

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


a__												=						{
    '''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__												=						'''hopper-medium-v2'''
			a__												=						gym.make(env_name)

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

			env.seed(0)
			a__												=						env.reset()
			a__												=						0
			a__												=						0
			a__												=						1000
			a__												=						[obs.copy()]
			try:
						for t in tqdm.tqdm(range(T)):
									# call the policy
									a__												=						pipeline(obs, planning_horizon=32)

									# execute action in environment
									a__						,     a__						,     a__						,     a__												=						env.step(denorm_actions)
									a__												=						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__												=						next_observation
			except KeyboardInterrupt:
						pass

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











'''simple docstring'''



import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      ComputeEnvironment.AMAZON_SAGEMAKER
      UpperCamelCase_             =      True
      UpperCamelCase_             =      '''ml.p3.2xlarge'''
      UpperCamelCase_             =      '''accelerate_sagemaker_execution_role'''
      UpperCamelCase_             =      '''hf-sm'''
      UpperCamelCase_             =      '''us-east-1'''
      UpperCamelCase_             =      1
      UpperCamelCase_             =      '''accelerate-sagemaker-1'''
      UpperCamelCase_             =      '''1.6'''
      UpperCamelCase_             =      '''4.4'''
      UpperCamelCase_             =      '''train.py'''
      UpperCamelCase_             =      [
          '''--model_name_or_path''',
          '''bert''',
          '''--do_train''',
          '''False''',
          '''--epochs''',
          '''3''',
          '''--learning_rate''',
          '''5e-5''',
          '''--max_steps''',
          '''50.5''',
      ]
      UpperCamelCase_             =      [
          '''--model_name_or_path''',
          '''bert''',
          '''--do_train''',
          '''--do_test''',
          '''False''',
          '''--do_predict''',
          '''--epochs''',
          '''3''',
          '''--learning_rate''',
          '''5e-5''',
          '''--max_steps''',
          '''50.5''',
      ]





class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      def 					A__   (			self      :		int		)	->	Dict:


       '''simple docstring'''

       lowercase       : str						=_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
       assert isinstance(converted_args['''model_name_or_path''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''do_train''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''epochs''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''learning_rate''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''max_steps''']  ,   UpperCAmelCase		)

       with pytest.raises(UpperCAmelCase		):
        _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)



