


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


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

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

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

         # execute action in environment
         UpperCAmelCase_      ,     UpperCAmelCase_      ,     UpperCAmelCase_      ,     UpperCAmelCase_   :				Any						      = env.step(denorm_actions)
         UpperCAmelCase_   :				Optional[int]						      = 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_   :				Union[str, 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(lowercase__    )		,					'''Tatoeba directory does not exist.'''    )
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[Any]:
             a_      :	Optional[Any]							=						tempfile.mkdtemp()
             return TatoebaConverter(save_dir=SCREAMING_SNAKE_CASE__  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             self.resolver.convert_models(['heb-eng']  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             a_							,	a_      :	Dict							=						self.resolver.write_model_card('opus-mt-he-en'  ,							dry_run=SCREAMING_SNAKE_CASE__  )
             assert mmeta["long_pair"] == "heb-eng"


