



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}''')










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__  :  int								=					logging.get_logger(__name__)

A__  :  Optional[int]								=					{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''data2vec-vision'''




							def __init__(       self     :       Tuple				,				A_     :       List[Any]=7_6_8				,				A_     :       Union[str, Any]=1_2				,				A_     :       Dict=1_2				,				A_     :       List[Any]=3_0_7_2				,				A_     :       Dict="gelu"				,				A_     :       Tuple=0.0				,				A_     :       Dict=0.0				,				A_     :       List[str]=0.02				,				A_     :       List[str]=1e-12				,				A_     :       Tuple=2_2_4				,				A_     :       Dict=1_6				,				A_     :       Optional[int]=3				,				A_     :       Optional[int]=False				,				A_     :       Any=False				,				A_     :       Tuple=False				,				A_     :       Optional[int]=False				,				A_     :       int=0.1				,				A_     :       Union[str, Any]=0.1				,				A_     :       List[Any]=True				,				A_     :       List[Any]=[3, 5, 7, 1_1]				,				A_     :       Union[str, Any]=[1, 2, 3, 6]				,				A_     :       Optional[int]=True				,				A_     :       Any=0.4				,				A_     :       str=2_5_6				,				A_     :       Optional[int]=1				,				A_     :       str=False				,				A_     :       Optional[int]=2_5_5				,				**A_     :       Optional[int]				,				):
									super().__init__(**A_)

									lowerCAmelCase_			:     str									= hidden_size
									lowerCAmelCase_			:     List[str]									= num_hidden_layers
									lowerCAmelCase_			:     Optional[Any]									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Union[str, Any]									= hidden_act
									lowerCAmelCase_			:     List[Any]									= hidden_dropout_prob
									lowerCAmelCase_			:     Tuple									= attention_probs_dropout_prob
									lowerCAmelCase_			:     str									= initializer_range
									lowerCAmelCase_			:     Tuple									= layer_norm_eps

									lowerCAmelCase_			:     List[Any]									= image_size
									lowerCAmelCase_			:     List[Any]									= patch_size
									lowerCAmelCase_			:     Any									= num_channels
									lowerCAmelCase_			:     Any									= use_mask_token
									lowerCAmelCase_			:     Optional[int]									= use_absolute_position_embeddings
									lowerCAmelCase_			:     str									= use_relative_position_bias
									lowerCAmelCase_			:     Optional[Any]									= use_shared_relative_position_bias
									lowerCAmelCase_			:     Dict									= layer_scale_init_value
									lowerCAmelCase_			:     Tuple									= drop_path_rate
									lowerCAmelCase_			:     Optional[int]									= use_mean_pooling
									# decode head attributes (semantic segmentation)
									lowerCAmelCase_			:     Any									= out_indices
									lowerCAmelCase_			:     int									= pool_scales
									# auxiliary head attributes (semantic segmentation)
									lowerCAmelCase_			:     Dict									= use_auxiliary_head
									lowerCAmelCase_			:     str									= auxiliary_loss_weight
									lowerCAmelCase_			:     Optional[Any]									= auxiliary_channels
									lowerCAmelCase_			:     str									= auxiliary_num_convs
									lowerCAmelCase_			:     str									= auxiliary_concat_input
									lowerCAmelCase_			:     str									= semantic_loss_ignore_index





class 				__snake_case						(     UpperCamelCase_						):
							_a			      = version.parse('''1.11'''						)




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







							@property
							def    UpperCAmelCase__			(       self     :       Dict):
									return 1e-4





