


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__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 # we don't care about the past rewards in this model

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

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

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

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

 return action_preds[0, -1]


# build the environment

lowercase_           =	gym.make('Hopper-v3')
lowercase_           =	env.observation_space.shape[0]
lowercase_           =	env.action_space.shape[0]
lowercase_           =	1_0_0_0
lowercase_           =	'cuda'
lowercase_           =	1000.0  # normalization for rewards/returns
lowercase_           =	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowercase_           =	np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
lowercase_           =	np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
lowercase_           =	torch.from_numpy(state_mean).to(device=device)
lowercase_           =	torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
       lowercase_    ,lowercase_           =	0, 0
       lowercase_           =	env.reset()
       lowercase_           =	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
       lowercase_           =	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
       lowercase_           =	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
       lowercase_           =	torch.zeros(0, device=device, dtype=torch.floataa)

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

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

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

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

              lowercase_           =	target_return[0, -1] - (reward / scale)
              lowercase_           =	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
              lowercase_           =	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
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase					,     __lowerCamelCase		:							str							=						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       UpperCamelCase__     (  ):
 __lowerCamelCase		:							List[Any]							=						[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
 __lowerCamelCase		:							int							=						int(input('Enter the element to be searched:'						)						)
 print(SCREAMING_SNAKE_CASE__						)
 search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE__			,       len(SCREAMING_SNAKE_CASE__						)			,       len(mat[0]						)			,       SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()

