import math
import numpy as np
from einops import repeat
import torch
import torch.nn.functional as F


def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
    """
    Create sinusoidal timestep embeddings.
    :param timesteps: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an [N x dim] Tensor of positional embeddings.
    """
    if not repeat_only:
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period)
            * torch.arange(start=0, end=half, dtype=torch.float32)
            / half
        ).to(device=timesteps.device)
        args = timesteps[:, None].float() * freqs[None]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        if dim % 2:
            embedding = torch.cat(
                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
            )
    else:
        embedding = repeat(timesteps, "b -> b d", d=dim)
    return embedding


def make_beta_schedule(
    schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
):
    if schedule == "linear":
        betas = (
            torch.linspace(
                linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64
            )
            ** 2
        )

    elif schedule == "cosine":
        timesteps = (
            torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
        )
        alphas = timesteps / (1 + cosine_s) * np.pi / 2
        alphas = torch.cos(alphas).pow(2)
        alphas = alphas / alphas[0]
        betas = 1 - alphas[1:] / alphas[:-1]
        betas = np.clip(betas, a_min=0, a_max=0.999)

    elif schedule == "sqrt_linear":
        betas = torch.linspace(
            linear_start, linear_end, n_timestep, dtype=torch.float64
        )
    elif schedule == "sqrt":
        betas = (
            torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
            ** 0.5
        )
    else:
        raise ValueError(f"schedule '{schedule}' unknown.")
    return betas.numpy()


def make_ddim_timesteps(
    ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True
):
    if ddim_discr_method == "uniform":
        c = num_ddpm_timesteps // num_ddim_timesteps
        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
    elif ddim_discr_method == "quad":
        ddim_timesteps = (
            (np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2
        ).astype(int)
    else:
        raise NotImplementedError(
            f'There is no ddim discretization method called "{ddim_discr_method}"'
        )

    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
    # add one to get the final alpha values right (the ones from first scale to data during sampling)
    steps_out = ddim_timesteps + 1
    if verbose:
        print(f"Selected timesteps for ddim sampler: {steps_out}")
    return steps_out


def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
    # select alphas for computing the variance schedule
    # print(f'ddim_timesteps={ddim_timesteps}, len_alphacums={len(alphacums)}')
    alphas = alphacums[ddim_timesteps]
    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())

    # according the the formula provided in https://arxiv.org/abs/2010.02502
    sigmas = eta * np.sqrt(
        (1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)
    )
    if verbose:
        print(
            f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}"
        )
        print(
            f"For the chosen value of eta, which is {eta}, "
            f"this results in the following sigma_t schedule for ddim sampler {sigmas}"
        )
    return sigmas, alphas, alphas_prev


def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
    """
    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].
    :param num_diffusion_timesteps: the number of betas to produce.
    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
                      produces the cumulative product of (1-beta) up to that
                      part of the diffusion process.
    :param max_beta: the maximum beta to use; use values lower than 1 to
                     prevent singularities.
    """
    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(t2) / alpha_bar(t1), max_beta))
    return np.array(betas)