# modified from https://github.com/isl-org/DPT
import torch
import torch.nn as nn
import torch.nn.functional as F

from .base_model import BaseModel
from .blocks import (
    FeatureFusionBlock,
    FeatureFusionBlock_custom,
    Interpolate,
    _make_encoder,
    forward_vit,
)


def _make_fusion_block(features, use_bn):
    return FeatureFusionBlock_custom(
        features,
        nn.ReLU(False),
        deconv=False,
        bn=use_bn,
        expand=False,
        align_corners=True,
    )


class DPT(BaseModel):
    def __init__(
        self,
        head,
        features=256,
        backbone="vitb_rn50_384",
        readout="project",
        channels_last=False,
        use_bn=False,
    ):

        super(DPT, self).__init__()

        self.channels_last = channels_last

        hooks = {
            "vitb_rn50_384": [0, 1, 8, 11],
            "vitb16_384": [2, 5, 8, 11],
            "vitl16_384": [5, 11, 17, 23],
        }

        # Instantiate backbone and reassemble blocks
        self.pretrained, self.scratch = _make_encoder(
            backbone,
            features,
            True, # Set to true of you want to train from scratch, uses ImageNet weights
            groups=1,
            expand=False,
            exportable=False,
            hooks=hooks[backbone],
            use_readout=readout,
        )

        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)

        self.scratch.output_conv = head


    def forward(self, x, get_feat=False):
        if self.channels_last == True:
            x.contiguous(memory_format=torch.channels_last)

        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)

        # res 8x -> 4x -> 2x -> 1x base_size
        # base_size = H / 32
        # all n_channels same (256 by default) after these
        layer_1_rn = self.scratch.layer1_rn(layer_1)
        layer_2_rn = self.scratch.layer2_rn(layer_2)
        layer_3_rn = self.scratch.layer3_rn(layer_3)
        layer_4_rn = self.scratch.layer4_rn(layer_4)

        # upsample by two with out changing n_channels each time, conv-sum for fusing
        path_4 = self.scratch.refinenet4(layer_4_rn)
        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)

        out = self.scratch.output_conv(path_1)

        # save the feat if required
        if get_feat:
            return out, layer_4
        
        return out

class DPTDepthModel(DPT):
    def __init__(self, path=None, non_negative=True, num_channels=1, **kwargs):
        features = kwargs["features"] if "features" in kwargs else 256

        head = nn.Sequential(
            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.Conv2d(32, num_channels, kernel_size=1, stride=1, padding=0),
            nn.ReLU(True) if non_negative else nn.Identity(),
            nn.Identity(),
        )
        nn.init.constant_(head[-3].bias, 0.05)
        super().__init__(head, **kwargs)

        if path is not None:
           self.load(path)

    def forward(self, image, get_feat=False):
        x = image * 2 - 1
        if get_feat:
            output, feat = super().forward(x, get_feat=get_feat)
            output = output.clamp(min=0, max=1)
            return output, feat
        else:
            output = super().forward(x, get_feat=get_feat).clamp(min=0, max=1)
            return output