

def 		UpperCamelCase_(    snake_case__:						List[Any]     ) ->				bool:
 # must be
 UpperCAmelCase__           =       [list(_UpperCAmelCase     ) for row in matrix]
 if matrix and isinstance(_UpperCAmelCase						,					_UpperCAmelCase     ):
  if isinstance(matrix[0]						,					_UpperCAmelCase     ):
   UpperCAmelCase__           =       0
   for row in matrix:
    if prev_len == 0:
     UpperCAmelCase__           =       len(_UpperCAmelCase     )
     UpperCAmelCase__           =       True
    else:
     UpperCAmelCase__           =       prev_len == len(_UpperCAmelCase     )
  else:
   UpperCAmelCase__           =       True
 else:
  UpperCAmelCase__           =       False

 return result
def 		UpperCamelCase_(    snake_case__:						str     ) ->				None:
 if check_matrix(_UpperCAmelCase     ) and len(_UpperCAmelCase     ) > 0:
  UpperCAmelCase__           =       [list(_UpperCAmelCase     ) for row in a]
  UpperCAmelCase__           =       len(_UpperCAmelCase     )
  if isinstance(a[0]						,					_UpperCAmelCase     ):
   UpperCAmelCase__           =       len(a[0]     )
  else:
   for dat in a:
    print(_UpperCAmelCase     )
   return

  # horizotal printing increasing
  for i in range(0						,					_UpperCAmelCase     ):
   print(a[0][i]     )
  # vertical printing down
  for i in range(1						,					_UpperCAmelCase     ):
   print(a[i][mat_col - 1]     )
  # horizotal printing decreasing
  if mat_row > 1:
   for i in range(mat_col - 2						,					-1						,					-1     ):
    print(a[mat_row - 1][i]     )
        # vertical printing up
  for i in range(mat_row - 2						,					0						,					-1     ):
   print(a[i][0]     )
  UpperCAmelCase__           =       [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
  if len(_UpperCAmelCase     ) > 0:
   spiral_print_clockwise(_UpperCAmelCase     )
  else:
   return
 else:
  print('Not a valid matrix'     )
  return
def 		UpperCamelCase_(    snake_case__:						Dict     ) ->				list[int]:
 if matrix:
  return list(matrix.pop(0     )     ) + spiral_traversal(list(zip(*_UpperCAmelCase     )     )[::-1]     )
 else:
  return []


# driver code
if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _UpperCamelCase     =    [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
 spiral_print_clockwise(a)








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_(    snake_case__:						int						,					snake_case__:						int						,					snake_case__:						Union[str, Any]						,					snake_case__:						Any						,					snake_case__:						Any						,					snake_case__:						Union[str, Any]     ) ->				Any:
 # we don't care about the past rewards in this model

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

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

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

 UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       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

_UpperCamelCase     =    gym.make('''Hopper-v3''')
_UpperCamelCase     =    env.observation_space.shape[0]
_UpperCamelCase     =    env.action_space.shape[0]
_UpperCamelCase     =    1000
_UpperCamelCase     =    '''cuda'''
_UpperCamelCase     =    1_0_0_0.0  # normalization for rewards/returns
_UpperCamelCase     =    3600 / 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



