

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     (      _A					:							Union[str, Any]      ,  _A					:							int      ,  _A					:							Dict      ,  _A					:							Any      ,  _A					:							str      ,  _A					:							List[str]		)				->Union[str, Any]:



							"""simple docstring"""
							# we don't care about the past rewards in this model

							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=_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

__A	:   Tuple	=					gym.make('Hopper-v3')
__A	:   List[str]	=					env.observation_space.shape[0]
__A	:   Dict	=					env.action_space.shape[0]
__A	:   Dict	=					10_00
__A	:   Optional[Any]	=					'cuda'
__A	:   Tuple	=					10_00.0  # normalization for rewards/returns
__A	:   List[str]	=					36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__A	:   Dict	=					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	:   Union[str, Any]	=					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	:   Union[str, Any]	=					torch.from_numpy(state_mean).to(device=device)
__A	:   Tuple	=					torch.from_numpy(state_std).to(device=device)

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

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

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

		__A	:   Optional[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),
		)
		__A	:   int	=					action
		__A	:   Optional[Any]	=					action.detach().cpu().numpy()

		__A,		__A,		__A,		__A	:   List[str]	=					env.step(action)

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

		__A	:   List[Any]	=					target_return[0, -1] - (reward / scale)
		__A	:   Tuple	=					torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		__A	:   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 math
from collections.abc import Generator




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(i for i in range(1      ,  (max_n + 1)		))
							for i in (n for n in numbers if n > 1):
														for j in range(2      ,  _A		):
																					if (i % j) == 0:
																												break
														else:
																					yield i




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(i for i in range(1      ,  (max_n + 1)		))
							for i in (n for n in numbers if n > 1):
														# only need to check for factors up to sqrt(i)
														lowerCamelCase_								=int(math.sqrt(_A		)		) + 1
														for j in range(2      ,  _A		):
																					if (i % j) == 0:
																												break
														else:
																					yield i




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(i for i in range(1      ,  (max_n + 1)      ,  2		))
							# It's useless to test even numbers as they will not be prime
							if max_n > 2:
														yield 2  # Because 2 will not be tested, it's necessary to yield it now
							for i in (n for n in numbers if n > 1):
														lowerCamelCase_								=int(math.sqrt(_A		)		) + 1
														for j in range(3      ,  _A      ,  2		):
																					# As we removed the even numbers, we don't need them now
																					if (i % j) == 0:
																												break
														else:
																					yield i




def        __UpperCamelCase     (      )				->Dict:



							"""simple docstring"""
							from timeit import timeit

							lowerCamelCase_								="""from __main__ import slow_primes, primes, fast_primes"""
							print(timeit("""slow_primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)
							print(timeit("""primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)
							print(timeit("""fast_primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)


if __name__ == "__main__":
	__A	:   Union[str, Any]	=					int(input('Calculate primes up to:\n>> ').strip())
	for ret in primes(number):
		print(ret)
	benchmark()
