from dataclasses import dataclass
import torch
import torch.nn as nn

T = torch.Tensor


@dataclass(frozen=True)
class StyleAlignedArgs:
    share_group_norm: bool = True
    share_layer_norm: bool = True
    share_attention: bool = True
    adain_queries: bool = True
    adain_keys: bool = True
    adain_values: bool = False
    full_attention_share: bool = False
    shared_score_scale: float = 1.0
    shared_score_shift: float = 0.0
    only_self_level: float = 0.0


def expand_first(
    feat: T,
    scale=1.0,
) -> T:
    b = feat.shape[0]
    feat_style = torch.stack((feat[0], feat[b // 2])).unsqueeze(1)
    if scale == 1:
        feat_style = feat_style.expand(2, b // 2, *feat.shape[1:])
    else:
        feat_style = feat_style.repeat(1, b // 2, 1, 1, 1)
        feat_style = torch.cat([feat_style[:, :1], scale * feat_style[:, 1:]], dim=1)
    return feat_style.reshape(*feat.shape)


def concat_first(feat: T, dim=2, scale=1.0) -> T:
    feat_style = expand_first(feat, scale=scale)
    return torch.cat((feat, feat_style), dim=dim)


def calc_mean_std(feat, eps: float = 1e-5) -> tuple[T, T]:
    feat_std = (feat.var(dim=-2, keepdims=True) + eps).sqrt()
    feat_mean = feat.mean(dim=-2, keepdims=True)
    return feat_mean, feat_std


def adain(feat: T) -> T:
    feat_mean, feat_std = calc_mean_std(feat)
    feat_style_mean = expand_first(feat_mean)
    feat_style_std = expand_first(feat_std)
    feat = (feat - feat_mean) / feat_std
    feat = feat * feat_style_std + feat_style_mean
    return feat


def register_shared_norm(
    pipeline,
    share_group_norm: bool = True,
    share_layer_norm: bool = True,
):
    def register_norm_forward(
        norm_layer: nn.GroupNorm | nn.LayerNorm,
    ) -> nn.GroupNorm | nn.LayerNorm:
        if not hasattr(norm_layer, "orig_forward"):
            setattr(norm_layer, "orig_forward", norm_layer.forward)
        orig_forward = norm_layer.orig_forward

        def forward_(hidden_states: T) -> T:
            n = hidden_states.shape[-2]
            hidden_states = concat_first(hidden_states, dim=-2)
            hidden_states = orig_forward(hidden_states)
            return hidden_states[..., :n, :]

        norm_layer.forward = forward_  # type: ignore
        return norm_layer

    def get_norm_layers(
        pipeline_, norm_layers_: dict[str, list[nn.GroupNorm | nn.LayerNorm]]
    ):
        if isinstance(pipeline_, nn.LayerNorm) and share_layer_norm:
            norm_layers_["layer"].append(pipeline_)
        if isinstance(pipeline_, nn.GroupNorm) and share_group_norm:
            norm_layers_["group"].append(pipeline_)
        else:
            for layer in pipeline_.children():
                get_norm_layers(layer, norm_layers_)

    norm_layers = {"group": [], "layer": []}
    get_norm_layers(pipeline.unet, norm_layers)
    return [register_norm_forward(layer) for layer in norm_layers["group"]] + [
        register_norm_forward(layer) for layer in norm_layers["layer"]
    ]

def _get_switch_vec(total_num_layers, level):
    if level == 0:
        return torch.zeros(total_num_layers, dtype=torch.bool)
    if level == 1:
        return torch.ones(total_num_layers, dtype=torch.bool)
    to_flip = level > .5
    if to_flip:
        level = 1 - level
    num_switch = int(level * total_num_layers)
    vec = torch.arange(total_num_layers)
    vec = vec % (total_num_layers // num_switch)
    vec = vec == 0
    if to_flip:
        vec = ~vec
    return vec

def init_attention_processors(pipeline, style_aligned_args: StyleAlignedArgs | None = None):
    attn_procs = {}
    unet = pipeline.unet
    number_of_self, number_of_cross = 0, 0
    num_self_layers = len([name for name in unet.attn_processors.keys() if 'attn1' in name])
    if style_aligned_args is None:
        only_self_vec = _get_switch_vec(num_self_layers, 1)
    else:
        only_self_vec = _get_switch_vec(num_self_layers, style_aligned_args.only_self_level)
    for i, name in enumerate(unet.attn_processors.keys()):
        is_self_attention = 'attn1' in name
        if is_self_attention:
            number_of_self += 1
            if style_aligned_args is None or only_self_vec[i // 2]:
                attn_procs[name] = DefaultAttentionProcessor()
            else:
                attn_procs[name] = SharedAttentionProcessor(style_aligned_args)
        else:
            number_of_cross += 1
            attn_procs[name] = DefaultAttentionProcessor()

    unet.set_attn_processor(attn_procs)


class StyleAlignedPatch:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("MODEL",),
                "style_image": ("IMAGE",),
            }
        }

    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"
    CATEGORY = "custom_node_experiments"

    def __init__(self) -> None:
        self.args = StyleAlignedArgs()
        self.norm_layers = register_shared_norm(
            None, self.args.share_group_norm, self.args.share_layer_norm
        )

    def patch(self, model, style_image):
        m = model.clone()
        sd = model.model_state_dict()
        return (m,)


NODE_CLASS_MAPPINGS = {
    "StyleAlignedPatch": StyleAlignedPatch,
}