



"""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 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_	,			lowercase_	,			lowercase_	,			lowercase_		):
						# 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=lowercase_	,			actions=lowercase_	,			rewards=lowercase_	,			returns_to_go=lowercase_	,			timesteps=lowercase_	,			attention_mask=lowercase_	,			return_dict=lowercase_	,			)

						return action_preds[0, -1]


# build the environment

snake_case_         = gym.make("""Hopper-v3""")
snake_case_         = env.observation_space.shape[0]
snake_case_         = env.action_space.shape[0]
snake_case_         = 1000
snake_case_         = """cuda"""
snake_case_         = 1000.0  # normalization for rewards/returns
snake_case_         = 3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case_         = 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_         = 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_         = torch.from_numpy(state_mean).to(device=device)
snake_case_         = torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
				snake_case_  ,     snake_case_         = 0, 0
				snake_case_         = env.reset()
				snake_case_         = torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
				snake_case_         = torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
				snake_case_         = torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
				snake_case_         = torch.zeros(0, device=device, dtype=torch.floataa)

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

								snake_case_         = 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_         = action
								snake_case_         = action.detach().cpu().numpy()

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

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

								snake_case_         = target_return[0, -1] - (reward / scale)
								snake_case_         = torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
								snake_case_         = 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 collections import Counter

def 				_lowerCAmelCase   (     lowercase_		):
						return sum(socks_by_color // 2 for socks_by_color in Counter(lowercase_		).values()		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				snake_case_         = [int(x) for x in input("""Enter socks by color :""").rstrip().split()]
				print(f'''sock_merchant({colors}) = {sock_merchant(colors)}''')





