import torch
from torch import nn
from torch.nn import functional as F
import os


class BasicConv2d(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
        super().__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=False)

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


class Block35(nn.Module):

    def __init__(self, scale=1.0):
        super().__init__()

        self.scale = scale

        self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(256, 32, kernel_size=1, stride=1),
            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
        )

        self.branch2 = nn.Sequential(
            BasicConv2d(256, 32, kernel_size=1, stride=1),
            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1),
            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
        )

        self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1)
        self.relu = 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 Block17(nn.Module):

    def __init__(self, scale=1.0):
        super().__init__()

        self.scale = scale

        self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(896, 128, kernel_size=1, stride=1),
            BasicConv2d(128, 128, kernel_size=(1,7), stride=1, padding=(0,3)),
            BasicConv2d(128, 128, kernel_size=(7,1), stride=1, padding=(3,0))
        )

        self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1)
        self.relu = 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 Block8(nn.Module):

    def __init__(self, scale=1.0, noReLU=False):
        super().__init__()

        self.scale = scale
        self.noReLU = noReLU

        self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(1792, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 192, kernel_size=(1,3), stride=1, padding=(0,1)),
            BasicConv2d(192, 192, kernel_size=(3,1), stride=1, padding=(1,0))
        )

        self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1)
        if not self.noReLU:
            self.relu = 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 Mixed_6a(nn.Module):

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

        self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2)

        self.branch1 = nn.Sequential(
            BasicConv2d(256, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1),
            BasicConv2d(192, 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 Mixed_7a(nn.Module):

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

        self.branch0 = nn.Sequential(
            BasicConv2d(896, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 384, kernel_size=3, stride=2)
        )

        self.branch1 = nn.Sequential(
            BasicConv2d(896, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 256, kernel_size=3, stride=2)
        )

        self.branch2 = nn.Sequential(
            BasicConv2d(896, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
            BasicConv2d(256, 256, kernel_size=3, stride=2)
        )

        self.branch3 = 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 InceptionResnetV1(nn.Module):
    """Inception Resnet V1 model with optional loading of pretrained weights.

    Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface
    datasets. Pretrained state_dicts are automatically downloaded on model instantiation if
    requested and cached in the torch cache. Subsequent instantiations use the cache rather than
    redownloading.

    Keyword Arguments:
        pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'.
            (default: {None})
        classify {bool} -- Whether the model should output classification probabilities or feature
            embeddings. (default: {False})
        num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not
            equal to that used for the pretrained model, the final linear layer will be randomly
            initialized. (default: {None})
        dropout_prob {float} -- Dropout probability. (default: {0.6})
    """
    def __init__(self, classify=False, dropout_prob=0.6, device=None):
        super().__init__()

        # Set simple attributes
        self.classify = classify
        self.num_classes = 8631

        # Define layers
        self.conv2d_1a = BasicConv2d(3, 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 = 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.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2)
        self.repeat_1 = nn.Sequential(
            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_2 = 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),
        )
        self.mixed_7a = Mixed_7a()
        self.repeat_3 = nn.Sequential(
            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.avgpool_1a = nn.AdaptiveAvgPool2d(1)
        self.dropout = nn.Dropout(dropout_prob)
        self.last_linear = nn.Linear(1792, 512, bias=False)
        self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True)
        self.logits = nn.Linear(512, self.num_classes)
        load_weights(self)

        self.device = torch.device('cpu')
        if device is not None:
            self.device = device
            self.to(device)

    def forward(self, x):
        """Calculate embeddings or logits given a batch of input image tensors.

        Arguments:
            x {torch.tensor} -- Batch of image tensors representing faces.

        Returns:
            torch.tensor -- Batch of embedding vectors or multinomial logits.
        """
        x = self.conv2d_1a(x)
        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.conv2d_4b(x)
        x = self.repeat_1(x)
        x = self.mixed_6a(x)
        x = self.repeat_2(x)
        x = self.mixed_7a(x)
        x = self.repeat_3(x)
        x = self.block8(x)
        x = self.avgpool_1a(x)
        x = self.dropout(x)
        x = self.last_linear(x.view(x.shape[0], -1))
        x = self.last_bn(x)
        if self.classify:
            x = self.logits(x)
        else:
            x = F.normalize(x, p=2, dim=1)
        return x


def load_weights(mdl):
    features_path = state_dict_path = os.path.join(os.path.dirname(__file__), 'vggface2-dict/20180402-114759-vggface2-features.pt')
    logits_path = state_dict_path = os.path.join(os.path.dirname(__file__), 'vggface2-dict/20180402-114759-vggface2-logits.pt')
    state_dict = {}
    for i, path in enumerate([features_path, logits_path]):
        state_dict.update(torch.load(path))
    mdl.load_state_dict(state_dict)


def get_torch_home():
    torch_home = os.path.expanduser(
        os.getenv(
            'TORCH_HOME',
            os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')
        )
    )
    return torch_home