from typing import Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput


'''
helper functions:   append_zero(),
                    t_to_sigma(),
                    get_sigmas(),
                    append_dims(),
                    CFGDenoiserForward(),
                    get_scalings(),
                    DSsigma_to_t(),
                    DiscreteEpsDDPMDenoiserForward(),
                    to_d(),
                    get_ancestral_step()
need cleaning 
'''


def append_zero(x):
    return torch.cat([x, x.new_zeros([1])])


def t_to_sigma(t, sigmas):
    t = t.float()
    low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
    return (1 - w) * sigmas[low_idx] + w * sigmas[high_idx]


def get_sigmas(sigmas, n=None):
    if n is None:
        return append_zero(sigmas.flip(0))
    t_max = len(sigmas) - 1  # = 999
    t = torch.linspace(t_max, 0, n, device=sigmas.device, dtype=sigmas.dtype)
    return append_zero(t_to_sigma(t, sigmas))

# from k_samplers utils.py


def append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
    return x[(...,) + (None,) * dims_to_append]


def CFGDenoiserForward(Unet, x_in, sigma_in, cond_in, cond_scale, quantize=False, DSsigmas=None):
    # x_in = torch.cat([x] * 2)#A# concat the latent
    # sigma_in = torch.cat([sigma] * 2) #A# concat sigma
    # cond_in = torch.cat([uncond, cond])
    # uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
    # uncond, cond = DiscreteEpsDDPMDenoiserForward(Unet,x_in, sigma_in,DSsigmas=DSsigmas, cond=cond_in).chunk(2)
    # return uncond + (cond - uncond) * cond_scale
    noise_pred = DiscreteEpsDDPMDenoiserForward(
        Unet, x_in, sigma_in, quantize=quantize, DSsigmas=DSsigmas, cond=cond_in)
    return noise_pred

# from k_samplers sampling.py


def to_d(x, sigma, denoised):
    """Converts a denoiser output to a Karras ODE derivative."""
    return (x - denoised) / append_dims(sigma.to(denoised.device), x.ndim)


def get_scalings(sigma):
    sigma_data = 1.
    c_out = -sigma
    c_in = 1 / (sigma ** 2 + sigma_data ** 2) ** 0.5
    return c_out, c_in

# DiscreteSchedule DS


def DSsigma_to_t(sigma, quantize=False, DSsigmas=None):
    dists = torch.abs(sigma - DSsigmas[:, None])
    if quantize:
        return torch.argmin(dists, dim=0).view(sigma.shape)
    low_idx, high_idx = torch.sort(torch.topk(dists, dim=0, k=2, largest=False).indices, dim=0)[0]
    low, high = DSsigmas[low_idx], DSsigmas[high_idx]
    w = (low - sigma) / (low - high)
    w = w.clamp(0, 1)
    t = (1 - w) * low_idx + w * high_idx
    return t.view(sigma.shape)


def DiscreteEpsDDPMDenoiserForward(Unet, input, sigma, DSsigmas=None, quantize=False, **kwargs):
    sigma = sigma.to(dtype=input.dtype, device=Unet.device)
    DSsigmas = DSsigmas.to(dtype=input.dtype, device=Unet.device)
    c_out, c_in = [append_dims(x, input.ndim) for x in get_scalings(sigma)]
    # print(f">>>>>>>>>>> {input.dtype} {c_in.dtype} {sigma.dtype} {DSsigmas.dtype}")
    eps = Unet(input * c_in, DSsigma_to_t(sigma, quantize=quantize, DSsigmas=DSsigmas),
               encoder_hidden_states=kwargs['cond']).sample
    return input + eps * c_out


# from k_samplers sampling.py
def get_ancestral_step(sigma_from, sigma_to):
    """Calculates the noise level (sigma_down) to step down to and the amount
    of noise to add (sigma_up) when doing an ancestral sampling step."""
    sigma_up = (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5
    sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
    return sigma_down, sigma_up


'''
Euler Ancestral Scheduler
'''


class EulerAScheduler(SchedulerMixin, ConfigMixin):
    """
    Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
    the VE column of Table 1 from [1] for reference.

    [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
    https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
    differential equations." https://arxiv.org/abs/2011.13456

    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
    [`~ConfigMixin.from_config`] functions.

    For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of
    Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the
    optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper.

    Args:
        sigma_min (`float`): minimum noise magnitude
        sigma_max (`float`): maximum noise magnitude
        s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling.
            A reasonable range is [1.000, 1.011].
        s_churn (`float`): the parameter controlling the overall amount of stochasticity.
            A reasonable range is [0, 100].
        s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity).
            A reasonable range is [0, 10].
        s_max (`float`): the end value of the sigma range where we add noise.
            A reasonable range is [0.2, 80].

    """

    @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[np.ndarray] = None,
    ):
        if trained_betas is not None:
            self.betas = torch.from_numpy(trained_betas)
        if 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)

        # setable values
        self.num_inference_steps = None
        self.timesteps = np.arange(0, num_train_timesteps)[::-1]

    # A# take number of steps as input
    # A# store 1) number of steps 2) timesteps 3) schedule

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

        Args:
            num_inference_steps (`int`):
                the number of diffusion steps used when generating samples with a pre-trained model.
        """

        self.num_inference_steps = num_inference_steps
        self.DSsigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
        self.sigmas = get_sigmas(self.DSsigmas, self.num_inference_steps).to(device=device)
        self.timesteps = np.arange(0, self.num_inference_steps)

    def step(
        self,
        model_output: torch.FloatTensor,
        timestep: int,
        timestep_prev: int,
        sample: torch.FloatTensor,
        generator: torch.Generator = None,
        return_dict: bool = True,
    ) -> Union[SchedulerOutput, Tuple]:
        """
        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
        process from the learned model outputs (most often the predicted noise).

        Args:
            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
            sigma_hat (`float`): TODO
            sigma_prev (`float`): TODO
            sample_hat (`torch.FloatTensor`): TODO
            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class

            EulerAOutput: updated sample in the diffusion chain and derivative (TODO double check).
        Returns:
            [`~schedulers.scheduling_karras_ve.EulerAOutput`] or `tuple`:
            [`~schedulers.scheduling_karras_ve.EulerAOutput`] if `return_dict` is True, otherwise a `tuple`. When
            returning a tuple, the first element is the sample tensor.

        """
        s = self.sigmas[timestep]
        s_prev = self.sigmas[timestep_prev]
        latents = sample

        sigma_down, sigma_up = get_ancestral_step(s, s_prev)
        d = to_d(latents, s, model_output)
        dt = sigma_down - s
        latents = latents + d * dt
        latents = latents + torch.randn(latents.shape, layout=latents.layout, device=latents.device, dtype=latents.dtype,
                                        generator=generator) * sigma_up

        return SchedulerOutput(prev_sample=latents)

    def step_correct(
        self,
        model_output: torch.FloatTensor,
        sigma_hat: float,
        sigma_prev: float,
        sample_hat: torch.FloatTensor,
        sample_prev: torch.FloatTensor,
        derivative: torch.FloatTensor,
        return_dict: bool = True,
    ) -> Union[SchedulerOutput, Tuple]:
        """
        Correct the predicted sample based on the output model_output of the network. TODO complete description

        Args:
            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
            sigma_hat (`float`): TODO
            sigma_prev (`float`): TODO
            sample_hat (`torch.FloatTensor`): TODO
            sample_prev (`torch.FloatTensor`): TODO
            derivative (`torch.FloatTensor`): TODO
            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class

        Returns:
            prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO

        """
        pred_original_sample = sample_prev + sigma_prev * model_output
        derivative_corr = (sample_prev - pred_original_sample) / sigma_prev
        sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

        if not return_dict:
            return (sample_prev, derivative)

        return SchedulerOutput(prev_sample=sample_prev)

    def add_noise(
        self,
        original_samples: torch.FloatTensor,
        noise: torch.FloatTensor,
        timesteps: torch.IntTensor,
    ) -> torch.FloatTensor:
        sigmas = self.sigmas.to(original_samples.device)
        timesteps = timesteps.to(original_samples.device)

        sigma = sigmas[timesteps].flatten()
        while len(sigma.shape) < len(original_samples.shape):
            sigma = sigma.unsqueeze(-1)

        noisy_samples = original_samples + noise * sigma
        return noisy_samples