


'''simple docstring'''






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 		a__    (							a__  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   )	->							List[str]:
														super().__init__()
														lowerCAmelCase__	       =     value_function
														lowerCAmelCase__	       =     unet
														lowerCAmelCase__	       =     scheduler
														lowerCAmelCase__	       =     env
														lowerCAmelCase__	       =     env.get_dataset()
														lowerCAmelCase__	       =     {}
														for key in self.data.keys():
																					try:
																												lowerCAmelCase__	       =     self.data[key].mean()
																					except:  # noqa: E722
																												pass
														lowerCAmelCase__	       =     {}
														for key in self.data.keys():
																					try:
																												lowerCAmelCase__	       =     self.data[key].std()
																					except:  # noqa: E722
																												pass
														lowerCAmelCase__	       =     env.observation_space.shape[0]
														lowerCAmelCase__	       =     env.action_space.shape[0]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Dict:
														return (x_in - self.means[key]) / self.stds[key]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Any:
														return x_in * self.stds[key] + self.means[key]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							List[str]:
														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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Any:
														for key, val in cond.items():
																					lowerCAmelCase__	       =     val.clone()
														return x_in







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							List[Any]:
														lowerCAmelCase__	       =     x.shape[0]
														lowerCAmelCase__	       =     None
														for i in tqdm.tqdm(self.scheduler.timesteps       ):
																					# create batch of timesteps to pass into model
																					lowerCAmelCase__	       =     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
																																			lowerCAmelCase__	       =     self.value_function(x.permute(0 ,   2 ,   1       ) ,   lowerCamelCase_       ).sample
																																			lowerCAmelCase__	       =     torch.autograd.grad([y.sum()] ,   [x]       )[0]

																																			lowerCAmelCase__	       =     self.scheduler._get_variance(lowerCamelCase_       )
																																			lowerCAmelCase__	       =     torch.exp(0.5 * posterior_variance       )
																																			lowerCAmelCase__	       =     model_std * grad

																												lowerCAmelCase__	       =     0
																												lowerCAmelCase__	       =     x.detach()
																												lowerCAmelCase__	       =     x + scale * grad
																												lowerCAmelCase__	       =     self.reset_xa(lowerCamelCase_ ,   lowerCamelCase_ ,   self.action_dim       )

																					lowerCAmelCase__	       =     self.unet(x.permute(0 ,   2 ,   1       ) ,   lowerCamelCase_       ).sample.permute(0 ,   2 ,   1       )

																					# TODO: verify deprecation of this kwarg
																					lowerCAmelCase__	       =     self.scheduler.step(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   predict_epsilon=lowerCamelCase_       )['''prev_sample''']

																					# apply conditions to the trajectory (set the initial state)
																					lowerCAmelCase__	       =     self.reset_xa(lowerCamelCase_ ,   lowerCamelCase_ ,   self.action_dim       )
																					lowerCAmelCase__	       =     self.to_torch(lowerCamelCase_       )
														return x, y







							def __call__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=64 ,   lowerCamelCase_=32 ,   lowerCamelCase_=2 ,   lowerCamelCase_=0.1       )	->							Any:
														# normalize the observations and create  batch dimension
														lowerCAmelCase__	       =     self.normalize(lowerCamelCase_ ,   '''observations'''       )
														lowerCAmelCase__	       =     obs[None].repeat(lowerCamelCase_ ,   axis=0       )

														lowerCAmelCase__	       =     {0: self.to_torch(lowerCamelCase_       )}
														lowerCAmelCase__	       =     (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)
														lowerCAmelCase__	       =     randn_tensor(lowerCamelCase_ ,   device=self.unet.device       )
														lowerCAmelCase__	       =     self.reset_xa(lowerCamelCase_ ,   lowerCamelCase_ ,   self.action_dim       )
														lowerCAmelCase__	       =     self.to_torch(lowerCamelCase_       )

														# run the diffusion process
														lowerCAmelCase__       , lowerCAmelCase__	       =     self.run_diffusion(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )

														# sort output trajectories by value
														lowerCAmelCase__	       =     y.argsort(0 ,   descending=lowerCamelCase_       ).squeeze()
														lowerCAmelCase__	       =     x[sorted_idx]
														lowerCAmelCase__	       =     sorted_values[:, :, : self.action_dim]
														lowerCAmelCase__	       =     actions.detach().cpu().numpy()
														lowerCAmelCase__	       =     self.de_normalize(lowerCamelCase_ ,   key='''actions'''       )

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

														lowerCAmelCase__	       =     denorm_actions[selected_index, 0]
														return denorm_actions



'''simple docstring'''






from collections.abc import Iterable
from typing import Any




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ = None       )	->							List[str]:
														lowerCAmelCase__	       =     value
														lowerCAmelCase__	       =     None  # Added in order to delete a node easier
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     None







							def __repr__(		self       )	->							str:
														from pprint import pformat

														if self.left is None and self.right is None:
																					return str(self.value       )
														return pformat({F"""{self.value}""": (self.left, self.right)} ,   indent=1       )




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ = None       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     root







							def __str__(		self       )	->							str:
														return str(self.root       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if new_children is not None:  # reset its kids
																					lowerCAmelCase__	       =     node.parent
														if node.parent is not None:  # reset its parent
																					if self.is_right(lowerCamelCase_       ):  # If it is the right children
																												lowerCAmelCase__	       =     new_children
																					else:
																												lowerCAmelCase__	       =     new_children
														else:
																					lowerCAmelCase__	       =     new_children







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							bool:
														if node.parent and node.parent.right:
																					return node == node.parent.right
														return False







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							bool:
														return self.root is None







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     Node(lowerCamelCase_       )  # create a new Node
														if self.empty():  # if Tree is empty
																					lowerCAmelCase__	       =     new_node  # set its root
														else:  # Tree is not empty
																					lowerCAmelCase__	       =     self.root  # from root
																					if parent_node is None:
																												return
																					while True:  # While we don't get to a leaf
																												if value < parent_node.value:  # We go left
																																			if parent_node.left is None:
																																										lowerCAmelCase__	       =     new_node  # We insert the new node in a leaf
																																										break
																																			else:
																																										lowerCAmelCase__	       =     parent_node.left
																												else:
																																			if parent_node.right is None:
																																										lowerCAmelCase__	       =     new_node
																																										break
																																			else:
																																										lowerCAmelCase__	       =     parent_node.right
																					lowerCAmelCase__	       =     parent_node







							def   __SCREAMING_SNAKE_CASE							(		self ,   *lowerCamelCase_       )	->							None:
														for value in values:
																					self.__insert(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Node | None:
														if self.empty():
																					raise IndexError('''Warning: Tree is empty! please use another.'''       )
														else:
																					lowerCAmelCase__	       =     self.root
																					# use lazy evaluation here to avoid NoneType Attribute error
																					while node is not None and node.value is not value:
																												lowerCAmelCase__	       =     node.left if value < node.value else node.right
																					return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ = None       )	->							Node | None:
														if node is None:
																					if self.root is None:
																												return None
																					lowerCAmelCase__	       =     self.root

														if not self.empty():
																					while node.right is not None:
																												lowerCAmelCase__	       =     node.right
														return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ = None       )	->							Node | None:
														if node is None:
																					lowerCAmelCase__	       =     self.root
														if self.root is None:
																					return None
														if not self.empty():
																					lowerCAmelCase__	       =     self.root
																					while node.left is not None:
																												lowerCAmelCase__	       =     node.left
														return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     self.search(lowerCamelCase_       )  # Look for the node with that label
														if node is not None:
																					if node.left is None and node.right is None:  # If it has no children
																												self.__reassign_nodes(lowerCamelCase_ ,   lowerCamelCase_       )
																					elif node.left is None:  # Has only right children
																												self.__reassign_nodes(lowerCamelCase_ ,   node.right       )
																					elif node.right is None:  # Has only left children
																												self.__reassign_nodes(lowerCamelCase_ ,   node.left       )
																					else:
																												lowerCAmelCase__	       =     self.get_max(
																												    node.left       )  # Gets the max value of the left branch
																												self.remove(tmp_node.value       )  # type: ignore
																												lowerCAmelCase__	       =     (
																												    tmp_node.value  # type: ignore
																												)  # Assigns the value to the node to delete and keep tree structure







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Iterable:
														if node is not None:
																					yield node  # Preorder Traversal
																					yield from self.preorder_traverse(node.left       )
																					yield from self.preorder_traverse(node.right       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=None       )	->							Any:
														if traversal_function is None:
																					return self.preorder_traverse(self.root       )
														else:
																					return traversal_function(self.root       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if node:
																					self.inorder(lowerCamelCase_ ,   node.left       )
																					arr.append(node.value       )
																					self.inorder(lowerCamelCase_ ,   node.right       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							int:
														lowerCAmelCase__	       =     []
														self.inorder(lowerCamelCase_ ,   lowerCamelCase_       )  # append all values to list using inorder traversal
														return arr[k - 1]


def  _snake_case       (					A	)						->   list[Node]:
							lowerCAmelCase__	       =     []
							if curr_node is not None:
														lowerCAmelCase__	       =     postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
							return node_list


def  _snake_case       (					)						->   None:
							lowerCAmelCase__	       =     (8, 3, 6, 1, 10, 14, 13, 4, 7)
							lowerCAmelCase__	       =     BinarySearchTree()
							for i in testlist:
														t.insert(A	)

							# Prints all the elements of the list in order traversal
							print(A	)

							if t.search(6	) is not None:
														print('''The value 6 exists'''	)
							else:
														print('''The value 6 doesn\'t exist'''	)

							if t.search(-1	) is not None:
														print('''The value -1 exists'''	)
							else:
														print('''The value -1 doesn\'t exist'''	)

							if not t.empty():
														print('''Max Value: '''			,   t.get_max().value	)  # type: ignore
														print('''Min Value: '''			,   t.get_min().value	)  # type: ignore

							for i in testlist:
														t.remove(A	)
														print(A	)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)