



'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase_     :       Dict								=						logging.get_logger(__name__)


if is_vision_available():
							import PIL
class 	lowerCamelCase_							(					snake_case__			):
						_lowerCAmelCase     :   int	     =				['pixel_values']







						def __init__(  self   :      str				,    lowerCAmelCase__   :      List[str] = True				,    lowerCAmelCase__   :      Dict = None				,    lowerCAmelCase__   :      Any = PILImageResampling.BICUBIC				,    lowerCAmelCase__   :      Tuple = True				,    lowerCAmelCase__   :      List[str] = None				,    lowerCAmelCase__   :      str = True				,    lowerCAmelCase__   :      str = 1 / 2_55				,    lowerCAmelCase__   :      List[Any] = True				,    lowerCAmelCase__   :      List[Any] = None				,    lowerCAmelCase__   :      Optional[Any] = None				,    lowerCAmelCase__   :      Dict = True				,    **lowerCAmelCase__   :      Optional[Any]				,    ):

													"""simple docstring"""
													super().__init__(**lowercase_				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			size if size is not None else {"shortest_edge": 2_24}
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			get_size_dict(lowercase_				,    default_to_square=lowercase_				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE					:      Any     					=			get_size_dict(lowercase_				,    default_to_square=lowercase_				,    param_name='''crop_size'''				)

													SCREAMING_SNAKE_CASE					:      str     					=			do_resize
													SCREAMING_SNAKE_CASE					:      Dict     					=			size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			resample
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			do_center_crop
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			crop_size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			do_rescale
													SCREAMING_SNAKE_CASE					:      str     					=			rescale_factor
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			do_normalize
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_std if image_std is not None else OPENAI_CLIP_STD
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			do_convert_rgb







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      List[str] = PILImageResampling.BICUBIC				,    lowerCAmelCase__   :      Union[str, Any] = None				,    **lowerCAmelCase__   :      int				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			get_size_dict(lowercase_				,    default_to_square=lowercase_				)
													if "shortest_edge" not in size:
																				raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			get_resize_output_image_size(lowercase_				,    size=size['''shortest_edge''']				,    default_to_square=lowercase_				)
													return resize(lowercase_				,    size=lowercase_				,    resample=lowercase_				,    data_format=lowercase_				,    **lowercase_				)







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      Dict = None				,    **lowerCAmelCase__   :      List[Any]				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			get_size_dict(lowercase_				)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
													return center_crop(lowercase_				,    size=(size['''height'''], size['''width'''])				,    data_format=lowercase_				,    **lowercase_				)







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      List[str] = None				,    **lowerCAmelCase__   :      Any				,    ):

													"""simple docstring"""
													return rescale(lowercase_				,    scale=lowercase_				,    data_format=lowercase_				,    **lowercase_				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Optional[int] = None				,    **lowerCAmelCase__   :      Dict				,    ):

													"""simple docstring"""
													return normalize(lowercase_				,    mean=lowercase_				,    std=lowercase_				,    data_format=lowercase_				,    **lowercase_				)







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      int = None				,    lowerCAmelCase__   :      int = None				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      int = None				,    lowerCAmelCase__   :      Union[str, Any] = None				,    lowerCAmelCase__   :      int = None				,    lowerCAmelCase__   :      List[Any] = None				,    lowerCAmelCase__   :      Tuple = None				,    lowerCAmelCase__   :      Tuple = None				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      List[Any] = None				,    lowerCAmelCase__   :      Any = None				,    lowerCAmelCase__   :      Any = ChannelDimension.FIRST				,    **lowerCAmelCase__   :      Any				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			size if size is not None else self.size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			get_size_dict(lowercase_				,    param_name='''size'''				,    default_to_square=lowercase_				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE					:      Dict     					=			do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			get_size_dict(lowercase_				,    param_name='''crop_size'''				,    default_to_square=lowercase_				)
													SCREAMING_SNAKE_CASE					:      int     					=			do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE					:      Tuple     					=			do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE					:      List[str]     					=			image_std if image_std is not None else self.image_std
													SCREAMING_SNAKE_CASE					:      List[str]     					=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			make_list_of_images(lowercase_				)

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

													if do_resize and size is None:
																				raise ValueError('''Size 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.'''				)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																				SCREAMING_SNAKE_CASE					:      Any     					=			[convert_to_rgb(lowercase_				) for image in images]

													# All transformations expect numpy arrays.
													SCREAMING_SNAKE_CASE					:      List[str]     					=			[to_numpy_array(lowercase_				) for image in images]

													if do_resize:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			[self.resize(image=lowercase_				,    size=lowercase_				,    resample=lowercase_				) for image in images]

													if do_center_crop:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			[self.center_crop(image=lowercase_				,    size=lowercase_				) for image in images]

													if do_rescale:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			[self.rescale(image=lowercase_				,    scale=lowercase_				) for image in images]

													if do_normalize:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[self.normalize(image=lowercase_				,    mean=lowercase_				,    std=lowercase_				) for image in images]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			[to_channel_dimension_format(lowercase_				,    lowercase_				) for image in images]

													SCREAMING_SNAKE_CASE					:      int     					=			{"pixel_values": images}
													return BatchFeature(data=lowercase_				,    tensor_type=lowercase_				)









'''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}')




