


import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							):
        super().__init__()
        lowercase_       :Optional[Any]     =       value_function
        lowercase_       :List[str]     =       unet
        lowercase_       :Optional[int]     =       scheduler
        lowercase_       :Optional[Any]     =       env
        lowercase_       :Optional[Any]     =       env.get_dataset()
        lowercase_       :List[str]     =       {}
        for key in self.data.keys():
             try:
                  lowercase_       :List[Any]     =       self.data[key].mean()
             except:  # noqa: E722
                  pass
        lowercase_       :Union[str, Any]     =       {}
        for key in self.data.keys():
             try:
                  lowercase_       :Optional[int]     =       self.data[key].std()
             except:  # noqa: E722
                  pass
        lowercase_       :Optional[Any]     =       env.observation_space.shape[0]
        lowercase_       :Optional[Any]     =       env.action_space.shape[0]
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return (x_in - self.means[key]) / self.stds[key]
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return x_in * self.stds[key] + self.means[key]
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        if type(UpperCamelCase_		) is dict:
             return {k: self.to_torch(UpperCamelCase_		) for k, v in x_in.items()}
        elif torch.is_tensor(UpperCamelCase_		):
             return x_in.to(self.unet.device		)
        return torch.tensor(UpperCamelCase_   ,							device=self.unet.device		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        for key, val in cond.items():
             lowercase_       :List[Any]     =       val.clone()
        return x_in
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       x.shape[0]
        lowercase_       :List[Any]     =       None
        for i in tqdm.tqdm(self.scheduler.timesteps		):
             # create batch of timesteps to pass into model
             lowercase_       :int     =       torch.full((batch_size,)   ,							UpperCamelCase_   ,							device=self.unet.device   ,							dtype=torch.long		)
             for _ in range(UpperCamelCase_		):
                  with torch.enable_grad():
                       x.requires_grad_()

                       # permute to match dimension for pre-trained models
                       lowercase_       :Tuple     =       self.value_function(x.permute(0   ,							2   ,							1		)   ,							UpperCamelCase_		).sample
                       lowercase_       :List[str]     =       torch.autograd.grad([y.sum()]   ,							[x]		)[0]

                       lowercase_       :Tuple     =       self.scheduler._get_variance(UpperCamelCase_		)
                       lowercase_       :Union[str, Any]     =       torch.exp(0.5 * posterior_variance		)
                       lowercase_       :Dict     =       model_std * grad

                  lowercase_       :Tuple     =       0
                  lowercase_       :Optional[int]     =       x.detach()
                  lowercase_       :Union[str, Any]     =       x + scale * grad
                  lowercase_       :Any     =       self.reset_xa(UpperCamelCase_   ,							UpperCamelCase_   ,							self.action_dim		)

             lowercase_       :Union[str, Any]     =       self.unet(x.permute(0   ,							2   ,							1		)   ,							UpperCamelCase_		).sample.permute(0   ,							2   ,							1		)

             # TODO: verify deprecation of this kwarg
             lowercase_       :List[str]     =       self.scheduler.step(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							predict_epsilon=UpperCamelCase_		)['''prev_sample''']

             # apply conditions to the trajectory (set the initial state)
             lowercase_       :List[str]     =       self.reset_xa(UpperCamelCase_   ,							UpperCamelCase_   ,							self.action_dim		)
             lowercase_       :int     =       self.to_torch(UpperCamelCase_		)
        return x, y




   def __call__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_=64   ,							UpperCamelCase_=32   ,							UpperCamelCase_=2   ,							UpperCamelCase_=0.1		):
        # normalize the observations and create  batch dimension
        lowercase_       :List[Any]     =       self.normalize(UpperCamelCase_   ,							'''observations'''		)
        lowercase_       :Tuple     =       obs[None].repeat(UpperCamelCase_   ,							axis=0		)

        lowercase_       :Any     =       {0: self.to_torch(UpperCamelCase_		)}
        lowercase_       :List[Any]     =       (batch_size, planning_horizon, self.state_dim + self.action_dim)

        # generate initial noise and apply our conditions (to make the trajectories start at current state)
        lowercase_       :Any     =       randn_tensor(UpperCamelCase_   ,							device=self.unet.device		)
        lowercase_       :List[str]     =       self.reset_xa(UpperCamelCase_   ,							UpperCamelCase_   ,							self.action_dim		)
        lowercase_       :Tuple     =       self.to_torch(UpperCamelCase_		)

        # run the diffusion process
        lowercase_       :List[Any]     =       self.run_diffusion(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

        # sort output trajectories by value
        lowercase_       :Tuple     =       y.argsort(0   ,							descending=UpperCamelCase_		).squeeze()
        lowercase_       :Dict     =       x[sorted_idx]
        lowercase_       :Dict     =       sorted_values[:, :, : self.action_dim]
        lowercase_       :str     =       actions.detach().cpu().numpy()
        lowercase_       :List[str]     =       self.de_normalize(UpperCamelCase_   ,							key='''actions'''		)

        # select the action with the highest value
        if y is not None:
             lowercase_       :int     =       0
        else:
             # if we didn't run value guiding, select a random action
             lowercase_       :List[Any]     =       np.random.randint(0   ,							UpperCamelCase_		)

        lowercase_       :Tuple     =       denorm_actions[selected_index, 0]
        return denorm_actions





def     UpperCamelCase    (      _a		)					->							int:
     '''simple docstring'''





     assert isinstance(_a       ,		_a		), f"The input value of [n={number}] is not an integer"

     if number == 1:
          return 2
     elif number < 1:
          lowercase_       :str     =       f"The input value of [n={number}] has to be > 0"
          raise ValueError(_a		)
     else:
          lowercase_       :List[str]     =       sylvester(number - 1		)
          lowercase_       :Union[str, Any]     =       num - 1
          lowercase_       :List[str]     =       num
          return lower * upper + 1


if __name__ == "__main__":
   print(f"The 8th number in Sylvester's sequence: {sylvester(8)}")

