import numpy as np
from tqdm import tqdm
import torch
import copy
from einops import rearrange, repeat

from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg
from lvdm.common import noise_like
from lvdm.common import extract_into_tensor

class DDIMSampler(object):
    def __init__(self, model, schedule="linear", num_chunk=4, dtype=torch.float32, **kwargs):
        super().__init__()
        self.model = model
        self.ddpm_num_timesteps = model.num_timesteps
        self.schedule = schedule
        self.counter = 0
        self.num_chunk = num_chunk

        if dtype is None:
            self.dtype = torch.float32
        else:
            self.dtype = dtype

    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., verbose=True, ddpm_from=1000,):
        
        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
                                                  num_ddpm_timesteps=ddpm_from,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(self.dtype).to(self.model.device)

        if self.model.use_dynamic_rescale:
            self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps]
            self.ddim_scale_arr_prev = torch.cat([self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]])

        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))

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer('sqrt_alphas_cumprod', to_torch(torch.sqrt(alphas_cumprod.cpu())))
        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(torch.sqrt(1. - alphas_cumprod.cpu())))
        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(torch.log(1. - alphas_cumprod.cpu())))
        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(torch.sqrt(1. / alphas_cumprod.cpu())))
        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(torch.sqrt(1. / 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', torch.sqrt(1. - 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.,
               mask=None,
               x0=None,
               temperature=1.,
               noise_dropout=0.,
               score_corrector=None,
               corrector_kwargs=None,
               verbose=True,
               schedule_verbose=False,
               log_every_t=100,
               unconditional_guidance_scale=1.,
               unconditional_conditioning=None,
               precision=None,
               fs=None,
               timestep_spacing='uniform', #uniform_trailing for starting from last timestep
               guidance_rescale=0.0,
               ddpm_from=1000,
               causal=False,
               chunk=8,
               cat_mask=False,
               sparse=False,
               **kwargs
            ):
        self.make_schedule(ddim_num_steps=S, ddim_discretize=timestep_spacing, ddim_eta=eta, verbose=schedule_verbose,ddpm_from=ddpm_from)

        # 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)
        elif len(shape) == 5:
            C, V, T, H, W = shape
            size = (batch_size, C, T, H, W)

        # 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}")
   
        outputs = self.ddim_sampling_causal(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,
                                                log_every_t=log_every_t,
                                                unconditional_guidance_scale=unconditional_guidance_scale,
                                                unconditional_conditioning=unconditional_conditioning,
                                                verbose=verbose,
                                                precision=precision,
                                                fs=fs,
                                                guidance_rescale=guidance_rescale,
                                                chunk=chunk,
                                                cat_mask=cat_mask,
                                                sparse=sparse,
                                                **kwargs)

        return outputs


    @torch.no_grad()
    def ddim_sampling_causal(self, cond, shape,
                      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., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                      unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True,precision=None,fs=None,guidance_rescale=0.0,
                      chunk=4,cat_mask=False,sparse=False,
                      **kwargs):
        
        device = self.model.betas.device        
        b = shape[0]
        
        img = cond['c_concat'][0].clone() # img: bv c f h w

        predefined_noise = kwargs.get("noise", None)

        if predefined_noise is None:
            img[:,:,-chunk:] = torch.randn_like(img[:,:,-chunk:])
        else:
            img[:,:,-chunk:] = predefined_noise[:,:,-chunk:]

        img_0 = cond['c_concat'][0][:,:,:-chunk].clone().data.cpu()

        if precision is not None:
            if precision == 16:
                img = img.to(dtype=torch.float16)

        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]

        return_intermediates = kwargs.pop("return_intermediates", True)
        if return_intermediates:
            intermediates = {'x_inter': [img.data.cpu()], 'pred_x0': [img.data.cpu()]}
        else:
            intermediates = None


        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

        clean_cond = kwargs.pop("clean_cond", False)

        img_out = [img_0, ] #previous frame show red channel

        total_frames = chunk*self.num_chunk
        

        for fstep in tqdm(range(total_frames//chunk)):

            b,c,f,h,w = img.shape
            

            img_ = img.clone()
            
            for i, step in enumerate(iterator):
                    
                
                index = total_steps - i - 1
                ts = torch.full((b,), step, device=device, dtype=torch.long)

                ## 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)
                    img = img_orig * mask + (1. - mask) * img # keep original & modify use img

                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,
                                        mask=mask,x0=x0,fs=fs,guidance_rescale=guidance_rescale,
                                        **kwargs)

                img_, pred_x0 = outs

                img_[:,:,:-chunk] = img[:,:,:-chunk]
                

                if callback: callback(i)
                if img_callback: img_callback(pred_x0, i)

                if return_intermediates and (index % log_every_t == 0 or index == total_steps - 1):
                    intermediates['x_inter'].append(img_[:,:,-chunk:])
                    intermediates['pred_x0'].append(pred_x0[:,:,-chunk:])

                torch.cuda.empty_cache()

            if sparse:
                add2mem = img_[:,:,-1:] # last generated frames
                img = img[:,:,:-chunk]
                if predefined_noise is None:
                    noise = torch.randn((b,c,chunk,h,w), device=device)
                else:
                    noise = predefined_noise[:,:,-chunk:]
                img = torch.cat([img[:,:,1:], add2mem, noise],dim=2)
                cond['c_concat'][0] = torch.cat([cond['c_concat'][0][:,:,1:-chunk],add2mem,add2mem.repeat(1,1,chunk,1,1)],dim=2) # condition on the generated frames
            
            else:
                img[:,:,-chunk:] = img_[:,:,-chunk:]
                if predefined_noise is None:
                    noise = torch.randn((b,c,chunk,h,w), device=device)
                else:
                    noise = predefined_noise[:,:,-chunk:]
                img = torch.cat([img[:,:,chunk:],noise],dim=2)
                cond['c_concat'][0] = torch.cat([cond['c_concat'][0][:,:,chunk:-chunk],img_[:,:,-chunk:],img_[:,:,-1:].repeat(1,1,chunk,1,1)],dim=2)

                if cat_mask:
                    cond['c_concat'][0] = cond['c_concat'][0]*cond['c_concat'][1]

            img_out.append(img_[:,:,-chunk:].data.cpu())
            

        img_out = torch.cat(img_out,dim=2)

        print('img_out:',img_out.max())

        return img_out, 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., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                      unconditional_guidance_scale=1., unconditional_conditioning=None,
                      uc_type=None, conditional_guidance_scale_temporal=None,mask=None,x0=None,guidance_rescale=0.0,**kwargs):
        b, *_, device = *x.shape, x.device
        if x.dim() == 5:
            is_video = True
        else:
            is_video = False

        input_dtype = x.dtype
        input_device = x.device

        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
            model_output = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
        else:
            ### do_classifier_free_guidance
            if isinstance(c, torch.Tensor) or isinstance(c, dict):
                e_t_cond = self.model.apply_model(x, t, c, **kwargs)
                e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
            else:
                raise NotImplementedError

            model_output = e_t_uncond + unconditional_guidance_scale * (e_t_cond - e_t_uncond)

            if guidance_rescale > 0.0:
                model_output = rescale_noise_cfg(model_output, e_t_cond, guidance_rescale=guidance_rescale)

        if self.model.parameterization == "v":
            e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
        else:
            e_t = model_output

        if score_corrector is not None:
            assert self.model.parameterization == "eps", 'not implemented'
            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
        sigmas = self.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
        if self.model.parameterization != "v":
            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        else:
            pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)


        if self.model.use_dynamic_rescale:
            scale_t = torch.full(size, self.ddim_scale_arr[index], device=device)
            prev_scale_t = torch.full(size, self.ddim_scale_arr_prev[index], device=device)
            rescale = (prev_scale_t / scale_t)
            pred_x0 *= rescale

        if quantize_denoised:
            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
        
        # direction pointing to x_t
        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t

        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
    
        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise

        x_prev = x_prev.to(dtype=input_dtype, device=input_device)
        pred_x0 = pred_x0.to(dtype=input_dtype, device=input_device)

        return x_prev, pred_x0


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

        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)
            if callback: callback(i)
        return x_dec


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

