

import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
     import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin


@require_torch
class  A						(							unittest.TestCase    ,							_UpperCAmelCase ):







    """simple docstring"""





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





        '''simple docstring'''







        A__          =   load_tool('text-to-speech'		)
        self.tool.setup()


    def 						snake_case__     (      self :       int		)->    Optional[Any]:





        '''simple docstring'''







        torch.manual_seed(0		)
        A__          =   self.tool('hey'		)
        A__          =   result.to_raw()
        self.assertTrue(
            torch.allclose(
                resulting_tensor[:3],torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]		),)		)


    def 						snake_case__     (      self :       int		)->    Union[str, Any]:





        '''simple docstring'''







        torch.manual_seed(0		)
        A__          =   self.tool('hey'		)
        A__          =   result.to_raw()
        self.assertTrue(
            torch.allclose(
                resulting_tensor[:3],torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]		),)		)






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__  :  Optional[int]								=					'''hopper-medium-v2'''
						A__  :  int								=					gym.make(env_name)

						A__  :  Optional[int]								=					ValueGuidedRLPipeline.from_pretrained(
						    '''bglick13/hopper-medium-v2-value-function-hor32''',
						    env=env,
						)

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

																		# execute action in environment
																		A__					,     A__					,     A__					,     A__  :  str								=					env.step(denorm_actions)
																		A__  :  Dict								=					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__  :  List[str]								=					next_observation
						except KeyboardInterrupt:
												pass

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





