#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
import paddle.nn.functional as F

from .celoss import CELoss
from .dmlloss import DMLLoss
from .distanceloss import DistanceLoss
from .rkdloss import RKdAngle, RkdDistance
from .kldivloss import KLDivLoss
from .dkdloss import DKDLoss
from .dist_loss import DISTLoss
from .multilabelloss import MultiLabelLoss
from .mgd_loss import MGDLoss


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",
                 weight_ratio=False,
                 sum_across_class_dim=False,
                 key=None,
                 name="loss_dml"):
        super().__init__(act=act, sum_across_class_dim=sum_across_class_dim)
        assert isinstance(model_name_pairs, list)
        self.key = key
        self.model_name_pairs = model_name_pairs
        self.name = name
        self.weight_ratio = weight_ratio

    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]
            if self.weight_ratio is True:
                loss = super().forward(out1, out2, batch)
            else:
                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=[],
                 act=None,
                 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
        assert act in [None, "sigmoid", "softmax"]
        if act == "sigmoid":
            self.act = nn.Sigmoid()
        elif act == "softmax":
            self.act = nn.Softmax(axis=-1)
        else:
            self.act = None

    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]
            if self.act is not None:
                out1 = self.act(out1)
                out2 = self.act(out2)
            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


class DistillationKLDivLoss(KLDivLoss):
    """
    DistillationKLDivLoss
    """

    def __init__(self,
                 model_name_pairs=[],
                 temperature=4,
                 key=None,
                 name="loss_kl"):
        super().__init__(temperature=temperature)
        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 DistillationDKDLoss(DKDLoss):
    """
    DistillationDKDLoss
    """

    def __init__(self,
                 model_name_pairs=[],
                 key=None,
                 temperature=1.0,
                 alpha=1.0,
                 beta=1.0,
                 use_target_as_gt=False,
                 name="loss_dkd"):
        super().__init__(
            temperature=temperature,
            alpha=alpha,
            beta=beta,
            use_target_as_gt=use_target_as_gt)
        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, batch)
            loss_dict[f"{self.name}_{pair[0]}_{pair[1]}"] = loss
        return loss_dict


class DistillationMultiLabelLoss(MultiLabelLoss):
    """
    DistillationMultiLabelLoss
    """

    def __init__(self,
                 model_names=[],
                 epsilon=None,
                 size_sum=False,
                 weight_ratio=False,
                 key=None,
                 name="loss_mll"):
        super().__init__(
            epsilon=epsilon, size_sum=size_sum, weight_ratio=weight_ratio)
        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 DistillationDISTLoss(DISTLoss):
    """
    DistillationDISTLoss
    """

    def __init__(self,
                 model_name_pairs=[],
                 key=None,
                 beta=1.0,
                 gamma=1.0,
                 name="loss_dist"):
        super().__init__(beta=beta, gamma=gamma)
        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)
            loss_dict[f"{self.name}_{pair[0]}_{pair[1]}"] = loss
        return loss_dict


class DistillationPairLoss(nn.Layer):
    """
    DistillationPairLoss
    """

    def __init__(self,
                 base_loss_name,
                 model_name_pairs=[],
                 s_keys=None,
                 t_keys=None,
                 name="loss",
                 **kwargs):
        super().__init__()
        self.loss_func = eval(base_loss_name)(**kwargs)
        if not isinstance(s_keys, list):
            s_keys = [s_keys]
        if not isinstance(t_keys, list):
            t_keys = [t_keys]
        self.s_keys = s_keys
        self.t_keys = t_keys
        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]]
            out1 = [out1[k] if k is not None else out1 for k in self.s_keys]
            out2 = [out2[k] if k is not None else out2 for k in self.t_keys]
            for feat_idx, (o1, o2) in enumerate(zip(out1, out2)):
                loss = self.loss_func.forward(o1, o2)
                if isinstance(loss, dict):
                    for k in loss:
                        loss_dict[
                            f"{self.name}_{idx}_{feat_idx}_{pair[0]}_{pair[1]}_{k}"] = loss[
                                k]
                else:
                    loss_dict[
                        f"{self.name}_{idx}_{feat_idx}_{pair[0]}_{pair[1]}"] = loss
        return loss_dict