import math
import copy
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
from multiprocessing import cpu_count

import torch
from torch import nn, einsum
import torch.nn.functional as F
from torch.amp import autocast
from torch.utils.data import Dataset, DataLoader

from torch.optim import Adam
from torchvision import transforms as T, utils

from einops import rearrange, reduce
from einops.layers.torch import Rearrange

from PIL import Image
from tqdm.auto import tqdm
from ema_pytorch import EMA

from accelerate import Accelerator

# from denoising_diffusion_pytorch.version import __version__

# constants

ModelPrediction =  namedtuple('ModelPrediction', ['pred_noise', 'pred_x_start'])

# helpers functions

def exists(x):
    return x is not None

def default(val, d):
    if exists(val):
        return val
    return d() if callable(d) else d

def identity(t, *args, **kwargs):
    return t

def cycle(dl):
    while True:
        for data in dl:
            yield data

def has_int_squareroot(num):
    return (math.sqrt(num) ** 2) == num

def num_to_groups(num, divisor):
    groups = num // divisor
    remainder = num % divisor
    arr = [divisor] * groups
    if remainder > 0:
        arr.append(remainder)
    return arr

def convert_image_to_fn(img_type, image):
    if image.mode != img_type:
        return image.convert(img_type)
    return image

# normalization functions

def normalize_to_neg_one_to_one(img):
    return img * 2 - 1

def unnormalize_to_zero_to_one(t):
    return (t + 1) * 0.5

# small helper modules

class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn

    def forward(self, x, *args, **kwargs):
        return self.fn(x, *args, **kwargs) + x

def Upsample(dim, dim_out = None):
    return nn.Sequential(
        nn.Upsample(scale_factor = 2, mode = 'nearest'),
        nn.Conv2d(dim, default(dim_out, dim), 3, padding = 1)
    )

def Downsample(dim, dim_out = None):
    return nn.Sequential(
        Rearrange('b c (h p1) (w p2) -> b (c p1 p2) h w', p1 = 2, p2 = 2),
        nn.Conv2d(dim * 4, default(dim_out, dim), 1)
    )

class RMSNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))

    def forward(self, x):
        return F.normalize(x, dim = 1) * self.g * (x.shape[-1] ** 0.5)

class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.fn = fn
        self.norm = RMSNorm(dim)

    def forward(self, x):
        x = self.norm(x)
        return self.fn(x)

# sinusoidal positional embeds

class SinusoidalPosEmb(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
        emb = x[:, None] * emb[None, :]
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb

class RandomOrLearnedSinusoidalPosEmb(nn.Module):
    """ following @crowsonkb 's lead with random (learned optional) sinusoidal pos emb """
    """ https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """

    def __init__(self, dim, is_random = False):
        super().__init__()
        assert (dim % 2) == 0
        half_dim = dim // 2
        self.weights = nn.Parameter(torch.randn(half_dim), requires_grad = not is_random)

    def forward(self, x):
        x = rearrange(x, 'b -> b 1')
        freqs = x * rearrange(self.weights, 'd -> 1 d') * 2 * math.pi
        fouriered = torch.cat((freqs.sin(), freqs.cos()), dim = -1)
        fouriered = torch.cat((x, fouriered), dim = -1)
        return fouriered

# building block modules

class Block(nn.Module):
    def __init__(self, dim, dim_out):
        super().__init__()
        self.proj = nn.Conv2d(dim, dim_out, 3, padding = 1)
        self.norm = RMSNorm(dim_out)
        self.act = nn.SiLU()

    def forward(self, x, scale_shift = None):
        x = self.proj(x)
        x = self.norm(x)

        if exists(scale_shift):
            scale, shift = scale_shift
            x = x * (scale + 1) + shift

        x = self.act(x)
        return x

class ResnetBlock(nn.Module):
    def __init__(self, dim, dim_out, *, time_emb_dim = None):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.SiLU(),
            nn.Linear(time_emb_dim, dim_out * 2)
        ) if exists(time_emb_dim) else None

        self.block1 = Block(dim, dim_out)
        self.block2 = Block(dim_out, dim_out)
        self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()

    def forward(self, x, time_emb = None):

        scale_shift = None
        if exists(self.mlp) and exists(time_emb):
            time_emb = self.mlp(time_emb)
            time_emb = rearrange(time_emb, 'b c -> b c 1 1')
            scale_shift = time_emb.chunk(2, dim = 1)

        h = self.block1(x, scale_shift = scale_shift)

        h = self.block2(h)

        return h + self.res_conv(x)

class LinearAttention(nn.Module):
    def __init__(self, dim, heads = 4, dim_head = 32):
        super().__init__()
        self.scale = dim_head ** -0.5
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)

        self.to_out = nn.Sequential(
            nn.Conv2d(hidden_dim, dim, 1),
            RMSNorm(dim)
        )

    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x).chunk(3, dim = 1)
        q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)

        q = q.softmax(dim = -2)
        k = k.softmax(dim = -1)

        q = q * self.scale

        context = torch.einsum('b h d n, b h e n -> b h d e', k, v)

        out = torch.einsum('b h d e, b h d n -> b h e n', context, q)
        out = rearrange(out, 'b h c (x y) -> b (h c) x y', h = self.heads, x = h, y = w)
        return self.to_out(out)

class Attention(nn.Module):
    def __init__(self, dim, heads = 4, dim_head = 32):
        super().__init__()
        self.scale = dim_head ** -0.5
        self.heads = heads
        hidden_dim = dim_head * heads

        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
        self.to_out = nn.Conv2d(hidden_dim, dim, 1)

    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x).chunk(3, dim = 1)
        q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)

        q = q * self.scale

        sim = einsum('b h d i, b h d j -> b h i j', q, k)
        attn = sim.softmax(dim = -1)
        out = einsum('b h i j, b h d j -> b h i d', attn, v)

        out = rearrange(out, 'b h (x y) d -> b (h d) x y', x = h, y = w)
        return self.to_out(out)

# model

class Unet(nn.Module):
    def __init__(
        self,
        dim,
        init_dim = None,
        out_dim = None,
        dim_mults=(1, 2, 4, 8),
        channels = 3,
        self_condition = False,
        learned_variance = False,
        learned_sinusoidal_cond = False,
        random_fourier_features = False,
        learned_sinusoidal_dim = 16
    ):
        super().__init__()

        # determine dimensions

        self.channels = channels
        self.self_condition = self_condition
        input_channels = channels * (2 if self_condition else 1)

        init_dim = default(init_dim, dim)
        self.init_conv = nn.Conv2d(input_channels, init_dim, 7, padding = 3)

        dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
        in_out = list(zip(dims[:-1], dims[1:]))

        # time embeddings

        time_dim = dim * 4

        self.random_or_learned_sinusoidal_cond = learned_sinusoidal_cond or random_fourier_features

        if self.random_or_learned_sinusoidal_cond:
            sinu_pos_emb = RandomOrLearnedSinusoidalPosEmb(learned_sinusoidal_dim, random_fourier_features)
            fourier_dim = learned_sinusoidal_dim + 1
        else:
            sinu_pos_emb = SinusoidalPosEmb(dim)
            fourier_dim = dim

        self.time_mlp = nn.Sequential(
            sinu_pos_emb,
            nn.Linear(fourier_dim, time_dim),
            nn.GELU(),
            nn.Linear(time_dim, time_dim)
        )

        # layers

        self.downs = nn.ModuleList([])
        self.ups = nn.ModuleList([])
        num_resolutions = len(in_out)

        for ind, (dim_in, dim_out) in enumerate(in_out):
            is_last = ind >= (num_resolutions - 1)

            self.downs.append(nn.ModuleList([
                ResnetBlock(dim_in, dim_in, time_emb_dim = time_dim),
                ResnetBlock(dim_in, dim_in, time_emb_dim = time_dim),
                Residual(PreNorm(dim_in, LinearAttention(dim_in))),
                Downsample(dim_in, dim_out) if not is_last else nn.Conv2d(dim_in, dim_out, 3, padding = 1)
            ]))

        mid_dim = dims[-1]
        self.mid_block1 = ResnetBlock(mid_dim, mid_dim, time_emb_dim = time_dim)
        self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim)))
        self.mid_block2 = ResnetBlock(mid_dim, mid_dim, time_emb_dim = time_dim)

        for ind, (dim_in, dim_out) in enumerate(reversed(in_out)):
            is_last = ind == (len(in_out) - 1)

            self.ups.append(nn.ModuleList([
                ResnetBlock(dim_out + dim_in, dim_out, time_emb_dim = time_dim),
                ResnetBlock(dim_out + dim_in, dim_out, time_emb_dim = time_dim),
                Residual(PreNorm(dim_out, LinearAttention(dim_out))),
                Upsample(dim_out, dim_in) if not is_last else  nn.Conv2d(dim_out, dim_in, 3, padding = 1)
            ]))

        default_out_dim = channels * (1 if not learned_variance else 2)
        self.out_dim = default(out_dim, default_out_dim)

        self.final_res_block = ResnetBlock(dim * 2, dim, time_emb_dim = time_dim)
        self.final_conv = nn.Conv2d(dim, self.out_dim, 1)

    def forward(self, x, time, x_self_cond = None):
        if self.self_condition:
            x_self_cond = default(x_self_cond, lambda: torch.zeros_like(x))
            x = torch.cat((x_self_cond, x), dim = 1)

        x = self.init_conv(x)
        r = x.clone()

        t = self.time_mlp(time)

        h = []

        for block1, block2, attn, downsample in self.downs:
            x = block1(x, t)
            h.append(x)

            x = block2(x, t)
            x = attn(x)
            h.append(x)

            x = downsample(x)

        x = self.mid_block1(x, t)
        x = self.mid_attn(x)
        x = self.mid_block2(x, t)

        for block1, block2, attn, upsample in self.ups:
            x = torch.cat((x, h.pop()), dim = 1)
            x = block1(x, t)

            x = torch.cat((x, h.pop()), dim = 1)
            x = block2(x, t)
            x = attn(x)

            x = upsample(x)

        x = torch.cat((x, r), dim = 1)

        x = self.final_res_block(x, t)
        return self.final_conv(x)

# gaussian diffusion trainer class

def extract(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))

def linear_beta_schedule(timesteps):
    """
    linear schedule, proposed in original ddpm paper
    """
    scale = 1000 / timesteps
    beta_start = scale * 0.0001
    beta_end = scale * 0.02
    return torch.linspace(beta_start, beta_end, timesteps, dtype = torch.float64)

def cosine_beta_schedule(timesteps, s = 0.008):
    """
    cosine schedule
    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
    """
    steps = timesteps + 1
    t = torch.linspace(0, timesteps, steps, dtype = torch.float64) / timesteps
    alphas_cumprod = torch.cos((t + s) / (1 + s) * math.pi * 0.5) ** 2
    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
    return torch.clip(betas, 0, 0.999)

def sigmoid_beta_schedule(timesteps, start = -3, end = 3, tau = 1, clamp_min = 1e-5):
    """
    sigmoid schedule
    proposed in https://arxiv.org/abs/2212.11972 - Figure 8
    better for images > 64x64, when used during training
    """
    steps = timesteps + 1
    t = torch.linspace(0, timesteps, steps, dtype = torch.float64) / timesteps
    v_start = torch.tensor(start / tau).sigmoid()
    v_end = torch.tensor(end / tau).sigmoid()
    alphas_cumprod = (-((t * (end - start) + start) / tau).sigmoid() + v_end) / (v_end - v_start)
    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
    return torch.clip(betas, 0, 0.999)
    

    
class GaussianDiffusion(nn.Module):
    def __init__(
        self,
        model,
        *,
        image_size,
        timesteps = 1000,
        sampling_timesteps = None,
        objective = 'pred_noise',
        beta_schedule = 'sigmoid',
        schedule_fn_kwargs = dict(),
        ddim_sampling_eta = 0.,
        auto_normalize = True,
        min_snr_loss_weight = False,
        min_snr_gamma = 5
    ):
        super().__init__()
        assert not (type(self) == GaussianDiffusion and model.channels != model.out_dim)
        assert not model.random_or_learned_sinusoidal_cond

        self.model = model
        self.channels = self.model.channels
        self.self_condition = self.model.self_condition

        self.image_size = image_size

        self.objective = objective

        assert objective in {'pred_noise', 'pred_x0', 'pred_v'}, 'objective must be either pred_noise (predict noise) or pred_x0 (predict image start) or pred_v (predict v [v-parameterization as defined in appendix D of progressive distillation paper, used in imagen-video successfully])'

        if beta_schedule == 'linear':
            beta_schedule_fn = linear_beta_schedule
        elif beta_schedule == 'cosine':
            beta_schedule_fn = cosine_beta_schedule
        elif beta_schedule == 'sigmoid':
            beta_schedule_fn = sigmoid_beta_schedule
        else:
            raise ValueError(f'unknown beta schedule {beta_schedule}')

        betas = beta_schedule_fn(timesteps, **schedule_fn_kwargs)

        alphas = 1. - betas
        alphas_cumprod = torch.cumprod(alphas, dim=0)
        alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value = 1.)

        timesteps, = betas.shape
        self.num_timesteps = int(timesteps)

        # sampling related parameters

        self.sampling_timesteps = default(sampling_timesteps, timesteps) # default num sampling timesteps to number of timesteps at training

        assert self.sampling_timesteps <= timesteps
        self.is_ddim_sampling = self.sampling_timesteps < timesteps
        self.ddim_sampling_eta = ddim_sampling_eta

        # helper function to register buffer from float64 to float32

        register_buffer = lambda name, val: self.register_buffer(name, val.to(torch.float32))

        register_buffer('betas', betas)
        register_buffer('alphas_cumprod', alphas_cumprod)
        register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)

        # calculations for diffusion q(x_t | x_{t-1}) and others

        register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
        register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
        register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
        register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
        register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1))

        # calculations for posterior q(x_{t-1} | x_t, x_0)

        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)

        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)

        register_buffer('posterior_variance', posterior_variance)

        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain

        register_buffer('posterior_log_variance_clipped', torch.log(posterior_variance.clamp(min =1e-20)))
        register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
        register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))

        # loss weight

        snr = alphas_cumprod / (1 - alphas_cumprod)

        maybe_clipped_snr = snr.clone()
        if min_snr_loss_weight:
            maybe_clipped_snr.clamp_(max = min_snr_gamma)

        if objective == 'pred_noise':
            loss_weight = maybe_clipped_snr / snr
        elif objective == 'pred_x0':
            loss_weight = maybe_clipped_snr
        elif objective == 'pred_v':
            loss_weight = maybe_clipped_snr / (snr + 1)

        register_buffer('loss_weight', loss_weight)

        # auto-normalization of data [0, 1] -> [-1, 1] - can turn off by setting it to be False

        self.normalize = normalize_to_neg_one_to_one if auto_normalize else identity
        self.unnormalize = unnormalize_to_zero_to_one if auto_normalize else identity

    def predict_start_from_noise(self, x_t, t, noise):
        return (
            extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
            extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
        )

    def predict_noise_from_start(self, x_t, t, x0):
        return (
            (extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - x0) / \
            extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
        )

    def predict_v(self, x_start, t, noise):
        return (
            extract(self.sqrt_alphas_cumprod, t, x_start.shape) * noise -
            extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * x_start
        )

    def predict_start_from_v(self, x_t, t, v):
        return (
            extract(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
            extract(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
        )

    def q_posterior(self, x_start, x_t, t):
        posterior_mean = (
            extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
            extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
        )
        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
        return posterior_mean, posterior_variance, posterior_log_variance_clipped

    def model_predictions(self, x, t, x_self_cond = None, clip_x_start = False):
        model_output = self.model(x, t, x_self_cond)
        maybe_clip = partial(torch.clamp, min = -1., max = 1.) if clip_x_start else identity

        if self.objective == 'pred_noise':
            pred_noise = model_output
            x_start = self.predict_start_from_noise(x, t, pred_noise)
            x_start = maybe_clip(x_start)

        elif self.objective == 'pred_x0':
            x_start = model_output
            x_start = maybe_clip(x_start)
            pred_noise = self.predict_noise_from_start(x, t, x_start)

        elif self.objective == 'pred_v':
            v = model_output
            x_start = self.predict_start_from_v(x, t, v)
            x_start = maybe_clip(x_start)
            pred_noise = self.predict_noise_from_start(x, t, x_start)

        return ModelPrediction(pred_noise, x_start)

    def p_mean_variance(self, x, t, x_self_cond = None, clip_denoised = True):
        preds = self.model_predictions(x, t, x_self_cond)
        x_start = preds.pred_x_start

        if clip_denoised:
            x_start.clamp_(-1., 1.)

        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start = x_start, x_t = x, t = t)
        return model_mean, posterior_variance, posterior_log_variance, x_start
     
    def condition_mean(self, cond_fn, mean,variance, x, t, guidance_kwargs=None):
        """
        Compute the mean for the previous step, given a function cond_fn that
        computes the gradient of a conditional log probability with respect to
        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
        condition on y.
        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
        """
        # this fixes a bug in the official OpenAI implementation:
        # https://github.com/openai/guided-diffusion/issues/51 (see point 1)
        # use the predicted mean for the previous timestep to compute gradient
        gradient = cond_fn(mean, t, **guidance_kwargs)
        new_mean = (
            mean.float() + variance * gradient.float()
        )
        print("gradient: ",(variance * gradient.float()).mean())
        return new_mean

        
    @torch.no_grad()
    def p_sample(self, x, t: int, x_self_cond = None, cond_fn=None, guidance_kwargs=None):
        b, *_, device = *x.shape, x.device
        batched_times = torch.full((b,), t, device = x.device, dtype = torch.long)
        model_mean, variance, model_log_variance, x_start = self.p_mean_variance(
            x = x, t = batched_times, x_self_cond = x_self_cond, clip_denoised = True
        )
        if exists(cond_fn) and exists(guidance_kwargs):
            model_mean = self.condition_mean(cond_fn, model_mean, variance, x, batched_times, guidance_kwargs)
        
        noise = torch.randn_like(x) if t > 0 else 0. # no noise if t == 0
        pred_img = model_mean + (0.5 * model_log_variance).exp() * noise
        return pred_img, x_start

    @torch.no_grad()
    def p_sample_loop(self, shape, return_all_timesteps = False, cond_fn=None, guidance_kwargs=None):
        batch, device = shape[0], self.betas.device

        img = torch.randn(shape, device = device)
        imgs = [img]

        x_start = None

        for t in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps):
            self_cond = x_start if self.self_condition else None
            img, x_start = self.p_sample(img, t, self_cond, cond_fn, guidance_kwargs)
            imgs.append(img)

        ret = img if not return_all_timesteps else torch.stack(imgs, dim = 1)

        ret = self.unnormalize(ret)
        return ret

    @torch.no_grad()
    def ddim_sample(self, shape, return_all_timesteps = False, cond_fn=None, guidance_kwargs=None):
        batch, device, total_timesteps, sampling_timesteps, eta, objective = shape[0], self.betas.device, self.num_timesteps, self.sampling_timesteps, self.ddim_sampling_eta, self.objective

        times = torch.linspace(-1, total_timesteps - 1, steps = sampling_timesteps + 1)   # [-1, 0, 1, 2, ..., T-1] when sampling_timesteps == total_timesteps
        times = list(reversed(times.int().tolist()))
        time_pairs = list(zip(times[:-1], times[1:])) # [(T-1, T-2), (T-2, T-3), ..., (1, 0), (0, -1)]

        img = torch.randn(shape, device = device)
        imgs = [img]

        x_start = None

        for time, time_next in tqdm(time_pairs, desc = 'sampling loop time step'):
            time_cond = torch.full((batch,), time, device = device, dtype = torch.long)
            self_cond = x_start if self.self_condition else None
            pred_noise, x_start, *_ = self.model_predictions(img, time_cond, self_cond, clip_x_start = True)

            imgs.append(img)

            if time_next < 0:
                img = x_start
                continue

            alpha = self.alphas_cumprod[time]
            alpha_next = self.alphas_cumprod[time_next]

            sigma = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
            c = (1 - alpha_next - sigma ** 2).sqrt()

            noise = torch.randn_like(img)

            img = x_start * alpha_next.sqrt() + \
                  c * pred_noise + \
                  sigma * noise

        ret = img if not return_all_timesteps else torch.stack(imgs, dim = 1)

        ret = self.unnormalize(ret)
        return ret

    @torch.no_grad()
    def sample(self, batch_size = 16, return_all_timesteps = False, cond_fn=None, guidance_kwargs=None):
        image_size, channels = self.image_size, self.channels
        sample_fn = self.p_sample_loop if not self.is_ddim_sampling else self.ddim_sample
        return sample_fn((batch_size, channels, image_size, image_size), return_all_timesteps = return_all_timesteps, cond_fn=cond_fn, guidance_kwargs=guidance_kwargs)

    @torch.no_grad()
    def interpolate(self, x1, x2, t = None, lam = 0.5):
        b, *_, device = *x1.shape, x1.device
        t = default(t, self.num_timesteps - 1)

        assert x1.shape == x2.shape

        t_batched = torch.full((b,), t, device = device)
        xt1, xt2 = map(lambda x: self.q_sample(x, t = t_batched), (x1, x2))

        img = (1 - lam) * xt1 + lam * xt2

        x_start = None

        for i in tqdm(reversed(range(0, t)), desc = 'interpolation sample time step', total = t):
            self_cond = x_start if self.self_condition else None
            img, x_start = self.p_sample(img, i, self_cond)

        return img

    @autocast('cuda', enabled = False)
    def q_sample(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))

        return (
            extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
            extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
        )

    def p_losses(self, x_start, t, noise = None):
        b, c, h, w = x_start.shape
        noise = default(noise, lambda: torch.randn_like(x_start))

        # noise sample

        x = self.q_sample(x_start = x_start, t = t, noise = noise)

        # if doing self-conditioning, 50% of the time, predict x_start from current set of times
        # and condition with unet with that
        # this technique will slow down training by 25%, but seems to lower FID significantly

        x_self_cond = None
        if self.self_condition and random() < 0.5:
            with torch.no_grad():
                x_self_cond = self.model_predictions(x, t).pred_x_start
                x_self_cond.detach_()

        # predict and take gradient step

        model_out = self.model(x, t, x_self_cond)

        if self.objective == 'pred_noise':
            target = noise
        elif self.objective == 'pred_x0':
            target = x_start
        elif self.objective == 'pred_v':
            v = self.predict_v(x_start, t, noise)
            target = v
        else:
            raise ValueError(f'unknown objective {self.objective}')

        loss = F.mse_loss(model_out, target, reduction = 'none')
        loss = reduce(loss, 'b ... -> b', 'mean')

        loss = loss * extract(self.loss_weight, t, loss.shape)
        return loss.mean()

    def forward(self, img, *args, **kwargs):
        b, c, h, w, device, img_size, = *img.shape, img.device, self.image_size
        assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
        t = torch.randint(0, self.num_timesteps, (b,), device=device).long()

        img = self.normalize(img)
        return self.p_losses(img, t, *args, **kwargs)

# dataset classes

class Dataset(Dataset):
    def __init__(
        self,
        folder,
        image_size,
        exts = ['jpg', 'jpeg', 'png', 'tiff'],
        augment_horizontal_flip = False,
        convert_image_to = None
    ):
        super().__init__()
        self.folder = folder
        self.image_size = image_size
        self.paths = [p for ext in exts for p in Path(f'{folder}').glob(f'**/*.{ext}')]

        maybe_convert_fn = partial(convert_image_to_fn, convert_image_to) if exists(convert_image_to) else nn.Identity()

        self.transform = T.Compose([
            T.Lambda(maybe_convert_fn),
            T.Resize(image_size),
            T.RandomHorizontalFlip() if augment_horizontal_flip else nn.Identity(),
            T.CenterCrop(image_size),
            T.ToTensor()
        ])

    def __len__(self):
        return len(self.paths)

    def __getitem__(self, index):
        path = self.paths[index]
        img = Image.open(path)
        return self.transform(img)

# trainer class

class Trainer(object):
    def __init__(
        self,
        diffusion_model,
        folder,
        *,
        train_batch_size = 16,
        gradient_accumulate_every = 1,
        augment_horizontal_flip = True,
        train_lr = 1e-4,
        train_num_steps = 100000,
        ema_update_every = 10,
        ema_decay = 0.995,
        adam_betas = (0.9, 0.99),
        save_and_sample_every = 1000,
        num_samples = 25,
        results_folder = './results',
        amp = False,
        fp16 = False,
        split_batches = True,
        convert_image_to = None
    ):
        super().__init__()

        self.accelerator = Accelerator(
            split_batches = split_batches,
            mixed_precision = 'fp16' if fp16 else 'no'
        )

        self.accelerator.native_amp = amp

        self.model = diffusion_model

        assert has_int_squareroot(num_samples), 'number of samples must have an integer square root'
        self.num_samples = num_samples
        self.save_and_sample_every = save_and_sample_every

        self.batch_size = train_batch_size
        self.gradient_accumulate_every = gradient_accumulate_every

        self.train_num_steps = train_num_steps
        self.image_size = diffusion_model.image_size

        # dataset and dataloader

        self.ds = Dataset(folder, self.image_size, augment_horizontal_flip = augment_horizontal_flip, convert_image_to = convert_image_to)
        dl = DataLoader(self.ds, batch_size = train_batch_size, shuffle = True, pin_memory = True, num_workers = cpu_count())

        dl = self.accelerator.prepare(dl)
        self.dl = cycle(dl)

        # optimizer

        self.opt = Adam(diffusion_model.parameters(), lr = train_lr, betas = adam_betas)

        # for logging results in a folder periodically

        if self.accelerator.is_main_process:
            self.ema = EMA(diffusion_model, beta = ema_decay, update_every = ema_update_every)

        self.results_folder = Path(results_folder)
        self.results_folder.mkdir(exist_ok = True)

        # step counter state

        self.step = 0

        # prepare model, dataloader, optimizer with accelerator

        self.model, self.opt = self.accelerator.prepare(self.model, self.opt)

    def save(self, milestone):
        if not self.accelerator.is_local_main_process:
            return

        data = {
            'step': self.step,
            'model': self.accelerator.get_state_dict(self.model),
            'opt': self.opt.state_dict(),
            'ema': self.ema.state_dict(),
            'scaler': self.accelerator.scaler.state_dict() if exists(self.accelerator.scaler) else None,
            'version': __version__
        }

        torch.save(data, str(self.results_folder / f'model-{milestone}.pt'))

    def load(self, milestone):
        accelerator = self.accelerator
        device = accelerator.device

        data = torch.load(str(self.results_folder / f'model-{milestone}.pt'), map_location=device, weights_only=True)

        model = self.accelerator.unwrap_model(self.model)
        model.load_state_dict(data['model'])

        self.step = data['step']
        self.opt.load_state_dict(data['opt'])
        self.ema.load_state_dict(data['ema'])

        if 'version' in data:
            print(f"loading from version {data['version']}")

        if exists(self.accelerator.scaler) and exists(data['scaler']):
            self.accelerator.scaler.load_state_dict(data['scaler'])

    def train(self):
        accelerator = self.accelerator
        device = accelerator.device

        with tqdm(initial = self.step, total = self.train_num_steps, disable = not accelerator.is_main_process) as pbar:

            while self.step < self.train_num_steps:

                total_loss = 0.

                for _ in range(self.gradient_accumulate_every):
                    data = next(self.dl).to(device)

                    with self.accelerator.autocast():
                        loss = self.model(data)
                        loss = loss / self.gradient_accumulate_every
                        total_loss += loss.item()

                    self.accelerator.backward(loss)

                accelerator.clip_grad_norm_(self.model.parameters(), 1.0)
                pbar.set_description(f'loss: {total_loss:.4f}')

                accelerator.wait_for_everyone()

                self.opt.step()
                self.opt.zero_grad()

                accelerator.wait_for_everyone()

                self.step += 1
                if accelerator.is_main_process:
                    self.ema.to(device)
                    self.ema.update()

                    if self.step != 0 and self.step % self.save_and_sample_every == 0:
                        self.ema.ema_model.eval()

                        with torch.no_grad():
                            milestone = self.step // self.save_and_sample_every
                            batches = num_to_groups(self.num_samples, self.batch_size)
                            all_images_list = list(map(lambda n: self.ema.ema_model.sample(batch_size=n), batches))

                        all_images = torch.cat(all_images_list, dim = 0)
                        utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = int(math.sqrt(self.num_samples)))
                        self.save(milestone)

                pbar.update(1)

        accelerator.print('training complete')

if __name__ == '__main__':
    class Classifier(nn.Module):
        def __init__(self, image_size, num_classes, t_dim=1) -> None:
            super().__init__()
            self.linear_t = nn.Linear(t_dim, num_classes)
            self.linear_img = nn.Linear(image_size * image_size * 3, num_classes)
        def forward(self, x, t):
            """
            Args:
                x (_type_): [B, 3, N, N]
                t (_type_): [B,]

            Returns:
                    logits [B, num_classes]
            """
            B = x.shape[0]
            t = t.view(B, 1)
            logits = self.linear_t(t.float()) + self.linear_img(x.view(x.shape[0], -1))
            return logits
        
    def classifier_cond_fn(x, t, classifier, y, classifier_scale=1):
        """
        return the graident of the classifier outputing y wrt x.
        formally expressed as d_log(classifier(x, t)) / dx
        """
        assert y is not None
        with torch.enable_grad():
            x_in = x.detach().requires_grad_(True)
            logits = classifier(x_in, t)
            log_probs = F.log_softmax(logits, dim=-1)
            selected = log_probs[range(len(logits)), y.view(-1)]
            grad = torch.autograd.grad(selected.sum(), x_in)[0] * classifier_scale
            return grad
        
    
    
    model = Unet(
        dim = 64,
        dim_mults = (1, 2, 4, 8)
    )
    image_size = 128
    diffusion = GaussianDiffusion(
        model,
        image_size = image_size,
        timesteps = 1000   # number of steps
    )

    classifier = Classifier(image_size=image_size, num_classes=1000, t_dim=1)
    batch_size = 4
    sampled_images = diffusion.sample(
        batch_size = batch_size,
        cond_fn=classifier_cond_fn, 
        guidance_kwargs={
            "classifier":classifier,
            "y":torch.fill(torch.zeros(batch_size), 1).long(),
            "classifier_scale":1,
        }
    )
    sampled_images.shape # (4, 3, 128, 128)