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  (		lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  str						,						lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  Optional[Any]			):
 # we don't care about the past rewards in this model

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

 if model.config.max_length is not None:
  UpperCamelCase_      :		int				=      states[:, -model.config.max_length :]
  UpperCamelCase_      :		Tuple				=      actions[:, -model.config.max_length :]
  UpperCamelCase_      :		List[Any]				=      returns_to_go[:, -model.config.max_length :]
  UpperCamelCase_      :		Tuple				=      timesteps[:, -model.config.max_length :]

  # pad all tokens to sequence length
  UpperCamelCase_      :		Optional[Any]				=      torch.cat(
      [torch.zeros(model.config.max_length - states.shape[1]			), torch.ones(states.shape[1]			)]			)
  UpperCamelCase_      :		Optional[int]				=      attention_mask.to(dtype=torch.long						,						device=states.device			).reshape(1						,						-1			)
  UpperCamelCase_      :		str				=      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			)
  UpperCamelCase_      :		Union[str, Any]				=      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			)
  UpperCamelCase_      :		List[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			)
  UpperCamelCase_      :		Dict				=      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:
  UpperCamelCase_      :		List[str]				=      None

 UpperCamelCase_      :		Any				=      model(
     states=lowerCamelCase						,						actions=lowerCamelCase						,						rewards=lowerCamelCase						,						returns_to_go=lowerCamelCase						,						timesteps=lowerCamelCase						,						attention_mask=lowerCamelCase						,						return_dict=lowerCamelCase						,						)

 return action_preds[0, -1]


# build the environment

a_					      = gym.make('Hopper-v3')
a_					      = env.observation_space.shape[0]
a_					      = env.action_space.shape[0]
a_					      = 1_000
a_					      = 'cuda'
a_					      = 1_000.0  # normalization for rewards/returns
a_					      = 3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a_					      = 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,
    ]
)
a_					      = 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,
    ]
)
a_					      = torch.from_numpy(state_mean).to(device=device)
a_					      = torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
a_					      = DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
a_					      = model.to(device)
model.eval()

for ep in range(10):
   a_      ,				a_					      = 0, 0
   a_					      = env.reset()
   a_					      = torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   a_					      = torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   a_					      = torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   a_					      = torch.zeros(0, device=device, dtype=torch.floataa)

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

      a_					      = 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),
      )
      a_					      = action
      a_					      = action.detach().cpu().numpy()

      a_      ,				a_      ,				a_      ,				a_					      = env.step(action)

      a_					      = torch.from_numpy(state).to(device=device).reshape(1, state_dim)
      a_					      = torch.cat([states, cur_state], dim=0)
      a_					      = reward

      a_					      = target_return[0, -1] - (reward / scale)
      a_					      = torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
      a_					      = 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




from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

a_					      = {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Tuple				=      {start}
 UpperCamelCase_      :		Optional[Any]				=      [start]
 UpperCamelCase_      :		Queue				=      Queue()
 queue.put(lowerCamelCase			)
 while not queue.empty():
  UpperCamelCase_      :		Union[str, Any]				=      queue.get()
  for w in graph[v]:
   if w not in explored:
    explored.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.put(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Dict				=      {start}
 UpperCamelCase_      :		Optional[int]				=      [start]
 UpperCamelCase_      :		Union[str, Any]				=      deque([start]			)
 while queue:
  UpperCamelCase_      :		Optional[int]				=      queue.popleft()
  for child in graph[v]:
   if child not in visited:
    visited.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.append(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  str			):
 UpperCamelCase_      :		str				=      F"from __main__ import G, {name}"
 UpperCamelCase_      :		int				=      10000
 UpperCamelCase_      :		Optional[Any]				=      timeit(F"{name}(G, 'A')"						,						setup=lowerCamelCase						,						number=lowerCamelCase			)
 print(F"{name:<35} finished {number} runs in {res:.5f} seconds"			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   benchmark_function('breadth_first_search')
   benchmark_function('breadth_first_search_with_deque')
   # breadth_first_search                finished 10000 runs in 0.20999 seconds
   # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds



