




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 				UpperCAmelCase__  (							_A      ,      _A      ,      _A      ,      _A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  states.reshape(1      ,      -1      ,      model.config.state_dim			)
  a_		    =  actions.reshape(1      ,      -1      ,      model.config.act_dim			)
  a_		    =  returns_to_go.reshape(1      ,      -1      ,      1			)
  a_		    =  timesteps.reshape(1      ,      -1			)

  if model.config.max_length is not None:
    a_		    =  states[:, -model.config.max_length :]
    a_		    =  actions[:, -model.config.max_length :]
    a_		    =  returns_to_go[:, -model.config.max_length :]
    a_		    =  timesteps[:, -model.config.max_length :]

    # pad all tokens to sequence length
    a_		    =  torch.cat(
        [torch.zeros(model.config.max_length - states.shape[1]			), torch.ones(states.shape[1]			)]			)
    a_		    =  attention_mask.to(dtype=torch.long      ,      device=states.device			).reshape(1      ,      -1			)
    a_		    =  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			)
    a_		    =  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			)
    a_		    =  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			)
    a_		    =  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:
    a_		    =  None

  a_		    =  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

UpperCamelCase__       				=	gym.make('''Hopper-v3''')
UpperCamelCase__       				=	env.observation_space.shape[0]
UpperCamelCase__       				=	env.action_space.shape[0]
UpperCamelCase__       				=	1_000
UpperCamelCase__       				=	'''cuda'''
UpperCamelCase__       				=	1_0_0_0.0  # normalization for rewards/returns
UpperCamelCase__       				=	3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase__       				=	np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
UpperCamelCase__       				=	np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
UpperCamelCase__       				=	torch.from_numpy(state_mean).to(device=device)
UpperCamelCase__       				=	torch.from_numpy(state_std).to(device=device)

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

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

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

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

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

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

    UpperCamelCase__       				=	target_return[0, -1] - (reward / scale)
    UpperCamelCase__       				=	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    UpperCamelCase__       				=	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 hashlib import mda, shaaaa

__snake_case         =     (shaaaa, mda)







class       __snake_case :
 def __init__(	self		, snake_case__ = 8    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=0b0
       UpperCAmelCase     :			str     	=size
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.hash_(snake_case__    )
       self.bitarray |= h
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				bool:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.hash_(snake_case__    )
       return (h & self.bitarray) == h
 def __contains__(	self		, snake_case__    )						->				bool:
       '''simple docstring'''





       return self.exists(snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=bin(snake_case__    )[2:]
       return res.zfill(self.size    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       return self.format_bin(self.bitarray    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=0b0
       for func in HASH_FUNCTIONS:
             UpperCAmelCase     :			Optional[Any]     	=(
                 int.from_bytes(func(value.encode()    ).digest()		, '''little'''    ) % self.size
             )
             res |= 2**position
       return res
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       return self.format_bin(self.hash_(snake_case__    )    )






 @property
 def 						UpperCAmelCase__							(	self    )						->				float:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=bin(self.bitarray    ).count('''1'''    )
       return (n_ones / self.size) ** len(snake_case__    )
