



"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_UpperCamelCase: List[Any]										=     {
    '''sail/poolformer_s12''': '''https://huggingface.co/sail/poolformer_s12/resolve/main/config.json''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}


class      a__						(  UpperCamelCase_   ):
   _lowerCamelCase    							= 'poolformer'


   def __init__(							self					:							int,   lowerCAmelCase					:							List[str]=3,   lowerCAmelCase					:							List[Any]=16,   lowerCAmelCase					:							Union[str, Any]=16,   lowerCAmelCase					:							Optional[Any]=3,   lowerCAmelCase					:							Optional[Any]=4.0,   lowerCAmelCase					:							Union[str, Any]=[2, 2, 6, 2],   lowerCAmelCase					:							Optional[int]=[64, 128, 320, 512],   lowerCAmelCase					:							List[Any]=[7, 3, 3, 3],   lowerCAmelCase					:							str=[4, 2, 2, 2],   lowerCAmelCase					:							List[str]=[2, 1, 1, 1],   lowerCAmelCase					:							int=4,   lowerCAmelCase					:							Any=0.0,   lowerCAmelCase					:							List[Any]="gelu",   lowerCAmelCase					:							Tuple=True,   lowerCAmelCase					:							Optional[Any]=1e-5,   lowerCAmelCase					:							Optional[Any]=0.02,   **lowerCAmelCase					:							Any,   )			->				Optional[Any]:
         lowercase     :					Union[str, Any] 		=      num_channels
         lowercase     :					Optional[int] 		=      patch_size
         lowercase     :					Tuple 		=      stride
         lowercase     :					Dict 		=      padding
         lowercase     :					List[str] 		=      pool_size
         lowercase     :					Optional[int] 		=      hidden_sizes
         lowercase     :					Optional[Any] 		=      mlp_ratio
         lowercase     :					Optional[int] 		=      depths
         lowercase     :					int 		=      patch_sizes
         lowercase     :					int 		=      strides
         lowercase     :					Dict 		=      num_encoder_blocks
         lowercase     :					Tuple 		=      drop_path_rate
         lowercase     :					Union[str, Any] 		=      hidden_act
         lowercase     :					List[str] 		=      use_layer_scale
         lowercase     :					Optional[Any] 		=      layer_scale_init_value
         lowercase     :					Any 		=      initializer_range
         super().__init__(**A_	)




class      a__						(  UpperCamelCase_   ):
   _lowerCamelCase    							= version.parse('1.11'   )


   @property
   def 				lowercase (							self					:							List[str]	)			->				List[str]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]	)






   @property
   def 				lowercase (							self					:							List[str]	)			->				str:
         return 2e-3





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


A__  :  Union[str, 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__":
						A__  :  Optional[int]								=					'''hopper-medium-v2'''
						A__  :  int								=					gym.make(env_name)

						A__  :  Optional[int]								=					ValueGuidedRLPipeline.from_pretrained(
						    '''bglick13/hopper-medium-v2-value-function-hor32''',
						    env=env,
						)

						env.seed(0)
						A__  :  int								=					env.reset()
						A__  :  Optional[int]								=					0
						A__  :  Union[str, Any]								=					0
						A__  :  Union[str, Any]								=					1000
						A__  :  Optional[Any]								=					[obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__  :  Union[str, Any]								=					pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__					,     A__					,     A__					,     A__  :  str								=					env.step(denorm_actions)
																		A__  :  Dict								=					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__  :  List[str]								=					next_observation
						except KeyboardInterrupt:
												pass

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





