


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_    ,      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





import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




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





     return np.dot(_a       ,		_a		)






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							*,
       UpperCamelCase_ = np.inf   ,							UpperCamelCase_ = "linear"   ,							UpperCamelCase_ = 0.0   ,							):
        lowercase_       :Dict     =       regularization
        lowercase_       :Optional[Any]     =       gamma
        if kernel == "linear":
             lowercase_       :Optional[Any]     =       self.__linear
        elif kernel == "rbf":
             if self.gamma == 0:
                  raise ValueError('''rbf kernel requires gamma'''		)
             if not isinstance(self.gamma   ,							(float, int)		):
                  raise ValueError('''gamma must be float or int'''		)
             if not self.gamma > 0:
                  raise ValueError('''gamma must be > 0'''		)
             lowercase_       :Union[str, Any]     =       self.__rbf
             # in the future, there could be a default value like in sklearn
             # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
             # previously it was 1/(n_features)
        else:
             lowercase_       :str     =       f"Unknown kernel: {kernel}"
             raise ValueError(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.dot(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Tuple     =       observations
        lowercase_       :Union[str, Any]     =       classes

        # using Wolfe's Dual to calculate w.
        # Primal problem: minimize 1/2*norm_squared(w)
        #   constraint: yn(w . xn + b) >= 1
        #
        # With l a vector
        # Dual problem: maximize sum_n(ln) -
        #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
        #   constraint: self.C >= ln >= 0
        #           and sum_n(ln*yn) = 0
        # Then we get w using w = sum_n(ln*yn*xn)
        # At the end we can get b ~= mean(yn - w . xn)
        #
        # Since we use kernels, we only need l_star to calculate b
        # and to classify observations

        ((lowercase_)    ,      )       :Optional[Any]     =       np.shape(UpperCamelCase_		)

        def to_minimize(UpperCamelCase_		) -> float:
             lowercase_       :Dict     =       0
             ((lowercase_)    ,      )       :Tuple     =       np.shape(UpperCamelCase_		)
             for i in range(UpperCamelCase_		):
                  for j in range(UpperCamelCase_		):
                       s += (
                           candidate[i]
                           * candidate[j]
                           * classes[i]
                           * classes[j]
                           * self.kernel(observations[i]   ,							observations[j]		)
                       )
             return 1 / 2 * s - sum(UpperCamelCase_		)

        lowercase_       :Dict     =       LinearConstraint(UpperCamelCase_   ,							0   ,							0		)
        lowercase_       :Optional[Any]     =       Bounds(0   ,							self.regularization		)

        lowercase_       :Any     =       minimize(
            UpperCamelCase_   ,							np.ones(UpperCamelCase_		)   ,							bounds=UpperCamelCase_   ,							constraints=[ly_contraint]		).x
        lowercase_       :Tuple     =       l_star

        # calculating mean offset of separation plane to points
        lowercase_       :str     =       0
        for i in range(UpperCamelCase_		):
             for j in range(UpperCamelCase_		):
                  s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                      observations[i]   ,							observations[j]		)
        lowercase_       :List[str]     =       s / n




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Union[str, Any]     =       sum(
            self.optimum[n]
            * self.classes[n]
            * self.kernel(self.observations[n]   ,							UpperCamelCase_		)
            for n in range(len(self.classes		)		)		)
        return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

