






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      (			UpperCamelCase_				:    int			,				UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    Dict						)     ->      List[str]:

						"""simple docstring"""



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

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

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

						lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__			  =  model(
						    states=UpperCamelCase_			,				actions=UpperCamelCase_			,				rewards=UpperCamelCase_			,				returns_to_go=UpperCamelCase_			,				timesteps=UpperCamelCase_			,				attention_mask=UpperCamelCase_			,				return_dict=UpperCamelCase_			,				)

						return action_preds[0, -1]


# build the environment

__snake_case			:			int			   =   gym.make("""Hopper-v3""")
__snake_case			:			str			   =   env.observation_space.shape[0]
__snake_case			:			List[Any]			   =   env.action_space.shape[0]
__snake_case			:			Tuple			   =   10_00
__snake_case			:			int			   =   """cuda"""
__snake_case			:			Tuple			   =   1000.0  # normalization for rewards/returns
__snake_case			:			List[Any]			   =   36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case			:			List[str]			   =   np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
__snake_case			:			Optional[int]			   =   np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
__snake_case			:			int			   =   torch.from_numpy(state_mean).to(device=device)
__snake_case			:			int			   =   torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__snake_case			:			List[Any]			   =   DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
__snake_case			:			Optional[int]			   =   model.to(device)
model.eval()

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

					__snake_case			:			Optional[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			:			Tuple			   =   torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
										__snake_case			:			List[Any]			   =   torch.cat([rewards, torch.zeros(1, device=device)])

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

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

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

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













import numpy as np
import requests







def 		_UpperCamelCase      (			)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  requests.get(
						    'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
						    'master/Week1/ADRvsRating.csv'						)
						lowerCAmelCase__			  =  response.text.splitlines()
						lowerCAmelCase__			  =  []
						for item in lines:
												lowerCAmelCase__			  =  item.split(','						)
												data.append(UpperCamelCase_						)
						data.pop(0						)  # This is for removing the labels from the list
						lowerCAmelCase__			  =  np.matrix(UpperCamelCase_						)
						return dataset







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    int						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  len_data

						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta - (alpha / n) * sum_grad
						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    str			,				UpperCamelCase_				:    Any			,				UpperCamelCase_				:    str			,				UpperCamelCase_				:    List[Any]						)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.sum(np.square(UpperCamelCase_						)						)
						lowerCAmelCase__			  =  sum_elem / (2 * len_data)
						return error







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    List[Any]						)     ->      str:

						"""simple docstring"""



						lowerCAmelCase__			  =  10_0000
						lowerCAmelCase__			  =  0.000_1550

						lowerCAmelCase__			  =  data_x.shape[1]
						lowerCAmelCase__			  =  data_x.shape[0] - 1

						lowerCAmelCase__			  =  np.zeros((1, no_features)						)

						for i in range(0			,				UpperCamelCase_						):
												lowerCAmelCase__			  =  run_steep_gradient_descent(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												lowerCAmelCase__			  =  sum_of_square_error(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												print(F"At Iteration {i + 1} - Error is {error:.5f}"						)

						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Any						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  sum(abs(y - predicted_y[i]						) for i, y in enumerate(UpperCamelCase_						)						)
						return total / len(UpperCamelCase_						)







def 		_UpperCamelCase      (			)     ->      List[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  collect_dataset()

						lowerCAmelCase__			  =  data.shape[0]
						lowerCAmelCase__			  =  np.c_[np.ones(UpperCamelCase_						), data[:, :-1]].astype(UpperCamelCase_						)
						lowerCAmelCase__			  =  data[:, -1].astype(UpperCamelCase_						)

						lowerCAmelCase__			  =  run_linear_regression(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta.shape[1]
						print('Resultant Feature vector : '						)
						for i in range(0			,				UpperCamelCase_						):
												print(F"{theta[0, i]:.5f}"						)


if __name__ == "__main__":
					main()





