

"""simple docstring"""



class        a    :



    """simple docstring"""



    def __init__(  self:			Optional[Any]     ,    UpperCamelCase:			Any							):






         """simple docstring"""


         A__				      =	val
         # Number of nodes in left subtree
         A__				      =	0
         A__				      =	None
         A__				      =	None
         A__				      =	None





    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			Tuple							):






         """simple docstring"""


         assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

         if self.val < other.val:
              A__				      =	self.right
              A__				      =	None
              if self.right:
                   A__				      =	other
              A__				      =	other
              A__				      =	self.left_tree_size * 2 + 1
              return self
         else:
              A__				      =	other.right
              A__				      =	None
              if other.right:
                   A__				      =	self
              A__				      =	self
              A__				      =	other.left_tree_size * 2 + 1
              return other



class        a    :



    """simple docstring"""



    def __init__(  self:			str     ,    UpperCamelCase:			Optional[int]=None     ,    UpperCamelCase:			Union[str, Any]=None     ,    UpperCamelCase:			int=0							):






         """simple docstring"""


         A__				      =	heap_size
         A__				      =	bottom_root
         A__				      =	min_node

    def 					UpperCamelCase						(  self:			List[str]     ,    UpperCamelCase:			List[str]							):






         """simple docstring"""


         if other.size == 0:
              return None
         if self.size == 0:
              A__				      =	other.size
              A__				      =	other.bottom_root
              A__				      =	other.min_node
              return None
         # Update size
         A__				      =	self.size + other.size

         # Update min.node
         if self.min_node.val > other.min_node.val:
              A__				      =	other.min_node
         # Merge

         # Order roots by left_subtree_size
         A__				      =	[]
         A__				      =	self.bottom_root, other.bottom_root
         while i or j:
              if i and ((not j) or i.left_tree_size < j.left_tree_size):
                   combined_roots_list.append((i, True)							)
                   A__				      =	i.parent
              else:
                   combined_roots_list.append((j, False)							)
                   A__				      =	j.parent
        # Insert links between them
         for i in range(len(a_							) - 1							):
              if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
                   A__				      =	combined_roots_list[i + 1][0]
                   A__				      =	combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
         A__				      =	combined_roots_list[0][0]
         while i.parent:
              if (
                  (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
              ) or (
                  i.left_tree_size == i.parent.left_tree_size
                  and i.left_tree_size != i.parent.parent.left_tree_size
              ):
                   # Neighbouring Nodes
                   A__				      =	i.left
                   A__				      =	i.parent.parent

                   # Merging trees
                   A__				      =	i.merge_trees(i.parent							)

                   # Updating links
                   A__				      =	previous_node
                   A__				      =	next_node
                   if previous_node:
                        A__				      =	i
                   if next_node:
                        A__				      =	i
              else:
                   A__				      =	i.parent
        # Updating self.bottom_root
         while i.left:
              A__				      =	i.left
         A__				      =	i

         # Update other
         A__				      =	self.size
         A__				      =	self.bottom_root
         A__				      =	self.min_node

         # Return the merged heap
         return self

    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			Optional[int]							):






         """simple docstring"""


         if self.size == 0:
              A__				      =	Node(a_							)
              A__				      =	1
              A__				      =	self.bottom_root
         else:
              # Create new node
              A__				      =	Node(a_							)

              # Update size
              self.size += 1

              # update min_node
              if val < self.min_node.val:
                   A__				      =	new_node
              # Put new_node as a bottom_root in heap
              A__				      =	new_node
              A__				      =	self.bottom_root
              A__				      =	new_node

              # Consecutively merge roots with same left_tree_size
              while (
                  self.bottom_root.parent
                  and self.bottom_root.left_tree_size
                  == self.bottom_root.parent.left_tree_size
              ):
                   # Next node
                   A__				      =	self.bottom_root.parent.parent

                   # Merge
                   A__				      =	self.bottom_root.merge_trees(self.bottom_root.parent							)

                   # Update Links
                   A__				      =	next_node
                   A__				      =	None
                   if next_node:
                        A__				      =	self.bottom_root

    def 					UpperCamelCase						(  self:			Optional[Any]							):






         """simple docstring"""


         return self.min_node.val

    def 					UpperCamelCase						(  self:			Optional[int]							):






         """simple docstring"""


         return self.size == 0

    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


         A__				      =	self.min_node.val

         # Last element in heap corner case
         if self.size == 1:
              # Update size
              A__				      =	0

              # Update bottom root
              A__				      =	None

              # Update min_node
              A__				      =	None

              return min_value
         # No right subtree corner case
         # The structure of the tree implies that this should be the bottom root
         # and there is at least one other root
         if self.min_node.right is None:
              # Update size
              self.size -= 1

              # Update bottom root
              A__				      =	self.bottom_root.parent
              A__				      =	None

              # Update min_node
              A__				      =	self.bottom_root
              A__				      =	self.bottom_root.parent
              while i:
                   if i.val < self.min_node.val:
                        A__				      =	i
                   A__				      =	i.parent
              return min_value
         # General case
         # Find the BinomialHeap of the right subtree of min_node
         A__				      =	self.min_node.right
         A__				      =	None
         A__				      =	bottom_of_new
         A__				      =	1

         # Size, min_node and bottom_root
         while bottom_of_new.left:
              A__				      =	size_of_new * 2 + 1
              A__				      =	bottom_of_new.left
              if bottom_of_new.val < min_of_new.val:
                   A__				      =	bottom_of_new
        # Corner case of single root on top left path
         if (not self.min_node.left) and (not self.min_node.parent):
              A__				      =	size_of_new
              A__				      =	bottom_of_new
              A__				      =	min_of_new
              # print("Single root, multiple nodes case")
              return min_value
         # Remaining cases
         # Construct heap of right subtree
         A__				      =	BinomialHeap(
             bottom_root=a_     ,    min_node=a_     ,    heap_size=a_							)

         # Update size
         A__				      =	self.size - 1 - size_of_new

         # Neighbour nodes
         A__				      =	self.min_node.left
         A__				      =	self.min_node.parent

         # Initialize new bottom_root and min_node
         A__				      =	previous_node or next_node
         A__				      =	next_node

         # Update links of previous_node and search below for new min_node and
         # bottom_root
         if previous_node:
              A__				      =	next_node

              # Update bottom_root and search for min_node below
              A__				      =	previous_node
              A__				      =	previous_node
              while self.bottom_root.left:
                   A__				      =	self.bottom_root.left
                   if self.bottom_root.val < self.min_node.val:
                        A__				      =	self.bottom_root
         if next_node:
              A__				      =	previous_node

              # Search for new min_node above min_node
              A__				      =	next_node
              while i:
                   if i.val < self.min_node.val:
                        A__				      =	i
                   A__				      =	i.parent
        # Merge heaps
         self.merge_heaps(a_							)

         return min_value

    def 					UpperCamelCase						(  self:			Dict							):






         """simple docstring"""


         A__				      =	self.bottom_root
         while top_root.parent:
              A__				      =	top_root.parent
         # preorder
         A__				      =	[]
         self.__traversal(a_     ,    a_							)
         return heap_pre_order

    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			int     ,    UpperCamelCase:			Union[str, Any]     ,    UpperCamelCase:			Optional[int]=0							):






         """simple docstring"""


         if curr_node:
              preorder.append((curr_node.val, level)							)
              self.__traversal(curr_node.left     ,    a_     ,    level + 1							)
              self.__traversal(curr_node.right     ,    a_     ,    level + 1							)
         else:
              preorder.append(("""#""", level)							)





    def __str__(  self:			int							):






         """simple docstring"""


         if self.is_empty():
              return ""
         A__				      =	self.pre_order()

         return "\n".join(("""-""" * level + str(a_							)) for value, level in preorder_heap							)


# Unit Tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()










"""simple docstring"""
import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.






def 						lowercase				(	_snake_case		: Tuple	,  _snake_case		: Tuple	,  _snake_case		: Optional[Any]	,  _snake_case		: str	,  _snake_case		: int	,  _snake_case		: List[str]	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		Dict         =							states.reshape(1	,  -1	,  model.config.state_dim	)
	__snake_case      :		List[str]         =							actions.reshape(1	,  -1	,  model.config.act_dim	)
	__snake_case      :		Union[str, Any]         =							returns_to_go.reshape(1	,  -1	,  1	)
	__snake_case      :		Any         =							timesteps.reshape(1	,  -1	)

	if model.config.max_length is not None:
		__snake_case      :		Union[str, Any]         =							states[:, -model.config.max_length :]
		__snake_case      :		Any         =							actions[:, -model.config.max_length :]
		__snake_case      :		Tuple         =							returns_to_go[:, -model.config.max_length :]
		__snake_case      :		Union[str, Any]         =							timesteps[:, -model.config.max_length :]

		# pad all tokens to sequence length
		__snake_case      :		Dict         =							torch.cat(
		    [torch.zeros(model.config.max_length - states.shape[1]	), torch.ones(states.shape[1]	)]	)
		__snake_case      :		str         =							attention_mask.to(dtype=torch.long	,  device=states.device	).reshape(1	,  -1	)
		__snake_case      :		Dict         =							torch.cat(
		    [
		        torch.zeros(
		            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)	,  device=states.device	,  ),
		        states,
		    ]	,  dim=1	,  ).to(dtype=torch.floataa	)
		__snake_case      :		Tuple         =							torch.cat(
		    [
		        torch.zeros(
		            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)	,  device=actions.device	,  ),
		        actions,
		    ]	,  dim=1	,  ).to(dtype=torch.floataa	)
		__snake_case      :		str         =							torch.cat(
		    [
		        torch.zeros(
		            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)	,  device=returns_to_go.device	,  ),
		        returns_to_go,
		    ]	,  dim=1	,  ).to(dtype=torch.floataa	)
		__snake_case      :		Union[str, Any]         =							torch.cat(
		    [
		        torch.zeros(
		            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])	,  device=timesteps.device	),
		        timesteps,
		    ]	,  dim=1	,  ).to(dtype=torch.long	)
	else:
		__snake_case      :		int         =							None

	__snake_case      ,				__snake_case      ,				__snake_case      :		Optional[int]         =							model(
	    states=_snake_case	,  actions=_snake_case	,  rewards=_snake_case	,  returns_to_go=_snake_case	,  timesteps=_snake_case	,  attention_mask=_snake_case	,  return_dict=_snake_case	,  )

	return action_preds[0, -1]


# build the environment

SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							gym.make("""Hopper-v3""")
SCREAMING_SNAKE_CASE					:       List[str]							   =							env.observation_space.shape[0]
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							env.action_space.shape[0]
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							1000
SCREAMING_SNAKE_CASE					:       Tuple							   =							"""cuda"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1_000.0  # normalization for rewards/returns
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
SCREAMING_SNAKE_CASE					:       List[Any]							   =							np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
SCREAMING_SNAKE_CASE					:       List[str]							   =							torch.from_numpy(state_mean).to(device=device)
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
SCREAMING_SNAKE_CASE					:       List[Any]							   =							DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							model.to(device)
model.eval()

for ep in range(10):
			SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE					:       str							   =							0, 0
			SCREAMING_SNAKE_CASE					:       int							   =							env.reset()
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
			SCREAMING_SNAKE_CASE					:       str							   =							torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
			SCREAMING_SNAKE_CASE					:       Tuple							   =							torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							torch.zeros(0, device=device, dtype=torch.floataa)

			SCREAMING_SNAKE_CASE					:       int							   =							torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
			for t in range(max_ep_len):
						env.render()
						# add padding
						SCREAMING_SNAKE_CASE					:       str							   =							torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							torch.cat([rewards, torch.zeros(1, device=device)])

						SCREAMING_SNAKE_CASE					:       Dict							   =							get_action(
						    model,
						    (states.to(dtype=torch.floataa) - state_mean) / state_std,
						    actions.to(dtype=torch.floataa),
						    rewards.to(dtype=torch.floataa),
						    target_return.to(dtype=torch.floataa),
						    timesteps.to(dtype=torch.long),
						)
						SCREAMING_SNAKE_CASE					:       Optional[int]							   =							action
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							action.detach().cpu().numpy()

						SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE					:       List[str]							   =							env.step(action)

						SCREAMING_SNAKE_CASE					:       Dict							   =							torch.from_numpy(state).to(device=device).reshape(1, state_dim)
						SCREAMING_SNAKE_CASE					:       int							   =							torch.cat([states, cur_state], dim=0)
						SCREAMING_SNAKE_CASE					:       List[str]							   =							reward

						SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							target_return[0, -1] - (reward / scale)
						SCREAMING_SNAKE_CASE					:       Dict							   =							torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

						episode_return += reward
						episode_length += 1

						if done:
									break





