



from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from ..image_utils import load_image


if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowerCAmelCase							:  Union[str, Any]            =						logging.get_logger(__name__)


lowerCAmelCase							:  Optional[int]            =						Dict[str, Any]
lowerCAmelCase							:  Any            =						List[Prediction]



@add_end_docstrings(UpperCAmelCase_     )
class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					str       ,						*lowerCAmelCase__  :					List[Any]       ,						**lowerCAmelCase__  :					int):
									super().__init__(*__a       ,						**__a)

									if self.framework == "tf":
															raise ValueError(F"The {self.__class__} is only available in PyTorch.")

									requires_backends(self       ,						"vision")
									self.check_model_type(
									    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						**lowerCAmelCase__  :					int):
									SCREAMING_SNAKE_CASE_:			str  		=	{}
									if "threshold" in kwargs:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	kwargs["threshold"]
									return {}, {}, postprocess_kwargs



			def __call__(			self  :					Tuple       ,						*lowerCAmelCase__  :					Optional[Any]       ,						**lowerCAmelCase__  :					Dict):
									return super().__call__(*__a       ,						**__a)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	load_image(__a)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.IntTensor([[image.height, image.width]])
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.image_processor(images=[image]       ,						return_tensors="pt")
									if self.tokenizer is not None:
															SCREAMING_SNAKE_CASE_:			Any  		=	self.tokenizer(text=inputs["words"]       ,						boxes=inputs["boxes"]       ,						return_tensors="pt")
									SCREAMING_SNAKE_CASE_:			int  		=	target_size
									return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	model_inputs.pop("target_size")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.model(**__a)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	outputs.__class__({"target_size": target_size, **outputs})
									if self.tokenizer is not None:
															SCREAMING_SNAKE_CASE_:			Dict  		=	model_inputs["bbox"]
									return model_outputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Dict=0.9):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	model_outputs["target_size"]
									if self.tokenizer is not None:
															# This is a LayoutLMForTokenClassification variant.
															# The OCR got the boxes and the model classified the words.
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	target_size[0].tolist()

															def unnormalize(lowerCAmelCase__  :					Optional[int]):
																					return self._get_bounding_box(
																					    torch.Tensor(
																					        [
																					            (width * bbox[0] / 1000),
																					            (height * bbox[1] / 1000),
																					            (width * bbox[2] / 1000),
																					            (height * bbox[3] / 1000),
																					        ]))

															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[Any]  		=	model_outputs["logits"].squeeze(0).softmax(dim=-1).max(dim=-1)
															SCREAMING_SNAKE_CASE_:			Tuple  		=	[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
															SCREAMING_SNAKE_CASE_:			Dict  		=	[unnormalize(__a) for bbox in model_outputs["bbox"].squeeze(0)]
															SCREAMING_SNAKE_CASE_:			int  		=	["score", "label", "box"]
															SCREAMING_SNAKE_CASE_:			Tuple  		=	[dict(zip(__a       ,						__a)) for vals in zip(scores.tolist()       ,						__a       ,						__a) if vals[0] > threshold]
									else:
															# This is a regular ForObjectDetectionModel
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.image_processor.post_process_object_detection(__a       ,						__a       ,						__a)
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	raw_annotations[0]
															SCREAMING_SNAKE_CASE_:			Tuple  		=	raw_annotation["scores"]
															SCREAMING_SNAKE_CASE_:			Tuple  		=	raw_annotation["labels"]
															SCREAMING_SNAKE_CASE_:			str  		=	raw_annotation["boxes"]

															SCREAMING_SNAKE_CASE_:			int  		=	scores.tolist()
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[self.model.config.idalabel[label.item()] for label in labels]
															SCREAMING_SNAKE_CASE_:			Tuple  		=	[self._get_bounding_box(__a) for box in boxes]

															# {"scores": [...], ...} --> [{"score":x, ...}, ...]
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	["score", "label", "box"]
															SCREAMING_SNAKE_CASE_:			Dict  		=	[
															    dict(zip(__a       ,						__a))
															    for vals in zip(raw_annotation["scores"]       ,						raw_annotation["labels"]       ,						raw_annotation["boxes"])
															]

									return annotation




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[Any]):
									if self.framework != "pt":
															raise ValueError("The ObjectDetectionPipeline is only available in PyTorch.")
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[int]  		=	box.int().tolist()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{
									    "xmin": xmin,
									    "ymin": ymin,
									    "xmax": xmax,
									    "ymax": ymax,
									}
									return bbox









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}''')






