# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F

from sklearn.metrics import hamming_loss
from sklearn.metrics import accuracy_score as accuracy_metric
from sklearn.metrics import multilabel_confusion_matrix
from sklearn.preprocessing import binarize


class TopkAcc(nn.Layer):
    def __init__(self, topk=(1, 5)):
        super().__init__()
        assert isinstance(topk, (int, list, tuple))
        if isinstance(topk, int):
            topk = [topk]
        self.topk = topk

    def forward(self, x, label):
        if isinstance(x, dict):
            x = x["logits"]

        metric_dict = dict()
        for k in self.topk:
            metric_dict["top{}".format(k)] = paddle.metric.accuracy(
                x, label, k=k)
        return metric_dict


class mAP(nn.Layer):
    def __init__(self):
        super().__init__()

    def forward(self, similarities_matrix, query_img_id, gallery_img_id,
                keep_mask):
        metric_dict = dict()

        choosen_indices = paddle.argsort(
            similarities_matrix, axis=1, descending=True)
        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
        gallery_labels_transpose = paddle.broadcast_to(
            gallery_labels_transpose,
            shape=[
                choosen_indices.shape[0], gallery_labels_transpose.shape[1]
            ])
        choosen_label = paddle.index_sample(gallery_labels_transpose,
                                            choosen_indices)
        equal_flag = paddle.equal(choosen_label, query_img_id)
        if keep_mask is not None:
            keep_mask = paddle.index_sample(
                keep_mask.astype('float32'), choosen_indices)
            equal_flag = paddle.logical_and(equal_flag,
                                            keep_mask.astype('bool'))
        equal_flag = paddle.cast(equal_flag, 'float32')

        num_rel = paddle.sum(equal_flag, axis=1)
        num_rel = paddle.greater_than(num_rel, paddle.to_tensor(0.))
        num_rel_index = paddle.nonzero(num_rel.astype("int"))
        num_rel_index = paddle.reshape(num_rel_index, [num_rel_index.shape[0]])
        equal_flag = paddle.index_select(equal_flag, num_rel_index, axis=0)

        acc_sum = paddle.cumsum(equal_flag, axis=1)
        div = paddle.arange(acc_sum.shape[1]).astype("float32") + 1
        precision = paddle.divide(acc_sum, div)

        #calc map
        precision_mask = paddle.multiply(equal_flag, precision)
        ap = paddle.sum(precision_mask, axis=1) / paddle.sum(equal_flag, axis=1)
        metric_dict["mAP"] = paddle.mean(ap).numpy()[0]
        return metric_dict


class mINP(nn.Layer):
    def __init__(self):
        super().__init__()

    def forward(self, similarities_matrix, query_img_id, gallery_img_id,
                keep_mask):
        metric_dict = dict()

        choosen_indices = paddle.argsort(
            similarities_matrix, axis=1, descending=True)
        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
        gallery_labels_transpose = paddle.broadcast_to(
            gallery_labels_transpose,
            shape=[
                choosen_indices.shape[0], gallery_labels_transpose.shape[1]
            ])
        choosen_label = paddle.index_sample(gallery_labels_transpose,
                                            choosen_indices)
        equal_flag = paddle.equal(choosen_label, query_img_id)
        if keep_mask is not None:
            keep_mask = paddle.index_sample(
                keep_mask.astype('float32'), choosen_indices)
            equal_flag = paddle.logical_and(equal_flag,
                                            keep_mask.astype('bool'))
        equal_flag = paddle.cast(equal_flag, 'float32')

        num_rel = paddle.sum(equal_flag, axis=1)
        num_rel = paddle.greater_than(num_rel, paddle.to_tensor(0.))
        num_rel_index = paddle.nonzero(num_rel.astype("int"))
        num_rel_index = paddle.reshape(num_rel_index, [num_rel_index.shape[0]])
        equal_flag = paddle.index_select(equal_flag, num_rel_index, axis=0)

        #do accumulative sum
        div = paddle.arange(equal_flag.shape[1]).astype("float32") + 2
        minus = paddle.divide(equal_flag, div)
        auxilary = paddle.subtract(equal_flag, minus)
        hard_index = paddle.argmax(auxilary, axis=1).astype("float32")
        all_INP = paddle.divide(paddle.sum(equal_flag, axis=1), hard_index)
        mINP = paddle.mean(all_INP)
        metric_dict["mINP"] = mINP.numpy()[0]
        return metric_dict


class Recallk(nn.Layer):
    def __init__(self, topk=(1, 5)):
        super().__init__()
        assert isinstance(topk, (int, list, tuple))
        if isinstance(topk, int):
            topk = [topk]
        self.topk = topk

    def forward(self, similarities_matrix, query_img_id, gallery_img_id,
                keep_mask):
        metric_dict = dict()

        #get cmc
        choosen_indices = paddle.argsort(
            similarities_matrix, axis=1, descending=True)
        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
        gallery_labels_transpose = paddle.broadcast_to(
            gallery_labels_transpose,
            shape=[
                choosen_indices.shape[0], gallery_labels_transpose.shape[1]
            ])
        choosen_label = paddle.index_sample(gallery_labels_transpose,
                                            choosen_indices)
        equal_flag = paddle.equal(choosen_label, query_img_id)
        if keep_mask is not None:
            keep_mask = paddle.index_sample(
                keep_mask.astype('float32'), choosen_indices)
            equal_flag = paddle.logical_and(equal_flag,
                                            keep_mask.astype('bool'))
        equal_flag = paddle.cast(equal_flag, 'float32')
        real_query_num = paddle.sum(equal_flag, axis=1)
        real_query_num = paddle.sum(
            paddle.greater_than(real_query_num, paddle.to_tensor(0.)).astype(
                "float32"))

        acc_sum = paddle.cumsum(equal_flag, axis=1)
        mask = paddle.greater_than(acc_sum,
                                   paddle.to_tensor(0.)).astype("float32")
        all_cmc = (paddle.sum(mask, axis=0) / real_query_num).numpy()

        for k in self.topk:
            metric_dict["recall{}".format(k)] = all_cmc[k - 1]
        return metric_dict


class Precisionk(nn.Layer):
    def __init__(self, topk=(1, 5)):
        super().__init__()
        assert isinstance(topk, (int, list, tuple))
        if isinstance(topk, int):
            topk = [topk]
        self.topk = topk

    def forward(self, similarities_matrix, query_img_id, gallery_img_id,
                keep_mask):
        metric_dict = dict()

        #get cmc
        choosen_indices = paddle.argsort(
            similarities_matrix, axis=1, descending=True)
        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
        gallery_labels_transpose = paddle.broadcast_to(
            gallery_labels_transpose,
            shape=[
                choosen_indices.shape[0], gallery_labels_transpose.shape[1]
            ])
        choosen_label = paddle.index_sample(gallery_labels_transpose,
                                            choosen_indices)
        equal_flag = paddle.equal(choosen_label, query_img_id)
        if keep_mask is not None:
            keep_mask = paddle.index_sample(
                keep_mask.astype('float32'), choosen_indices)
            equal_flag = paddle.logical_and(equal_flag,
                                            keep_mask.astype('bool'))
        equal_flag = paddle.cast(equal_flag, 'float32')

        Ns = paddle.arange(gallery_img_id.shape[0]) + 1
        equal_flag_cumsum = paddle.cumsum(equal_flag, axis=1)
        Precision_at_k = (paddle.mean(equal_flag_cumsum, axis=0) / Ns).numpy()

        for k in self.topk:
            metric_dict["precision@{}".format(k)] = Precision_at_k[k - 1]

        return metric_dict


class DistillationTopkAcc(TopkAcc):
    def __init__(self, model_key, feature_key=None, topk=(1, 5)):
        super().__init__(topk=topk)
        self.model_key = model_key
        self.feature_key = feature_key

    def forward(self, x, label):
        if isinstance(x, dict):
            x = x[self.model_key]
        if self.feature_key is not None:
            x = x[self.feature_key]
        return super().forward(x, label)


class GoogLeNetTopkAcc(TopkAcc):
    def __init__(self, topk=(1, 5)):
        super().__init__()
        assert isinstance(topk, (int, list, tuple))
        if isinstance(topk, int):
            topk = [topk]
        self.topk = topk

    def forward(self, x, label):
        return super().forward(x[0], label)


class MutiLabelMetric(object):
    def __init__(self):
        pass

    def _multi_hot_encode(self, logits, threshold=0.5):
        return binarize(logits, threshold=threshold)

    def __call__(self, output):
        output = F.sigmoid(output)
        preds = self._multi_hot_encode(logits=output.numpy(), threshold=0.5)
        return preds


class HammingDistance(MutiLabelMetric):
    """
    Soft metric based label for multilabel classification
    Returns:
        The smaller the return value is, the better model is.
    """

    def __init__(self):
        super().__init__()

    def __call__(self, output, target):
        preds = super().__call__(output)
        metric_dict = dict()
        metric_dict["HammingDistance"] = paddle.to_tensor(
            hamming_loss(target, preds))
        return metric_dict


class AccuracyScore(MutiLabelMetric):
    """
    Hard metric for multilabel classification
    Args:
        base: ["sample", "label"], default="sample"
            if "sample", return metric score based sample,
            if "label", return metric score based label.
    Returns:
        accuracy:
    """

    def __init__(self, base="label"):
        super().__init__()
        assert base in ["sample", "label"], 'must be one of ["sample", "label"]'
        self.base = base

    def __call__(self, output, target):
        preds = super().__call__(output)
        metric_dict = dict()
        if self.base == "sample":
            accuracy = accuracy_metric(target, preds)
        elif self.base == "label":
            mcm = multilabel_confusion_matrix(target, preds)
            tns = mcm[:, 0, 0]
            fns = mcm[:, 1, 0]
            tps = mcm[:, 1, 1]
            fps = mcm[:, 0, 1]
            accuracy = (sum(tps) + sum(tns)) / (
                sum(tps) + sum(tns) + sum(fns) + sum(fps))
            precision = sum(tps) / (sum(tps) + sum(fps))
            recall = sum(tps) / (sum(tps) + sum(fns))
            F1 = 2 * (accuracy * recall) / (accuracy + recall)
        metric_dict["AccuracyScore"] = paddle.to_tensor(accuracy)
        return metric_dict