





from decimal import Decimal, getcontext
from math import ceil, factorial




def      a__			(       lowercase__	):






   '''simple docstring'''






   if not isinstance(lowercase__	,     lowercase__	):
      raise TypeError("Undefined for non-integers"	)
   elif precision < 1:
      raise ValueError("Undefined for non-natural numbers"	)

   UpperCAmelCase_           =precision
   UpperCAmelCase_           =ceil(precision / 1_4	)
   UpperCAmelCase_           =4_2_6_8_8_0 * Decimal(1_0_0_0_5	).sqrt()
   UpperCAmelCase_           =1
   UpperCAmelCase_           =1_3_5_9_1_4_0_9
   UpperCAmelCase_           =Decimal(lowercase__	)
   for k in range(1	,     lowercase__	):
      UpperCAmelCase_           =factorial(6 * k	) // (factorial(3 * k	) * factorial(lowercase__	) ** 3)
      linear_term += 5_4_5_1_4_0_1_3_4
      exponential_term *= -2_6_2_5_3_7_4_1_2_6_4_0_7_6_8_0_0_0
      partial_sum += Decimal(multinomial_term * linear_term	) / exponential_term
   return str(constant_term / partial_sum	)[:-1]


if __name__ == "__main__":
     __lowercase	: List[str]       =50
     print(f"""The first {n} digits of pi is: {pi(n)}""")







import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase						:  List[Any]									=			{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
					_UpperCAmelCase						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

					_UpperCAmelCase						:  Tuple									=			ValueGuidedRLPipeline.from_pretrained(
					    """bglick13/hopper-medium-v2-value-function-hor32""",
					    env=env,
					)

					env.seed(0)
					_UpperCAmelCase						:  Tuple									=			env.reset()
					_UpperCAmelCase						:  str									=			0
					_UpperCAmelCase						:  Union[str, Any]									=			0
					_UpperCAmelCase						:  List[str]									=			1000
					_UpperCAmelCase						:  Optional[int]									=			[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase						:  int									=			pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  Tuple									=			env.get_normalized_score(total_reward)

															# update return
															total_reward += reward
															total_score += score
															print(
															    F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
															    F''' {total_score}'''
															)

															# save observations for rendering
															rollout.append(next_observation.copy())

															_UpperCAmelCase						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

					print(F'''Total reward: {total_reward}''')




