#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 paddle
import paddle.nn as nn

from .celoss import CELoss
from .dmlloss import DMLLoss
from .distanceloss import DistanceLoss
from .rkdloss import RKdAngle, RkdDistance


class DistillationCELoss(CELoss):
    """
    DistillationCELoss
    """

    def __init__(self,
                 model_name_pairs=[],
                 epsilon=None,
                 key=None,
                 name="loss_ce"):
        super().__init__(epsilon=epsilon)
        assert isinstance(model_name_pairs, list)
        self.key = key
        self.model_name_pairs = model_name_pairs
        self.name = name

    def forward(self, predicts, batch):
        loss_dict = dict()
        for idx, pair in enumerate(self.model_name_pairs):
            out1 = predicts[pair[0]]
            out2 = predicts[pair[1]]
            if self.key is not None:
                out1 = out1[self.key]
                out2 = out2[self.key]
            loss = super().forward(out1, out2)
            for key in loss:
                loss_dict["{}_{}_{}".format(key, pair[0], pair[1])] = loss[key]
        return loss_dict


class DistillationGTCELoss(CELoss):
    """
    DistillationGTCELoss
    """

    def __init__(self,
                 model_names=[],
                 epsilon=None,
                 key=None,
                 name="loss_gt_ce"):
        super().__init__(epsilon=epsilon)
        assert isinstance(model_names, list)
        self.key = key
        self.model_names = model_names
        self.name = name

    def forward(self, predicts, batch):
        loss_dict = dict()
        for name in self.model_names:
            out = predicts[name]
            if self.key is not None:
                out = out[self.key]
            loss = super().forward(out, batch)
            for key in loss:
                loss_dict["{}_{}".format(key, name)] = loss[key]
        return loss_dict


class DistillationDMLLoss(DMLLoss):
    """
    """

    def __init__(self,
                 model_name_pairs=[],
                 act="softmax",
                 key=None,
                 name="loss_dml"):
        super().__init__(act=act)
        assert isinstance(model_name_pairs, list)
        self.key = key
        self.model_name_pairs = model_name_pairs
        self.name = name

    def forward(self, predicts, batch):
        loss_dict = dict()
        for idx, pair in enumerate(self.model_name_pairs):
            out1 = predicts[pair[0]]
            out2 = predicts[pair[1]]
            if self.key is not None:
                out1 = out1[self.key]
                out2 = out2[self.key]
            loss = super().forward(out1, out2)
            if isinstance(loss, dict):
                for key in loss:
                    loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1],
                                                   idx)] = loss[key]
            else:
                loss_dict["{}_{}".format(self.name, idx)] = loss
        return loss_dict


class DistillationDistanceLoss(DistanceLoss):
    """
    """

    def __init__(self,
                 mode="l2",
                 model_name_pairs=[],
                 key=None,
                 name="loss_",
                 **kargs):
        super().__init__(mode=mode, **kargs)
        assert isinstance(model_name_pairs, list)
        self.key = key
        self.model_name_pairs = model_name_pairs
        self.name = name + mode

    def forward(self, predicts, batch):
        loss_dict = dict()
        for idx, pair in enumerate(self.model_name_pairs):
            out1 = predicts[pair[0]]
            out2 = predicts[pair[1]]
            if self.key is not None:
                out1 = out1[self.key]
                out2 = out2[self.key]
            loss = super().forward(out1, out2)
            for key in loss:
                loss_dict["{}_{}_{}".format(self.name, key, idx)] = loss[key]
        return loss_dict


class DistillationRKDLoss(nn.Layer):
    def __init__(self,
                 target_size=None,
                 model_name_pairs=(["Student", "Teacher"], ),
                 student_keepkeys=[],
                 teacher_keepkeys=[]):
        super().__init__()
        self.student_keepkeys = student_keepkeys
        self.teacher_keepkeys = teacher_keepkeys
        self.model_name_pairs = model_name_pairs
        assert len(self.student_keepkeys) == len(self.teacher_keepkeys)

        self.rkd_angle_loss = RKdAngle(target_size=target_size)
        self.rkd_dist_loss = RkdDistance(target_size=target_size)

    def __call__(self, predicts, batch):
        loss_dict = {}
        for m1, m2 in self.model_name_pairs:
            for idx, (
                    student_name, teacher_name
            ) in enumerate(zip(self.student_keepkeys, self.teacher_keepkeys)):
                student_out = predicts[m1][student_name]
                teacher_out = predicts[m2][teacher_name]

                loss_dict[f"loss_angle_{idx}_{m1}_{m2}"] = self.rkd_angle_loss(
                    student_out, teacher_out)
                loss_dict[f"loss_dist_{idx}_{m1}_{m2}"] = self.rkd_dist_loss(
                    student_out, teacher_out)

        return loss_dict
