import nodes
import node_helpers
import torch
import comfy.model_management


class CLIPTextEncodeHunyuanDiT:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "clip": ("CLIP", ),
            "bert": ("STRING", {"multiline": True, "dynamicPrompts": True}),
            "mt5xl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
            }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "encode"

    CATEGORY = "advanced/conditioning"

    def encode(self, clip, bert, mt5xl):
        tokens = clip.tokenize(bert)
        tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"]

        return (clip.encode_from_tokens_scheduled(tokens), )

class EmptyHunyuanLatentVideo:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                              "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                              "length": ("INT", {"default": 25, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "generate"

    CATEGORY = "latent/video"

    def generate(self, width, height, length, batch_size=1):
        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
        return ({"samples":latent}, )

PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = (
    "<|start_header_id|>system<|end_header_id|>\n\n<image>\nDescribe the video by detailing the following aspects according to the reference image: "
    "1. The main content and theme of the video."
    "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
    "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
    "4. background environment, light, style and atmosphere."
    "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n"
    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
    "<|start_header_id|>assistant<|end_header_id|>\n\n"
)

class TextEncodeHunyuanVideo_ImageToVideo:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "clip": ("CLIP", ),
            "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
            "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
            }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "encode"

    CATEGORY = "advanced/conditioning"

    def encode(self, clip, clip_vision_output, prompt):
        tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected)
        return (clip.encode_from_tokens_scheduled(tokens), )


class HunyuanImageToVideo:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"positive": ("CONDITIONING", ),
                             "vae": ("VAE", ),
                             "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                             "length": ("INT", {"default": 53, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
                },
                "optional": {"start_image": ("IMAGE", ),
                }}

    RETURN_TYPES = ("CONDITIONING", "LATENT")
    RETURN_NAMES = ("positive", "latent")
    FUNCTION = "encode"

    CATEGORY = "conditioning/video_models"

    def encode(self, positive, vae, width, height, length, batch_size, start_image=None):
        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, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)

            concat_latent_image = vae.encode(start_image)
            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})

        out_latent = {}
        out_latent["samples"] = latent
        return (positive, out_latent)


NODE_CLASS_MAPPINGS = {
    "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT,
    "TextEncodeHunyuanVideo_ImageToVideo": TextEncodeHunyuanVideo_ImageToVideo,
    "EmptyHunyuanLatentVideo": EmptyHunyuanLatentVideo,
    "HunyuanImageToVideo": HunyuanImageToVideo,
}