import torch
from diffusers import DiffusionPipeline
import tqdm

from diffusers.models.unet_1d import UNet1DModel
from diffusers.utils.dummy_pt_objects import DDPMScheduler


class ValueGuidedDiffuserPipeline(DiffusionPipeline):
    def __init__(self, value_function: UNet1DModel, unet: UNet1DModel, scheduler: DDPMScheduler, env, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.value_function = value_function
        self.unet = unet
        self.scheduler = scheduler
        self.env = env
        self.data = env.get_dataset()
        self.means = dict((key, val.mean(axis=0)) for key, val in self.data.items())
        self.stds = dict((key, val.std(axis=0)) for key, val in self.data.items())
        self.device = self.unet.device
        self.state_dim = env.observation_space.shape[0]
        self.action_dim = env.action_space.shape[0]

    def normalize(self, x_in, key):
        return (x_in - self.means[key]) / self.stds[key]

    def de_normalize(self, x_in, key):
        return x_in * self.stds[key] + self.means[key]

    def to_torch(self, x_in):

        if type(x_in) is dict:
            return {k: self.to_torch(v) for k, v in x_in.items()}
        elif torch.is_tensor(x_in):
            return x_in.to(self.device)
        return torch.tensor(x_in, device=self.device)

    def reset_x0(self, x_in, cond, act_dim):
        for key, val in cond.items():
            x_in[:, key, act_dim:] = val.clone()
        return x_in

    def run_diffusion(self, x, conditions, n_guide_steps, scale):
        batch_size = x.shape[0]
        y = None
        for i in tqdm.tqdm(self.scheduler.timesteps):
            # create batch of timesteps to pass into model
            timesteps = torch.full((batch_size,), i, device=self.device, dtype=torch.long)
            # 3. call the sample function
            for _ in range(n_guide_steps):
                with torch.enable_grad():
                    x.requires_grad_()
                    y = self.value_function(x, timesteps).sample
                    grad = torch.autograd.grad([y.sum()], [x])[0]

                    posterior_variance = self.scheduler._get_variance(i)
                    model_std = torch.exp(0.5 * posterior_variance)
                    grad = model_std * grad
                grad[timesteps < 2] = 0
                x = x.detach()
                x = x + scale * grad
                x = self.reset_x0(x, conditions, self.action_dim)
            # with torch.no_grad():
            prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1)
            x = self.scheduler.step(prev_x, i, x, predict_epsilon=False)["prev_sample"]

            # 4. apply conditions to the trajectory
            x = self.reset_x0(x, conditions, self.action_dim)
            x = self.to_torch(x, device=self.device)
        # y = network(x, timesteps).sample
        return x, y

    def __call__(self, obs, batch_size=64, planning_horizon=20, n_guide_steps=2, scale=0.1):
        obs = self.normalize(obs, "observations")
        obs = obs[None].repeat(batch_size, axis=0)
        conditions = {0: self.to_torch(obs)}
        shape = (batch_size, planning_horizon, self.state_dim + self.action_dim)
        x1 = torch.randn(shape, device=self.device)
        x = self.reset_x0(x1, conditions, self.action_dim)
        x = self.to_torch(x)
        x, y = self.run_diffusion(x, conditions, n_guide_steps, scale)
        sorted_idx = y.argsort(0, descending=True).squeeze()
        sorted_values = x[sorted_idx]
        actions = sorted_values[:, :, : self.action_dim]
        actions = actions.detach().cpu().numpy()
        denorm_actions = self.de_normalize(actions, key="actions")
        # denorm_actions = denorm_actions[np.random.randint(config['n_samples']), 0]
        denorm_actions = denorm_actions[0, 0]
        return denorm_actions