

"""simple docstring"""


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


a					:Dict    = {
    "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					:Dict    = "hopper-medium-v2"
  a					:Any    = gym.make(env_name)

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

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

      # execute action in environment
      a    ,a    ,a    ,a					:Union[str, Any]    = env.step(denorm_actions)
      a					: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())

      a					:Optional[int]    = next_observation
  except KeyboardInterrupt:
    pass

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







"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a					:List[Any]    = logging.get_logger(__name__)

a					:Optional[int]    = {
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}

class      __a							(UpperCamelCase_   ,	UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = """focalnet"""

     def __init__(						self	,    _a=224	,    _a=4	,    _a=3	,    _a=96	,    _a=False	,    _a=[192, 384, 768, 768]	,    _a=[2, 2, 6, 2]	,    _a=[2, 2, 2, 2]	,    _a=[3, 3, 3, 3]	,    _a="gelu"	,    _a=4.0	,    _a=0.0	,    _a=0.1	,    _a=False	,    _a=1E-4	,    _a=False	,    _a=False	,    _a=False	,    _a=0.02	,    _a=1E-5	,    _a=32	,    _a=None	,    _a=None	,    **_a	,    )  ->      Optional[Any]:


           """simple docstring"""


           super().__init__(**_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	image_size
           SCREAMING_SNAKE_CASE__   :      str      		=	patch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_channels
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	embed_dim
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	use_conv_embed
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	hidden_sizes
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	depths
           SCREAMING_SNAKE_CASE__   :      Any      		=	focal_levels
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	focal_windows
           SCREAMING_SNAKE_CASE__   :      Any      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	mlp_ratio
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	drop_path_rate
           SCREAMING_SNAKE_CASE__   :      str      		=	use_layerscale
           SCREAMING_SNAKE_CASE__   :      int      		=	layerscale_value
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	use_post_layernorm
           SCREAMING_SNAKE_CASE__   :      Any      		=	use_post_layernorm_in_modulation
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	normalize_modulator
           SCREAMING_SNAKE_CASE__   :      str      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Any      		=	layer_norm_eps
           SCREAMING_SNAKE_CASE__   :      Any      		=	encoder_stride
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""stem"""] + [f'''stage{idx}''' for idx in range(1	,    len(self.depths  ) + 1  )]
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	get_aligned_output_features_output_indices(
               out_features=_a	,    out_indices=_a	,    stage_names=self.stage_names  )




