


"""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__			(					__lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						int:
				# 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=__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

a_											=     gym.make("Hopper-v3")
a_											=     env.observation_space.shape[0]
a_											=     env.action_space.shape[0]
a_											=     10_00
a_											=     "cuda"
a_											=     10_00.0  # normalization for rewards/returns
a_											=     36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a_											=     np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
a_											=     np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
a_											=     torch.from_numpy(state_mean).to(device=device)
a_											=     torch.from_numpy(state_std).to(device=device)

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

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

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

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

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

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

				a_											=     target_return[0, -1] - (reward / scale)
				a_											=     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
				a_											=     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 math import factorial






def   a__			(					__lowercase  ,      __lowercase )						->						List[Any]:
				return factorial(__lowercase ) / (factorial(__lowercase ) * factorial(n - r ))






def   a__			(					)						->						List[str]:
				_A       	=      0

				for i in range(1  ,      101 ):
								for j in range(1  ,      i + 1 ):
												if combinations(__lowercase  ,      __lowercase ) > 1E6:
																total += 1
				return total


if __name__ == "__main__":
		print(solution())