import torch
from comfy import model_management
from comfy.sd import load_model_weights, ModelPatcher, VAE, CLIP, model_lora_keys_unet, model_lora_keys_clip
from comfy import utils
from comfy import clip_vision
from comfy.ldm.util import instantiate_from_config
from .convert_from_ckpt import convert_unet_checkpoint
from omegaconf import OmegaConf


def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None):
    sd = utils.load_torch_file(ckpt_path)
    sd_keys = sd.keys()
    clip = None
    clipvision = None
    vae = None

    fp16 = model_management.should_use_fp16()

    class WeightsLoader(torch.nn.Module):
        pass

    w = WeightsLoader()
    load_state_dict_to = []
    if output_vae:
        vae = VAE()
        w.first_stage_model = vae.first_stage_model
        load_state_dict_to = [w]

    if output_clip:
        clip_config = {}
        if "cond_stage_model.model.transformer.resblocks.22.attn.out_proj.weight" in sd_keys:
            clip_config['target'] = 'ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
        else:
            clip_config['target'] = 'ldm.modules.encoders.modules.FrozenCLIPEmbedder'
        clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
        w.cond_stage_model = clip.cond_stage_model
        load_state_dict_to = [w]

    clipvision_key = "embedder.model.visual.transformer.resblocks.0.attn.in_proj_weight"
    noise_aug_config = None
    if clipvision_key in sd_keys:
        size = sd[clipvision_key].shape[1]

        if output_clipvision:
            clipvision = clip_vision.load_clipvision_from_sd(sd)

        noise_aug_key = "noise_augmentor.betas"
        if noise_aug_key in sd_keys:
            noise_aug_config = {}
            params = {}
            noise_schedule_config = {}
            noise_schedule_config["timesteps"] = sd[noise_aug_key].shape[0]
            noise_schedule_config["beta_schedule"] = "squaredcos_cap_v2"
            params["noise_schedule_config"] = noise_schedule_config
            noise_aug_config['target'] = "ldm.modules.encoders.noise_aug_modules.CLIPEmbeddingNoiseAugmentation"
            if size == 1280: #h
                params["timestep_dim"] = 1024
            elif size == 1024: #l
                params["timestep_dim"] = 768
            noise_aug_config['params'] = params

    sd_config = {
        "linear_start": 0.00085,
        "linear_end": 0.012,
        "num_timesteps_cond": 1,
        "log_every_t": 200,
        "timesteps": 1000,
        "first_stage_key": "jpg",
        "cond_stage_key": "txt",
        "image_size": 64,
        "channels": 4,
        "cond_stage_trainable": False,
        "monitor": "val/loss_simple_ema",
        "scale_factor": 0.18215,
        "use_ema": False,
    }

    unet_config = {
        "use_checkpoint": True,
        "image_size": 32,
        "out_channels": 4,
        "attention_resolutions": [
            4,
            2,
            1
        ],
        "num_res_blocks": 2,
        "channel_mult": [
            1,
            2,
            4,
            4
        ],
        "use_spatial_transformer": True,
        "transformer_depth": 1,
        "legacy": False
    }

    if len(sd['model.diffusion_model.input_blocks.1.1.proj_in.weight'].shape) == 2:
        unet_config['use_linear_in_transformer'] = True

    unet_config["use_fp16"] = fp16
    unet_config["model_channels"] = sd['model.diffusion_model.input_blocks.0.0.weight'].shape[0]
    unet_config["in_channels"] = sd['model.diffusion_model.input_blocks.0.0.weight'].shape[1]
    unet_config["context_dim"] = sd['model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight'].shape[1]

    sd_config["unet_config"] = {"target": "ldm.modules.diffusionmodules.openaimodel.UNetModel", "params": unet_config}
    model_config = {"target": "ldm.models.diffusion.ddpm.LatentDiffusion", "params": sd_config}

    if noise_aug_config is not None: #SD2.x unclip model
        sd_config["noise_aug_config"] = noise_aug_config
        sd_config["image_size"] = 96
        sd_config["embedding_dropout"] = 0.25
        sd_config["conditioning_key"] = 'crossattn-adm'
        model_config["target"] = "ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion"
    elif unet_config["in_channels"] > 4: #inpainting model
        sd_config["conditioning_key"] = "hybrid"
        sd_config["finetune_keys"] = None
        model_config["target"] = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
    else:
        sd_config["conditioning_key"] = "crossattn"

    if unet_config["context_dim"] == 1024:
        unet_config["num_head_channels"] = 64 #SD2.x
    else:
        unet_config["num_heads"] = 8 #SD1.x

    unclip = 'model.diffusion_model.label_emb.0.0.weight'
    if unclip in sd_keys:
        unet_config["num_classes"] = "sequential"
        unet_config["adm_in_channels"] = sd[unclip].shape[1]

    if unet_config["context_dim"] == 1024 and unet_config["in_channels"] == 4: #only SD2.x non inpainting models are v prediction
        k = "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.norm1.bias"
        out = sd[k]
        if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
            sd_config["parameterization"] = 'v'

    model = instantiate_from_config(model_config)
    model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)

    with torch.inference_mode(mode=False):
        model.model.diffusion_model = convert_unet_checkpoint(sd, OmegaConf.create({"model": model_config}))
        if model_management.xformers_enabled():
            model.model.diffusion_model.enable_xformers_memory_efficient_attention()

    #if fp16:
    #    model = model.half()

    return (ModelPatcher(model), clip, vae, clipvision)


def load_lora(path, to_load):
    lora = utils.load_torch_file(path)
    patch_dict = {}
    loaded_keys = set()
    for x in to_load:
        alpha_name = "{}.alpha".format(x)
        alpha = None
        if alpha_name in lora.keys():
            alpha = lora[alpha_name].item()
            loaded_keys.add(alpha_name)

        A_name = "{}.lora_up.weight".format(x)
        B_name = "{}.lora_down.weight".format(x)
        mid_name = "{}.lora_mid.weight".format(x)

        if A_name in lora.keys():
            mid = None
            if mid_name in lora.keys():
                mid = lora[mid_name]
                loaded_keys.add(mid_name)
            patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid)
            loaded_keys.add(A_name)
            loaded_keys.add(B_name)

        hada_w1_a_name = "{}.hada_w1_a".format(x)
        hada_w1_b_name = "{}.hada_w1_b".format(x)
        hada_w2_a_name = "{}.hada_w2_a".format(x)
        hada_w2_b_name = "{}.hada_w2_b".format(x)
        hada_t1_name = "{}.hada_t1".format(x)
        hada_t2_name = "{}.hada_t2".format(x)
        if hada_w1_a_name in lora.keys():
            hada_t1 = None
            hada_t2 = None
            if hada_t1_name in lora.keys():
                hada_t1 = lora[hada_t1_name]
                hada_t2 = lora[hada_t2_name]
                loaded_keys.add(hada_t1_name)
                loaded_keys.add(hada_t2_name)

            patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)
            loaded_keys.add(hada_w1_a_name)
            loaded_keys.add(hada_w1_b_name)
            loaded_keys.add(hada_w2_a_name)
            loaded_keys.add(hada_w2_b_name)

    return patch_dict


def use_lora(pretrained_LoRA_path, model, alpha):
    LORA_PREFIX_UNET = "lora_unet"
    LORA_PREFIX_TEXT_ENCODER = "lora_te"
    state_dict = utils.load_torch_file(pretrained_LoRA_path)

    visited = []

    # directly update weight in diffusers model
    for key in state_dict:

        # it is suggested to print out the key, it usually will be something like below
        # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

        # as we have set the alpha beforehand, so just skip
        if ".alpha" in key or key in visited:
            continue
        if "text" in key:
            continue
        else:
            layer_infos = key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_")
            curr_layer = model.model.model.diffusion_model

        # find the target layer
        temp_name = layer_infos.pop(0)
        while len(layer_infos) > -1:
            try:
                curr_layer = curr_layer.__getattr__(temp_name)
                if len(layer_infos) > 0:
                    temp_name = layer_infos.pop(0)
                elif len(layer_infos) == 0:
                    break
            except Exception:
                if len(temp_name) > 0:
                    temp_name += "_" + layer_infos.pop(0)
                else:
                    temp_name = layer_infos.pop(0)

        pair_keys = []
        if "lora_down" in key:
            pair_keys.append(key.replace("lora_down", "lora_up"))
            pair_keys.append(key)
        else:
            pair_keys.append(key)
            pair_keys.append(key.replace("lora_up", "lora_down"))

        # update weight
        if len(state_dict[pair_keys[0]].shape) == 4:
            weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32)
            weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32)
            curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
        else:
            weight_up = state_dict[pair_keys[0]].to(torch.float32)
            weight_down = state_dict[pair_keys[1]].to(torch.float32)
            curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down)

        # update visited list
        for item in pair_keys:
            visited.append(item)
    return model


def load_lora_for_models(model, clip, lora_path, strength_model, strength_clip):
    key_map = model_lora_keys_unet(model.model)
    key_map = model_lora_keys_clip(clip.cond_stage_model, key_map)
    loaded = load_lora(lora_path, key_map)
    new_modelpatcher = model.clone()
    new_modelpatcher = use_lora(lora_path, new_modelpatcher, strength_model)
    new_clip = clip.clone()
    new_clip.add_patches(loaded, strength_clip)

    return (new_modelpatcher, new_clip)