import math
from typing import Dict, Optional

import torch
import torchvision.transforms.functional as FF
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection

from diffusers import StableDiffusionPipeline
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers


try:
    from compel import Compel
except ImportError:
    Compel = None

KBASE = "ADDBASE"
KCOMM = "ADDCOMM"
KBRK = "BREAK"


class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
    r"""
    Args for Regional Prompting Pipeline:
        rp_args:dict
        Required
            rp_args["mode"]: cols, rows, prompt, prompt-ex
        for cols, rows mode
            rp_args["div"]: ex) 1;1;1(Divide into 3 regions)
        for prompt, prompt-ex mode
            rp_args["th"]: ex) 0.5,0.5,0.6 (threshold for prompt mode)

        Optional
            rp_args["save_mask"]: True/False (save masks in prompt mode)
            rp_args["power"]: int (power for attention maps in prompt mode)
            rp_args["base_ratio"]:
                float (Sets the ratio of the base prompt)
                ex) 0.2 (20%*BASE_PROMPT + 80%*REGION_PROMPT)
                [Use base prompt](https://github.com/hako-mikan/sd-webui-regional-prompter?tab=readme-ov-file#use-base-prompt)

    Pipeline for text-to-image generation using Stable Diffusion.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPImageProcessor,
        image_encoder: CLIPVisionModelWithProjection = None,
        requires_safety_checker: bool = True,
    ):
        super().__init__(
            vae,
            text_encoder,
            tokenizer,
            unet,
            scheduler,
            safety_checker,
            feature_extractor,
            image_encoder,
            requires_safety_checker,
        )
        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
        )

    @torch.no_grad()
    def __call__(
        self,
        prompt: str,
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: str = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        rp_args: Dict[str, str] = None,
    ):
        active = KBRK in prompt[0] if isinstance(prompt, list) else KBRK in prompt
        use_base = KBASE in prompt[0] if isinstance(prompt, list) else KBASE in prompt
        if negative_prompt is None:
            negative_prompt = "" if isinstance(prompt, str) else [""] * len(prompt)

        device = self._execution_device
        regions = 0

        self.base_ratio = float(rp_args["base_ratio"]) if "base_ratio" in rp_args else 0.0
        self.power = int(rp_args["power"]) if "power" in rp_args else 1

        prompts = prompt if isinstance(prompt, list) else [prompt]
        n_prompts = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt]
        self.batch = batch = num_images_per_prompt * len(prompts)

        if use_base:
            bases = prompts.copy()
            n_bases = n_prompts.copy()

            for i, prompt in enumerate(prompts):
                parts = prompt.split(KBASE)
                if len(parts) == 2:
                    bases[i], prompts[i] = parts
                elif len(parts) > 2:
                    raise ValueError(f"Multiple instances of {KBASE} found in prompt: {prompt}")
            for i, prompt in enumerate(n_prompts):
                n_parts = prompt.split(KBASE)
                if len(n_parts) == 2:
                    n_bases[i], n_prompts[i] = n_parts
                elif len(n_parts) > 2:
                    raise ValueError(f"Multiple instances of {KBASE} found in negative prompt: {prompt}")

            all_bases_cn, _ = promptsmaker(bases, num_images_per_prompt)
            all_n_bases_cn, _ = promptsmaker(n_bases, num_images_per_prompt)

        all_prompts_cn, all_prompts_p = promptsmaker(prompts, num_images_per_prompt)
        all_n_prompts_cn, _ = promptsmaker(n_prompts, num_images_per_prompt)

        equal = len(all_prompts_cn) == len(all_n_prompts_cn)

        if Compel:
            compel = Compel(tokenizer=self.tokenizer, text_encoder=self.text_encoder)

            def getcompelembs(prps):
                embl = []
                for prp in prps:
                    embl.append(compel.build_conditioning_tensor(prp))
                return torch.cat(embl)

            conds = getcompelembs(all_prompts_cn)
            unconds = getcompelembs(all_n_prompts_cn)
            base_embs = getcompelembs(all_bases_cn) if use_base else None
            base_n_embs = getcompelembs(all_n_bases_cn) if use_base else None
            # When using base, it seems more reasonable to use base prompts as prompt_embeddings rather than regional prompts
            embs = getcompelembs(prompts) if not use_base else base_embs
            n_embs = getcompelembs(n_prompts) if not use_base else base_n_embs

            if use_base and self.base_ratio > 0:
                conds = self.base_ratio * base_embs + (1 - self.base_ratio) * conds
                unconds = self.base_ratio * base_n_embs + (1 - self.base_ratio) * unconds

            prompt = negative_prompt = None
        else:
            conds = self.encode_prompt(prompts, device, 1, True)[0]
            unconds = (
                self.encode_prompt(n_prompts, device, 1, True)[0]
                if equal
                else self.encode_prompt(all_n_prompts_cn, device, 1, True)[0]
            )

            if use_base and self.base_ratio > 0:
                base_embs = self.encode_prompt(bases, device, 1, True)[0]
                base_n_embs = (
                    self.encode_prompt(n_bases, device, 1, True)[0]
                    if equal
                    else self.encode_prompt(all_n_bases_cn, device, 1, True)[0]
                )

                conds = self.base_ratio * base_embs + (1 - self.base_ratio) * conds
                unconds = self.base_ratio * base_n_embs + (1 - self.base_ratio) * unconds

            embs = n_embs = None

        if not active:
            pcallback = None
            mode = None
        else:
            if any(x in rp_args["mode"].upper() for x in ["COL", "ROW"]):
                mode = "COL" if "COL" in rp_args["mode"].upper() else "ROW"
                ocells, icells, regions = make_cells(rp_args["div"])

            elif "PRO" in rp_args["mode"].upper():
                regions = len(all_prompts_p[0])
                mode = "PROMPT"
                reset_attnmaps(self)
                self.ex = "EX" in rp_args["mode"].upper()
                self.target_tokens = target_tokens = tokendealer(self, all_prompts_p)
                thresholds = [float(x) for x in rp_args["th"].split(",")]

            orig_hw = (height, width)
            revers = True

            def pcallback(s_self, step: int, timestep: int, latents: torch.Tensor, selfs=None):
                if "PRO" in mode:  # in Prompt mode, make masks from sum of attention maps
                    self.step = step

                    if len(self.attnmaps_sizes) > 3:
                        self.history[step] = self.attnmaps.copy()
                        for hw in self.attnmaps_sizes:
                            allmasks = []
                            basemasks = [None] * batch
                            for tt, th in zip(target_tokens, thresholds):
                                for b in range(batch):
                                    key = f"{tt}-{b}"
                                    _, mask, _ = makepmask(self, self.attnmaps[key], hw[0], hw[1], th, step)
                                    mask = mask.unsqueeze(0).unsqueeze(-1)
                                    if self.ex:
                                        allmasks[b::batch] = [x - mask for x in allmasks[b::batch]]
                                        allmasks[b::batch] = [torch.where(x > 0, 1, 0) for x in allmasks[b::batch]]
                                    allmasks.append(mask)
                                    basemasks[b] = mask if basemasks[b] is None else basemasks[b] + mask
                            basemasks = [1 - mask for mask in basemasks]
                            basemasks = [torch.where(x > 0, 1, 0) for x in basemasks]
                            allmasks = basemasks + allmasks

                            self.attnmasks[hw] = torch.cat(allmasks)
                        self.maskready = True
                return latents

            def hook_forward(module):
                # diffusers==0.23.2
                def forward(
                    hidden_states: torch.Tensor,
                    encoder_hidden_states: Optional[torch.Tensor] = None,
                    attention_mask: Optional[torch.Tensor] = None,
                    temb: Optional[torch.Tensor] = None,
                    scale: float = 1.0,
                ) -> torch.Tensor:
                    attn = module
                    xshape = hidden_states.shape
                    self.hw = (h, w) = split_dims(xshape[1], *orig_hw)

                    if revers:
                        nx, px = hidden_states.chunk(2)
                    else:
                        px, nx = hidden_states.chunk(2)

                    if equal:
                        hidden_states = torch.cat(
                            [px for i in range(regions)] + [nx for i in range(regions)],
                            0,
                        )
                        encoder_hidden_states = torch.cat([conds] + [unconds])
                    else:
                        hidden_states = torch.cat([px for i in range(regions)] + [nx], 0)
                        encoder_hidden_states = torch.cat([conds] + [unconds])

                    residual = hidden_states

                    if attn.spatial_norm is not None:
                        hidden_states = attn.spatial_norm(hidden_states, temb)

                    input_ndim = hidden_states.ndim

                    if input_ndim == 4:
                        batch_size, channel, height, width = hidden_states.shape
                        hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

                    batch_size, sequence_length, _ = (
                        hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
                    )

                    if attention_mask is not None:
                        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
                        attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

                    if attn.group_norm is not None:
                        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

                    query = attn.to_q(hidden_states)

                    if encoder_hidden_states is None:
                        encoder_hidden_states = hidden_states
                    elif attn.norm_cross:
                        encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

                    key = attn.to_k(encoder_hidden_states)
                    value = attn.to_v(encoder_hidden_states)

                    inner_dim = key.shape[-1]
                    head_dim = inner_dim // attn.heads

                    query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

                    key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
                    value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

                    # the output of sdp = (batch, num_heads, seq_len, head_dim)
                    # TODO: add support for attn.scale when we move to Torch 2.1
                    hidden_states = scaled_dot_product_attention(
                        self,
                        query,
                        key,
                        value,
                        attn_mask=attention_mask,
                        dropout_p=0.0,
                        is_causal=False,
                        getattn="PRO" in mode,
                    )

                    hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
                    hidden_states = hidden_states.to(query.dtype)

                    # linear proj
                    hidden_states = attn.to_out[0](hidden_states)
                    # dropout
                    hidden_states = attn.to_out[1](hidden_states)

                    if input_ndim == 4:
                        hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

                    if attn.residual_connection:
                        hidden_states = hidden_states + residual

                    hidden_states = hidden_states / attn.rescale_output_factor

                    #### Regional Prompting Col/Row mode
                    if any(x in mode for x in ["COL", "ROW"]):
                        reshaped = hidden_states.reshape(hidden_states.size()[0], h, w, hidden_states.size()[2])
                        center = reshaped.shape[0] // 2
                        px = reshaped[0:center] if equal else reshaped[0:-batch]
                        nx = reshaped[center:] if equal else reshaped[-batch:]
                        outs = [px, nx] if equal else [px]
                        for out in outs:
                            c = 0
                            for i, ocell in enumerate(ocells):
                                for icell in icells[i]:
                                    if "ROW" in mode:
                                        out[
                                            0:batch,
                                            int(h * ocell[0]) : int(h * ocell[1]),
                                            int(w * icell[0]) : int(w * icell[1]),
                                            :,
                                        ] = out[
                                            c * batch : (c + 1) * batch,
                                            int(h * ocell[0]) : int(h * ocell[1]),
                                            int(w * icell[0]) : int(w * icell[1]),
                                            :,
                                        ]
                                    else:
                                        out[
                                            0:batch,
                                            int(h * icell[0]) : int(h * icell[1]),
                                            int(w * ocell[0]) : int(w * ocell[1]),
                                            :,
                                        ] = out[
                                            c * batch : (c + 1) * batch,
                                            int(h * icell[0]) : int(h * icell[1]),
                                            int(w * ocell[0]) : int(w * ocell[1]),
                                            :,
                                        ]
                                    c += 1
                        px, nx = (px[0:batch], nx[0:batch]) if equal else (px[0:batch], nx)
                        hidden_states = torch.cat([nx, px], 0) if revers else torch.cat([px, nx], 0)
                        hidden_states = hidden_states.reshape(xshape)

                    #### Regional Prompting Prompt mode
                    elif "PRO" in mode:
                        px, nx = (
                            torch.chunk(hidden_states) if equal else hidden_states[0:-batch],
                            hidden_states[-batch:],
                        )

                        if (h, w) in self.attnmasks and self.maskready:

                            def mask(input):
                                out = torch.multiply(input, self.attnmasks[(h, w)])
                                for b in range(batch):
                                    for r in range(1, regions):
                                        out[b] = out[b] + out[r * batch + b]
                                return out

                            px, nx = (mask(px), mask(nx)) if equal else (mask(px), nx)
                        px, nx = (px[0:batch], nx[0:batch]) if equal else (px[0:batch], nx)
                        hidden_states = torch.cat([nx, px], 0) if revers else torch.cat([px, nx], 0)
                    return hidden_states

                return forward

            def hook_forwards(root_module: torch.nn.Module):
                for name, module in root_module.named_modules():
                    if "attn2" in name and module.__class__.__name__ == "Attention":
                        module.forward = hook_forward(module)

            hook_forwards(self.unet)

        output = StableDiffusionPipeline(**self.components)(
            prompt=prompt,
            prompt_embeds=embs,
            negative_prompt=negative_prompt,
            negative_prompt_embeds=n_embs,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback_on_step_end=pcallback,
        )

        if "save_mask" in rp_args:
            save_mask = rp_args["save_mask"]
        else:
            save_mask = False

        if mode == "PROMPT" and save_mask:
            saveattnmaps(
                self,
                output,
                height,
                width,
                thresholds,
                num_inference_steps // 2,
                regions,
            )

        return output


### Make prompt list for each regions
def promptsmaker(prompts, batch):
    out_p = []
    plen = len(prompts)
    for prompt in prompts:
        add = ""
        if KCOMM in prompt:
            add, prompt = prompt.split(KCOMM)
            add = add.strip() + " "
        prompts = [p.strip() for p in prompt.split(KBRK)]
        out_p.append([add + p for i, p in enumerate(prompts)])
    out = [None] * batch * len(out_p[0]) * len(out_p)
    for p, prs in enumerate(out_p):  # inputs prompts
        for r, pr in enumerate(prs):  # prompts for regions
            start = (p + r * plen) * batch
            out[start : start + batch] = [pr] * batch  # P1R1B1,P1R1B2...,P1R2B1,P1R2B2...,P2R1B1...
    return out, out_p


### make regions from ratios
### ";" makes outercells, "," makes inner cells
def make_cells(ratios):
    if ";" not in ratios and "," in ratios:
        ratios = ratios.replace(",", ";")
    ratios = ratios.split(";")
    ratios = [inratios.split(",") for inratios in ratios]

    icells = []
    ocells = []

    def startend(cells, array):
        current_start = 0
        array = [float(x) for x in array]
        for value in array:
            end = current_start + (value / sum(array))
            cells.append([current_start, end])
            current_start = end

    startend(ocells, [r[0] for r in ratios])

    for inratios in ratios:
        if 2 > len(inratios):
            icells.append([[0, 1]])
        else:
            add = []
            startend(add, inratios[1:])
            icells.append(add)
    return ocells, icells, sum(len(cell) for cell in icells)


def make_emblist(self, prompts):
    with torch.no_grad():
        tokens = self.tokenizer(
            prompts,
            max_length=self.tokenizer.model_max_length,
            padding=True,
            truncation=True,
            return_tensors="pt",
        ).input_ids.to(self.device)
        embs = self.text_encoder(tokens, output_hidden_states=True).last_hidden_state.to(self.device, dtype=self.dtype)
    return embs


def split_dims(xs, height, width):
    def repeat_div(x, y):
        while y > 0:
            x = math.ceil(x / 2)
            y = y - 1
        return x

    scale = math.ceil(math.log2(math.sqrt(height * width / xs)))
    dsh = repeat_div(height, scale)
    dsw = repeat_div(width, scale)
    return dsh, dsw


##### for prompt mode
def get_attn_maps(self, attn):
    height, width = self.hw
    target_tokens = self.target_tokens
    if (height, width) not in self.attnmaps_sizes:
        self.attnmaps_sizes.append((height, width))

    for b in range(self.batch):
        for t in target_tokens:
            power = self.power
            add = attn[b, :, :, t[0] : t[0] + len(t)] ** (power) * (self.attnmaps_sizes.index((height, width)) + 1)
            add = torch.sum(add, dim=2)
            key = f"{t}-{b}"
            if key not in self.attnmaps:
                self.attnmaps[key] = add
            else:
                if self.attnmaps[key].shape[1] != add.shape[1]:
                    add = add.view(8, height, width)
                    add = FF.resize(add, self.attnmaps_sizes[0], antialias=None)
                    add = add.reshape_as(self.attnmaps[key])

                self.attnmaps[key] = self.attnmaps[key] + add


def reset_attnmaps(self):  # init parameters in every batch
    self.step = 0
    self.attnmaps = {}  # made from attention maps
    self.attnmaps_sizes = []  # height,width set of u-net blocks
    self.attnmasks = {}  # made from attnmaps for regions
    self.maskready = False
    self.history = {}


def saveattnmaps(self, output, h, w, th, step, regions):
    masks = []
    for i, mask in enumerate(self.history[step].values()):
        img, _, mask = makepmask(self, mask, h, w, th[i % len(th)], step)
        if self.ex:
            masks = [x - mask for x in masks]
            masks.append(mask)
            if len(masks) == regions - 1:
                output.images.extend([FF.to_pil_image(mask) for mask in masks])
                masks = []
        else:
            output.images.append(img)


def makepmask(
    self, mask, h, w, th, step
):  # make masks from attention cache return [for preview, for attention, for Latent]
    th = th - step * 0.005
    if 0.05 >= th:
        th = 0.05
    mask = torch.mean(mask, dim=0)
    mask = mask / mask.max().item()
    mask = torch.where(mask > th, 1, 0)
    mask = mask.float()
    mask = mask.view(1, *self.attnmaps_sizes[0])
    img = FF.to_pil_image(mask)
    img = img.resize((w, h))
    mask = FF.resize(mask, (h, w), interpolation=FF.InterpolationMode.NEAREST, antialias=None)
    lmask = mask
    mask = mask.reshape(h * w)
    mask = torch.where(mask > 0.1, 1, 0)
    return img, mask, lmask


def tokendealer(self, all_prompts):
    for prompts in all_prompts:
        targets = [p.split(",")[-1] for p in prompts[1:]]
        tt = []

        for target in targets:
            ptokens = (
                self.tokenizer(
                    prompts,
                    max_length=self.tokenizer.model_max_length,
                    padding=True,
                    truncation=True,
                    return_tensors="pt",
                ).input_ids
            )[0]
            ttokens = (
                self.tokenizer(
                    target,
                    max_length=self.tokenizer.model_max_length,
                    padding=True,
                    truncation=True,
                    return_tensors="pt",
                ).input_ids
            )[0]

            tlist = []

            for t in range(ttokens.shape[0] - 2):
                for p in range(ptokens.shape[0]):
                    if ttokens[t + 1] == ptokens[p]:
                        tlist.append(p)
            if tlist != []:
                tt.append(tlist)

    return tt


def scaled_dot_product_attention(
    self,
    query,
    key,
    value,
    attn_mask=None,
    dropout_p=0.0,
    is_causal=False,
    scale=None,
    getattn=False,
) -> torch.Tensor:
    # Efficient implementation equivalent to the following:
    L, S = query.size(-2), key.size(-2)
    scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
    attn_bias = torch.zeros(L, S, dtype=query.dtype, device=self.device)
    if is_causal:
        assert attn_mask is None
        temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
        attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
        attn_bias.to(query.dtype)

    if attn_mask is not None:
        if attn_mask.dtype == torch.bool:
            attn_mask.masked_fill_(attn_mask.logical_not(), float("-inf"))
        else:
            attn_bias += attn_mask
    attn_weight = query @ key.transpose(-2, -1) * scale_factor
    attn_weight += attn_bias
    attn_weight = torch.softmax(attn_weight, dim=-1)
    if getattn:
        get_attn_maps(self, attn_weight)
    attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
    return attn_weight @ value
