# InceptionResNetv2 derived from:
# https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/inceptionresnetv2.py
import torch
import torch.nn

import thelper.nn
import thelper.nn.coordconv


class BasicConv2d(torch.nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
        super().__init__()
        self.conv = torch.nn.Conv2d(in_planes, out_planes,
                                    kernel_size=kernel_size, stride=stride,
                                    padding=padding, bias=False)  # verify bias false
        self.bn = torch.nn.BatchNorm2d(out_planes,
                                       eps=0.001,  # value found in tensorflow
                                       momentum=0.1,  # default pytorch value
                                       affine=True)
        self.relu = torch.nn.ReLU(inplace=False)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class Mixed_5b(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)
        self.branch1 = torch.nn.Sequential(
            BasicConv2d(192, 48, kernel_size=1, stride=1),
            BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
        )
        self.branch2 = torch.nn.Sequential(
            BasicConv2d(192, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
        )
        self.branch3 = torch.nn.Sequential(
            torch.nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
            BasicConv2d(192, 64, kernel_size=1, stride=1)
        )

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class Block35(torch.nn.Module):

    def __init__(self, scale=1.0):
        super().__init__()
        self.scale = scale
        self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)
        self.branch1 = torch.nn.Sequential(
            BasicConv2d(320, 32, kernel_size=1, stride=1),
            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
        )
        self.branch2 = torch.nn.Sequential(
            BasicConv2d(320, 32, kernel_size=1, stride=1),
            BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
            BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
        )
        self.conv2d = torch.nn.Conv2d(128, 320, kernel_size=1, stride=1)
        self.relu = torch.nn.ReLU(inplace=False)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), 1)
        out = self.conv2d(out)
        out = out * self.scale + x
        out = self.relu(out)
        return out


class Mixed_6a(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)
        self.branch1 = torch.nn.Sequential(
            BasicConv2d(320, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
            BasicConv2d(256, 384, kernel_size=3, stride=2)
        )
        self.branch2 = torch.nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), 1)
        return out


class Block17(torch.nn.Module):

    def __init__(self, scale=1.0):
        super().__init__()
        self.scale = scale
        self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)
        self.branch1 = torch.nn.Sequential(
            BasicConv2d(1088, 128, kernel_size=1, stride=1),
            BasicConv2d(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),
            BasicConv2d(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0))
        )
        self.conv2d = torch.nn.Conv2d(384, 1088, kernel_size=1, stride=1)
        self.relu = torch.nn.ReLU(inplace=False)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        out = torch.cat((x0, x1), 1)
        out = self.conv2d(out)
        out = out * self.scale + x
        out = self.relu(out)
        return out


class Mixed_7a(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.branch0 = torch.nn.Sequential(
            BasicConv2d(1088, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 384, kernel_size=3, stride=2)
        )
        self.branch1 = torch.nn.Sequential(
            BasicConv2d(1088, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 288, kernel_size=3, stride=2)
        )
        self.branch2 = torch.nn.Sequential(
            BasicConv2d(1088, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
            BasicConv2d(288, 320, kernel_size=3, stride=2)
        )
        self.branch3 = torch.nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class Block8(torch.nn.Module):

    def __init__(self, scale=1.0, noReLU=False):
        super(Block8, self).__init__()
        self.scale = scale
        self.noReLU = noReLU
        self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)
        self.branch1 = torch.nn.Sequential(
            BasicConv2d(2080, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),
            BasicConv2d(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
        )
        self.conv2d = torch.nn.Conv2d(448, 2080, kernel_size=1, stride=1)
        if not self.noReLU:
            self.relu = torch.nn.ReLU(inplace=False)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        out = torch.cat((x0, x1), 1)
        out = self.conv2d(out)
        out = out * self.scale + x
        if not self.noReLU:
            out = self.relu(out)
        return out


class InceptionResNetV2(thelper.nn.Module):

    def __init__(self, task, input_channels=3):
        # note: must always forward args to base class to keep backup
        super().__init__(task, input_channels=input_channels)
        self.conv2d_1a = BasicConv2d(input_channels, 32, kernel_size=3, stride=2)
        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
        self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.maxpool_3a = torch.nn.MaxPool2d(3, stride=2)
        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
        self.maxpool_5a = torch.nn.MaxPool2d(3, stride=2)
        self.mixed_5b = Mixed_5b()
        self.repeat = torch.nn.Sequential(
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17)
        )
        self.mixed_6a = Mixed_6a()
        self.repeat_1 = torch.nn.Sequential(
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10)
        )
        self.mixed_7a = Mixed_7a()
        self.repeat_2 = torch.nn.Sequential(
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20)
        )
        self.block8 = Block8(noReLU=True)
        self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
        self.avgpool_1a = torch.nn.AvgPool2d(8, count_include_pad=False)
        self.last_linear = torch.nn.Linear(1536, 1000)
        self.set_task(task)

    def features(self, input):
        x = self.conv2d_1a(input)
        x = self.conv2d_2a(x)
        x = self.conv2d_2b(x)
        x = self.maxpool_3a(x)
        x = self.conv2d_3b(x)
        x = self.conv2d_4a(x)
        x = self.maxpool_5a(x)
        x = self.mixed_5b(x)
        x = self.repeat(x)
        x = self.mixed_6a(x)
        x = self.repeat_1(x)
        x = self.mixed_7a(x)
        x = self.repeat_2(x)
        x = self.block8(x)
        x = self.conv2d_7b(x)
        return x

    def logits(self, features):
        x = self.avgpool_1a(features)
        x = x.view(x.size(0), -1)
        x = self.last_linear(x)
        return x

    def forward(self, input):
        x = self.features(input)
        x = self.logits(x)
        return x

    def set_task(self, task):
        assert isinstance(task, thelper.tasks.Classification), "missing impl for non-classif task type"
        num_classes = len(task.class_names)
        if self.last_linear.out_features != num_classes:
            self.last_linear = torch.nn.Linear(1536, num_classes)
        self.task = task
