'''simple docstring'''




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


__SCREAMING_SNAKE_CASE       :     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__":
  __SCREAMING_SNAKE_CASE       :     Dict     =  """hopper-medium-v2"""
  __SCREAMING_SNAKE_CASE       :     Optional[int]     =  gym.make(env_name)

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

  env.seed(0)
  __SCREAMING_SNAKE_CASE       :     Any     =  env.reset()
  __SCREAMING_SNAKE_CASE       :     str     =  0
  __SCREAMING_SNAKE_CASE       :     Optional[int]     =  0
  __SCREAMING_SNAKE_CASE       :     Any     =  1_000
  __SCREAMING_SNAKE_CASE       :     str     =  [obs.copy()]
  try:
    for t in tqdm.tqdm(range(T)):
      # call the policy
      __SCREAMING_SNAKE_CASE       :     int     =  pipeline(obs, planning_horizon=32)

      # execute action in environment
      __SCREAMING_SNAKE_CASE     ,					__SCREAMING_SNAKE_CASE     ,					__SCREAMING_SNAKE_CASE     ,					__SCREAMING_SNAKE_CASE       :     Any     =  env.step(denorm_actions)
      __SCREAMING_SNAKE_CASE       :     int     =  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())

      __SCREAMING_SNAKE_CASE       :     int     =  next_observation
  except KeyboardInterrupt:
    pass

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


'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    float:


       """simple docstring"""







       def get_matched_characters(_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    ) -> str:
              _UpperCAmelCase     :  Tuple							      =    []
              _UpperCAmelCase     :  Dict							      =    min(len(_stra    )			,	len(_stra    )    ) // 2
              for i, l in enumerate(_stra    ):
                     _UpperCAmelCase     :  int							      =    int(max(0			,	i - limit    )    )
                     _UpperCAmelCase     :  Any							      =    int(min(i + limit + 1			,	len(_stra    )    )    )
                     if l in _stra[left:right]:
                            matched.append(_UpperCAmelCase    )
                            _UpperCAmelCase     :  List[Any]							      =    F"""{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}"""

              return "".join(_UpperCAmelCase    )

       # matching characters
       _UpperCAmelCase     :  Union[str, Any]							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    len(_UpperCAmelCase    )

       # transposition
       _UpperCAmelCase     :  Optional[Any]							      =    (
           len([(ca, ca) for ca, ca in zip(_UpperCAmelCase			,	_UpperCAmelCase    ) if ca != ca]    ) // 2
       )

       if not match_count:
              _UpperCAmelCase     :  Dict							      =    0.0
       else:
              _UpperCAmelCase     :  Optional[int]							      =    (
                  1
                  / 3
                  * (
                      match_count / len(_UpperCAmelCase    )
                      + match_count / len(_UpperCAmelCase    )
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       _UpperCAmelCase     :  str							      =    0
       for ca, ca in zip(stra[:4]			,	stra[:4]    ):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))

