings_list[1]
    embeddings = sklearn.preprocessing.normalize(embeddings)
    logging.info(embeddings.shape)
    logging.info("infer time:%f" % time_consumed)
    _, _, accuracy, val, val_std, far = evaluate(
        embeddings, issame_list, nrof_folds=nfolds
    )
    acc2, std2 = np.mean(accuracy), np.std(accuracy)
    return acc1, std1, acc2, std2, _xnorm, embeddings_list


def dumpR(data_set, backbone, batch_size, name="", data_extra=None, label_shape=None):
    print("dump verification embedding..")
    data_list = data_set[0]
    issame_list = data_set[1]
    embeddings_list = []
    time_consumed = 0.0
    for i in range(len(data_list)):
        data = data_list[i]
        embeddings = None
        ba = 0
        while ba < data.shape[0]:
            bb = min(ba + batch_size, data.shape[0])
            count = bb - ba

            _data = nd.slice_axis(data, axis=0, begin=bb - batch_size, end=bb)
            time0 = datetime.datetime.now()
            if data_extra is None:
                db = mx.io.DataBatch(data=(_data,), label=(_label,))
            else:
                db = mx.io.DataBatch(data=(_data, _data_extra), label=(_label,))
            model.forward(db, is_train=False)
            net_out = model.get_outputs()
            _embeddings = net_out[0].asnumpy()
            time_now = datetime.datetime.now()
            diff = time_now - time0
            time_consumed += diff.total_seconds()
            if embeddings is None:
                embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
            embeddings[ba:bb, :] = _embeddings[(batch_size - count) :, :]
            ba = bb
        embeddings_list.append(embeddings)
    embeddings = embeddings_list[0] + embeddings_list[1]
    embeddings = sklearn.preprocessing.normalize(embeddings)
    actual_issame = np.asarray(issame_list)
    outname = os.path.join("temp.bin")
    with open(outname, "wb") as f:
        pickle.dump((embeddings, issame_list), f, protocol=pickle.HIGHEST_PROTOCOL)
