from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
	from .features import FeatureType





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    = 4_2
				lowerCamelCase_    = None
				# Automatically constructed
				lowerCamelCase_    = '''dict'''
				lowerCamelCase_    = None
				lowerCamelCase_    = field(default='''Translation''' ,	init=__UpperCamelCase ,	repr=__UpperCamelCase  )
				def __call__(						self ):



						"""simple docstring"""





						return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						from .features import Value

						return {k: Value('string' ) for k in sorted(self.languages )}





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    = None
				lowerCamelCase_    = None
				lowerCamelCase_    = None
				# Automatically constructed
				lowerCamelCase_    = '''dict'''
				lowerCamelCase_    = None
				lowerCamelCase_    = field(default='''TranslationVariableLanguages''' ,	init=__UpperCamelCase ,	repr=__UpperCamelCase  )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				sorted(set(self.languages ) ) if self.languages else None
						A_		:    List[str]         =				len(self.languages ) if self.languages else None
				def __call__(						self ):



						"""simple docstring"""





						return pa.struct({'language': pa.list_(pa.string() ), 'translation': pa.list_(pa.string() )} )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				set(self.languages )
						if self.languages and set(lowercase ) - lang_set:
								raise ValueError(
								    F'''Some languages in example ({', '.join(sorted(set(lowercase ) - lang_set ) )}) are not in valid set ({', '.join(lowercase )}).''' )

						# Convert dictionary into tuples, splitting out cases where there are
						# multiple translations for a single language.
						A_		:    Dict         =				[]
						for lang, text in translation_dict.items():
								if isinstance(lowercase		,     lowercase ):
										translation_tuples.append((lang, text) )
								else:
										translation_tuples.extend([(lang, el) for el in text] )

        # Ensure translations are in ascending order by language code.
						A_		:    List[Any]         =				zip(*sorted(lowercase ) )

						return {"language": languages, "translation": translations}







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						from .features import Sequence, Value

						return {
						    "language": Sequence(Value('string' ) ),
						    "translation": Sequence(Value('string' ) ),
						}







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}""")






