import torch
import torchvision
from torch import nn


class ImageEncoder(nn.Module):
    """
    Global image encoder
    """

    def __init__(self, backbone="resnet34", pretrained=True, latent_size=128):
        """
        :param backbone Backbone network. Assumes it is resnet*
        e.g. resnet34 | resnet50
        :param num_layers number of resnet layers to use, 1-5
        :param pretrained Whether to use model pretrained on ImageNet
        """
        super().__init__()
        self.model = getattr(torchvision.models, backbone)(pretrained=pretrained)
        self.model.fc = nn.Sequential()
        self.register_buffer("latent", torch.empty(1, 1), persistent=False)
        # self.latent (B, L)
        self.latent_size = latent_size
        if latent_size != 512:
            self.fc = nn.Linear(512, latent_size)

    def index(self, uv, cam_z=None, image_size=(), z_bounds=()):
        """
        Params ignored (compatibility)
        :param uv (B, N, 2) only used for shape
        :return latent vector (B, L, N)
        """
        return self.latent.unsqueeze(-1).expand(-1, -1, uv.shape[1])

    def forward(self, x):
        """
        For extracting ResNet's features.
        :param x image (B, C, H, W)
        :return latent (B, latent_size)
        """
        x = x.to(device=self.latent.device)
        x = self.model.conv1(x)
        x = self.model.bn1(x)
        x = self.model.relu(x)

        x = self.model.maxpool(x)
        x = self.model.layer1(x)
        x = self.model.layer2(x)
        x = self.model.layer3(x)
        x = self.model.layer4(x)

        x = self.model.avgpool(x)
        x = torch.flatten(x, 1)

        if self.latent_size != 512:
            x = self.fc(x)

        self.latent = x  # (B, latent_size)
        return self.latent

    @classmethod
    def from_conf(cls, conf):
        return cls(
            conf.get_string("backbone"),
            pretrained=conf.get_bool("pretrained", True),
            latent_size=conf.get_int("latent_size", 128),
        )