

import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
A_       : Any         =			{
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
A_       : Optional[int]         =			'ETAOINSHRDLCUMWFGYPBVKJXQZ'
A_       : Optional[Any]         =			'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

def 						UpperCamelCase					(lowercase_: str					)     ->      dict[str, int]:
	A__							:    List[Any]        =		{letter: 0 for letter in string.ascii_uppercase}
	for letter in message.upper():
		if letter in LETTERS:
			letter_count[letter] += 1

	return letter_count

def 						UpperCamelCase					(lowercase_: tuple					)     ->      str:
	return x[0]

def 						UpperCamelCase					(lowercase_: str					)     ->      str:
	A__							:    Union[str, Any]        =		get_letter_count(lowercase_					)
	A__							:    dict[int, list[str]]        =		{
	    freq: [] for letter, freq in letter_to_freq.items()
	}
	for letter in LETTERS:
		freq_to_letter[letter_to_freq[letter]].append(lowercase_					)

	A__							:    dict[int, str]        =		{}

	for freq in freq_to_letter:
		freq_to_letter[freq].sort(key=ETAOIN.find    ,      reverse=lowercase_					)
		A__							:    str        =		"""""".join(freq_to_letter[freq]					)

	A__							:    Optional[Any]        =		list(freq_to_letter_str.items()					)
	freq_pairs.sort(key=lowercase_    ,      reverse=lowercase_					)

	A__							:    list[str]        =		[freq_pair[1] for freq_pair in freq_pairs]

	return "".join(lowercase_					)

def 						UpperCamelCase					(lowercase_: str					)     ->      int:
	A__							:    List[str]        =		get_frequency_order(lowercase_					)
	A__							:    int        =		0
	for common_letter in ETAOIN[:6]:
		if common_letter in freq_order[:6]:
			match_score += 1

	for uncommon_letter in ETAOIN[-6:]:
		if uncommon_letter in freq_order[-6:]:
			match_score += 1

	return match_score


if __name__ == "__main__":
	import doctest

	doctest.testmod()






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


A_       : List[str]         =			{
    '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__":
	A_       : Optional[int]         =			'hopper-medium-v2'
	A_       : List[Any]         =			gym.make(env_name)

	A_       : str         =			ValueGuidedRLPipeline.from_pretrained(
	    'bglick13/hopper-medium-v2-value-function-hor32',
	    env=env,
	)

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

			# execute action in environment
			A_       ,			A_       ,			A_       ,			A_       : Dict         =			env.step(denorm_actions)
			A_       : List[str]         =			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())

			A_       : int         =			next_observation
	except KeyboardInterrupt:
		pass

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



