






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        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




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







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :str     ):
					return (x_in - self.means[key]) / self.stds[key]







				def 				__a   ( self     :int  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :Tuple     ):
					return x_in * self.stds[key] + self.means[key]







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







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :Tuple     ):
					for key, val in cond.items():
						UpperCamelCase__ :str						=      val.clone()
					return x_in







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

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

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

							UpperCamelCase__ :Optional[Any]						=      0
							UpperCamelCase__ :Dict						=      x.detach()
							UpperCamelCase__ :int						=      x + scale * grad
							UpperCamelCase__ :int						=      self.reset_xa(lowerCamelCase__  ,     lowerCamelCase__  ,     self.action_dim     )

						UpperCamelCase__ :List[str]						=      self.unet(x.permute(0  ,     2  ,     1     )  ,     lowerCamelCase__     ).sample.permute(0  ,     2  ,     1     )

						# TODO: verify deprecation of this kwarg
						UpperCamelCase__ :List[str]						=      self.scheduler.step(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     predict_epsilon=lowerCamelCase__     )["""prev_sample"""]

						# apply conditions to the trajectory (set the initial state)
						UpperCamelCase__ :Optional[Any]						=      self.reset_xa(lowerCamelCase__  ,     lowerCamelCase__  ,     self.action_dim     )
						UpperCamelCase__ :Optional[int]						=      self.to_torch(lowerCamelCase__     )
					return x, y







				def __call__( self     :Optional[Any]  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :str=64  ,     lowerCamelCase__     :Tuple=32  ,     lowerCamelCase__     :Dict=2  ,     lowerCamelCase__     :str=0.1     ):
					# normalize the observations and create  batch dimension
					UpperCamelCase__ :List[str]						=      self.normalize(lowerCamelCase__  ,     """observations"""     )
					UpperCamelCase__ :List[str]						=      obs[None].repeat(lowerCamelCase__  ,     axis=0     )

					UpperCamelCase__ :int						=      {0: self.to_torch(lowerCamelCase__     )}
					UpperCamelCase__ :Dict						=      (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)
					UpperCamelCase__ :Any						=      randn_tensor(lowerCamelCase__  ,     device=self.unet.device     )
					UpperCamelCase__ :Optional[int]						=      self.reset_xa(lowerCamelCase__  ,     lowerCamelCase__  ,     self.action_dim     )
					UpperCamelCase__ :List[Any]						=      self.to_torch(lowerCamelCase__     )

					# run the diffusion process
					UpperCamelCase__				,		UpperCamelCase__ :Union[str, Any]						=      self.run_diffusion(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )

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

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

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







'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np


def 				lowercase_					(     __A			:							dict       ,   __A			:							str       ,   __A			:							set       ,   __A			:							set       ,   __A			:							dict       ,   __A			:							dict       ,   __A			:							PriorityQueue       ,   __A			:							dict       ,   __A			:							float | int       ,   )							->   float | int:



 """simple docstring"""




 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  lowercase       : str						=cst_fwd.get(__A       ,   np.inf      )
  lowercase       : str						=cst_fwd[v] + d
  if new_cost_f < old_cost_f:
   queue.put((new_cost_f, nxt)      )
   lowercase       : int						=new_cost_f
   lowercase       : Optional[int]						=v
  if nxt in visited_backward:
   if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
    lowercase       : List[Any]						=cst_fwd[v] + d + cst_bwd[nxt]
 return shortest_distance


def 				lowercase_					(     __A			:							str       ,   __A			:							str       ,   __A			:							dict       ,   __A			:							dict      )							->   int:



 """simple docstring"""




 lowercase       : Optional[Any]						=-1

 lowercase       : Tuple						=set()
 lowercase       : str						=set()
 lowercase       : List[str]						={source: 0}
 lowercase       : List[str]						={destination: 0}
 lowercase       : Tuple						={source: None}
 lowercase       : List[Any]						={destination: None}
 lowercase       : PriorityQueue[Any]						=PriorityQueue()
 lowercase       : PriorityQueue[Any]						=PriorityQueue()

 lowercase       : Any						=np.inf

 queue_forward.put((0, source)      )
 queue_backward.put((0, destination)      )

 if source == destination:
  return 0

 while not queue_forward.empty() and not queue_backward.empty():
  lowercase	,		lowercase       : int						=queue_forward.get()
  visited_forward.add(__A      )

  lowercase	,		lowercase       : int						=queue_backward.get()
  visited_backward.add(__A      )

  lowercase       : Dict						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

  lowercase       : List[Any]						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

  if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
   break

 if shortest_distance != np.inf:
  lowercase       : Union[str, Any]						=shortest_distance
 return shortest_path_distance


SCREAMING_SNAKE_CASE							=						{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
SCREAMING_SNAKE_CASE							=						{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
      import doctest

      doctest.testmod()



