


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


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    '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 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """fnet"""




      def __init__(    self:	Optional[int]						,				a:	List[Any]=3_2000						,				a:	str=768						,				a:	Tuple=12						,				a:	Optional[int]=3072						,				a:	List[str]="gelu_new"						,				a:	Optional[Any]=0.1						,				a:	Any=512						,				a:	List[str]=4						,				a:	Union[str, Any]=0.0_2						,				a:	Optional[int]=1e-12						,				a:	List[Any]=False						,				a:	Optional[Any]=512						,				a:	Dict=3						,				a:	Dict=1						,				a:	Dict=2						,				**a:	Any						,				):
       super().__init__(pad_token_id=a						,				bos_token_id=a						,				eos_token_id=a						,				**a    )

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





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}""")

