





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


UpperCAmelCase_					:   Tuple 							=						{
    """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__":
      UpperCAmelCase_					:   List[str] 							=						"""hopper-medium-v2"""
      UpperCAmelCase_					:   Dict 							=						gym.make(env_name)

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

      env.seed(0)
      UpperCAmelCase_					:   int 							=						env.reset()
      UpperCAmelCase_					:   List[str] 							=						0
      UpperCAmelCase_					:   Union[str, Any] 							=						0
      UpperCAmelCase_					:   List[str] 							=						10_00
      UpperCAmelCase_					:   Dict 							=						[obs.copy()]
      try:
            for t in tqdm.tqdm(range(T)):
                  # call the policy
                  UpperCAmelCase_					:   Any 							=						pipeline(obs, planning_horizon=32)

                  # execute action in environment
                  UpperCAmelCase_							,						UpperCAmelCase_							,						UpperCAmelCase_							,						UpperCAmelCase_					:   List[str] 							=						env.step(denorm_actions)
                  UpperCAmelCase_					:   str 							=						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())

                  UpperCAmelCase_					:   Dict 							=						next_observation
      except KeyboardInterrupt:
            pass

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_					:   Tuple 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   List[str] 							=						{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """fnet"""







  def __init__(	self    ,						_lowercase=32_000    ,						_lowercase=768    ,						_lowercase=12    ,						_lowercase=3_072    ,						_lowercase="gelu_new"    ,						_lowercase=0.1    ,						_lowercase=512    ,						_lowercase=4    ,						_lowercase=0.02    ,						_lowercase=1e-12    ,						_lowercase=False    ,						_lowercase=512    ,						_lowercase=3    ,						_lowercase=1    ,						_lowercase=2    ,						**_lowercase    ,						):
       super().__init__(pad_token_id=_lowercase    ,						bos_token_id=_lowercase    ,						eos_token_id=_lowercase    ,						**_lowercase						)

       lowerCAmelCase_					:       Optional[Any]    =		vocab_size
       lowerCAmelCase_					:       List[str]    =		max_position_embeddings
       lowerCAmelCase_					:       str    =		hidden_size
       lowerCAmelCase_					:       Any    =		num_hidden_layers
       lowerCAmelCase_					:       Optional[int]    =		intermediate_size
       lowerCAmelCase_					:       int    =		hidden_act
       lowerCAmelCase_					:       List[str]    =		hidden_dropout_prob
       lowerCAmelCase_					:       Tuple    =		initializer_range
       lowerCAmelCase_					:       int    =		type_vocab_size
       lowerCAmelCase_					:       str    =		layer_norm_eps
       lowerCAmelCase_					:       Optional[Any]    =		use_tpu_fourier_optimizations
       lowerCAmelCase_					:       Union[str, Any]    =		tpu_short_seq_length
