



'''simple docstring'''





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


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

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

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

                     # execute action in environment
                     a__      :				Union[str, Any]   	=			env.step(denorm_actions)
                     a__      :				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__      :				Optional[int]   	=			next_observation
       except KeyboardInterrupt:
              pass

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





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class       UpperCAmelCase__(							unittest.TestCase     ,							lowerCamelCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[str])	->				Any:




         """simple docstring"""

         lowercase__     =  load_tool('text-classification')
         self.tool.setup()
         lowercase__     =  load_tool('text-classification'			,				remote=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.tool('That\'s quite cool'			,				['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')


   def 			UpperCAmelCase		(						self  :			int)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.remote_tool('That\'s quite cool'			,				['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.tool(text='That\'s quite cool'			,				labels=['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')



   def 			UpperCAmelCase		(						self  :			Any)	->				Any:




         """simple docstring"""

         lowercase__     =  self.remote_tool(text='That\'s quite cool'			,				labels=['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')
