code: 
  
  
  '''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 _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A						,				A						,				A ) -> str:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				states.reshape(1						,				-1						,				model.config.state_dim )
         lowercase__       =				actions.reshape(1						,				-1						,				model.config.act_dim )
         lowercase__       =				returns_to_go.reshape(1						,				-1						,				1 )
         lowercase__       =				timesteps.reshape(1						,				-1 )
  
         if model.config.max_length is not None:
                lowercase__       =				states[:, -model.config.max_length :]
                lowercase__       =				actions[:, -model.config.max_length :]
                lowercase__       =				returns_to_go[:, -model.config.max_length :]
                lowercase__       =				timesteps[:, -model.config.max_length :]
  
                # pad all tokens to sequence length
                lowercase__       =				torch.cat(
                    [torch.zeros(model.config.max_length - states.shape[1] ), torch.ones(states.shape[1] )] )
                lowercase__       =				attention_mask.to(dtype=torch.long						,				device=states.device ).reshape(1						,				-1 )
                lowercase__       =				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 )
                lowercase__       =				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 )
                lowercase__       =				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 )
                lowercase__       =				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:
                lowercase__       =				None
  
         lowercase__    ,lowercase__    ,lowercase__       =				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
  
  lowerCamelCase							:     Optional[Any]								=			gym.make('Hopper-v3')
  lowerCamelCase							:     Dict								=			env.observation_space.shape[0]
  lowerCamelCase							:     List[Any]								=			env.action_space.shape[0]
  lowerCamelCase							:     Optional[int]								=			1_000
  lowerCamelCase							:     List[Any]								=			'cuda'
  lowerCamelCase							:     List[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							:     Union[str, Any]								=			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							:     Tuple								=			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							:     List[str]								=			torch.from_numpy(state_mean).to(device=device)
  lowerCamelCase							:     List[Any]								=			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  lowerCamelCase							:     str								=			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
  lowerCamelCase							:     List[Any]								=			model.to(device)
  model.eval()
  
  for ep in range(10):
   lowerCamelCase		,						lowerCamelCase							:     Optional[Any]								=			0, 0
   lowerCamelCase							:     Union[str, Any]								=			env.reset()
   lowerCamelCase							:     Any								=			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   lowerCamelCase							:     Optional[int]								=			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   lowerCamelCase							:     str								=			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   lowerCamelCase							:     str								=			torch.zeros(0, device=device, dtype=torch.floataa)
  
   lowerCamelCase							:     int								=			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
   for t in range(max_ep_len):
    env.render()
    # add padding
    lowerCamelCase							:     str								=			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    lowerCamelCase							:     List[str]								=			torch.cat([rewards, torch.zeros(1, device=device)])
  
    lowerCamelCase							:     Dict								=			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							:     Optional[Any]								=			action
    lowerCamelCase							:     int								=			action.detach().cpu().numpy()
  
    lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase							:     Optional[Any]								=			env.step(action)
  
    lowerCamelCase							:     str								=			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    lowerCamelCase							:     List[Any]								=			torch.cat([states, cur_state], dim=0)
    lowerCamelCase							:     Optional[Any]								=			reward
  
    lowerCamelCase							:     Optional[int]								=			target_return[0, -1] - (reward / scale)
    lowerCamelCase							:     Dict								=			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    lowerCamelCase							:     Optional[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
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  from collections import Counter
  
  def _SCREAMING_SNAKE_CASE   (A ) -> list[int]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				[True] * n
         lowercase__       =				False
         lowercase__       =				False
         lowercase__       =				True
  
         for i in range(3						,				int(n**0.5 + 1 )						,				2 ):
                lowercase__       =				i * 2
                while index < n:
                       lowercase__       =				False
                       lowercase__       =				index + i
  
         lowercase__       =				[2]
  
         for i in range(3						,				A						,				2 ):
                if is_prime[i]:
                       primes.append(A )
  
         return primes
  
  def _SCREAMING_SNAKE_CASE   (A ) -> list[list[int]]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				str(A )
         lowercase__       =				[]
         lowercase__       =				['''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''']
  
         for duplicate in Counter(A ) - Counter(set(A ) ):
                lowercase__       =				[int(number_str.replace(A						,				A ) ) for digit in digits]
                replacements.append(A )
  
         return replacements
  
  def _SCREAMING_SNAKE_CASE   (A = 8 ) -> int:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				set()
  
         # Filter primes with less than 3 replaceable digits
         lowercase__       =				{
             x for x in set(prime_sieve(1_000_000 ) ) if len(str(A ) ) - len(set(str(A ) ) ) >= 3
         }
  
         for prime in primes:
                if prime in numbers_checked:
                       continue
  
                lowercase__       =				digit_replacements(A )
  
                for family in replacements:
                       numbers_checked.update(A )
                       lowercase__       =				primes.intersection(A )
  
                       if len(A ) != family_length:
                              continue
  
                       return min(A )
  
         return -1
  
  
  if __name__ == "__main__":
   print(solution())
  
  
  
  
  