# 1st edit by https://github.com/comfyanonymous/ComfyUI
# 2nd edit by Forge Official


from enum import Enum

import ldm_patched.ldm.modules.attention
import ldm_patched.modules.conds
import ldm_patched.modules.model_management
import ldm_patched.modules.ops
import torch

from ldm_patched.ldm.modules.diffusionmodules.openaimodel import Timestep, UNetModel
from ldm_patched.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
from ldm_patched.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
from ldm_patched.modules.model_sampling import (
    EPS,
    V_PREDICTION,
    ModelSamplingContinuousEDM,
    ModelSamplingDiscrete,
)

from modules.shared import opts

from . import utils


class ModelType(Enum):
    EPS = 1
    V_PREDICTION = 2
    V_PREDICTION_EDM = 3


def model_sampling(model_config, model_type):
    s = ModelSamplingDiscrete

    if model_type == ModelType.EPS:
        c = EPS
    elif model_type == ModelType.V_PREDICTION:
        c = V_PREDICTION
    elif model_type == ModelType.V_PREDICTION_EDM:
        c = V_PREDICTION
        s = ModelSamplingContinuousEDM

    class ModelSampling(s, c):
        pass

    return ModelSampling(model_config)


class BaseModel(torch.nn.Module):
    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
        super().__init__()

        unet_config = model_config.unet_config
        self.latent_format = model_config.latent_format
        self.model_config = model_config
        self.manual_cast_dtype = model_config.manual_cast_dtype

        if not unet_config.get("disable_unet_model_creation", False):
            operations = None

            if getattr(opts, "cublas_fast", False):
                if ldm_patched.modules.model_management.prefer_fp8():
                    print("\n\nERROR: cublas_ops requires fp16\n\n")
                else:
                    try:
                        operations = ldm_patched.modules.ops.cublas_ops
                        print("using fast cublas ops")
                    except AttributeError:
                        print("\n\nERROR: failed to import cublas_ops\n\n")
            elif getattr(opts, "fp8_fast", False):
                if ldm_patched.modules.model_management.support_fp8():
                    operations = ldm_patched.modules.ops.fp8_ops
                    print("using fast fp8 ops")
                else:
                    print("\n\nERROR: fast fp8 ops is not supported\n\n")
            if operations is None:
                if self.manual_cast_dtype is not None:
                    operations = ldm_patched.modules.ops.manual_cast
                else:
                    operations = ldm_patched.modules.ops.disable_weight_init

            self.diffusion_model = UNetModel(**unet_config, device=device, operations=operations)

        self.model_type = model_type
        self.model_sampling = model_sampling(model_config, model_type)

        self.adm_channels = unet_config.get("adm_in_channels", None)
        if self.adm_channels is None:
            self.adm_channels = 0
        self.inpaint_model = False
        print("model_type", model_type.name)
        print("UNet ADM Dimension", self.adm_channels)

    def apply_model(
        self,
        x,
        t,
        c_concat=None,
        c_crossattn=None,
        control=None,
        transformer_options={},
        **kwargs,
    ):
        sigma = t
        xc = self.model_sampling.calculate_input(sigma, x)
        if c_concat is not None:
            xc = torch.cat([xc] + [c_concat], dim=1)

        context = c_crossattn
        dtype = self.get_dtype()

        if self.manual_cast_dtype is not None:
            dtype = self.manual_cast_dtype

        xc = xc.to(dtype)
        t = self.model_sampling.timestep(t).float()
        context = context.to(dtype)
        extra_conds = {}
        for o in kwargs:
            extra = kwargs[o]
            if hasattr(extra, "dtype"):
                if extra.dtype != torch.int and extra.dtype != torch.long:
                    extra = extra.to(dtype)
            extra_conds[o] = extra

        model_output = self.diffusion_model(
            xc,
            t,
            context=context,
            control=control,
            transformer_options=transformer_options,
            **extra_conds,
        ).float()
        return self.model_sampling.calculate_denoised(sigma, model_output, x)

    def get_dtype(self):
        return self.diffusion_model.dtype

    def is_adm(self):
        return self.adm_channels > 0

    def encode_adm(self, **kwargs):
        return None

    def extra_conds(self, **kwargs):
        out = {}
        if self.inpaint_model:
            concat_keys = ("mask", "masked_image")
            cond_concat = []
            denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
            concat_latent_image = kwargs.get("concat_latent_image", None)
            if concat_latent_image is None:
                concat_latent_image = kwargs.get("latent_image", None)
            else:
                concat_latent_image = self.process_latent_in(concat_latent_image)

            noise = kwargs.get("noise", None)
            device = kwargs["device"]

            if concat_latent_image.shape[1:] != noise.shape[1:]:
                concat_latent_image = utils.common_upscale(
                    concat_latent_image,
                    noise.shape[-1],
                    noise.shape[-2],
                    "bilinear",
                    "center",
                )

            concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0])

            if len(denoise_mask.shape) == len(noise.shape):
                denoise_mask = denoise_mask[:, :1]

            denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
            if denoise_mask.shape[-2:] != noise.shape[-2:]:
                denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
            denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])

            def blank_inpaint_image_like(latent_image):
                blank_image = torch.ones_like(latent_image)
                # these are the values for "zero" in pixel space translated to latent space
                blank_image[:, 0] *= 0.8223
                blank_image[:, 1] *= -0.6876
                blank_image[:, 2] *= 0.6364
                blank_image[:, 3] *= 0.1380
                return blank_image

            for ck in concat_keys:
                if denoise_mask is not None:
                    if ck == "mask":
                        cond_concat.append(denoise_mask.to(device))
                    elif ck == "masked_image":
                        cond_concat.append(concat_latent_image.to(device))  # NOTE: the latent_image should be masked by the mask in pixel space
                else:
                    if ck == "mask":
                        cond_concat.append(torch.ones_like(noise)[:, :1])
                    elif ck == "masked_image":
                        cond_concat.append(blank_inpaint_image_like(noise))
            data = torch.cat(cond_concat, dim=1)
            out["c_concat"] = ldm_patched.modules.conds.CONDNoiseShape(data)

        adm = self.encode_adm(**kwargs)
        if adm is not None:
            out["y"] = ldm_patched.modules.conds.CONDRegular(adm)

        cross_attn = kwargs.get("cross_attn", None)
        if cross_attn is not None:
            out["c_crossattn"] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn)

        return out

    def load_model_weights(self, sd, unet_prefix=""):
        to_load = {}
        keys = list(sd.keys())
        for k in keys:
            if k.startswith(unet_prefix):
                to_load[k[len(unet_prefix) :]] = sd.pop(k)

        to_load = self.model_config.process_unet_state_dict(to_load)
        m, u = self.diffusion_model.load_state_dict(to_load, strict=False)
        if len(m) > 0:
            print("unet missing:", m)

        if len(u) > 0:
            print("unet unexpected:", u)
        del to_load
        return self

    def process_latent_in(self, latent):
        return self.latent_format.process_in(latent)

    def process_latent_out(self, latent):
        return self.latent_format.process_out(latent)

    def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):
        extra_sds = []
        if clip_state_dict is not None:
            extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict))
        if vae_state_dict is not None:
            extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict))
        if clip_vision_state_dict is not None:
            extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict))

        unet_state_dict = self.diffusion_model.state_dict()
        unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)

        if self.get_dtype() == torch.float16:
            extra_sds = map(lambda sd: utils.convert_sd_to(sd, torch.float16), extra_sds)

        if self.model_type == ModelType.V_PREDICTION:
            unet_state_dict["v_pred"] = torch.tensor([])

        for sd in extra_sds:
            unet_state_dict.update(sd)

        return unet_state_dict

    def set_inpaint(self):
        self.inpaint_model = True

    def memory_required(self, input_shape):
        area = input_shape[0] * input_shape[2] * input_shape[3]
        dtype = self.manual_cast_dtype if self.manual_cast_dtype is not None else self.get_dtype()
        dtype_size = ldm_patched.modules.model_management.dtype_size(dtype)

        if (
            ldm_patched.modules.model_management.xformers_enabled()
            or ldm_patched.modules.model_management.sage_enabled()
            or ldm_patched.modules.model_management.flash_enabled()
        ):
            scaler = 1.28
        else:
            scaler = 1.65
            if ldm_patched.ldm.modules.attention._ATTN_PRECISION == "fp32":
                dtype_size = 4

        return scaler * area * dtype_size * 16384


def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
    adm_inputs = []
    weights = []
    noise_aug = []
    for unclip_cond in unclip_conditioning:
        for adm_cond in unclip_cond["clip_vision_output"].image_embeds:
            weight = unclip_cond["strength"]
            noise_augment = unclip_cond["noise_augmentation"]
            noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
            c_adm, noise_level_emb = noise_augmentor(
                adm_cond.to(device),
                noise_level=torch.tensor([noise_level], device=device),
                seed=seed,
            )
            adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
            weights.append(weight)
            noise_aug.append(noise_augment)
            adm_inputs.append(adm_out)

    if len(noise_aug) > 1:
        adm_out = torch.stack(adm_inputs).sum(0)
        noise_augment = noise_augment_merge
        noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
        c_adm, noise_level_emb = noise_augmentor(
            adm_out[:, : noise_augmentor.time_embed.dim],
            noise_level=torch.tensor([noise_level], device=device),
        )
        adm_out = torch.cat((c_adm, noise_level_emb), 1)

    return adm_out


def sdxl_pooled(args, noise_augmentor):
    if "unclip_conditioning" in args:
        return unclip_adm(
            args.get("unclip_conditioning", None),
            args["device"],
            noise_augmentor,
            seed=args.get("seed", 0) - 10,
        )[:, :1280]
    else:
        return args["pooled_output"]


class SDXLRefiner(BaseModel):
    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
        super().__init__(model_config, model_type, device=device)
        self.embedder = Timestep(256)
        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(
            **{
                "noise_schedule_config": {
                    "timesteps": 1000,
                    "beta_schedule": "squaredcos_cap_v2",
                },
                "timestep_dim": 1280,
            }
        )

    def encode_adm(self, **kwargs):
        clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
        width = kwargs.get("width", 768)
        height = kwargs.get("height", 768)
        crop_w = kwargs.get("crop_w", 0)
        crop_h = kwargs.get("crop_h", 0)

        if kwargs.get("prompt_type", "") == "negative":
            aesthetic_score = kwargs.get("aesthetic_score", 2.5)
        else:
            aesthetic_score = kwargs.get("aesthetic_score", 6)

        out = []
        out.append(self.embedder(torch.Tensor([height])))
        out.append(self.embedder(torch.Tensor([width])))
        out.append(self.embedder(torch.Tensor([crop_h])))
        out.append(self.embedder(torch.Tensor([crop_w])))
        out.append(self.embedder(torch.Tensor([aesthetic_score])))
        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
        return torch.cat((clip_pooled.to(flat.device), flat), dim=1)


class SDXL(BaseModel):
    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
        super().__init__(model_config, model_type, device=device)
        self.embedder = Timestep(256)
        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(
            **{
                "noise_schedule_config": {
                    "timesteps": 1000,
                    "beta_schedule": "squaredcos_cap_v2",
                },
                "timestep_dim": 1280,
            }
        )

    def encode_adm(self, **kwargs):
        clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
        width = kwargs.get("width", 768)
        height = kwargs.get("height", 768)
        crop_w = kwargs.get("crop_w", 0)
        crop_h = kwargs.get("crop_h", 0)
        target_width = kwargs.get("target_width", width)
        target_height = kwargs.get("target_height", height)

        out = []
        out.append(self.embedder(torch.Tensor([height])))
        out.append(self.embedder(torch.Tensor([width])))
        out.append(self.embedder(torch.Tensor([crop_h])))
        out.append(self.embedder(torch.Tensor([crop_w])))
        out.append(self.embedder(torch.Tensor([target_height])))
        out.append(self.embedder(torch.Tensor([target_width])))
        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
        return torch.cat((clip_pooled.to(flat.device), flat), dim=1)


class SD_X4Upscaler(BaseModel):
    def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):
        super().__init__(model_config, model_type, device=device)
        self.noise_augmentor = ImageConcatWithNoiseAugmentation(
            noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02},
            max_noise_level=350,
        )

    def extra_conds(self, **kwargs):
        out = {}

        image = kwargs.get("concat_image", None)
        noise = kwargs.get("noise", None)
        noise_augment = kwargs.get("noise_augmentation", 0.0)
        device = kwargs["device"]
        seed = kwargs["seed"] - 10

        noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment)

        if image is None:
            image = torch.zeros_like(noise)[:, :3]

        if image.shape[1:] != noise.shape[1:]:
            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")

        noise_level = torch.tensor([noise_level], device=device)
        if noise_augment > 0:
            image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed)

        image = utils.resize_to_batch_size(image, noise.shape[0])

        out["c_concat"] = ldm_patched.modules.conds.CONDNoiseShape(image)
        out["y"] = ldm_patched.modules.conds.CONDRegular(noise_level)
        return out