


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


_UpperCAmelCase            =		{
    """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            =		"""hopper-medium-v2"""
	_UpperCAmelCase            =		gym.make(env_name)

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

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

			# execute action in environment
			_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase            =		env.step(denorm_actions)
			_UpperCAmelCase            =		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            =		next_observation
	except KeyboardInterrupt:
		pass

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







_UpperCAmelCase            =		"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Tuple         =				input('Enter message: '     )
		A_		:    int         =				input('Enter key [alphanumeric]: '     )
		A_		:    Optional[Any]         =				input('Encrypt/Decrypt [e/d]: '     )

		if mode.lower().startswith('e'     ):
				A_		:    List[Any]         =				'encrypt'
				A_		:    int         =				encrypt_message(__lowercase						,__lowercase     )
		elif mode.lower().startswith('d'     ):
				A_		:    Optional[Any]         =				'decrypt'
				A_		:    Dict         =				decrypt_message(__lowercase						,__lowercase     )

		print(f'''\n{mode.title()}ed message:'''     )
		print(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		return translate_message(__lowercase						,__lowercase						,'encrypt'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		return translate_message(__lowercase						,__lowercase						,'decrypt'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    Tuple         =				[]
		A_		:    str         =				0
		A_		:    Optional[int]         =				key.upper()

		for symbol in message:
				A_		:    Optional[Any]         =				LETTERS.find(symbol.upper()     )
				if num != -1:
						if mode == "encrypt":
								num += LETTERS.find(key[key_index]     )
						elif mode == "decrypt":
								num -= LETTERS.find(key[key_index]     )

						num %= len(__lowercase     )

						if symbol.isupper():
								translated.append(LETTERS[num]     )
						elif symbol.islower():
								translated.append(LETTERS[num].lower()     )

						key_index += 1
						if key_index == len(__lowercase     ):
								A_		:    str         =				0
				else:
						translated.append(__lowercase     )
		return "".join(__lowercase     )


if __name__ == "__main__":
	main()






