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


_lowerCamelCase					:   Union[str, 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__":
							_lowerCamelCase					:   Any							=		'''hopper-medium-v2'''
							_lowerCamelCase					:   Any							=		gym.make(env_name)

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

							env.seed(0)
							_lowerCamelCase					:   int							=		env.reset()
							_lowerCamelCase					:   List[str]							=		0
							_lowerCamelCase					:   List[Any]							=		0
							_lowerCamelCase					:   int							=		10_00
							_lowerCamelCase					:   List[Any]							=		[obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					_lowerCamelCase					:   Optional[int]							=		pipeline(obs, planning_horizon=32)

																					# execute action in environment
																					_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase					:   Union[str, Any]							=		env.step(denorm_actions)
																					_lowerCamelCase					:   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())

																					_lowerCamelCase					:   Optional[Any]							=		next_observation
							except KeyboardInterrupt:
														pass

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





import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property


@unittest.skipUnless(os.path.exists(a     )				,       """Tatoeba directory does not exist."""     )
class 						lowercase    (     unittest.TestCase     ):




							@cached_property
							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       tempfile.mkdtemp()
												return TatoebaConverter(save_dir=_UpperCamelCase      )




							@slow
							def 	__snake_case(						self   :					Tuple      )					->				List[Any]:



												'''simple docstring'''

												self.resolver.convert_models(["heb-eng"]      )





							@slow
							def 	__snake_case(						self   :					Any      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.resolver.write_model_card("opus-mt-he-en"	,							dry_run=_UpperCamelCase      )
												assert mmeta["long_pair"] == "heb-eng"




