"""SAMPLING ONLY."""

import torch
import numpy as np
from tqdm import tqdm

from ldm.modules.diffusionmodules.util import (
    make_ddim_sampling_parameters,
    make_ddim_timesteps,
    noise_like,
    extract_into_tensor,
)

from utils.mpi.mpi import render_scene_from_mpi_torch
import math
import scipy.ndimage
from src.featglac.losses import compute_foreground_loss


class DDIMSampler(object):
    def __init__(self, model, schedule="linear", **kwargs):
        super().__init__()
        self.model = model
        self.ddpm_num_timesteps = model.num_timesteps
        self.schedule = schedule

    def register_buffer(self, name, attr):
        if type(attr) == torch.Tensor:
            if attr.device != torch.device("cuda"):
                attr = attr.to(torch.device("cuda"))
        setattr(self, name, attr)

    def make_schedule(
        self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True
    ):
        self.ddim_timesteps = make_ddim_timesteps(
            ddim_discr_method=ddim_discretize,
            num_ddim_timesteps=ddim_num_steps,
            num_ddpm_timesteps=self.ddpm_num_timesteps,
            verbose=verbose,
        )
        alphas_cumprod = self.model.alphas_cumprod
        assert (
            alphas_cumprod.shape[0] == self.ddpm_num_timesteps
        ), "alphas have to be defined for each timestep"
        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)

        self.register_buffer("betas", to_torch(self.model.betas))
        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
        self.register_buffer(
            "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
        )

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer(
            "sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_one_minus_alphas_cumprod",
            to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
        )
        self.register_buffer(
            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_recipm1_alphas_cumprod",
            to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
        )

        # ddim sampling parameters
        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
            alphacums=alphas_cumprod.cpu(),
            ddim_timesteps=self.ddim_timesteps,
            eta=ddim_eta,
            verbose=verbose,
        )
        self.register_buffer("ddim_sigmas", ddim_sigmas)
        self.register_buffer("ddim_alphas", ddim_alphas)
        self.register_buffer("ddim_alphas_prev", ddim_alphas_prev)
        self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas))
        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
            (1 - self.alphas_cumprod_prev)
            / (1 - self.alphas_cumprod)
            * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
        )
        self.register_buffer(
            "ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps
        )

    # @torch.no_grad()
    def sample(
        self,
        S,
        batch_size,
        shape,
        conditioning=None,
        callback=None,
        normals_sequence=None,
        img_callback=None,
        quantize_x0=False,
        eta=0.0,
        mask=None,
        x0=None,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        verbose=True,
        x_T=None,
        log_every_t=100,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,  # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
        dynamic_threshold=None,
        ucg_schedule=None,
        mpi_data=None,
        **kwargs,
    ):
        if conditioning is not None:
            if isinstance(conditioning, dict):
                ctmp = conditioning[list(conditioning.keys())[0]]
                while isinstance(ctmp, list):
                    ctmp = ctmp[0]
                cbs = ctmp.shape[0]
                if cbs != batch_size:
                    print(
                        f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
                    )

            elif isinstance(conditioning, list):
                for ctmp in conditioning:
                    if ctmp.shape[0] != batch_size:
                        print(
                            f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
                        )

            else:
                if conditioning.shape[0] != batch_size:
                    print(
                        f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}"
                    )

        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
        # sampling
        C, H, W = shape
        size = (batch_size, C, H, W)
        print(f"Data shape for DDIM sampling is {size}, eta {eta}")

        samples, intermediates = self.ddim_sampling(
            conditioning,
            size,
            callback=callback,
            img_callback=img_callback,
            quantize_denoised=quantize_x0,
            mask=mask,
            x0=x0,
            ddim_use_original_steps=False,
            noise_dropout=noise_dropout,
            temperature=temperature,
            score_corrector=score_corrector,
            corrector_kwargs=corrector_kwargs,
            x_T=x_T,
            log_every_t=log_every_t,
            unconditional_guidance_scale=unconditional_guidance_scale,
            unconditional_conditioning=unconditional_conditioning,
            dynamic_threshold=dynamic_threshold,
            ucg_schedule=ucg_schedule,
            mpi_data=mpi_data,
        )
        return samples, intermediates

    # @torch.no_grad()
    def ddim_sampling(
        self,
        cond,
        shape,
        x_T=None,
        ddim_use_original_steps=False,
        callback=None,
        timesteps=None,
        quantize_denoised=False,
        mask=None,
        x0=None,
        img_callback=None,
        log_every_t=100,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        dynamic_threshold=None,
        ucg_schedule=None,
        mpi_data=None,
    ):
        device = self.model.betas.device
        b = shape[0]
        # x_T 1,4,64,64
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T

        if timesteps is None:
            timesteps = (
                self.ddpm_num_timesteps
                if ddim_use_original_steps
                else self.ddim_timesteps
            )
        elif timesteps is not None and not ddim_use_original_steps:
            subset_end = (
                int(
                    min(timesteps / self.ddim_timesteps.shape[0], 1)
                    * self.ddim_timesteps.shape[0]
                )
                - 1
            )
            timesteps = self.ddim_timesteps[:subset_end]

        intermediates = {"x_inter": [img], "pred_x0": [img]}
        time_range = (
            reversed(range(0, timesteps))
            if ddim_use_original_steps
            else np.flip(timesteps)
        )
        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
        print(f"Running DDIM Sampling with {total_steps} timesteps")

        ddim_latents = mpi_data["ddim_latents"]
        mpi_foreground_alpha = mpi_data["mpi_masks"][1]
        mpi_masks = mpi_data["mpi_masks"]
        # mpi_timesteps = mpi_data["mpi_timesteps"]
        do_mpi = mpi_data["do_mpi"]
        # amodal_mask = mpi_data.get("amodal_mask",None)
        bg_change_latent = mpi_data.get("bg_change_latent", None)
        activations_fg = mpi_data.get("activation_fore")

        # get corrrespondences
        bg_mask, fg_mask = mpi_data.get("mpi_orig_mask")
        correspondences = np.where(fg_mask == 1)
        corr_y, corr_x = correspondences[0], correspondences[1]
        corr_x, corr_y = torch.tensor(corr_x), torch.tensor(corr_y)
        correspondences = torch.stack((corr_x, corr_y, corr_x, corr_y), dim=-1)

        processed_correspondences = self.process_correspondences(
            correspondences, img_res=512, bg_erosion=0
        )
        bg_correspondences = processed_correspondences
        fg_correspondences = processed_correspondences

        guidance_max_step = 51
        fg_weight = 1.5 * 30  # 30
        bg_weight = 1.25 * 30  # 30
        denoising_weight_schedule = []

        fg_weight_falloff = np.linspace(fg_weight, fg_weight, guidance_max_step)
        bg_weight_falloff = np.linspace(bg_weight, bg_weight, guidance_max_step)

        for t_idx in range(guidance_max_step):
            fg_weights = [0.0, 0.2, 8.5]
            bg_weights = [0.0, 0.2, 1.5]

            denoising_weight_schedule.append(
                (
                    t_idx,
                    (np.array(fg_weights) * fg_weight_falloff[t_idx]).tolist(),
                    (np.array(bg_weights) * bg_weight_falloff[t_idx]).tolist(),
                )
            )
        denoising_weight_schedule.append(
            (guidance_max_step, [0.0, 0.0, 0.0], [0.0, 0.0, 0.0])
        )

        optimization_weight_schedule = [
            (0, [3.5, 3.5, 3.5], [2.5, 2.5, 2.5]),
            (1, [1.25, 1.25, 1.25], [2.5, 2.5, 2.5]),
            (2, [2.5, 2.5, 2.5], [2.5, 2.5, 2.5]),
            (3, [2.5, 2.5, 2.5], [2.5, 2.5, 2.5]),
            (4, [1.5, 1.5, 1.5], [1.25, 1.25, 1.25]),
        ]
        self.guidance_weight_schedule = StepGuidanceWeightSchedule(
            denoising_steps=denoising_weight_schedule,
            optimization_steps=optimization_weight_schedule,
        )

        iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps)

        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full((b,), step, device=device, dtype=torch.long)

            if mask is not None:
                assert x0 is not None
                img_orig = self.model.q_sample(
                    x0, ts
                )  # TODO: deterministic forward pass?
                img = img_orig * mask + (1.0 - mask) * img

            if ucg_schedule is not None:
                assert len(ucg_schedule) == len(time_range)
                unconditional_guidance_scale = ucg_schedule[i]

            # if(i == 30):
            if i == 30:
                img = (
                    img * mpi_masks[0]
                    + ddim_latents[-i - 1].to(torch.float16) * mpi_masks[1]
                )

            outs = self.p_sample_ddim(
                img,
                cond,
                ts,
                index=index,
                use_original_steps=ddim_use_original_steps,
                quantize_denoised=quantize_denoised,
                temperature=temperature,
                noise_dropout=noise_dropout,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
                dynamic_threshold=dynamic_threshold,
                t_idx=i,
                correspondance=fg_correspondences,
                orig_activations=activations_fg,
            )
            img, pred_x0 = outs
            # print("latents mean and std :", img.mean(), img.std())

            if callback:
                callback(i)
            if img_callback:
                img_callback(pred_x0, i)
            if index % log_every_t == 0 or index == total_steps - 1:
                intermediates["x_inter"].append(img)
                intermediates["pred_x0"].append(pred_x0)

        return img, intermediates

    # @torch.no_grad()
    def p_sample_ddim(
        self,
        x,
        c,
        t,
        index,
        repeat_noise=False,
        use_original_steps=False,
        quantize_denoised=False,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        dynamic_threshold=None,
        t_idx=0,
        correspondance=None,
        orig_activations=None,
    ):
        b, *_, device = *x.shape, x.device

        # latent update as per diffusion handles
        torch.set_grad_enabled(True)
        for param in self.model.model.diffusion_model.parameters():
            param.requires_grad = True
        for param in self.model.model.diffusion_model.parameters():
            param.grad = None

        x.requires_grad = True
        # x = x.to(torch.float16)
        with torch.enable_grad():
            # if(t_idx > 30 and t_idx < 48):
            if t_idx > 30 and t_idx < 48:
                # if(t_idx < 39):
                for num_iter in range(3):  # 3
                    fgw, bgw = self.guidance_weight_schedule(t_idx, num_iter)
                    x = torch.nn.parameter.Parameter(x.detach(), requires_grad=True)
                    model_output, activations = self.model.apply_model(
                        x, t, c, return_activations=True
                    )

                    activations_size = (
                        orig_activations[2][t_idx].shape[-2],
                        orig_activations[2][t_idx].shape[-1],
                    )

                    loss = 0.0
                    for act_idx in range(len(orig_activations)):
                        if fgw != 0.0:
                            loss += fgw[act_idx] * compute_foreground_loss(
                                activations=activations[act_idx][0],
                                activations_orig=orig_activations[act_idx][t_idx],
                                processed_correspondences=correspondance,
                                patch_size=1,
                                activations_size=activations_size,
                            )

                    loss.backward()
                    grad_cond = x.grad
                    x = x - 0.2 * grad_cond  # 0.2
        torch.set_grad_enabled(False)
        ############################

        if unconditional_conditioning is None or unconditional_guidance_scale == 1.0:
            model_output = self.model.apply_model(x, t, c)
        else:
            model_t = self.model.apply_model(x, t, c)
            model_uncond = self.model.apply_model(x, t, unconditional_conditioning)
            model_output = model_uncond + unconditional_guidance_scale * (
                model_t - model_uncond
            )

        if self.model.parameterization == "v":
            e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
        else:
            e_t = model_output

        if score_corrector is not None:
            assert self.model.parameterization == "eps", "not implemented"
            e_t = score_corrector.modify_score(
                self.model, e_t, x, t, c, **corrector_kwargs
            )

        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
        alphas_prev = (
            self.model.alphas_cumprod_prev
            if use_original_steps
            else self.ddim_alphas_prev
        )
        sqrt_one_minus_alphas = (
            self.model.sqrt_one_minus_alphas_cumprod
            if use_original_steps
            else self.ddim_sqrt_one_minus_alphas
        )
        sigmas = (
            self.model.ddim_sigmas_for_original_num_steps
            if use_original_steps
            else self.ddim_sigmas
        )
        # select parameters corresponding to the currently considered timestep
        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
        sqrt_one_minus_at = torch.full(
            (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
        )

        # current prediction for x_0
        if self.model.parameterization != "v":
            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        else:
            pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)

        if quantize_denoised:
            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)

        if dynamic_threshold is not None:
            raise NotImplementedError()

        # direction pointing to x_t
        dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.0:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
        return x_prev, pred_x0

    # @torch.no_grad()
    def encode(
        self,
        x0,
        c,
        t_enc,
        use_original_steps=False,
        return_intermediates=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        callback=None,
    ):
        timesteps = (
            np.arange(self.ddpm_num_timesteps)
            if use_original_steps
            else self.ddim_timesteps
        )
        num_reference_steps = timesteps.shape[0]

        assert t_enc <= num_reference_steps
        num_steps = t_enc

        if use_original_steps:
            alphas_next = self.alphas_cumprod[:num_steps]
            alphas = self.alphas_cumprod_prev[:num_steps]
        else:
            alphas_next = self.ddim_alphas[:num_steps]
            alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])

        x_next = x0
        intermediates = []
        inter_steps = []
        for i in tqdm(range(num_steps), desc="Encoding Image"):
            t = torch.full(
                (x0.shape[0],), timesteps[i], device=self.model.device, dtype=torch.long
            )
            if unconditional_guidance_scale == 1.0:
                noise_pred = self.model.apply_model(x_next, t, c)
            else:
                assert unconditional_conditioning is not None
                e_t_uncond, noise_pred = torch.chunk(
                    self.model.apply_model(
                        torch.cat((x_next, x_next)),
                        torch.cat((t, t)),
                        torch.cat((unconditional_conditioning, c)),
                    ),
                    2,
                )
                noise_pred = e_t_uncond + unconditional_guidance_scale * (
                    noise_pred - e_t_uncond
                )

            xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
            weighted_noise_pred = (
                alphas_next[i].sqrt()
                * ((1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt())
                * noise_pred
            )
            x_next = xt_weighted + weighted_noise_pred
            if (
                return_intermediates
                and i % (num_steps // return_intermediates) == 0
                and i < num_steps - 1
            ):
                intermediates.append(x_next)
                inter_steps.append(i)
            elif return_intermediates and i >= num_steps - 2:
                intermediates.append(x_next)
                inter_steps.append(i)
            if callback:
                callback(i)

        out = {"x_encoded": x_next, "intermediate_steps": inter_steps}
        if return_intermediates:
            out.update({"intermediates": intermediates})
        return x_next, out

    # @torch.no_grad()
    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
        # fast, but does not allow for exact reconstruction
        # t serves as an index to gather the correct alphas
        if use_original_steps:
            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
        else:
            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas

        if noise is None:
            noise = torch.randn_like(x0)
        return (
            extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
            + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise
        )

    # @torch.no_grad()
    def decode(
        self,
        x_latent,
        cond,
        t_start,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        use_original_steps=False,
        callback=None,
    ):

        timesteps = (
            np.arange(self.ddpm_num_timesteps)
            if use_original_steps
            else self.ddim_timesteps
        )
        timesteps = timesteps[:t_start]

        time_range = np.flip(timesteps)
        total_steps = timesteps.shape[0]
        print(f"Running DDIM Sampling with {total_steps} timesteps")

        iterator = tqdm(time_range, desc="Decoding image", total=total_steps)
        x_dec = x_latent
        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full(
                (x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long
            )
            x_dec, _ = self.p_sample_ddim(
                x_dec,
                cond,
                ts,
                index=index,
                use_original_steps=use_original_steps,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
            )
            if callback:
                callback(i)
        return x_dec

    def process_correspondences(self, correspondences, img_res, bg_erosion=0):

        original_x, original_y, transformed_x, transformed_y = torch.split(
            correspondences, 1, dim=-1
        )

        # Since np.split creates arrays of shape (N, 1), we'll squeeze them to get back to shape (N,)
        original_x = original_x.squeeze()
        original_y = original_y.squeeze()
        transformed_x = transformed_x.squeeze()
        transformed_y = transformed_y.squeeze()

        # bg_mask_orig = np.zeros((img_res, img_res))

        # bg_mask_trans = np.zeros((img_res, img_res))

        visible_orig_x = []
        visible_orig_y = []
        visible_trans_x = []
        visible_trans_y = []

        for x, y, tx, ty in zip(original_x, original_y, transformed_x, transformed_y):
            if (tx >= 0 and tx < img_res) and (ty >= 0 and ty < img_res):
                visible_orig_x.append(x)
                visible_orig_y.append(y)
                visible_trans_x.append(tx)
                visible_trans_y.append(ty)

        # for x, y in zip(visible_orig_x, visible_orig_y):
        #     bg_mask_orig[y,x] = 1

        # for x, y in zip(visible_trans_x, visible_trans_y):
        #     bg_mask_trans[y,x] = 1

        original_x, original_y, transformed_x, transformed_y = (
            np.array(visible_orig_x, dtype=np.int64),
            np.array(visible_orig_y, dtype=np.int64),
            np.array(visible_trans_x, dtype=np.int64),
            np.array(visible_trans_y, dtype=np.int64),
        )

        original_x, original_y = original_x // (img_res // 64), original_y // (
            img_res // 64
        )
        transformed_x, transformed_y = transformed_x // (
            img_res // 64
        ), transformed_y // (img_res // 64)

        bg_mask_orig = np.ones(shape=[64, 64], dtype=np.bool_)
        if len(original_x) > 0:
            bg_mask_orig[original_y, original_x] = False

        bg_mask_trans = np.ones(shape=[64, 64], dtype=np.bool_)
        if len(transformed_x) > 0:
            bg_mask_trans[transformed_y, transformed_x] = False

        if bg_erosion > 0:
            bg_mask_orig = scipy.ndimage.binary_erosion(
                bg_mask_orig, iterations=bg_erosion
            )
            bg_mask_trans = scipy.ndimage.binary_erosion(
                bg_mask_trans, iterations=bg_erosion
            )

        bg_y, bg_x = np.nonzero(bg_mask_orig & bg_mask_trans)
        bg_y_orig, bg_x_orig = np.nonzero(bg_mask_orig)
        bg_y_trans, bg_x_trans = np.nonzero(bg_mask_trans)

        # # Create sets for original and transformed pixels
        # original_pixels = set(zip(original_x, original_y))
        # transformed_pixels = set(zip(transformed_x, transformed_y))

        # # Create a set of all pixels in a 64x64 image
        # all_pixels = {(x, y) for x in range(64) for y in range(64)}

        # # Find pixels not in either of the original or transformed sets
        # bg_pixels = all_pixels - (original_pixels | transformed_pixels)

        # # Extract background_x and background_y
        # bg_x = np.array([x for x, y in bg_pixels])
        # bg_y = np.array([y for x, y in bg_pixels])

        # bg_pixels_orig = all_pixels - (original_pixels)

        # bg_x_orig = np.array([x for x, y in bg_pixels_orig])
        # bg_y_orig = np.array([y for x, y in bg_pixels_orig])

        # bg_pixels_trans = all_pixels - (transformed_pixels)

        # bg_x_trans = np.array([x for x, y in bg_pixels_trans])
        # bg_y_trans = np.array([y for x, y in bg_pixels_trans])

        processed_correspondences = {
            "original_x": original_x,
            "original_y": original_y,
            "transformed_x": transformed_x,
            "transformed_y": transformed_y,
            "background_x": bg_x,
            "background_y": bg_y,
            "background_x_orig": bg_x_orig,
            "background_y_orig": bg_y_orig,
            "background_x_trans": bg_x_trans,
            "background_y_trans": bg_y_trans,
        }

        return processed_correspondences


class GuidanceWeightSchedule:

    def __init__(self):
        pass

    def __call__(self, denoising_step: int, optimization_step: int):
        fg_weights = [1.0] * 3
        bg_weights = [1.0] * 3
        return fg_weights, bg_weights


class StepGuidanceWeightSchedule(GuidanceWeightSchedule):

    def __init__(self, denoising_steps, optimization_steps):

        super().__init__()

        if not all(
            len(fg_weights) == len(bg_weights)
            for _, fg_weights, bg_weights in denoising_steps
        ):
            raise ValueError(
                "Number of foreground and background weights do not match."
            )
        if not all(
            len(fg_weights) == len(bg_weights)
            for _, fg_weights, bg_weights in optimization_steps
        ):
            raise ValueError(
                "Number of foreground and background weights do not match."
            )
        if len(denoising_steps[0][1]) != len(optimization_steps[0][1]):
            raise ValueError(
                "Number of denoising and optimization weights do not match."
            )

        self.denoising_steps = sorted(denoising_steps, key=lambda step: step[0])
        self.optimization_steps = sorted(optimization_steps, key=lambda step: step[0])

    def __call__(self, denoising_step: int, optimization_step: int):

        denoising_fg_weights = None
        denoising_bg_weights = None
        optimization_fg_weights = None
        optimization_bg_weights = None

        for step, fg_weights, bg_weights in reversed(self.denoising_steps):
            if denoising_step >= step:
                denoising_fg_weights = fg_weights
                denoising_bg_weights = bg_weights
                break
        for step, fg_weights, bg_weights in reversed(self.optimization_steps):
            if optimization_step >= step:
                optimization_fg_weights = fg_weights
                optimization_bg_weights = bg_weights
                break

        if any(
            weight is None
            for weight in [
                denoising_fg_weights,
                denoising_bg_weights,
                optimization_fg_weights,
                optimization_bg_weights,
            ]
        ):
            raise ValueError(
                f"Could not find weights for denoising step {denoising_step} and optimization step {optimization_step}."
            )

        fg_weights = [
            dw * ow for dw, ow in zip(denoising_fg_weights, optimization_fg_weights)
        ]
        bg_weights = [
            dw * ow for dw, ow in zip(denoising_bg_weights, optimization_bg_weights)
        ]

        return fg_weights, bg_weights
