# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
import torch
import gguf
import types
import logging
import numpy as np

import comfy.sd
import comfy.utils
import comfy.model_management
import folder_paths

from .ops import GGMLTensor, GGMLOps

# TODO: This causes gguf files to show up in the main unet loader
folder_paths.folder_names_and_paths["unet"][1].add(".gguf")

def gguf_sd_loader(path):
    """
    Read state dict as fake tensors
    """
    reader = gguf.GGUFReader(path)
    sd = {}
    dt = {}
    for tensor in reader.tensors:
        sd[str(tensor.name)] = GGMLTensor(
            torch.from_numpy(tensor.data), # mmap
            tensor_type = tensor.tensor_type,
            tensor_shape = torch.Size(
                np.flip(list(tensor.shape))
            )
        )
        dt[str(tensor.tensor_type)] = dt.get(str(tensor.tensor_type), 0) + 1

    # sanity check debug print
    print("\nggml_sd_loader:")
    for k,v in dt.items():
        print(f" {k:30}{v:3}")
    print("\n")
    return sd

class UnetLoaderGGUF:
    @classmethod
    def INPUT_TYPES(s):
        unet_names = [x for x in folder_paths.get_filename_list("unet") if x.endswith(".gguf")]
        return {
            "required": {
                "unet_name": (unet_names,),
            }
        }

    RETURN_TYPES = ("MODEL",)
    FUNCTION = "load_unet"
    CATEGORY = "bootleg"
    TITLE = "Unet Loader (GGUF)"

    def load_unet(self, unet_name):
        unet_path = folder_paths.get_full_path("unet", unet_name)
        sd = gguf_sd_loader(unet_path)
        model = comfy.sd.load_diffusion_model_state_dict(
            sd, model_options={"custom_operations": GGMLOps}
        )
        if model is None:
            logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
            raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
        self.patch_calculate_weight(model)
        return (model,)

    def patch_calculate_weight(self, model):
        def calculate_weight(self, patches, weight, key):
            if isinstance(weight, GGMLTensor):
                qtype = weight.tensor_type
                # TODO: don't even store these in a custom format
                if qtype in [gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16]:
                    return self.calculate_weight_orig(patches, weight, key)
                else:
                    weight.patches.append((self.calculate_weight_orig, patches, key))
                    return weight
            else:
                return self.calculate_weight_orig(patches, weight, key)

        # make sure custom patch logic is used as to not mangle quants
        model.calculate_weight_orig = model.calculate_weight
        model.calculate_weight = types.MethodType(calculate_weight, model)

        # make sure this logic is preserved for cloned patchers
        def clone(self, *args, **kwargs):
            new = self.clone_orig(*args, **kwargs)
            new.calculate_weight_orig = new.calculate_weight
            new.calculate_weight = types.MethodType(calculate_weight, model)
            return new

        model.clone_orig = model.clone
        model.clone = types.MethodType(clone, model)

        return model

NODE_CLASS_MAPPINGS = {
    "UnetLoaderGGUF": UnetLoaderGGUF,
}