





'''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 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


  """simple docstring"""



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

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

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

  SCREAMING_SNAKE_CASE				:							str						=	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         =   1000
__UpperCAmelCase         =   """cuda"""
__UpperCAmelCase         =   1000.0  # normalization for rewards/returns
__UpperCAmelCase         =   3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__UpperCAmelCase         =   np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
__UpperCAmelCase         =   np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
__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










'''simple docstring'''




from __future__ import annotations



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[Any]						=	m - 1, 0
		while i >= 0 and j < n:
				if key == mat[i][j]:
						print(f'''Key {key} found at row- {i + 1} column- {j + 1}'''					)
						return
				if key < mat[i][j]:
						i -= 1
				else:
						j += 1
		print(f'''Key {key} not found'''					)



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
		SCREAMING_SNAKE_CASE				:							Dict						=	int(input("""Enter the element to be searched:"""					)					)
		print(lowerCamelCase_					)
		search_in_a_sorted_matrix(lowerCamelCase_	,	len(lowerCamelCase_					)	,	len(mat[0]					)	,	lowerCamelCase_					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()



