


import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							str		 =						get_activation('swish'    )

       self.assertIsInstance(a						,				nn.SiLU    )

       self.assertEqual(act(torch.tensor(-100						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertNotEqual(act(torch.tensor(-1						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertEqual(act(torch.tensor(0						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertEqual(act(torch.tensor(20						,				dtype=torch.floataa    )    ).item()						,				20    )




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							int		 =						get_activation('silu'    )

       self.assertIsInstance(a						,				nn.SiLU    )

       self.assertEqual(act(torch.tensor(-100						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertNotEqual(act(torch.tensor(-1						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertEqual(act(torch.tensor(0						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertEqual(act(torch.tensor(20						,				dtype=torch.floataa    )    ).item()						,				20    )




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							str		 =						get_activation('mish'    )

       self.assertIsInstance(a						,				nn.Mish    )

       self.assertEqual(act(torch.tensor(-200						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertNotEqual(act(torch.tensor(-1						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertEqual(act(torch.tensor(0						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertEqual(act(torch.tensor(20						,				dtype=torch.floataa    )    ).item()						,				20    )




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							Union[str, Any]		 =						get_activation('gelu'    )

       self.assertIsInstance(a						,				nn.GELU    )

       self.assertEqual(act(torch.tensor(-100						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertNotEqual(act(torch.tensor(-1						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertEqual(act(torch.tensor(0						,				dtype=torch.floataa    )    ).item()						,				0    )
       self.assertEqual(act(torch.tensor(20						,				dtype=torch.floataa    )    ).item()						,				20    )





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


lowercase_	   =     {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    '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__":
       lowercase_	   =     'hopper-medium-v2'
       lowercase_	   =     gym.make(env_name)

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

       env.seed(0)
       lowercase_	   =     env.reset()
       lowercase_	   =     0
       lowercase_	   =     0
       lowercase_	   =     1_0_0_0
       lowercase_	   =     [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     lowercase_	   =     pipeline(obs, planning_horizon=3_2)

                     # execute action in environment
                     lowercase_   ,lowercase_   ,lowercase_   ,lowercase_	   =     env.step(denorm_actions)
                     lowercase_	   =     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())

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

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

