# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# DISCLAIMER: check https://arxiv.org/abs/2309.05019
# The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

import math
from typing import List, Optional, Tuple, Union, Callable

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.torch_utils import randn_tensor
from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
def betas_for_alpha_bar(
        num_diffusion_timesteps,
        max_beta=0.999,
        alpha_transform_type="cosine",
):
    """
    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
    (1-beta) over time from t = [0,1].

    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
    to that part of the diffusion process.


    Args:
        num_diffusion_timesteps (`int`): the number of betas to produce.
        max_beta (`float`): the maximum beta to use; use values lower than 1 to
                     prevent singularities.
        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
                     Choose from `cosine` or `exp`

    Returns:
        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
    """
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(t):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(t):
            return math.exp(t * -12.0)

    else:
        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")

    betas = []
    for i in range(num_diffusion_timesteps):
        t1 = i / num_diffusion_timesteps
        t2 = (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
    return torch.tensor(betas, dtype=torch.float32)


class SASolverScheduler(SchedulerMixin, ConfigMixin):
    """
    `SASolverScheduler` is a fast dedicated high-order solver for diffusion SDEs.

    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
    methods the library implements for all schedulers such as loading and saving.

    Args:
        num_train_timesteps (`int`, defaults to 1000):
            The number of diffusion steps to train the model.
        beta_start (`float`, defaults to 0.0001):
            The starting `beta` value of inference.
        beta_end (`float`, defaults to 0.02):
            The final `beta` value.
        beta_schedule (`str`, defaults to `"linear"`):
            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
        trained_betas (`np.ndarray`, *optional*):
            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
        predictor_order (`int`, defaults to 2):
            The predictor order which can be `1` or `2` or `3` or '4'. It is recommended to use `predictor_order=2` for guided
            sampling, and `predictor_order=3` for unconditional sampling.
        corrector_order (`int`, defaults to 2):
            The corrector order which can be `1` or `2` or `3` or '4'. It is recommended to use `corrector_order=2` for guided
            sampling, and `corrector_order=3` for unconditional sampling.
        predictor_corrector_mode (`str`, defaults to `PEC`):
            The predictor-corrector mode can be `PEC` or 'PECE'. It is recommended to use `PEC` mode for fast
            sampling, and `PECE` for high-quality sampling (PECE needs around twice model evaluations as PEC).
        prediction_type (`str`, defaults to `epsilon`, *optional*):
            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
            Video](https://imagen.research.google/video/paper.pdf) paper).
        thresholding (`bool`, defaults to `False`):
            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
            as Stable Diffusion.
        dynamic_thresholding_ratio (`float`, defaults to 0.995):
            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
        sample_max_value (`float`, defaults to 1.0):
            The threshold value for dynamic thresholding. Valid only when `thresholding=True` and
            `algorithm_type="dpmsolver++"`.
        algorithm_type (`str`, defaults to `data_prediction`):
            Algorithm type for the solver; can be `data_prediction` or `noise_prediction`. It is recommended to use `data_prediction`
            with `solver_order=2` for guided sampling like in Stable Diffusion.
        lower_order_final (`bool`, defaults to `True`):
            Whether to use lower-order solvers in the final steps. Default = True.
        use_karras_sigmas (`bool`, *optional*, defaults to `False`):
            Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
            the sigmas are determined according to a sequence of noise levels {σi}.
        lambda_min_clipped (`float`, defaults to `-inf`):
            Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the
            cosine (`squaredcos_cap_v2`) noise schedule.
        variance_type (`str`, *optional*):
            Set to "learned" or "learned_range" for diffusion models that predict variance. If set, the model's output
            contains the predicted Gaussian variance.
        timestep_spacing (`str`, defaults to `"linspace"`):
            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
        steps_offset (`int`, defaults to 0):
            An offset added to the inference steps. You can use a combination of `offset=1` and
            `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
            Diffusion.
    """

    _compatibles = [e.name for e in KarrasDiffusionSchedulers]
    order = 1

    @register_to_config
    def __init__(
            self,
            num_train_timesteps: int = 1000,
            beta_start: float = 0.0001,
            beta_end: float = 0.02,
            beta_schedule: str = "linear",
            trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
            predictor_order: int = 2,
            corrector_order: int = 2,
            predictor_corrector_mode: str = 'PEC',
            prediction_type: str = "epsilon",
            tau_func: Callable = lambda t: 1 if t >= 200 and t <= 800 else 0,
            thresholding: bool = False,
            dynamic_thresholding_ratio: float = 0.995,
            sample_max_value: float = 1.0,
            algorithm_type: str = "data_prediction",
            lower_order_final: bool = True,
            use_karras_sigmas: Optional[bool] = False,
            lambda_min_clipped: float = -float("inf"),
            variance_type: Optional[str] = None,
            timestep_spacing: str = "linspace",
            steps_offset: int = 0,
    ):
        if trained_betas is not None:
            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
        elif beta_schedule == "linear":
            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            self.betas = (
                    torch.linspace(beta_start ** 0.5, beta_end ** 0.5, num_train_timesteps, dtype=torch.float32) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            self.betas = betas_for_alpha_bar(num_train_timesteps)
        else:
            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")

        self.alphas = 1.0 - self.betas
        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
        # Currently we only support VP-type noise schedule
        self.alpha_t = torch.sqrt(self.alphas_cumprod)
        self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
        self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)

        # standard deviation of the initial noise distribution
        self.init_noise_sigma = 1.0

        if algorithm_type not in ["data_prediction", "noise_prediction"]:
            raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")

        # setable values
        self.num_inference_steps = None
        timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()
        self.timesteps = torch.from_numpy(timesteps)
        self.timestep_list = [None] * max(predictor_order, corrector_order - 1)
        self.model_outputs = [None] * max(predictor_order, corrector_order - 1)

        self.tau_func = tau_func
        self.predict_x0 = algorithm_type == "data_prediction"
        self.lower_order_nums = 0
        self.last_sample = None

    def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torch.device] = None):
        """
        Sets the discrete timesteps used for the diffusion chain (to be run before inference).

        Args:
            num_inference_steps (`int`):
                The number of diffusion steps used when generating samples with a pre-trained model.
            device (`str` or `torch.device`, *optional*):
                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
        """
        # Clipping the minimum of all lambda(t) for numerical stability.
        # This is critical for cosine (squaredcos_cap_v2) noise schedule.
        clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped)
        last_timestep = ((self.config.num_train_timesteps - clipped_idx).numpy()).item()

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
            timesteps = (
                np.linspace(0, last_timestep - 1, num_inference_steps + 1).round()[::-1][:-1].copy().astype(np.int64)
            )

        elif self.config.timestep_spacing == "leading":
            step_ratio = last_timestep // (num_inference_steps + 1)
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            timesteps = (np.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(np.int64)
            timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
            step_ratio = self.config.num_train_timesteps / num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            timesteps = np.arange(last_timestep, 0, -step_ratio).round().copy().astype(np.int64)
            timesteps -= 1
        else:
            raise ValueError(
                f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
            )

        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
        if self.config.use_karras_sigmas:
            log_sigmas = np.log(sigmas)
            sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
            timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()
            timesteps = np.flip(timesteps).copy().astype(np.int64)

        self.sigmas = torch.from_numpy(sigmas)

        # when num_inference_steps == num_train_timesteps, we can end up with
        # duplicates in timesteps.
        _, unique_indices = np.unique(timesteps, return_index=True)
        timesteps = timesteps[np.sort(unique_indices)]

        self.timesteps = torch.from_numpy(timesteps).to(device)

        self.num_inference_steps = len(timesteps)

        self.model_outputs = [
                                 None,
                             ] * max(self.config.predictor_order, self.config.corrector_order - 1)
        self.lower_order_nums = 0
        self.last_sample = None

    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
    def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
        """
        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
        photorealism as well as better image-text alignment, especially when using very large guidance weights."

        https://arxiv.org/abs/2205.11487
        """
        dtype = sample.dtype
        batch_size, channels, height, width = sample.shape

        if dtype not in (torch.float32, torch.float64):
            sample = sample.float()  # upcast for quantile calculation, and clamp not implemented for cpu half

        # Flatten sample for doing quantile calculation along each image
        sample = sample.reshape(batch_size, channels * height * width)

        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"

        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
        s = torch.clamp(
            s, min=1, max=self.config.sample_max_value
        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]

        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
        sample = torch.clamp(sample, -s, s) / s  # "we threshold xt0 to the range [-s, s] and then divide by s"

        sample = sample.reshape(batch_size, channels, height, width)
        sample = sample.to(dtype)

        return sample

    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
    def _sigma_to_t(self, sigma, log_sigmas):
        # get log sigma
        log_sigma = np.log(sigma)

        # get distribution
        dists = log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
        high_idx = low_idx + 1

        low = log_sigmas[low_idx]
        high = log_sigmas[high_idx]

        # interpolate sigmas
        w = (low - log_sigma) / (low - high)
        w = np.clip(w, 0, 1)

        # transform interpolation to time range
        t = (1 - w) * low_idx + w * high_idx
        t = t.reshape(sigma.shape)
        return t

    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
    def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor:
        """Constructs the noise schedule of Karras et al. (2022)."""

        sigma_min: float = in_sigmas[-1].item()
        sigma_max: float = in_sigmas[0].item()

        rho = 7.0  # 7.0 is the value used in the paper
        ramp = np.linspace(0, 1, num_inference_steps)
        min_inv_rho = sigma_min ** (1 / rho)
        max_inv_rho = sigma_max ** (1 / rho)
        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas

    def convert_model_output(
            self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor
    ) -> torch.FloatTensor:
        """
        Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is
        designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an
        integral of the data prediction model.

        <Tip>

        The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise
        prediction and data prediction models.

        </Tip>

        Args:
            model_output (`torch.FloatTensor`):
                The direct output from the learned diffusion model.
            timestep (`int`):
                The current discrete timestep in the diffusion chain.
            sample (`torch.FloatTensor`):
                A current instance of a sample created by the diffusion process.

        Returns:
            `torch.FloatTensor`:
                The converted model output.
        """

        # SA-Solver_data_prediction needs to solve an integral of the data prediction model.
        if self.config.algorithm_type in ["data_prediction"]:
            if self.config.prediction_type == "epsilon":
                # SA-Solver only needs the "mean" output.
                if self.config.variance_type in ["learned", "learned_range"]:
                    model_output = model_output[:, :3]
                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
                x0_pred = (sample - sigma_t * model_output) / alpha_t
            elif self.config.prediction_type == "sample":
                x0_pred = model_output
            elif self.config.prediction_type == "v_prediction":
                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
                x0_pred = alpha_t * sample - sigma_t * model_output
            else:
                raise ValueError(
                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                    " `v_prediction` for the SASolverScheduler."
                )

            if self.config.thresholding:
                x0_pred = self._threshold_sample(x0_pred)

            return x0_pred

        # SA-Solver_noise_prediction needs to solve an integral of the noise prediction model.
        elif self.config.algorithm_type in ["noise_prediction"]:
            if self.config.prediction_type == "epsilon":
                # SA-Solver only needs the "mean" output.
                if self.config.variance_type in ["learned", "learned_range"]:
                    epsilon = model_output[:, :3]
                else:
                    epsilon = model_output
            elif self.config.prediction_type == "sample":
                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
                epsilon = (sample - alpha_t * model_output) / sigma_t
            elif self.config.prediction_type == "v_prediction":
                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
                epsilon = alpha_t * model_output + sigma_t * sample
            else:
                raise ValueError(
                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                    " `v_prediction` for the SASolverScheduler."
                )

            if self.config.thresholding:
                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
                x0_pred = (sample - sigma_t * epsilon) / alpha_t
                x0_pred = self._threshold_sample(x0_pred)
                epsilon = (sample - alpha_t * x0_pred) / sigma_t

            return epsilon

    def get_coefficients_exponential_negative(self, order, interval_start, interval_end):
        """
        Calculate the integral of exp(-x) * x^order dx from interval_start to interval_end
        """
        assert order in [0, 1, 2, 3], "order is only supported for 0, 1, 2 and 3"

        if order == 0:
            return torch.exp(-interval_end) * (torch.exp(interval_end - interval_start) - 1)
        elif order == 1:
            return torch.exp(-interval_end) * (
                        (interval_start + 1) * torch.exp(interval_end - interval_start) - (interval_end + 1))
        elif order == 2:
            return torch.exp(-interval_end) * (
                        (interval_start ** 2 + 2 * interval_start + 2) * torch.exp(interval_end - interval_start) - (
                            interval_end ** 2 + 2 * interval_end + 2))
        elif order == 3:
            return torch.exp(-interval_end) * (
                        (interval_start ** 3 + 3 * interval_start ** 2 + 6 * interval_start + 6) * torch.exp(
                    interval_end - interval_start) - (interval_end ** 3 + 3 * interval_end ** 2 + 6 * interval_end + 6))

    def get_coefficients_exponential_positive(self, order, interval_start, interval_end, tau):
        """
        Calculate the integral of exp(x(1+tau^2)) * x^order dx from interval_start to interval_end
        """
        assert order in [0, 1, 2, 3], "order is only supported for 0, 1, 2 and 3"

        # after change of variable(cov)
        interval_end_cov = (1 + tau ** 2) * interval_end
        interval_start_cov = (1 + tau ** 2) * interval_start

        if order == 0:
            return torch.exp(interval_end_cov) * (1 - torch.exp(-(interval_end_cov - interval_start_cov))) / (
            (1 + tau ** 2))
        elif order == 1:
            return torch.exp(interval_end_cov) * ((interval_end_cov - 1) - (interval_start_cov - 1) * torch.exp(
                -(interval_end_cov - interval_start_cov))) / ((1 + tau ** 2) ** 2)
        elif order == 2:
            return torch.exp(interval_end_cov) * ((interval_end_cov ** 2 - 2 * interval_end_cov + 2) - (
                        interval_start_cov ** 2 - 2 * interval_start_cov + 2) * torch.exp(
                -(interval_end_cov - interval_start_cov))) / ((1 + tau ** 2) ** 3)
        elif order == 3:
            return torch.exp(interval_end_cov) * (
                        (interval_end_cov ** 3 - 3 * interval_end_cov ** 2 + 6 * interval_end_cov - 6) - (
                            interval_start_cov ** 3 - 3 * interval_start_cov ** 2 + 6 * interval_start_cov - 6) * torch.exp(
                    -(interval_end_cov - interval_start_cov))) / ((1 + tau ** 2) ** 4)

    def lagrange_polynomial_coefficient(self, order, lambda_list):
        """
        Calculate the coefficient of lagrange polynomial
        """

        assert order in [0, 1, 2, 3]
        assert order == len(lambda_list) - 1
        if order == 0:
            return [[1]]
        elif order == 1:
            return [[1 / (lambda_list[0] - lambda_list[1]), -lambda_list[1] / (lambda_list[0] - lambda_list[1])],
                    [1 / (lambda_list[1] - lambda_list[0]), -lambda_list[0] / (lambda_list[1] - lambda_list[0])]]
        elif order == 2:
            denominator1 = (lambda_list[0] - lambda_list[1]) * (lambda_list[0] - lambda_list[2])
            denominator2 = (lambda_list[1] - lambda_list[0]) * (lambda_list[1] - lambda_list[2])
            denominator3 = (lambda_list[2] - lambda_list[0]) * (lambda_list[2] - lambda_list[1])
            return [[1 / denominator1,
                     (-lambda_list[1] - lambda_list[2]) / denominator1,
                     lambda_list[1] * lambda_list[2] / denominator1],

                    [1 / denominator2,
                     (-lambda_list[0] - lambda_list[2]) / denominator2,
                     lambda_list[0] * lambda_list[2] / denominator2],

                    [1 / denominator3,
                     (-lambda_list[0] - lambda_list[1]) / denominator3,
                     lambda_list[0] * lambda_list[1] / denominator3]
                    ]
        elif order == 3:
            denominator1 = (lambda_list[0] - lambda_list[1]) * (lambda_list[0] - lambda_list[2]) * (
                        lambda_list[0] - lambda_list[3])
            denominator2 = (lambda_list[1] - lambda_list[0]) * (lambda_list[1] - lambda_list[2]) * (
                        lambda_list[1] - lambda_list[3])
            denominator3 = (lambda_list[2] - lambda_list[0]) * (lambda_list[2] - lambda_list[1]) * (
                        lambda_list[2] - lambda_list[3])
            denominator4 = (lambda_list[3] - lambda_list[0]) * (lambda_list[3] - lambda_list[1]) * (
                        lambda_list[3] - lambda_list[2])
            return [[1 / denominator1,
                     (-lambda_list[1] - lambda_list[2] - lambda_list[3]) / denominator1,
                     (lambda_list[1] * lambda_list[2] + lambda_list[1] * lambda_list[3] + lambda_list[2] * lambda_list[
                         3]) / denominator1,
                     (-lambda_list[1] * lambda_list[2] * lambda_list[3]) / denominator1],

                    [1 / denominator2,
                     (-lambda_list[0] - lambda_list[2] - lambda_list[3]) / denominator2,
                     (lambda_list[0] * lambda_list[2] + lambda_list[0] * lambda_list[3] + lambda_list[2] * lambda_list[
                         3]) / denominator2,
                     (-lambda_list[0] * lambda_list[2] * lambda_list[3]) / denominator2],

                    [1 / denominator3,
                     (-lambda_list[0] - lambda_list[1] - lambda_list[3]) / denominator3,
                     (lambda_list[0] * lambda_list[1] + lambda_list[0] * lambda_list[3] + lambda_list[1] * lambda_list[
                         3]) / denominator3,
                     (-lambda_list[0] * lambda_list[1] * lambda_list[3]) / denominator3],

                    [1 / denominator4,
                     (-lambda_list[0] - lambda_list[1] - lambda_list[2]) / denominator4,
                     (lambda_list[0] * lambda_list[1] + lambda_list[0] * lambda_list[2] + lambda_list[1] * lambda_list[
                         2]) / denominator4,
                     (-lambda_list[0] * lambda_list[1] * lambda_list[2]) / denominator4]

                    ]

    def get_coefficients_fn(self, order, interval_start, interval_end, lambda_list, tau):
        assert order in [1, 2, 3, 4]
        assert order == len(lambda_list), 'the length of lambda list must be equal to the order'
        coefficients = []
        lagrange_coefficient = self.lagrange_polynomial_coefficient(order - 1, lambda_list)
        for i in range(order):
            coefficient = 0
            for j in range(order):
                if self.predict_x0:

                    coefficient += lagrange_coefficient[i][j] * self.get_coefficients_exponential_positive(
                        order - 1 - j, interval_start, interval_end, tau)
                else:
                    coefficient += lagrange_coefficient[i][j] * self.get_coefficients_exponential_negative(
                        order - 1 - j, interval_start, interval_end)
            coefficients.append(coefficient)
        assert len(coefficients) == order, 'the length of coefficients does not match the order'
        return coefficients

    def stochastic_adams_bashforth_update(
            self,
            model_output: torch.FloatTensor,
            prev_timestep: int,
            sample: torch.FloatTensor,
            noise: torch.FloatTensor,
            order: int,
            tau: torch.FloatTensor,
    ) -> torch.FloatTensor:
        """
        One step for the SA-Predictor.

        Args:
            model_output (`torch.FloatTensor`):
                The direct output from the learned diffusion model at the current timestep.
            prev_timestep (`int`):
                The previous discrete timestep in the diffusion chain.
            sample (`torch.FloatTensor`):
                A current instance of a sample created by the diffusion process.
            order (`int`):
                The order of SA-Predictor at this timestep.

        Returns:
            `torch.FloatTensor`:
                The sample tensor at the previous timestep.
        """

        assert noise is not None
        timestep_list = self.timestep_list
        model_output_list = self.model_outputs
        s0, t = self.timestep_list[-1], prev_timestep
        lambda_t, lambda_s0 = self.lambda_t[t], self.lambda_t[s0]
        alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0]
        sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0]
        gradient_part = torch.zeros_like(sample)
        h = lambda_t - lambda_s0
        lambda_list = []

        for i in range(order):
            lambda_list.append(self.lambda_t[timestep_list[-(i + 1)]])

        gradient_coefficients = self.get_coefficients_fn(order, lambda_s0, lambda_t, lambda_list, tau)

        x = sample

        if self.predict_x0:
            if order == 2:  ## if order = 2 we do a modification that does not influence the convergence order similar to unipc. Note: This is used only for few steps sampling.
                # The added term is O(h^3). Empirically we find it will slightly improve the image quality.
                # ODE case
                # gradient_coefficients[0] += 1.0 * torch.exp(lambda_t) * (h ** 2 / 2 - (h - 1 + torch.exp(-h))) / (ns.marginal_lambda(t_prev_list[-1]) - ns.marginal_lambda(t_prev_list[-2]))
                # gradient_coefficients[1] -= 1.0 * torch.exp(lambda_t) * (h ** 2 / 2 - (h - 1 + torch.exp(-h))) / (ns.marginal_lambda(t_prev_list[-1]) - ns.marginal_lambda(t_prev_list[-2]))
                gradient_coefficients[0] += 1.0 * torch.exp((1 + tau ** 2) * lambda_t) * (
                            h ** 2 / 2 - (h * (1 + tau ** 2) - 1 + torch.exp((1 + tau ** 2) * (-h))) / (
                                (1 + tau ** 2) ** 2)) / (self.lambda_t[timestep_list[-1]] - self.lambda_t[
                    timestep_list[-2]])
                gradient_coefficients[1] -= 1.0 * torch.exp((1 + tau ** 2) * lambda_t) * (
                            h ** 2 / 2 - (h * (1 + tau ** 2) - 1 + torch.exp((1 + tau ** 2) * (-h))) / (
                                (1 + tau ** 2) ** 2)) / (self.lambda_t[timestep_list[-1]] - self.lambda_t[
                    timestep_list[-2]])

        for i in range(order):
            if self.predict_x0:

                gradient_part += (1 + tau ** 2) * sigma_t * torch.exp(- tau ** 2 * lambda_t) * gradient_coefficients[
                    i] * model_output_list[-(i + 1)]
            else:
                gradient_part += -(1 + tau ** 2) * alpha_t * gradient_coefficients[i] * model_output_list[-(i + 1)]

        if self.predict_x0:
            noise_part = sigma_t * torch.sqrt(1 - torch.exp(-2 * tau ** 2 * h)) * noise
        else:
            noise_part = tau * sigma_t * torch.sqrt(torch.exp(2 * h) - 1) * noise

        if self.predict_x0:
            x_t = torch.exp(-tau ** 2 * h) * (sigma_t / sigma_s0) * x + gradient_part + noise_part
        else:
            x_t = (alpha_t / alpha_s0) * x + gradient_part + noise_part

        x_t = x_t.to(x.dtype)
        return x_t

    def stochastic_adams_moulton_update(
            self,
            this_model_output: torch.FloatTensor,
            this_timestep: int,
            last_sample: torch.FloatTensor,
            last_noise: torch.FloatTensor,
            this_sample: torch.FloatTensor,
            order: int,
            tau: torch.FloatTensor,
    ) -> torch.FloatTensor:
        """
        One step for the SA-Corrector.

        Args:
            this_model_output (`torch.FloatTensor`):
                The model outputs at `x_t`.
            this_timestep (`int`):
                The current timestep `t`.
            last_sample (`torch.FloatTensor`):
                The generated sample before the last predictor `x_{t-1}`.
            this_sample (`torch.FloatTensor`):
                The generated sample after the last predictor `x_{t}`.
            order (`int`):
                The order of SA-Corrector at this step.

        Returns:
            `torch.FloatTensor`:
                The corrected sample tensor at the current timestep.
        """

        assert last_noise is not None
        timestep_list = self.timestep_list
        model_output_list = self.model_outputs
        s0, t = self.timestep_list[-1], this_timestep
        lambda_t, lambda_s0 = self.lambda_t[t], self.lambda_t[s0]
        alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0]
        sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0]
        gradient_part = torch.zeros_like(this_sample)
        h = lambda_t - lambda_s0
        t_list = timestep_list + [this_timestep]
        lambda_list = []
        for i in range(order):
            lambda_list.append(self.lambda_t[t_list[-(i + 1)]])

        model_prev_list = model_output_list + [this_model_output]

        gradient_coefficients = self.get_coefficients_fn(order, lambda_s0, lambda_t, lambda_list, tau)

        x = last_sample

        if self.predict_x0:
            if order == 2:  ## if order = 2 we do a modification that does not influence the convergence order similar to UniPC. Note: This is used only for few steps sampling.
                # The added term is O(h^3). Empirically we find it will slightly improve the image quality.
                # ODE case
                # gradient_coefficients[0] += 1.0 * torch.exp(lambda_t) * (h / 2 - (h - 1 + torch.exp(-h)) / h)
                # gradient_coefficients[1] -= 1.0 * torch.exp(lambda_t) * (h / 2 - (h - 1 + torch.exp(-h)) / h)
                gradient_coefficients[0] += 1.0 * torch.exp((1 + tau ** 2) * lambda_t) * (
                            h / 2 - (h * (1 + tau ** 2) - 1 + torch.exp((1 + tau ** 2) * (-h))) / (
                                (1 + tau ** 2) ** 2 * h))
                gradient_coefficients[1] -= 1.0 * torch.exp((1 + tau ** 2) * lambda_t) * (
                            h / 2 - (h * (1 + tau ** 2) - 1 + torch.exp((1 + tau ** 2) * (-h))) / (
                                (1 + tau ** 2) ** 2 * h))

        for i in range(order):
            if self.predict_x0:
                gradient_part += (1 + tau ** 2) * sigma_t * torch.exp(- tau ** 2 * lambda_t) * gradient_coefficients[
                    i] * model_prev_list[-(i + 1)]
            else:
                gradient_part += -(1 + tau ** 2) * alpha_t * gradient_coefficients[i] * model_prev_list[-(i + 1)]

        if self.predict_x0:
            noise_part = sigma_t * torch.sqrt(1 - torch.exp(-2 * tau ** 2 * h)) * last_noise
        else:
            noise_part = tau * sigma_t * torch.sqrt(torch.exp(2 * h) - 1) * last_noise

        if self.predict_x0:
            x_t = torch.exp(-tau ** 2 * h) * (sigma_t / sigma_s0) * x + gradient_part + noise_part
        else:
            x_t = (alpha_t / alpha_s0) * x + gradient_part + noise_part

        x_t = x_t.to(x.dtype)
        return x_t

    def step(
            self,
            model_output: torch.FloatTensor,
            timestep: int,
            sample: torch.FloatTensor,
            generator=None,
            return_dict: bool = True,
    ) -> Union[SchedulerOutput, Tuple]:
        """
        Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
        the SA-Solver.

        Args:
            model_output (`torch.FloatTensor`):
                The direct output from learned diffusion model.
            timestep (`int`):
                The current discrete timestep in the diffusion chain.
            sample (`torch.FloatTensor`):
                A current instance of a sample created by the diffusion process.
            generator (`torch.Generator`, *optional*):
                A random number generator.
            return_dict (`bool`):
                Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.

        Returns:
            [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
                If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
                tuple is returned where the first element is the sample tensor.

        """
        if self.num_inference_steps is None:
            raise ValueError(
                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
            )

        if isinstance(timestep, torch.Tensor):
            timestep = timestep.to(self.timesteps.device)
        step_index = (self.timesteps == timestep).nonzero()
        if len(step_index) == 0:
            step_index = len(self.timesteps) - 1
        else:
            step_index = step_index.item()

        use_corrector = (
                step_index > 0 and self.last_sample is not None
        )

        model_output_convert = self.convert_model_output(model_output, timestep, sample)

        if use_corrector:
            current_tau = self.tau_func(self.timestep_list[-1])
            sample = self.stochastic_adams_moulton_update(
                this_model_output=model_output_convert,
                this_timestep=timestep,
                last_sample=self.last_sample,
                last_noise=self.last_noise,
                this_sample=sample,
                order=self.this_corrector_order,
                tau=current_tau,
            )

        prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1]

        for i in range(max(self.config.predictor_order, self.config.corrector_order - 1) - 1):
            self.model_outputs[i] = self.model_outputs[i + 1]
            self.timestep_list[i] = self.timestep_list[i + 1]

        self.model_outputs[-1] = model_output_convert
        self.timestep_list[-1] = timestep

        noise = randn_tensor(
            model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype
        )

        if self.config.lower_order_final:
            this_predictor_order = min(self.config.predictor_order, len(self.timesteps) - step_index)
            this_corrector_order = min(self.config.corrector_order, len(self.timesteps) - step_index + 1)
        else:
            this_predictor_order = self.config.predictor_order
            this_corrector_order = self.config.corrector_order

        self.this_predictor_order = min(this_predictor_order, self.lower_order_nums + 1)  # warmup for multistep
        self.this_corrector_order = min(this_corrector_order, self.lower_order_nums + 2)  # warmup for multistep
        assert self.this_predictor_order > 0
        assert self.this_corrector_order > 0

        self.last_sample = sample
        self.last_noise = noise

        current_tau = self.tau_func(self.timestep_list[-1])
        prev_sample = self.stochastic_adams_bashforth_update(
            model_output=model_output_convert,
            prev_timestep=prev_timestep,
            sample=sample,
            noise=noise,
            order=self.this_predictor_order,
            tau=current_tau,
        )

        if self.lower_order_nums < max(self.config.predictor_order, self.config.corrector_order - 1):
            self.lower_order_nums += 1

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=prev_sample)

    def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
        """
        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
        current timestep.

        Args:
            sample (`torch.FloatTensor`):
                The input sample.

        Returns:
            `torch.FloatTensor`:
                A scaled input sample.
        """
        return sample

    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
    def add_noise(
            self,
            original_samples: torch.FloatTensor,
            noise: torch.FloatTensor,
            timesteps: torch.IntTensor,
    ) -> torch.FloatTensor:
        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
        alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
        timesteps = timesteps.to(original_samples.device)

        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)

        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)

        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        return noisy_samples

    def __len__(self):
        return self.config.num_train_timesteps