from __future__ import annotations

import nodes
from comfy_api.latest import io


class CLIPTextEncodeSDXL(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="CLIPTextEncodeSDXL_V3",
            category="advanced/conditioning",
            inputs=[
                io.Clip.Input("clip"),
                io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION),
                io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION),
                io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                io.String.Input("text_g", multiline=True, dynamic_prompts=True),
                io.String.Input("text_l", multiline=True, dynamic_prompts=True),
            ],
            outputs=[io.Conditioning.Output()],
        )

    @classmethod
    def execute(cls, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l) -> io.NodeOutput:
        tokens = clip.tokenize(text_g)
        tokens["l"] = clip.tokenize(text_l)["l"]
        if len(tokens["l"]) != len(tokens["g"]):
            empty = clip.tokenize("")
            while len(tokens["l"]) < len(tokens["g"]):
                tokens["l"] += empty["l"]
            while len(tokens["l"]) > len(tokens["g"]):
                tokens["g"] += empty["g"]
        conditioning = clip.encode_from_tokens_scheduled(
            tokens,
            add_dict={
                "width": width,
                "height": height,
                "crop_w": crop_w,
                "crop_h": crop_h,
                "target_width": target_width,
                "target_height": target_height,
            },
        )
        return io.NodeOutput(conditioning)


class CLIPTextEncodeSDXLRefiner(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="CLIPTextEncodeSDXLRefiner_V3",
            category="advanced/conditioning",
            inputs=[
                io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
                io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                io.String.Input("text", multiline=True, dynamic_prompts=True),
                io.Clip.Input("clip"),
            ],
            outputs=[io.Conditioning.Output()],
        )

    @classmethod
    def execute(cls, ascore, width, height, text, clip) -> io.NodeOutput:
        tokens = clip.tokenize(text)
        conditioning = clip.encode_from_tokens_scheduled(
            tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}
        )
        return io.NodeOutput(conditioning)


NODES_LIST = [
    CLIPTextEncodeSDXL,
    CLIPTextEncodeSDXLRefiner,
]