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(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					# 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=__lowerCamelCase      ,				actions=__lowerCamelCase      ,				rewards=__lowerCamelCase      ,				returns_to_go=__lowerCamelCase      ,				timesteps=__lowerCamelCase      ,				attention_mask=__lowerCamelCase      ,				return_dict=__lowerCamelCase      ,				)

					return action_preds[0, -1]


# build the environment

lowerCamelCase_				:		str       						=       gym.make("""Hopper-v3""")
lowerCamelCase_				:		str       						=       env.observation_space.shape[0]
lowerCamelCase_				:		Optional[Any]       						=       env.action_space.shape[0]
lowerCamelCase_				:		List[Any]       						=       1_000
lowerCamelCase_				:		Union[str, Any]       						=       """cuda"""
lowerCamelCase_				:		str       						=       1_0_0_0.0  # normalization for rewards/returns
lowerCamelCase_				:		List[Any]       						=       3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowerCamelCase_				:		str       						=       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,
    ]
)
lowerCamelCase_				:		List[str]       						=       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,
    ]
)
lowerCamelCase_				:		Dict       						=       torch.from_numpy(state_mean).to(device=device)
lowerCamelCase_				:		Any       						=       torch.from_numpy(state_std).to(device=device)

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

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

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

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

		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_				:		Optional[int]       						=       env.step(action)

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

		lowerCamelCase_				:		str       						=       target_return[0, -1] - (reward / scale)
		lowerCamelCase_				:		Tuple       						=       torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		lowerCamelCase_				:		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





def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					sequence.append(__lowerCamelCase						)

					__a							=   0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__lowerCamelCase						):
										return None

					return index


if __name__ == "__main__":
	lowerCamelCase_				:		List[str]       						=       input("""Enter numbers separated by comma:\n""").strip()
	lowerCamelCase_				:		str       						=       [int(item) for item in user_input.split(""",""")]

	lowerCamelCase_				:		Tuple       						=       input("""Enter a single number to be found in the list:\n""")
	lowerCamelCase_				:		Optional[int]       						=       int(target_input)
	lowerCamelCase_				:		List[Any]       						=       sentinel_linear_search(sequence, target)
	if result is not None:
		print(F'''{target} found at positions: {result}''')
	else:
		print("""Not found""")




