



"""simple docstring"""







import cva
import numpy as np



def   a_	(			__a		,							__a		,							__a		,							__a      ):

     try:
          return int(image[x_coordinate][y_coordinate] >= center      )
     except (IndexError, TypeError):
          return 0



def   a_	(			__a		,							__a		,							__a      ):
     A__				=   image[x_coordinate][y_coordinate]
     A__				=   [1, 2, 4, 8, 16, 32, 64, 128]

     # skip get_neighbors_pixel if center is null
     if center is None:
          return 0

     # Starting from the top right, assigning value to pixels clockwise
     A__				=   [
         get_neighbors_pixel(__a		,							x_coordinate - 1		,							y_coordinate + 1		,							__a      ),
         get_neighbors_pixel(__a		,							__a		,							y_coordinate + 1		,							__a      ),
         get_neighbors_pixel(__a		,							x_coordinate - 1		,							__a		,							__a      ),
         get_neighbors_pixel(__a		,							x_coordinate + 1		,							y_coordinate + 1		,							__a      ),
         get_neighbors_pixel(__a		,							x_coordinate + 1		,							__a		,							__a      ),
         get_neighbors_pixel(__a		,							x_coordinate + 1		,							y_coordinate - 1		,							__a      ),
         get_neighbors_pixel(__a		,							__a		,							y_coordinate - 1		,							__a      ),
         get_neighbors_pixel(__a		,							x_coordinate - 1		,							y_coordinate - 1		,							__a      ),
     ]

     # Converting the binary value to decimal.
     return sum(
         binary_value * power for binary_value, power in zip(__a		,							__a      )      )


if __name__ == "__main__":
 # Reading the image and converting it to grayscale.
 __snake_case	:				Dict  					=			cva.imread(
     'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
 )

 # Create a numpy array as the same height and width of read image
 __snake_case	:				Optional[int]  					=			np.zeros((image.shape[0], image.shape[1]))

 # Iterating through the image and calculating the
 # local binary pattern value for each pixel.
 for i in range(0, image.shape[0]):
  for j in range(0, image.shape[1]):
   __snake_case	:				Union[str, Any]  					=			local_binary_value(image, i, j)

 cva.imshow('local binary pattern', lbp_image)
 cva.waitKey(0)
 cva.destroyAllWindows()








"""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_	(			__a		,							__a		,							__a		,							__a		,							__a		,							__a      ):
     # we don't care about the past rewards in this model

     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__	,     A__	,     A__				=   model(
         states=__a		,							actions=__a		,							rewards=__a		,							returns_to_go=__a		,							timesteps=__a		,							attention_mask=__a		,							return_dict=__a		,							)

     return action_preds[0, -1]


# build the environment

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

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

for ep in range(10):
 __snake_case					,  __snake_case	:				Union[str, Any]  					=			0, 0
 __snake_case	:				List[Any]  					=			env.reset()
 __snake_case	:				List[str]  					=			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 __snake_case	:				Union[str, Any]  					=			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 __snake_case	:				List[str]  					=			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 __snake_case	:				str  					=			torch.zeros(0, device=device, dtype=torch.floataa)

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

  __snake_case	:				Any  					=			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),
  )
  __snake_case	:				List[str]  					=			action
  __snake_case	:				int  					=			action.detach().cpu().numpy()

  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case	:				Union[str, Any]  					=			env.step(action)

  __snake_case	:				Tuple  					=			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  __snake_case	:				Dict  					=			torch.cat([states, cur_state], dim=0)
  __snake_case	:				Optional[Any]  					=			reward

  __snake_case	:				Union[str, Any]  					=			target_return[0, -1] - (reward / scale)
  __snake_case	:				Dict  					=			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  __snake_case	:				Any  					=			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



