import os
import sys
import math
sys.path.append(os.path.dirname(os.path.dirname(
    os.path.dirname(__file__))))  # noqa

import torch
from base.base_model import BaseModel
from torch.nn import (Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU,
                      Sigmoid, Dropout, MaxPool2d, AdaptiveAvgPool2d,
                      Sequential, Module, Parameter)
from collections import namedtuple
from utils.logging_config import logger


# Original Arcface Model, codes modified from https://github.com/TreB1eN/InsightFace_Pytorch/blob/master/model.py
class Flatten(Module):
    def forward(self, input):
        return input.view(input.size(0), -1)


def l2_norm(input, axis=1):
    norm = torch.norm(input, 2, axis, True)
    output = torch.div(input, norm)
    return output


class SEModule(Module):
    def __init__(self, channels, reduction):
        super(SEModule, self).__init__()
        self.avg_pool = AdaptiveAvgPool2d(1)
        self.fc1 = Conv2d(
            channels,
            channels // reduction,
            kernel_size=1,
            padding=0,
            bias=False)
        self.relu = ReLU(inplace=True)
        self.fc2 = Conv2d(
            channels // reduction,
            channels,
            kernel_size=1,
            padding=0,
            bias=False)
        self.sigmoid = Sigmoid()

    def forward(self, x):
        module_input = x
        x = self.avg_pool(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        return module_input * x


class bottleneck_IR(Module):
    def __init__(self, in_channel, depth, stride):
        super(bottleneck_IR, self).__init__()
        if in_channel == depth:
            self.shortcut_layer = MaxPool2d(1, stride)
        else:
            self.shortcut_layer = Sequential(
                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
                BatchNorm2d(depth))
        self.res_layer = Sequential(
            BatchNorm2d(in_channel),
            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
            PReLU(depth), Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
            BatchNorm2d(depth))

    def forward(self, x):
        shortcut = self.shortcut_layer(x)
        res = self.res_layer(x)
        return res + shortcut


class bottleneck_IR_SE(Module):
    def __init__(self, in_channel, depth, stride):
        super(bottleneck_IR_SE, self).__init__()
        if in_channel == depth:
            self.shortcut_layer = MaxPool2d(1, stride)
        else:
            self.shortcut_layer = Sequential(
                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
                BatchNorm2d(depth))
        self.res_layer = Sequential(
            BatchNorm2d(in_channel),
            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
            PReLU(depth), Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
            BatchNorm2d(depth), SEModule(depth, 16))

    def forward(self, x):
        shortcut = self.shortcut_layer(x)
        res = self.res_layer(x)
        return res + shortcut


class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
    '''A named tuple describing a ResNet block.'''


def get_block(in_channel, depth, num_units, stride=2):
    return [Bottleneck(in_channel, depth, stride)
            ] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]


def get_blocks(num_layers):
    if num_layers == 50:
        blocks = [
            get_block(in_channel=64, depth=64, num_units=3),
            get_block(in_channel=64, depth=128, num_units=4),
            get_block(in_channel=128, depth=256, num_units=14),
            get_block(in_channel=256, depth=512, num_units=3)
        ]
    elif num_layers == 100:
        blocks = [
            get_block(in_channel=64, depth=64, num_units=3),
            get_block(in_channel=64, depth=128, num_units=13),
            get_block(in_channel=128, depth=256, num_units=30),
            get_block(in_channel=256, depth=512, num_units=3)
        ]
    elif num_layers == 152:
        blocks = [
            get_block(in_channel=64, depth=64, num_units=3),
            get_block(in_channel=64, depth=128, num_units=8),
            get_block(in_channel=128, depth=256, num_units=36),
            get_block(in_channel=256, depth=512, num_units=3)
        ]
    return blocks


class Backbone(Module):
    def __init__(self, num_layers, drop_ratio, mode='ir_se'):
        super(Backbone, self).__init__()
        assert num_layers in [50, 100,
                              152], 'num_layers should be 50,100, or 152'
        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
        blocks = get_blocks(num_layers)
        if mode == 'ir':
            unit_module = bottleneck_IR
        elif mode == 'ir_se':
            unit_module = bottleneck_IR_SE
        self.input_layer = Sequential(
            Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64),
            PReLU(64))
        self.output_layer = Sequential(
            BatchNorm2d(512), Dropout(drop_ratio), Flatten(),
            Linear(512 * 7 * 7, 512), BatchNorm1d(512))
        modules = []
        for block in blocks:
            for bottleneck in block:
                modules.append(
                    unit_module(bottleneck.in_channel, bottleneck.depth,
                                bottleneck.stride))
        self.body = Sequential(*modules)

    def forward(self, x):
        x = self.input_layer(x)
        x = self.body(x)
        x = self.output_layer(x)
        return l2_norm(x)


class Arcface(Module):
    # implementation of additive margin softmax loss in https://arxiv.org/abs/1801.05599
    def __init__(self, embedding_size=512, classnum=51332, s=64., m=0.5):
        super(Arcface, self).__init__()
        self.classnum = classnum
        self.kernel = Parameter(torch.Tensor(embedding_size, classnum))
        # initial kernel
        self.kernel.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
        self.m = m  # the margin value, default is 0.5
        self.s = s  # scalar value default is 64, see normface https://arxiv.org/abs/1704.06369
        self.cos_m = math.cos(m)
        self.sin_m = math.sin(m)
        self.mm = self.sin_m * m  # issue 1
        self.threshold = math.cos(math.pi - m)

    def forward(self, embbedings, label):
        # weights norm
        nB = len(embbedings)
        kernel_norm = l2_norm(self.kernel, axis=0)
        # cos(theta+m)
        cos_theta = torch.mm(embbedings, kernel_norm)
        #         output = torch.mm(embbedings,kernel_norm)
        cos_theta = cos_theta.clamp(-1, 1)  # for numerical stability
        cos_theta_2 = torch.pow(cos_theta, 2)
        sin_theta_2 = 1 - cos_theta_2
        sin_theta = torch.sqrt(sin_theta_2)
        cos_theta_m = (cos_theta * self.cos_m - sin_theta * self.sin_m)
        # this condition controls the theta+m should in range [0, pi]
        #      0<=theta+m<=pi
        #     -m<=theta<=pi-m
        cond_v = cos_theta - self.threshold
        cond_mask = cond_v <= 0
        keep_val = (cos_theta - self.mm
                    )  # when theta not in [0,pi], use cosface instead
        cos_theta_m[cond_mask] = keep_val[cond_mask]
        output = cos_theta * 1.0  # a little bit hacky way to prevent in_place operation on cos_theta
        idx_ = torch.arange(0, nB, dtype=torch.long)
        output[idx_, label] = cos_theta_m[idx_, label]
        output *= self.s  # scale up in order to make softmax work, first introduced in normface
        return output


# Cosface head
class Am_softmax(Module):
    # implementation of additive margin softmax loss in https://arxiv.org/abs/1801.05599
    def __init__(self, embedding_size=512, classnum=51332):
        super(Am_softmax, self).__init__()
        self.classnum = classnum
        self.kernel = Parameter(torch.Tensor(embedding_size, classnum))
        # initial kernel
        self.kernel.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
        self.m = 0.35  # additive margin recommended by the paper
        self.s = 30.  # see normface https://arxiv.org/abs/1704.06369

    def forward(self, embbedings, label):
        kernel_norm = l2_norm(self.kernel, axis=0)
        cos_theta = torch.mm(embbedings, kernel_norm)
        cos_theta = cos_theta.clamp(-1, 1)  # for numerical stability
        phi = cos_theta - self.m
        label = label.view(-1, 1)  # size=(B,1)
        index = cos_theta.data * 0.0  # size=(B,Classnum)
        index.scatter_(1, label.data.view(-1, 1), 1)
        index = index.byte()
        output = cos_theta * 1.0
        output[index] = phi[index]  # only change the correct predicted output
        output *= self.s  # scale up in order to make softmax work, first introduced in normface
        return output


class FaceModelIRSE(BaseModel):
    def __init__(self, num_layers=50, drop_ratio=0.6, embedding_size=512, classnum=51332,
                 backbone_weights=None, head_type='ArcFace'):
        super().__init__()
        self.backbone = Backbone(num_layers=num_layers, drop_ratio=drop_ratio)
        if head_type == 'ArcFace':
            self.archead = Arcface(embedding_size=embedding_size, classnum=classnum, s=64., m=0.5)
        elif head_type == 'CosFace':
            self.archead = Am_softmax(embedding_size=embedding_size, classnum=classnum)
        else:
            raise NotImplementedError()

        if backbone_weights is not None:
            logger.info(f'Loading {backbone_weights} into {self.__class__}')
            state = torch.load(backbone_weights)
            self.backbone.load_state_dict(state)
        self.finetune_mode = ''
        self.set_finetune_mode('all')

    def forward(self, data_input):
        x = data_input['face_tensor']
        label = data_input['faceID']
        embedding = self.embedding(x)
        logits = self.logits(embedding, label)
        return {
            'logits': logits
        }

    def embedding(self, x):
        return self.backbone(x)

    def logits(self, embedding, label):
        return self.archead(embedding, label)

    def set_finetune_mode(self, mode='all'):
        if mode == self.finetune_mode:
            return
        if mode == 'all':
            for param in self.parameters():
                param.requires_grad = True
        elif mode == 'head':
            for param in self.parameters():
                param.requires_grad = False
            for param in self.archead.parameters():
                param.requires_grad = True
        else:
            raise NotImplementedError()
        self.finetune_mode = mode
