





"""simple docstring"""





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 tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


__A											=						"""sshleifer/bart-tiny-random"""
__A											=						"""patrickvonplaten/t5-tiny-random"""






@require_torch
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @cached_property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return AutoConfig.from_pretrained(__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :List[str]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=1 )
         self.assertEqual(student.config.num_hidden_layers       ,					1 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :Optional[int]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :List[Any]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=__UpperCAmelCase )
         self.assertEqual(student.config.encoder_layers       ,					1 )
         self.assertEqual(student.config.decoder_layers       ,					self.teacher_config.encoder_layers )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :Optional[int]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=1 )
         self.assertEqual(student.config.encoder_layers       ,					1 )
         self.assertEqual(student.config.decoder_layers       ,					1 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         with self.assertRaises(__UpperCAmelCase ):
           create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=__UpperCAmelCase       ,					d=__UpperCAmelCase )
