



'''simple docstring'''



import copy
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
from ..auto import CONFIG_MAPPING


lowerCAmelCase_     :       str								=						logging.get_logger(__name__)

lowerCAmelCase_     :       int								=						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}
class 	lowerCamelCase_							(					__A			):
						_lowerCAmelCase     :   Dict	     =				'conditional_detr'
						_lowerCAmelCase     :   str	     =				['past_key_values']
						_lowerCAmelCase     :   str	     =				{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						}







						def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      Tuple=True				,    lowerCAmelCase__   :      Optional[Any]=None				,    lowerCAmelCase__   :      str=3				,    lowerCAmelCase__   :      int=3_00				,    lowerCAmelCase__   :      List[str]=6				,    lowerCAmelCase__   :      List[Any]=20_48				,    lowerCAmelCase__   :      List[str]=8				,    lowerCAmelCase__   :      Tuple=6				,    lowerCAmelCase__   :      Any=20_48				,    lowerCAmelCase__   :      Tuple=8				,    lowerCAmelCase__   :      Dict=0.0				,    lowerCAmelCase__   :      Optional[Any]=0.0				,    lowerCAmelCase__   :      Union[str, Any]=True				,    lowerCAmelCase__   :      str="relu"				,    lowerCAmelCase__   :      List[Any]=2_56				,    lowerCAmelCase__   :      List[str]=0.1				,    lowerCAmelCase__   :      str=0.0				,    lowerCAmelCase__   :      int=0.0				,    lowerCAmelCase__   :      Union[str, Any]=0.02				,    lowerCAmelCase__   :      Union[str, Any]=1.0				,    lowerCAmelCase__   :      List[Any]=False				,    lowerCAmelCase__   :      Any="sine"				,    lowerCAmelCase__   :      Tuple="resnet50"				,    lowerCAmelCase__   :      str=True				,    lowerCAmelCase__   :      List[str]=False				,    lowerCAmelCase__   :      Optional[Any]=2				,    lowerCAmelCase__   :      Any=5				,    lowerCAmelCase__   :      Tuple=2				,    lowerCAmelCase__   :      Optional[int]=1				,    lowerCAmelCase__   :      Optional[Any]=1				,    lowerCAmelCase__   :      Tuple=2				,    lowerCAmelCase__   :      Any=5				,    lowerCAmelCase__   :      int=2				,    lowerCAmelCase__   :      str=0.25				,    **lowerCAmelCase__   :      int				,    ):

													"""simple docstring"""





													if backbone_config is not None and use_timm_backbone:
																				raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''				)

													if not use_timm_backbone:
																				if backbone_config is None:
																											logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''				)
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			CONFIG_MAPPING['resnet'](out_features=['''stage4''']				)
																				elif isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																											SCREAMING_SNAKE_CASE					:      int     					=			backbone_config.get('''model_type'''				)
																											SCREAMING_SNAKE_CASE					:      Any     					=			CONFIG_MAPPING[backbone_model_type]
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			config_class.from_dict(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			use_timm_backbone
													SCREAMING_SNAKE_CASE					:      Dict     					=			backbone_config
													SCREAMING_SNAKE_CASE					:      List[str]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      Dict     					=			num_queries
													SCREAMING_SNAKE_CASE					:      Dict     					=			d_model
													SCREAMING_SNAKE_CASE					:      str     					=			encoder_ffn_dim
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			encoder_layers
													SCREAMING_SNAKE_CASE					:      str     					=			encoder_attention_heads
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			decoder_ffn_dim
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			decoder_layers
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			decoder_attention_heads
													SCREAMING_SNAKE_CASE					:      Dict     					=			dropout
													SCREAMING_SNAKE_CASE					:      Any     					=			attention_dropout
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			activation_dropout
													SCREAMING_SNAKE_CASE					:      Dict     					=			activation_function
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			init_std
													SCREAMING_SNAKE_CASE					:      Dict     					=			init_xavier_std
													SCREAMING_SNAKE_CASE					:      Any     					=			encoder_layerdrop
													SCREAMING_SNAKE_CASE					:      str     					=			decoder_layerdrop
													SCREAMING_SNAKE_CASE					:      int     					=			encoder_layers
													SCREAMING_SNAKE_CASE					:      str     					=			auxiliary_loss
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			position_embedding_type
													SCREAMING_SNAKE_CASE					:      List[str]     					=			backbone
													SCREAMING_SNAKE_CASE					:      int     					=			use_pretrained_backbone
													SCREAMING_SNAKE_CASE					:      Dict     					=			dilation
													# Hungarian matcher
													SCREAMING_SNAKE_CASE					:      List[str]     					=			class_cost
													SCREAMING_SNAKE_CASE					:      str     					=			bbox_cost
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			giou_cost
													# Loss coefficients
													SCREAMING_SNAKE_CASE					:      Tuple     					=			mask_loss_coefficient
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			dice_loss_coefficient
													SCREAMING_SNAKE_CASE					:      List[str]     					=			cls_loss_coefficient
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			bbox_loss_coefficient
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			giou_loss_coefficient
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			focal_alpha
													super().__init__(is_encoder_decoder=lowerCAmelCase__				,    **lowerCAmelCase__				)







						@property
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													return self.encoder_attention_heads







						@property
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													return self.d_model







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			copy.deepcopy(self.__dict__				)
													if self.backbone_config is not None:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.backbone_config.to_dict()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.__class__.model_type
													return output


class 	lowerCamelCase_							(					__A			):
						_lowerCAmelCase     :   Optional[int]	     =				version.parse('1.11'			)







						@property
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													return OrderedDict(
													    [
													        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
													        ('''pixel_mask''', {0: '''batch'''}),
													    ]				)







						@property
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													return 1e-5







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													return 12









'''simple docstring'''



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


lowerCAmelCase_     :       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__":
							lowerCAmelCase_     :       int								=						'hopper-medium-v2'
							lowerCAmelCase_     :       str								=						gym.make(env_name)

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

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

																					# execute action in environment
																					lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_     :       Union[str, Any]								=						env.step(denorm_actions)
																					lowerCAmelCase_     :       Optional[int]								=						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_     :       Union[str, Any]								=						next_observation
							except KeyboardInterrupt:
														pass

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




