



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


lowerCAmelCase							:  int            =						logging.get_logger(__name__)

lowerCAmelCase							:  Any            =						{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class   __lowercase    (			__snake_case     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[Any]      							=					'mobilenet_v2'



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					str=3       ,						lowerCAmelCase__  :					int=224       ,						lowerCAmelCase__  :					int=1.0       ,						lowerCAmelCase__  :					Optional[Any]=8       ,						lowerCAmelCase__  :					Any=8       ,						lowerCAmelCase__  :					str=6       ,						lowerCAmelCase__  :					Optional[Any]=32       ,						lowerCAmelCase__  :					Optional[Any]=True       ,						lowerCAmelCase__  :					List[str]=True       ,						lowerCAmelCase__  :					Any="relu6"       ,						lowerCAmelCase__  :					List[Any]=True       ,						lowerCAmelCase__  :					List[str]=0.8       ,						lowerCAmelCase__  :					List[Any]=0.02       ,						lowerCAmelCase__  :					List[str]=0.001       ,						lowerCAmelCase__  :					Dict=255       ,						**lowerCAmelCase__  :					int       ,						):
									super().__init__(**lowerCAmelCase__)

									if depth_multiplier <= 0:
															raise ValueError("depth_multiplier must be greater than zero.")

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	num_channels
									SCREAMING_SNAKE_CASE_:			Any  		=	image_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	depth_multiplier
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	depth_divisible_by
									SCREAMING_SNAKE_CASE_:			Tuple  		=	min_depth
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	expand_ratio
									SCREAMING_SNAKE_CASE_:			str  		=	output_stride
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	first_layer_is_expansion
									SCREAMING_SNAKE_CASE_:			int  		=	finegrained_output
									SCREAMING_SNAKE_CASE_:			Tuple  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			str  		=	tf_padding
									SCREAMING_SNAKE_CASE_:			Dict  		=	classifier_dropout_prob
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			Tuple  		=	layer_norm_eps
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	semantic_loss_ignore_index







class   __lowercase    (			__snake_case     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Dict      							=					version.parse('''1.11'''     )



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									return OrderedDict([("pixel_values", {0: "batch"})])



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									if self.task == "image-classification":
															return OrderedDict([("logits", {0: "batch"})])
									else:
															return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])




			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									return 1E-4









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


lowerCAmelCase							:  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__":
						lowerCAmelCase							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

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

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

																		# execute action in environment
																		lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase							:  Dict            =						env.step(denorm_actions)
																		lowerCAmelCase							:  Tuple            =						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())

																		lowerCAmelCase							:  Tuple            =						next_observation
						except KeyboardInterrupt:
												pass

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






