



'''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


UpperCamelCase__:     int										=    logging.get_logger(__name__)


if is_vision_available():
						import PIL

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= ["""pixel_values"""]







	def __init__(		self    :  Union[str, Any]	,    __snake_case    :  bool = True	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  PILImageResampling = PILImageResampling.BICUBIC	,    __snake_case    :  bool = True	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  bool = True	,    __snake_case    :  Union[int, float] = 1 / 255	,    __snake_case    :  bool = True	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  bool = True	,    **__snake_case    :  List[str]	,    )    ->	None:
								super().__init__(**__snake_case	)
								UpperCAmelCase				: str											=	size if size is not None else {'''shortest_edge''': 224}
								UpperCAmelCase				: Optional[int]											=	get_size_dict(__snake_case	,    default_to_square=__snake_case	)
								UpperCAmelCase				: List[Any]											=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase				: str											=	get_size_dict(__snake_case	,    default_to_square=__snake_case	,    param_name='''crop_size'''	)

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







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  np.ndarray	,    __snake_case    :  Dict[str, int]	,    __snake_case    :  PILImageResampling = PILImageResampling.BICUBIC	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  List[Any]	,    )    ->	np.ndarray:
								UpperCAmelCase				: Dict											=	get_size_dict(__snake_case	,    default_to_square=__snake_case	)
								if "shortest_edge" not in size:
															raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""	)
								UpperCAmelCase				: Union[str, Any]											=	get_resize_output_image_size(__snake_case	,    size=size['''shortest_edge''']	,    default_to_square=__snake_case	)
								return resize(__snake_case	,    size=__snake_case	,    resample=__snake_case	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  np.ndarray	,    __snake_case    :  Dict[str, int]	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  Dict	,    )    ->	np.ndarray:
								UpperCAmelCase				: Optional[int]											=	get_size_dict(__snake_case	)
								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(__snake_case	,    size=(size['''height'''], size['''width'''])	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  List[Any]	,    __snake_case    :  np.ndarray	,    __snake_case    :  Union[int, float]	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  Dict	,    )    ->	Dict:
								return rescale(__snake_case	,    scale=__snake_case	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  np.ndarray	,    __snake_case    :  Union[float, List[float]]	,    __snake_case    :  Union[float, List[float]]	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  List[str]	,    )    ->	np.ndarray:
								return normalize(__snake_case	,    mean=__snake_case	,    std=__snake_case	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  Any	,    __snake_case    :  ImageInput	,    __snake_case    :  bool = None	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  PILImageResampling = None	,    __snake_case    :  bool = None	,    __snake_case    :  int = None	,    __snake_case    :  bool = None	,    __snake_case    :  float = None	,    __snake_case    :  bool = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  bool = None	,    __snake_case    :  Optional[Union[str, TensorType]] = None	,    __snake_case    :  Optional[ChannelDimension] = ChannelDimension.FIRST	,    **__snake_case    :  Optional[int]	,    )    ->	PIL.Image.Image:
								UpperCAmelCase				: int											=	do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase				: Dict											=	size if size is not None else self.size
								UpperCAmelCase				: Dict											=	get_size_dict(__snake_case	,    param_name='''size'''	,    default_to_square=__snake_case	)
								UpperCAmelCase				: int											=	resample if resample is not None else self.resample
								UpperCAmelCase				: Any											=	do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase				: Optional[int]											=	crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase				: int											=	get_size_dict(__snake_case	,    param_name='''crop_size'''	,    default_to_square=__snake_case	)
								UpperCAmelCase				: Any											=	do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase				: Union[str, Any]											=	rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase				: Dict											=	do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase				: Optional[Any]											=	image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase				: Tuple											=	image_std if image_std is not None else self.image_std
								UpperCAmelCase				: Dict											=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCAmelCase				: int											=	make_list_of_images(__snake_case	)

								if not valid_images(__snake_case	):
															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:
															UpperCAmelCase				: Optional[Any]											=	[convert_to_rgb(__snake_case	) for image in images]

								# All transformations expect numpy arrays.
								UpperCAmelCase				: int											=	[to_numpy_array(__snake_case	) for image in images]

								if do_resize:
															UpperCAmelCase				: List[Any]											=	[self.resize(image=__snake_case	,    size=__snake_case	,    resample=__snake_case	) for image in images]

								if do_center_crop:
															UpperCAmelCase				: Any											=	[self.center_crop(image=__snake_case	,    size=__snake_case	) for image in images]

								if do_rescale:
															UpperCAmelCase				: Any											=	[self.rescale(image=__snake_case	,    scale=__snake_case	) for image in images]

								if do_normalize:
															UpperCAmelCase				: Dict											=	[self.normalize(image=__snake_case	,    mean=__snake_case	,    std=__snake_case	) for image in images]

								UpperCAmelCase				: List[Any]											=	[to_channel_dimension_format(__snake_case	,    __snake_case	) for image in images]

								UpperCAmelCase				: Dict											=	{'''pixel_values''': images}
								return BatchFeature(data=__snake_case	,    tensor_type=__snake_case	)










'''simple docstring'''






import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Union[str, Any]	,    __snake_case    :  UNetaDModel	,    __snake_case    :  UNetaDModel	,    __snake_case    :  DDPMScheduler	,    __snake_case    :  List[Any]	,    )    ->	List[str]:
								super().__init__()
								UpperCAmelCase				: Any											=	value_function
								UpperCAmelCase				: List[str]											=	unet
								UpperCAmelCase				: Dict											=	scheduler
								UpperCAmelCase				: Union[str, Any]											=	env
								UpperCAmelCase				: Dict											=	env.get_dataset()
								UpperCAmelCase				: Tuple											=	{}
								for key in self.data.keys():
															try:
																						UpperCAmelCase				: Optional[int]											=	self.data[key].mean()
															except:  # noqa: E722
																						pass
								UpperCAmelCase				: Any											=	{}
								for key in self.data.keys():
															try:
																						UpperCAmelCase				: str											=	self.data[key].std()
															except:  # noqa: E722
																						pass
								UpperCAmelCase				: int											=	env.observation_space.shape[0]
								UpperCAmelCase				: Optional[Any]											=	env.action_space.shape[0]







	def   A			(		self    :  Any	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Union[str, Any]	)    ->	Optional[int]:
								return (x_in - self.means[key]) / self.stds[key]







	def   A			(		self    :  Dict	,    __snake_case    :  Any	,    __snake_case    :  Dict	)    ->	Any:
								return x_in * self.stds[key] + self.means[key]







	def   A			(		self    :  Any	,    __snake_case    :  int	)    ->	List[Any]:
								if type(__snake_case	) is dict:
															return {k: self.to_torch(__snake_case	) for k, v in x_in.items()}
								elif torch.is_tensor(__snake_case	):
															return x_in.to(self.unet.device	)
								return torch.tensor(__snake_case	,    device=self.unet.device	)







	def   A			(		self    :  int	,    __snake_case    :  Optional[int]	,    __snake_case    :  str	,    __snake_case    :  Union[str, Any]	)    ->	Dict:
								for key, val in cond.items():
															UpperCAmelCase				: List[Any]											=	val.clone()
								return x_in







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  Dict	,    __snake_case    :  List[Any]	,    __snake_case    :  Any	,    __snake_case    :  Dict	)    ->	Any:
								UpperCAmelCase				: Any											=	x.shape[0]
								UpperCAmelCase				: Any											=	None
								for i in tqdm.tqdm(self.scheduler.timesteps	):
															# create batch of timesteps to pass into model
															UpperCAmelCase				: Tuple											=	torch.full((batch_size,)	,    __snake_case	,    device=self.unet.device	,    dtype=torch.long	)
															for _ in range(__snake_case	):
																						with torch.enable_grad():
																													x.requires_grad_()

																													# permute to match dimension for pre-trained models
																													UpperCAmelCase				: Any											=	self.value_function(x.permute(0	,    2	,    1	)	,    __snake_case	).sample
																													UpperCAmelCase				: Dict											=	torch.autograd.grad([y.sum()]	,    [x]	)[0]

																													UpperCAmelCase				: Tuple											=	self.scheduler._get_variance(__snake_case	)
																													UpperCAmelCase				: Optional[int]											=	torch.exp(0.5 * posterior_variance	)
																													UpperCAmelCase				: str											=	model_std * grad

																						UpperCAmelCase				: Dict											=	0
																						UpperCAmelCase				: Optional[int]											=	x.detach()
																						UpperCAmelCase				: List[str]											=	x + scale * grad
																						UpperCAmelCase				: List[str]											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)

															UpperCAmelCase				: str											=	self.unet(x.permute(0	,    2	,    1	)	,    __snake_case	).sample.permute(0	,    2	,    1	)

															# TODO: verify deprecation of this kwarg
															UpperCAmelCase				: Optional[int]											=	self.scheduler.step(__snake_case	,    __snake_case	,    __snake_case	,    predict_epsilon=__snake_case	)['''prev_sample''']

															# apply conditions to the trajectory (set the initial state)
															UpperCAmelCase				: Any											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
															UpperCAmelCase				: List[str]											=	self.to_torch(__snake_case	)
								return x, y







	def __call__(		self    :  Any	,    __snake_case    :  List[str]	,    __snake_case    :  Optional[Any]=64	,    __snake_case    :  Optional[int]=32	,    __snake_case    :  Union[str, Any]=2	,    __snake_case    :  Optional[int]=0.1	)    ->	Tuple:
								# normalize the observations and create  batch dimension
								UpperCAmelCase				: Optional[Any]											=	self.normalize(__snake_case	,    '''observations'''	)
								UpperCAmelCase				: Optional[int]											=	obs[None].repeat(__snake_case	,    axis=0	)

								UpperCAmelCase				: Tuple											=	{0: self.to_torch(__snake_case	)}
								UpperCAmelCase				: Tuple											=	(batch_size, planning_horizon, self.state_dim + self.action_dim)

								# generate initial noise and apply our conditions (to make the trajectories start at current state)
								UpperCAmelCase				: Union[str, Any]											=	randn_tensor(__snake_case	,    device=self.unet.device	)
								UpperCAmelCase				: int											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
								UpperCAmelCase				: List[Any]											=	self.to_torch(__snake_case	)

								# run the diffusion process
								UpperCAmelCase							,	UpperCAmelCase				: Optional[int]											=	self.run_diffusion(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)

								# sort output trajectories by value
								UpperCAmelCase				: str											=	y.argsort(0	,    descending=__snake_case	).squeeze()
								UpperCAmelCase				: Tuple											=	x[sorted_idx]
								UpperCAmelCase				: List[Any]											=	sorted_values[:, :, : self.action_dim]
								UpperCAmelCase				: str											=	actions.detach().cpu().numpy()
								UpperCAmelCase				: List[str]											=	self.de_normalize(__snake_case	,    key='''actions'''	)

								# select the action with the highest value
								if y is not None:
															UpperCAmelCase				: List[Any]											=	0
								else:
															# if we didn't run value guiding, select a random action
															UpperCAmelCase				: Optional[int]											=	np.random.randint(0	,    __snake_case	)

								UpperCAmelCase				: Optional[int]											=	denorm_actions[selected_index, 0]
								return denorm_actions





