import nodes
import node_helpers
import torch
import comfy.model_management
import comfy.utils

from typing_extensions import override
from comfy_api.latest import ComfyExtension, io

class Kandinsky5ImageToVideo(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="Kandinsky5ImageToVideo",
            category="conditioning/video_models",
            inputs=[
                io.Conditioning.Input("positive"),
                io.Conditioning.Input("negative"),
                io.Vae.Input("vae"),
                io.Int.Input("width", default=768, min=16, max=nodes.MAX_RESOLUTION, step=16),
                io.Int.Input("height", default=512, min=16, max=nodes.MAX_RESOLUTION, step=16),
                io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=4),
                io.Int.Input("batch_size", default=1, min=1, max=4096),
                io.Image.Input("start_image", optional=True),
            ],
            outputs=[
                io.Conditioning.Output(display_name="positive"),
                io.Conditioning.Output(display_name="negative"),
                io.Latent.Output(display_name="latent"),
            ],
        )

    @classmethod
    def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None) -> io.NodeOutput:
        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
        if start_image is not None:
            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
            encoded = vae.encode(start_image[:, :, :, :3])
            concat_latent_image = latent.clone()
            concat_latent_image[:, :, :encoded.shape[2], :, :] = encoded

            mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
            mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0

            positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
            negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})

        out_latent = {}
        out_latent["samples"] = latent
        return io.NodeOutput(positive, negative, out_latent)


class Kandinsky5Extension(ComfyExtension):
    @override
    async def get_node_list(self) -> list[type[io.ComfyNode]]:
        return [
            Kandinsky5ImageToVideo,
        ]

async def comfy_entrypoint() -> Kandinsky5Extension:
    return Kandinsky5Extension()