


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	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]     ,			SCREAMING_SNAKE_CASE						:					int     ,			SCREAMING_SNAKE_CASE						:					Dict     ,			SCREAMING_SNAKE_CASE						:					Any     ,			SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					List[str]  ):







 """simple docstring"""



 UpperCamelCase__       :     List[Any] 							= states.reshape(1     ,			-1     ,			model.config.state_dim  )
 UpperCamelCase__       :     Optional[int] 							= actions.reshape(1     ,			-1     ,			model.config.act_dim  )
 UpperCamelCase__       :     List[Any] 							= returns_to_go.reshape(1     ,			-1     ,			1  )
 UpperCamelCase__       :     Any 							= timesteps.reshape(1     ,			-1  )

 if model.config.max_length is not None:
  UpperCamelCase__       :     Tuple 							= states[:, -model.config.max_length :]
  UpperCamelCase__       :     Dict 							= actions[:, -model.config.max_length :]
  UpperCamelCase__       :     List[Any] 							= returns_to_go[:, -model.config.max_length :]
  UpperCamelCase__       :     Optional[Any] 							= timesteps[:, -model.config.max_length :]

  # pad all tokens to sequence length
  UpperCamelCase__       :     Union[str, Any] 							= torch.cat(
      [torch.zeros(model.config.max_length - states.shape[1]  ), torch.ones(states.shape[1]  )]  )
  UpperCamelCase__       :     Dict 							= attention_mask.to(dtype=torch.long     ,			device=states.device  ).reshape(1     ,			-1  )
  UpperCamelCase__       :     Union[str, Any] 							= 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__       :     Optional[Any] 							= 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__       :     str 							= 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__       :     Tuple 							= 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__       :     str 							= None

 UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       :     Optional[int] 							= 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

__UpperCamelCase							:							Tuple    							=					gym.make("Hopper-v3")
__UpperCamelCase							:							List[str]    							=					env.observation_space.shape[0]
__UpperCamelCase							:							Dict    							=					env.action_space.shape[0]
__UpperCamelCase							:							Dict    							=					1000
__UpperCamelCase							:							Optional[Any]    							=					'cuda'
__UpperCamelCase							:							Tuple    							=					1000.0  # normalization for rewards/returns
__UpperCamelCase							:							List[str]    							=					3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__UpperCamelCase							:							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,
    ]
)
__UpperCamelCase							:							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,
    ]
)
__UpperCamelCase							:							Union[str, Any]    							=					torch.from_numpy(state_mean).to(device=device)
__UpperCamelCase							:							Tuple    							=					torch.from_numpy(state_std).to(device=device)

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

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

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

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

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

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

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