
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 __snake_case		(				lowerCAmelCase					):
    def __init__(  self		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           super().__init__()
           lowercase							:					int	     =				value_function
           lowercase							:					Union[str, Any]	     =				unet
           lowercase							:					Tuple	     =				scheduler
           lowercase							:					int	     =				env
           lowercase							:					str	     =				env.get_dataset()
           lowercase							:					int	     =				{}
           for key in self.data.keys():
                  try:
                         lowercase							:					Dict	     =				self.data[key].mean()
                  except:  # noqa: E722
                         pass
           lowercase							:					Optional[int]	     =				{}
           for key in self.data.keys():
                  try:
                         lowercase							:					Tuple	     =				self.data[key].std()
                  except:  # noqa: E722
                         pass
           lowercase							:					int	     =				env.observation_space.shape[0]
           lowercase							:					Optional[int]	     =				env.action_space.shape[0]
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







           for key, val in cond.items():
                  lowercase							:					str	     =				val.clone()
           return x_in
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				x.shape[0]
           lowercase							:					Optional[int]	     =				None
           for i in tqdm.tqdm(self.scheduler.timesteps				):
                  # create batch of timesteps to pass into model
                  lowercase							:					Tuple	     =				torch.full((batch_size,)		,snake_case		,device=self.unet.device		,dtype=torch.long				)
                  for _ in range(snake_case				):
                         with torch.enable_grad():
                                x.requires_grad_()

                                # permute to match dimension for pre-trained models
                                lowercase							:					int	     =				self.value_function(x.permute(0		,2		,1				)		,snake_case				).sample
                                lowercase							:					List[Any]	     =				torch.autograd.grad([y.sum()]		,[x]				)[0]

                                lowercase							:					int	     =				self.scheduler._get_variance(snake_case				)
                                lowercase							:					Any	     =				torch.exp(0.5 * posterior_variance				)
                                lowercase							:					List[Any]	     =				model_std * grad

                         lowercase							:					Optional[Any]	     =				0
                         lowercase							:					List[str]	     =				x.detach()
                         lowercase							:					Optional[Any]	     =				x + scale * grad
                         lowercase							:					Optional[int]	     =				self.reset_xa(snake_case		,snake_case		,self.action_dim				)

                  lowercase							:					Optional[Any]	     =				self.unet(x.permute(0		,2		,1				)		,snake_case				).sample.permute(0		,2		,1				)

                  # TODO: verify deprecation of this kwarg
                  lowercase							:					Optional[Any]	     =				self.scheduler.step(snake_case		,snake_case		,snake_case		,predict_epsilon=snake_case				)["""prev_sample"""]

                  # apply conditions to the trajectory (set the initial state)
                  lowercase							:					int	     =				self.reset_xa(snake_case		,snake_case		,self.action_dim				)
                  lowercase							:					Dict	     =				self.to_torch(snake_case				)
           return x, y
    def __call__(  self		,snake_case		,snake_case=64		,snake_case=32		,snake_case=2		,snake_case=0.1				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.normalize(snake_case		,"""observations"""				)
           lowercase							:					List[str]	     =				obs[None].repeat(snake_case		,axis=0				)

           lowercase							:					List[Any]	     =				{0: self.to_torch(snake_case				)}
           lowercase							:					Tuple	     =				(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							:					Dict	     =				randn_tensor(snake_case		,device=self.unet.device				)
           lowercase							:					Optional[Any]	     =				self.reset_xa(snake_case		,snake_case		,self.action_dim				)
           lowercase							:					str	     =				self.to_torch(snake_case				)

           # run the diffusion process
           lowercase							:					Any	     =				self.run_diffusion(snake_case		,snake_case		,snake_case		,snake_case				)

           # sort output trajectories by value
           lowercase							:					Dict	     =				y.argsort(0		,descending=snake_case				).squeeze()
           lowercase							:					Any	     =				x[sorted_idx]
           lowercase							:					int	     =				sorted_values[:, :, : self.action_dim]
           lowercase							:					List[Any]	     =				actions.detach().cpu().numpy()
           lowercase							:					List[str]	     =				self.de_normalize(snake_case		,key="""actions"""				)

           # select the action with the highest value
           if y is not None:
                  lowercase							:					Tuple	     =				0
           else:
                  # if we didn't run value guiding, select a random action
                  lowercase							:					int	     =				np.random.randint(0		,snake_case				)

           lowercase							:					Optional[Any]	     =				denorm_actions[selected_index, 0]
           return denorm_actions


def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     lowercase							:					str	     =				mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     lowercase							:					Optional[int]	     =				max(
                         mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     j - wt[i - 1]    ) + val[i - 1] ,     )
              lowercase							:					List[Any]	     =				val
       return f[i][j]







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Any:
       lowercase							:					Optional[int]	     =				[[0] * (w + 1) for _ in range(n + 1    )]

       for i in range(1 ,     n + 1    ):
              for w_ in range(1 ,     w + 1    ):
                     if wt[i - 1] <= w_:
                            lowercase							:					Union[str, Any]	     =				max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,     dp[i - 1][w_]    )
                     else:
                            lowercase							:					Any	     =				dp[i - 1][w_]

       return dp[n][w_], dp







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       if not (isinstance(SCREAMING_SNAKE_CASE__ ,     (list, tuple)    ) and isinstance(SCREAMING_SNAKE_CASE__ ,     (list, tuple)    )):
              raise ValueError(
                  """Both the weights and values vectors must be either lists or tuples"""    )

       lowercase							:					Optional[Any]	     =				len(SCREAMING_SNAKE_CASE__    )
       if num_items != len(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					Optional[Any]	     =				(
                  """The number of weights must be the same as the number of values.\n"""
                  f"But got {num_items} weights and {len(SCREAMING_SNAKE_CASE__    )} values"
              )
              raise ValueError(SCREAMING_SNAKE_CASE__    )
       for i in range(SCREAMING_SNAKE_CASE__    ):
              if not isinstance(wt[i] ,     SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Tuple	     =				(
                         """All weights must be integers but got weight of """
                         f"type {type(wt[i]    )} at index {i}"
                     )
                     raise TypeError(SCREAMING_SNAKE_CASE__    )

       lowercase     ,      lowercase							:					Any	     =				knapsack(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase							:					set	     =				set()
       _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       return optimal_val, example_optional_set







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       # for the current item i at a maximum weight j to be part of an optimal subset,
       # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
       # where i - 1 means considering only the previous items at the given maximum weight
       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     optimal_set.add(SCREAMING_SNAKE_CASE__    )
                     _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     i - 1 ,     j - wt[i - 1] ,     SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
  lowercase  : Dict						   =							[3, 2, 4, 4]
  lowercase  : List[Any]						   =							[4, 3, 2, 3]
  lowercase  : Tuple						   =							4
  lowercase  : Tuple						   =							6
  lowercase  : Tuple						   =							[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
  lowercase   ,  lowercase  : List[str]						   =							knapsack(w, wt, val, n)
  print(optimal_solution)
  print(mf_knapsack(n, wt, val, w))  # switched the n and w

  # testing the dynamic programming problem with example
  # the optimal subset for the above example are items 3 and 4
  lowercase   ,  lowercase  : Optional[int]						   =							knapsack_with_example_solution(w, wt, val)
  assert optimal_solution == 8
  assert optimal_subset == {3, 4}
  print("""optimal_value = """, optimal_solution)
  print("""An optimal subset corresponding to the optimal value""", optimal_subset)
