import comfy.supported_models_base
import comfy.latent_formats
import comfy.model_detection
import comfy.model_patcher
import comfy.model_base
import comfy.utils
import comfy.conds
import logging
import torch

from comfy import model_management
from .diffusers_convert import convert_state_dict
from .config import model_config_from_unet

class PixArtModel(comfy.model_base.BaseModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
    
    def extra_conds(self, **kwargs):
        out = super().extra_conds(**kwargs)

        for name in ["width", "height", "aspect_ratio", "img_hw"]: # TODO: remove last one
            out[name] = comfy.conds.CONDRegular(torch.tensor(name))
        return out

def load_pixart_state_dict(sd, model_options={}):
    # prefix / format
    sd = sd.get("model", sd) # ref ckpt
    diffusion_model_prefix = comfy.model_detection.unet_prefix_from_state_dict(sd)
    temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
    if len(temp_sd) > 0:
        sd = temp_sd

    # diffusers convert
    if "adaln_single.linear.weight" in sd:
        sd = convert_state_dict(sd)

    # model config
    model_config = model_config_from_unet(sd)

    # TODO: move lines below to utils
    parameters = comfy.utils.calculate_parameters(sd)
    load_device = model_management.get_torch_device()
    offload_device = comfy.model_management.unet_offload_device()

    dtype = model_options.get("dtype", None)
    weight_dtype = comfy.utils.weight_dtype(sd)
    unet_weight_dtype = list(model_config.supported_inference_dtypes)

    if weight_dtype is not None and model_config.scaled_fp8 is None:
        unet_weight_dtype.append(weight_dtype)

    if dtype is None:
        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
    else:
        unet_dtype = dtype

    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
    model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
    if model_options.get("fp8_optimizations", False):
        model_config.optimizations["fp8"] = True

    model = model_config.get_model(sd, "")
    model = model.to(offload_device).eval()
    model.load_model_weights(sd, "")
    left_over = sd.keys()
    if len(left_over) > 0:
        logging.info("left over keys in unet: {}".format(left_over))
    return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)