from __future__ import absolute_import, division, print_function
from collections import namedtuple

import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import constant_init, kaiming_init
from torch.nn.modules.batchnorm import _BatchNorm

from ..modelzoo import inceptionv4 as model_urls
from ..registry import BACKBONES

__all__ = ['Inception4']


class BasicConv2d(nn.Module):

    def __init__(self,
                 in_planes,
                 out_planes,
                 kernel_size,
                 stride=1,
                 padding=0):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(
            in_planes,
            out_planes,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            bias=False)  # verify bias false
        self.bn = nn.BatchNorm2d(
            out_planes,
            eps=0.001,  # value found in tensorflow
            momentum=0.1,  # default pytorch value
            affine=True)
        self.relu = nn.ReLU(inplace=True)

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


class Mixed_3a(nn.Module):

    def __init__(self):
        super(Mixed_3a, self).__init__()
        self.maxpool = nn.MaxPool2d(3, stride=2)
        self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)

    def forward(self, x):
        x0 = self.maxpool(x)
        x1 = self.conv(x)
        out = torch.cat((x0, x1), 1)
        return out


class Mixed_4a(nn.Module):

    def __init__(self):
        super(Mixed_4a, self).__init__()

        self.branch0 = nn.Sequential(
            BasicConv2d(160, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1))

        self.branch1 = nn.Sequential(
            BasicConv2d(160, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
            BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
            BasicConv2d(64, 96, kernel_size=(3, 3), stride=1))

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


class Mixed_5a(nn.Module):

    def __init__(self):
        super(Mixed_5a, self).__init__()
        self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
        self.maxpool = nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        x0 = self.conv(x)
        x1 = self.maxpool(x)
        out = torch.cat((x0, x1), 1)
        return out


class Inception_A(nn.Module):

    def __init__(self):
        super(Inception_A, self).__init__()
        self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(384, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1))

        self.branch2 = nn.Sequential(
            BasicConv2d(384, 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 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
            BasicConv2d(384, 96, 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 Reduction_A(nn.Module):

    def __init__(self):
        super(Reduction_A, self).__init__()
        self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)

        self.branch1 = nn.Sequential(
            BasicConv2d(384, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
            BasicConv2d(224, 256, kernel_size=3, stride=2))

        self.branch2 = 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 Inception_B(nn.Module):

    def __init__(self):
        super(Inception_B, self).__init__()
        self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(1024, 192, kernel_size=1, stride=1),
            BasicConv2d(
                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
            BasicConv2d(
                224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0)))

        self.branch2 = nn.Sequential(
            BasicConv2d(1024, 192, kernel_size=1, stride=1),
            BasicConv2d(
                192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)),
            BasicConv2d(
                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
            BasicConv2d(
                224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)),
            BasicConv2d(
                224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)))

        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
            BasicConv2d(1024, 128, 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 Reduction_B(nn.Module):

    def __init__(self):
        super(Reduction_B, self).__init__()

        self.branch0 = nn.Sequential(
            BasicConv2d(1024, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 192, kernel_size=3, stride=2))

        self.branch1 = nn.Sequential(
            BasicConv2d(1024, 256, kernel_size=1, stride=1),
            BasicConv2d(
                256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)),
            BasicConv2d(
                256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)),
            BasicConv2d(320, 320, kernel_size=3, stride=2))

        self.branch2 = 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 Inception_C(nn.Module):

    def __init__(self):
        super(Inception_C, self).__init__()

        self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)

        self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
        self.branch1_1a = BasicConv2d(
            384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
        self.branch1_1b = BasicConv2d(
            384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))

        self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
        self.branch2_1 = BasicConv2d(
            384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0))
        self.branch2_2 = BasicConv2d(
            448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1))
        self.branch2_3a = BasicConv2d(
            512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
        self.branch2_3b = BasicConv2d(
            512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))

        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
            BasicConv2d(1536, 256, kernel_size=1, stride=1))

    def forward(self, x):
        x0 = self.branch0(x)

        x1_0 = self.branch1_0(x)
        x1_1a = self.branch1_1a(x1_0)
        x1_1b = self.branch1_1b(x1_0)
        x1 = torch.cat((x1_1a, x1_1b), 1)

        x2_0 = self.branch2_0(x)
        x2_1 = self.branch2_1(x2_0)
        x2_2 = self.branch2_2(x2_1)
        x2_3a = self.branch2_3a(x2_2)
        x2_3b = self.branch2_3b(x2_2)
        x2 = torch.cat((x2_3a, x2_3b), 1)

        x3 = self.branch3(x)

        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class InceptionAux(nn.Module):

    def __init__(self, in_channels, num_classes):
        super(InceptionAux, self).__init__()
        self.conv0 = BasicConv2d(in_channels, 128, kernel_size=1)
        self.conv1 = BasicConv2d(128, 768, kernel_size=5)
        self.conv1.stddev = 0.01
        self.fc = nn.Linear(768, num_classes)
        self.fc.stddev = 0.001

    def forward(self, x):
        # N x 768 x 17 x 17
        x = F.avg_pool2d(x, kernel_size=5, stride=3)
        # N x 768 x 5 x 5
        x = self.conv0(x)
        # N x 128 x 5 x 5
        x = self.conv1(x)
        # N x 768 x 1 x 1
        # Adaptive average pooling
        x = F.adaptive_avg_pool2d(x, (1, 1))
        # N x 768 x 1 x 1
        x = torch.flatten(x, 1)
        # N x 768
        x = self.fc(x)
        # N x 1000
        return x


# class BasicConv2d(nn.Module):

#     def __init__(self, in_channels, out_channels, **kwargs):
#         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):
#         x = self.conv(x)
#         x = self.bn(x)
#         return F.relu(x, inplace=True)


@BACKBONES.register_module
class Inception4(nn.Module):
    """InceptionV4 backbone.

    Args:
        num_classes (int): The num_classes of InceptionV4. An extra fc will be used if
    """

    def __init__(self,
                 num_classes: int = 0,
                 p_dropout=0.2,
                 aux_logits: bool = True):
        super(Inception4, self).__init__()
        self.aux_logits = aux_logits
        # Modules
        self.features = nn.Sequential(
            BasicConv2d(3, 32, kernel_size=3, stride=2),
            BasicConv2d(32, 32, kernel_size=3, stride=1),
            BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
            Mixed_3a(),
            Mixed_4a(),
            Mixed_5a(),
            Inception_A(),
            Inception_A(),
            Inception_A(),
            Inception_A(),
            Reduction_A(),  # Mixed_6a
            Inception_B(),
            Inception_B(),
            Inception_B(),
            Inception_B(),
            Inception_B(),
            Inception_B(),
            Inception_B(),  # Mixed_6h 1024 x 17 x 17
            Reduction_B(),  # Mixed_7a
            Inception_C(),
            Inception_C(),
            Inception_C())

        if aux_logits:
            self.AuxLogits = InceptionAux(1024, num_classes)

        self.dropout = nn.Dropout(p_dropout)
        self.last_linear = None
        if num_classes > 0:
            self.last_linear = nn.Linear(1536, num_classes)

        self.default_pretrained_model_path = model_urls[
            self.__class__.__name__]

    @property
    def fc(self):
        return self.last_linear

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
                for m in self.modules():
                    if isinstance(m, nn.Conv2d):
                        kaiming_init(m, mode='fan_in', nonlinearity='relu')
                    elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
                        constant_init(m, 1)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def logits(self, features):
        x = F.adaptive_avg_pool2d(features, output_size=(1, 1))
        # x = F.avg_pool2d(features, kernel_size=adaptiveAvgPoolWidth)
        x = x.view(x.size(0), -1)  # B x 1536
        x = self.fc(x)
        # B x num_classes
        return x

    def forward(self, input: torch.Tensor):
        """_summary_

        Args:
            input (torch.Tensor): A RGB image tensor with shape B x C x H x W

        Returns:
            torch.Tensor: A feature tensor or a logit tensor when num_classes is 0 (default)
        """

        if self.training and self.aux_logits:
            x = self.features[:-4](input)
            aux = self.AuxLogits(x)
            x = self.features[-4:](x)
        else:
            x = self.features(input)
            aux = None

        if self.fc is not None:
            x = self.logits(x)

        return [aux, x]