






"""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


A__	:    Tuple			 = logging.get_logger(__name__)

A__	:    Optional[Any]			 = {
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:List[Any]             =			"levit"


			def __init__(				self					:  int       ,	snake_case__					:  Dict=224       ,	snake_case__					:  List[str]=3       ,	snake_case__					:  Dict=3       ,	snake_case__					:  Tuple=2       ,	snake_case__					:  Optional[int]=1       ,	snake_case__					:  int=16       ,	snake_case__					:  Optional[Any]=[128, 256, 384]       ,	snake_case__					:  Optional[Any]=[4, 8, 12]       ,	snake_case__					:  str=[4, 4, 4]       ,	snake_case__					:  Any=[16, 16, 16]       ,	snake_case__					:  Optional[int]=0       ,	snake_case__					:  Union[str, Any]=[2, 2, 2]       ,	snake_case__					:  Tuple=[2, 2, 2]       ,	snake_case__					:  List[Any]=0.02       ,	**snake_case__					:  int       ,	):
										super().__init__(**snake_case__ )
										lowerCamelCase_	:							Any									=image_size
										lowerCamelCase_	:							str									=num_channels
										lowerCamelCase_	:							Union[str, Any]									=kernel_size
										lowerCamelCase_	:							int									=stride
										lowerCamelCase_	:							str									=padding
										lowerCamelCase_	:							Optional[int]									=hidden_sizes
										lowerCamelCase_	:							int									=num_attention_heads
										lowerCamelCase_	:							Union[str, Any]									=depths
										lowerCamelCase_	:							int									=key_dim
										lowerCamelCase_	:							Dict									=drop_path_rate
										lowerCamelCase_	:							int									=patch_size
										lowerCamelCase_	:							Tuple									=attention_ratio
										lowerCamelCase_	:							int									=mlp_ratio
										lowerCamelCase_	:							Union[str, Any]									=initializer_range
										lowerCamelCase_	:							List[str]									=[
										    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
										    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
										]







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Tuple             =			version.parse("1.11"       )


			@property
			def      UpperCAmelCase__						(				self					:  Any ):
										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										    ] )


			@property
			def      UpperCAmelCase__						(				self					:  Tuple ):
										return 1E-4











"""simple docstring"""



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


A__	:    int			 = {
    '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__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

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

						env.seed(0)
						A__	:    Dict			 = env.reset()
						A__	:    Optional[int]			 = 0
						A__	:    str			 = 0
						A__	:    List[Any]			 = 1_000
						A__	:    Tuple			 = [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__	:    Any			 = env.step(denorm_actions)
																		A__	:    List[Any]			 = 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[Any]			 = next_observation
						except KeyboardInterrupt:
												pass

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



