"""Save CTransPath model in TorchScript format.

Adapted from https://github.com/Xiyue-Wang/TransPath

Licensed GPL 3.0.
"""

import sys

# Use the TIMM library with modifications by the CTransPath authors.
sys.path.append("timm-0.5.4/")

import timm
from timm.models.layers.helpers import to_2tuple
import torch
import torch.nn as nn

assert timm.__version__ == "0.5.4"


class ConvStem(nn.Module):
    def __init__(
        self,
        img_size=224,
        patch_size=4,
        in_chans=3,
        embed_dim=768,
        norm_layer=None,
        flatten=True,
    ):
        super().__init__()

        assert patch_size == 4
        assert embed_dim % 8 == 0

        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.flatten = flatten

        stem = []
        input_dim, output_dim = 3, embed_dim // 8
        for l in range(2):
            stem.append(
                nn.Conv2d(
                    input_dim,
                    output_dim,
                    kernel_size=3,
                    stride=2,
                    padding=1,
                    bias=False,
                )
            )
            stem.append(nn.BatchNorm2d(output_dim))
            stem.append(nn.ReLU(inplace=True))
            input_dim = output_dim
            output_dim *= 2
        stem.append(nn.Conv2d(input_dim, embed_dim, kernel_size=1))
        self.proj = nn.Sequential(*stem)

        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, H, W = x.shape
        assert (
            H == self.img_size[0] and W == self.img_size[1]
        ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        x = self.norm(x)
        return x


def ctranspath():
    model = timm.create_model(
        "swin_tiny_patch4_window7_224", embed_layer=ConvStem, pretrained=False
    )
    return model


model = ctranspath()
model.head = torch.nn.Identity()
td = torch.load("ctranspath.pth")
model.load_state_dict(td["model"], strict=True)

jitted = torch.jit.script(model)
torch.jit.save(jitted, "torchscript_model.pt")

torch.onnx.export(
    model,
    args=torch.ones(1, 3, 224, 224),
    f="model.onnx",
    input_names=["image"],
    output_names=["embedding"],
    dynamic_axes={
        "image": {0: "batch_size"},
        "embedding": {0: "batch_size"},
    },
)