import node_helpers
import comfy.utils
import math
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io

class TextEncodeQwenImageEdit(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="TextEncodeQwenImageEdit",
            category="advanced/conditioning",
            inputs=[
                io.Clip.Input("clip"),
                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
                io.Vae.Input("vae", optional=True),
                io.Image.Input("image", optional=True),
            ],
            outputs=[
                io.Conditioning.Output(),
            ],
        )

    @classmethod
    def execute(cls, clip, prompt, vae=None, image=None) -> io.NodeOutput:
        ref_latent = None
        if image is None:
            images = []
        else:
            samples = image.movedim(-1, 1)
            total = int(1024 * 1024)

            scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
            width = round(samples.shape[3] * scale_by)
            height = round(samples.shape[2] * scale_by)

            s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
            image = s.movedim(1, -1)
            images = [image[:, :, :, :3]]
            if vae is not None:
                ref_latent = vae.encode(image[:, :, :, :3])

        tokens = clip.tokenize(prompt, images=images)
        conditioning = clip.encode_from_tokens_scheduled(tokens)
        if ref_latent is not None:
            conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True)
        return io.NodeOutput(conditioning)


class TextEncodeQwenImageEditPlus(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="TextEncodeQwenImageEditPlus",
            category="advanced/conditioning",
            inputs=[
                io.Clip.Input("clip"),
                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
                io.Vae.Input("vae", optional=True),
                io.Image.Input("image1", optional=True),
                io.Image.Input("image2", optional=True),
                io.Image.Input("image3", optional=True),
                io.Image.Input("image4", optional=True),
                io.Int.Input("target_size", optional=True, default=896, min=128, max=2048, step=32),
            ],
            outputs=[
                io.Conditioning.Output(),
            ],
        )

    @classmethod
    def execute(cls, clip, prompt, vae=None, image1=None, image2=None, image3=None, image4=None, target_size=896) -> io.NodeOutput:
        ref_latents = []
        images = [image1, image2, image3, image4]
        images_vl = []
        llama_template = "<|im_start|>system\nDescribe key details of the input image (including any objects, characters, poses, facial features, clothing, setting, textures and style), then explain how the user's text instruction should alter, modify or recreate the image. Generate a new image that meets the user's requirements, which can vary from a small change to a completely new image using inputs as a guide.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
        image_prompt = ""

        for i, image in enumerate(images):
            if image is not None:
                samples = image.movedim(-1, 1)
                total = int(384 * 384)

                scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
                width = round(samples.shape[3] * scale_by)
                height = round(samples.shape[2] * scale_by)

                s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
                images_vl.append(s.movedim(1, -1))
                if vae is not None:
                    total = int(target_size * target_size)
                    scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
                    
                    height = int(samples.shape[2] * scale_by / 32) * 32
                    width = int(samples.shape[3] * scale_by / 32) * 32
                    
                    s = comfy.utils.common_upscale(samples, width, height, "lanczos", "center")                    
                    ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3]))

                image_prompt += "Picture {}: <|vision_start|><|image_pad|><|vision_end|>".format(i + 1)

        tokens = clip.tokenize(image_prompt + prompt, images=images_vl, llama_template=llama_template)
        conditioning = clip.encode_from_tokens_scheduled(tokens)
        if len(ref_latents) > 0:
            conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": ref_latents}, append=True)
        return io.NodeOutput(conditioning)

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


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