import pathlib
import torch
from torch.utils.data import DataLoader
import pathlib
from vfi_utils import load_file_from_direct_url, preprocess_frames, postprocess_frames, generic_frame_loop, InterpolationStateList
import typing
from comfy.model_management import get_torch_device
from .amt_arch import AMT_S, AMT_L, AMT_G, InputPadder

#https://github.com/MCG-NKU/AMT/tree/main/cfgs
CKPT_CONFIGS = {
    "amt-s.pth": {
        "network": AMT_S,
        "params": { "corr_radius": 3, "corr_lvls": 4, "num_flows": 3 }
    },
    "amt-l.pth": {
        "network": AMT_L,
        "params": { "corr_radius": 3, "corr_lvls": 4, "num_flows": 5 }
    },
    "amt-g.pth": {
        "network": AMT_G,
        "params": { "corr_radius": 3, "corr_lvls": 4, "num_flows": 5 }
    },
    "gopro_amt-s.pth": {
        "network": AMT_S,
        "params": { "corr_radius": 3, "corr_lvls": 4, "num_flows": 3 }
    }
}


MODEL_TYPE = pathlib.Path(__file__).parent.name

class AMT_VFI:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "ckpt_name": (list(CKPT_CONFIGS.keys()), ),
                "frames": ("IMAGE", ),
                "clear_cache_after_n_frames": ("INT", {"default": 1, "min": 1, "max": 100}),
                "multiplier": ("INT", {"default": 2, "min": 2, "max": 1000})
            },
            "optional": {
                "optional_interpolation_states": ("INTERPOLATION_STATES", )
            }
        }
    
    RETURN_TYPES = ("IMAGE", )
    FUNCTION = "vfi"
    CATEGORY = "ComfyUI-Frame-Interpolation/VFI"

    def vfi(
        self,
        ckpt_name: typing.AnyStr, 
        frames: torch.Tensor, 
        clear_cache_after_n_frames: typing.SupportsInt = 1,
        multiplier: typing.SupportsInt = 2,
        optional_interpolation_states: InterpolationStateList = None,
        **kwargs
    ):
        model_path = load_file_from_direct_url(MODEL_TYPE, f"https://huggingface.co/lalala125/AMT/resolve/main/{ckpt_name}")
        ckpt_config = CKPT_CONFIGS[ckpt_name]

        interpolation_model = ckpt_config["network"](**ckpt_config["params"])
        interpolation_model.load_state_dict(torch.load(model_path)["state_dict"])
        interpolation_model.eval().to(get_torch_device())

        frames = preprocess_frames(frames)
        padder = InputPadder(frames.shape, 16)
        frames = padder.pad(frames)
        
        def return_middle_frame(frame_0, frame_1, timestep, model):
            return model(
                frame_0, 
                frame_1,
                embt=torch.FloatTensor([timestep] * frame_0.shape[0]).view(frame_0.shape[0], 1, 1, 1).to(get_torch_device()),
                scale_factor=1.0,
                eval=True
            )["imgt_pred"]
        
        args = [interpolation_model]
        out = generic_frame_loop(type(self).__name__, frames, clear_cache_after_n_frames, multiplier, return_middle_frame, *args, 
                               interpolation_states=optional_interpolation_states, dtype=torch.float32)
        out = padder.unpad(out)
        out = postprocess_frames(out)
        return (out,)