



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

lowerCAmelCase							:  Optional[Any]            =						logging.get_logger(__name__)







def       A_				(   _UpperCAmelCase   ):
						if isinstance(lowercase__    ,     (list, tuple)   ) and isinstance(videos[0]    ,     (list, tuple)   ) and is_valid_image(videos[0][0]   ):
												return videos

						elif isinstance(lowercase__    ,     (list, tuple)   ) and is_valid_image(videos[0]   ):
												return [videos]

						elif is_valid_image(lowercase__   ):
												return [[videos]]

						raise ValueError(f"Could not make batched video from {videos}"   )



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					['''pixel_values''']



			def __init__(			self  :					List[str]       ,						lowerCAmelCase__  :					Optional[int] = True       ,						lowerCAmelCase__  :					Optional[Any] = None       ,						lowerCAmelCase__  :					int = PILImageResampling.BILINEAR       ,						lowerCAmelCase__  :					Dict = True       ,						lowerCAmelCase__  :					List[Any] = None       ,						lowerCAmelCase__  :					Optional[int] = True       ,						lowerCAmelCase__  :					Tuple = 1 / 255       ,						lowerCAmelCase__  :					str = True       ,						lowerCAmelCase__  :					List[Any] = True       ,						lowerCAmelCase__  :					Optional[int] = None       ,						lowerCAmelCase__  :					Any = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	size if size is not None else {'shortest_edge': 256}
									SCREAMING_SNAKE_CASE_:			str  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
									SCREAMING_SNAKE_CASE_:			str  		=	get_size_dict(lowerCAmelCase__       ,						param_name="crop_size")

									SCREAMING_SNAKE_CASE_:			int  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Any  		=	size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	do_center_crop
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	crop_size
									SCREAMING_SNAKE_CASE_:			List[str]  		=	resample
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			Any  		=	offset
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									SCREAMING_SNAKE_CASE_:			str  		=	image_std if image_std is not None else IMAGENET_STANDARD_STD



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					List[str] = PILImageResampling.BILINEAR       ,						lowerCAmelCase__  :					str = None       ,						**lowerCAmelCase__  :					int       ,						):
									SCREAMING_SNAKE_CASE_:			str  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									if "shortest_edge" in size:
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	get_resize_output_image_size(lowerCAmelCase__       ,						size["shortest_edge"]       ,						default_to_square=lowerCAmelCase__)
									elif "height" in size and "width" in size:
															SCREAMING_SNAKE_CASE_:			Any  		=	(size['height'], size['width'])
									else:
															raise ValueError(F"Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}")
									return resize(lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Dict = None       ,						**lowerCAmelCase__  :					Union[str, Any]       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	get_size_dict(lowerCAmelCase__)
									if "height" not in size or "width" not in size:
															raise ValueError(F"Size must have \'height\' and \'width\' as keys. Got {size.keys()}")
									return center_crop(lowerCAmelCase__       ,						size=(size["height"], size["width"])       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Union[str, Any]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[Any] = True       ,						lowerCAmelCase__  :					List[str] = None       ,						**lowerCAmelCase__  :					Optional[int]       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	image.astype(np.floataa)
									if offset:
															SCREAMING_SNAKE_CASE_:			int  		=	image - (scale / 2)
									return rescale(lowerCAmelCase__       ,						scale=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Optional[int] = None       ,						**lowerCAmelCase__  :					int       ,						):
									return normalize(lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					int = None       ,						lowerCAmelCase__  :					int = None       ,						lowerCAmelCase__  :					List[Any] = None       ,						lowerCAmelCase__  :					List[Any] = None       ,						lowerCAmelCase__  :					Dict = None       ,						lowerCAmelCase__  :					Tuple = None       ,						lowerCAmelCase__  :					Optional[int] = None       ,						lowerCAmelCase__  :					List[Any] = None       ,						lowerCAmelCase__  :					Optional[Any] = None       ,						lowerCAmelCase__  :					Optional[int] = None       ,						lowerCAmelCase__  :					Any = None       ,						lowerCAmelCase__  :					List[Any] = ChannelDimension.FIRST       ,						):
									if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True.")

									if do_center_crop and crop_size is None:
															raise ValueError("Crop size must be specified if do_center_crop is True.")

									if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True.")

									if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True.")

									if offset and not do_rescale:
															raise ValueError("For offset, do_rescale must also be set to True.")

									# All transformations expect numpy arrays.
									SCREAMING_SNAKE_CASE_:			int  		=	to_numpy_array(lowerCAmelCase__)

									if do_resize:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.resize(image=lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__)

									if do_center_crop:
															SCREAMING_SNAKE_CASE_:			int  		=	self.center_crop(lowerCAmelCase__       ,						size=lowerCAmelCase__)

									if do_rescale:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.rescale(image=lowerCAmelCase__       ,						scale=lowerCAmelCase__       ,						offset=lowerCAmelCase__)

									if do_normalize:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.normalize(image=lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	to_channel_dimension_format(lowerCAmelCase__       ,						lowerCAmelCase__)
									return image




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					int = None       ,						lowerCAmelCase__  :					str = None       ,						lowerCAmelCase__  :					str = None       ,						lowerCAmelCase__  :					int = None       ,						lowerCAmelCase__  :					Dict = None       ,						lowerCAmelCase__  :					str = None       ,						lowerCAmelCase__  :					Optional[int] = None       ,						lowerCAmelCase__  :					Optional[Any] = None       ,						lowerCAmelCase__  :					int = None       ,						lowerCAmelCase__  :					Dict = None       ,						lowerCAmelCase__  :					Dict = None       ,						lowerCAmelCase__  :					Tuple = None       ,						lowerCAmelCase__  :					Tuple = ChannelDimension.FIRST       ,						**lowerCAmelCase__  :					Union[str, Any]       ,						):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_center_crop if do_center_crop is not None else self.do_center_crop
									SCREAMING_SNAKE_CASE_:			str  		=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_:			Dict  		=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_:			Tuple  		=	offset if offset is not None else self.offset
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_:			Dict  		=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_:			str  		=	image_std if image_std is not None else self.image_std

									SCREAMING_SNAKE_CASE_:			List[str]  		=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE_:			Dict  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	crop_size if crop_size is not None else self.crop_size
									SCREAMING_SNAKE_CASE_:			Dict  		=	get_size_dict(lowerCAmelCase__       ,						param_name="crop_size")

									if not valid_images(lowerCAmelCase__):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray.")

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	make_batched(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	[
									    [
									        self._preprocess_image(
									            image=lowerCAmelCase__       ,						do_resize=lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						do_center_crop=lowerCAmelCase__       ,						crop_size=lowerCAmelCase__       ,						do_rescale=lowerCAmelCase__       ,						rescale_factor=lowerCAmelCase__       ,						offset=lowerCAmelCase__       ,						do_normalize=lowerCAmelCase__       ,						image_mean=lowerCAmelCase__       ,						image_std=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						)
									        for img in video
									    ]
									    for video in videos
									]

									SCREAMING_SNAKE_CASE_:			int  		=	{'pixel_values': videos}
									return BatchFeature(data=lowerCAmelCase__       ,						tensor_type=lowerCAmelCase__)









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}''')






