import numpy as np
from tqdm import tqdm
import torch
from lvdm.models.utils_diffusion import (
    make_ddim_sampling_parameters,
    make_ddim_timesteps,
)
from lvdm.common import noise_like


class DDIMSampler(object):
    def __init__(self, model, schedule="linear", **kwargs):
        super().__init__()
        self.model = model
        self.ddpm_num_timesteps = model.num_timesteps
        self.schedule = schedule
        self.counter = 0

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

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

        self.register_buffer("betas", to_torch(self.model.betas))
        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
        self.register_buffer(
            "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
        )
        self.use_scale = self.model.use_scale
        print("DDIM scale", self.use_scale)

        if self.use_scale:
            self.register_buffer("scale_arr", to_torch(self.model.scale_arr))
            ddim_scale_arr = self.scale_arr.cpu()[self.ddim_timesteps]
            self.register_buffer("ddim_scale_arr", ddim_scale_arr)
            ddim_scale_arr = np.asarray(
                [self.scale_arr.cpu()[0]]
                + self.scale_arr.cpu()[self.ddim_timesteps[:-1]].tolist()
            )
            self.register_buffer("ddim_scale_arr_prev", ddim_scale_arr)

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

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

    @torch.no_grad()
    def sample(
        self,
        S,
        batch_size,
        shape,
        conditioning=None,
        callback=None,
        normals_sequence=None,
        img_callback=None,
        quantize_x0=False,
        eta=0.0,
        mask=None,
        x0=None,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        verbose=True,
        schedule_verbose=False,
        x_T=None,
        log_every_t=100,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
        **kwargs,
    ):

        # check condition bs
        if conditioning is not None:
            if isinstance(conditioning, dict):
                try:
                    cbs = conditioning[list(conditioning.keys())[0]].shape[0]
                except:
                    cbs = conditioning[list(conditioning.keys())[0]][0].shape[0]

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

        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=schedule_verbose)

        # make shape
        if len(shape) == 3:
            C, H, W = shape
            size = (batch_size, C, H, W)
        elif len(shape) == 4:
            C, T, H, W = shape
            size = (batch_size, C, T, H, W)
        # print(f'Data shape for DDIM sampling is {size}, eta {eta}')

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

    @torch.no_grad()
    def ddim_sampling(
        self,
        cond,
        shape,
        x_T=None,
        ddim_use_original_steps=False,
        callback=None,
        timesteps=None,
        quantize_denoised=False,
        mask=None,
        x0=None,
        img_callback=None,
        log_every_t=100,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        verbose=True,
        cond_tau=1.0,
        target_size=None,
        start_timesteps=None,
        **kwargs,
    ):
        device = self.model.betas.device
        print("ddim device", device)
        b = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T

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

        intermediates = {"x_inter": [img], "pred_x0": [img]}
        time_range = (
            reversed(range(0, timesteps))
            if ddim_use_original_steps
            else np.flip(timesteps)
        )
        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
        if verbose:
            iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps)
        else:
            iterator = time_range

        init_x0 = False
        clean_cond = kwargs.pop("clean_cond", False)
        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full((b,), step, device=device, dtype=torch.long)
            if start_timesteps is not None:
                assert x0 is not None
                if step > start_timesteps * time_range[0]:
                    continue
                elif not init_x0:
                    img = self.model.q_sample(x0, ts)
                    init_x0 = True

            # use mask to blend noised original latent (img_orig) & new sampled latent (img)
            if mask is not None:
                assert x0 is not None
                if clean_cond:
                    img_orig = x0
                else:
                    img_orig = self.model.q_sample(
                        x0, ts
                    )  # TODO: deterministic forward pass? <ddim inversion>
                img = (
                    img_orig * mask + (1.0 - mask) * img
                )  # keep original & modify use img

            index_clip = int((1 - cond_tau) * total_steps)
            if index <= index_clip and target_size is not None:
                target_size_ = [
                    target_size[0],
                    target_size[1] // 8,
                    target_size[2] // 8,
                ]
                img = torch.nn.functional.interpolate(
                    img,
                    size=target_size_,
                    mode="nearest",
                )
            outs = self.p_sample_ddim(
                img,
                cond,
                ts,
                index=index,
                use_original_steps=ddim_use_original_steps,
                quantize_denoised=quantize_denoised,
                temperature=temperature,
                noise_dropout=noise_dropout,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
                x0=x0,
                **kwargs,
            )

            img, pred_x0 = outs
            if callback:
                callback(i)
            if img_callback:
                img_callback(pred_x0, i)

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

        return img, intermediates

    @torch.no_grad()
    def p_sample_ddim(
        self,
        x,
        c,
        t,
        index,
        repeat_noise=False,
        use_original_steps=False,
        quantize_denoised=False,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        uc_type=None,
        conditional_guidance_scale_temporal=None,
        **kwargs,
    ):
        b, *_, device = *x.shape, x.device
        if x.dim() == 5:
            is_video = True
        else:
            is_video = False
        if unconditional_conditioning is None or unconditional_guidance_scale == 1.0:
            e_t = self.model.apply_model(x, t, c, **kwargs)  # unet denoiser
        else:
            # with unconditional condition
            if isinstance(c, torch.Tensor):
                e_t = self.model.apply_model(x, t, c, **kwargs)
                e_t_uncond = self.model.apply_model(
                    x, t, unconditional_conditioning, **kwargs
                )
            elif isinstance(c, dict):
                e_t = self.model.apply_model(x, t, c, **kwargs)
                e_t_uncond = self.model.apply_model(
                    x, t, unconditional_conditioning, **kwargs
                )
            else:
                raise NotImplementedError
            # text cfg
            if uc_type is None:
                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
            else:
                if uc_type == "cfg_original":
                    e_t = e_t + unconditional_guidance_scale * (e_t - e_t_uncond)
                elif uc_type == "cfg_ours":
                    e_t = e_t + unconditional_guidance_scale * (e_t_uncond - e_t)
                else:
                    raise NotImplementedError
            # temporal guidance
            if conditional_guidance_scale_temporal is not None:
                e_t_temporal = self.model.apply_model(x, t, c, **kwargs)
                e_t_image = self.model.apply_model(
                    x, t, c, no_temporal_attn=True, **kwargs
                )
                e_t = e_t + conditional_guidance_scale_temporal * (
                    e_t_temporal - e_t_image
                )

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

        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
        alphas_prev = (
            self.model.alphas_cumprod_prev
            if use_original_steps
            else self.ddim_alphas_prev
        )
        sqrt_one_minus_alphas = (
            self.model.sqrt_one_minus_alphas_cumprod
            if use_original_steps
            else self.ddim_sqrt_one_minus_alphas
        )
        sigmas = (
            self.model.ddim_sigmas_for_original_num_steps
            if use_original_steps
            else self.ddim_sigmas
        )
        # select parameters corresponding to the currently considered timestep

        if is_video:
            size = (b, 1, 1, 1, 1)
        else:
            size = (b, 1, 1, 1)
        a_t = torch.full(size, alphas[index], device=device)
        a_prev = torch.full(size, alphas_prev[index], device=device)
        sigma_t = torch.full(size, sigmas[index], device=device)
        sqrt_one_minus_at = torch.full(
            size, sqrt_one_minus_alphas[index], device=device
        )

        # current prediction for x_0
        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        if quantize_denoised:
            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
        # direction pointing to x_t
        dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t

        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.0:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)

        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
        if self.use_scale:
            scale_arr = (
                self.model.scale_arr if use_original_steps else self.ddim_scale_arr
            )
            scale_t = torch.full(size, scale_arr[index], device=device)
            scale_arr_prev = (
                self.model.scale_arr_prev
                if use_original_steps
                else self.ddim_scale_arr_prev
            )
            scale_t_prev = torch.full(size, scale_arr_prev[index], device=device)
            pred_x0 /= scale_t
            x_prev = a_prev.sqrt() * scale_t_prev * pred_x0 + dir_xt + noise
        else:
            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise

        return x_prev, pred_x0

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

        if noise is None:
            noise = torch.randn_like(x0)

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

        return (
            extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
            + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise
        )

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

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

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

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