


'''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








'''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())




