"""GoogLeNet

Paper: https://arxiv.org/pdf/1409.4842.pdf
Adapted from: https://github.com/pytorch/vision/blob/master/torchvision/models/googlenet.py

Copyright 2021 | farabio
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Any
from farabio.utils.helpers import get_num_parameters

__all__ = ['GoogLeNet', 'googlenet']


class GoogLeNet(nn.Module):
    def __init__(
        self,
        n_classes: int = 1000,
    ) -> None:
        super(GoogLeNet, self).__init__()

        self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.conv2 = BasicConv2d(64, 64, kernel_size=1)
        self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
        self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
        self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
        self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
        self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
        self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
        self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
        self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
        self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
        self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(0.2)
        self.fc = nn.Linear(1024, n_classes)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.conv1(x)
        x = self.maxpool1(x)

        x = self.conv2(x)
        x = self.conv3(x)
        x = self.maxpool2(x)

        x = self.inception3a(x)
        x = self.inception3b(x)
        x = self.maxpool3(x)

        x = self.inception4a(x)

        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        x = self.inception4e(x)
        x = self.maxpool4(x)

        x = self.inception5a(x)
        x = self.inception5b(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.dropout(x)
        x = self.fc(x)

        return x


class Inception(nn.Module):
    def __init__(
        self,
        in_channels: int,
        ch1x1: int,
        ch3x3red: int,
        ch3x3: int,
        ch5x5red: int,
        ch5x5: int,
        pool_proj: int
    ) -> None:
        super(Inception, self).__init__()

        self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)

        self.branch2 = nn.Sequential(
            BasicConv2d(in_channels, ch3x3red, kernel_size=1),
            BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1)
        )

        self.branch3 = nn.Sequential(
            BasicConv2d(in_channels, ch5x5red, kernel_size=1),
            BasicConv2d(ch5x5red, ch5x5, kernel_size=3, padding=1)
        )

        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),
            BasicConv2d(in_channels, pool_proj, kernel_size=1)
        )

    def _forward(self, x: torch.Tensor) -> List[torch.Tensor]:
        branch1 = self.branch1(x)
        branch2 = self.branch2(x)
        branch3 = self.branch3(x)
        branch4 = self.branch4(x)

        outputs = [branch1, branch2, branch3, branch4]
        return outputs

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        outputs = self._forward(x)
        return torch.cat(outputs, 1)


class BasicConv2d(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        **kwargs: Any
    ) -> None:
        super(BasicConv2d, self).__init__()

        self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.conv(x)
        x = self.bn(x)
        x = F.relu(x, inplace=True)

        return x


def googlenet(**kwargs: Any) -> GoogLeNet:
    model = GoogLeNet(**kwargs)
    return model


def test():
    x = torch.randn(1, 3, 224, 224)

    model = googlenet()
    y = model(x)

    print("Trainable parameters: ", get_num_parameters(model))
    print("in shape: ", x.shape, ", out shape: ", y.shape)


# test()