



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 				__A					(A_      ):





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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						return (x_in - self.means[key]) / self.stds[key]





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						return x_in * self.stds[key] + self.means[key]





			def        _snake_case    (self							,    __magic_name__				):
						if type(__magic_name__				) is dict:
									return {k: self.to_torch(__magic_name__				) for k, v in x_in.items()}
						elif torch.is_tensor(__magic_name__				):
									return x_in.to(self.unet.device				)
						return torch.tensor(__magic_name__							,    device=self.unet.device				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						for key, val in cond.items():
									lowerCamelCase__ :      Dict								=		val.clone()
						return x_in





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Optional[Any]								=		x.shape[0]
						lowerCamelCase__ :      List[str]								=		None
						for i in tqdm.tqdm(self.scheduler.timesteps				):
									# create batch of timesteps to pass into model
									lowerCamelCase__ :      Optional[Any]								=		torch.full((batch_size,)							,    __magic_name__							,    device=self.unet.device							,    dtype=torch.long				)
									for _ in range(__magic_name__				):
												with torch.enable_grad():
															x.requires_grad_()

															# permute to match dimension for pre-trained models
															lowerCamelCase__ :      str								=		self.value_function(x.permute(0							,    2							,    1				)							,    __magic_name__				).sample
															lowerCamelCase__ :      List[str]								=		torch.autograd.grad([y.sum()]							,    [x]				)[0]

															lowerCamelCase__ :      int								=		self.scheduler._get_variance(__magic_name__				)
															lowerCamelCase__ :      Tuple								=		torch.exp(0.5 * posterior_variance				)
															lowerCamelCase__ :      int								=		model_std * grad

												lowerCamelCase__ :      Dict								=		0
												lowerCamelCase__ :      Any								=		x.detach()
												lowerCamelCase__ :      Any								=		x + scale * grad
												lowerCamelCase__ :      Any								=		self.reset_xa(__magic_name__							,    __magic_name__							,    self.action_dim				)

									lowerCamelCase__ :      int								=		self.unet(x.permute(0							,    2							,    1				)							,    __magic_name__				).sample.permute(0							,    2							,    1				)

									# TODO: verify deprecation of this kwarg
									lowerCamelCase__ :      Optional[Any]								=		self.scheduler.step(__magic_name__							,    __magic_name__							,    __magic_name__							,    predict_epsilon=__magic_name__				)["""prev_sample"""]

									# apply conditions to the trajectory (set the initial state)
									lowerCamelCase__ :      Dict								=		self.reset_xa(__magic_name__							,    __magic_name__							,    self.action_dim				)
									lowerCamelCase__ :      Union[str, Any]								=		self.to_torch(__magic_name__				)
						return x, y





			def __call__(self							,    __magic_name__							,    __magic_name__=64							,    __magic_name__=32							,    __magic_name__=2							,    __magic_name__=0.1				):
						# normalize the observations and create  batch dimension
						lowerCamelCase__ :      int								=		self.normalize(__magic_name__							,    """observations"""				)
						lowerCamelCase__ :      int								=		obs[None].repeat(__magic_name__							,    axis=0				)

						lowerCamelCase__ :      List[str]								=		{0: self.to_torch(__magic_name__				)}
						lowerCamelCase__ :      List[str]								=		(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)
						lowerCamelCase__ :      List[Any]								=		randn_tensor(__magic_name__							,    device=self.unet.device				)
						lowerCamelCase__ :      Any								=		self.reset_xa(__magic_name__							,    __magic_name__							,    self.action_dim				)
						lowerCamelCase__ :      Any								=		self.to_torch(__magic_name__				)

						# run the diffusion process
						lowerCamelCase__ :      List[str]								=		self.run_diffusion(__magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				)

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

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

						lowerCamelCase__ :      List[str]								=		denorm_actions[selected_index, 0]
						return denorm_actions








import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


_lowercase     							=						logging.get_logger(__name__)






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Optional[int] )  ->Tuple:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		nn.functional.normalize(UpperCamelCase )
			lowerCamelCase__ :      Tuple								=		nn.functional.normalize(UpperCamelCase )
			return torch.mm(UpperCamelCase      ,			normalized_text_embeds.t() )
class 				__A					( A_      ):
			UpperCamelCase					:Optional[Any]     =			CLIPConfig

			UpperCamelCase					:Optional[Any]     =			['''CLIPEncoderLayer''']





			def __init__(self							,    __magic_name__				):
						super().__init__(__magic_name__				)

						lowerCamelCase__ :      Union[str, Any]								=		CLIPVisionModel(config.vision_config				)
						lowerCamelCase__ :      Any								=		nn.Linear(config.vision_config.hidden_size							,    config.projection_dim							,    bias=__magic_name__				)

						lowerCamelCase__ :      Any								=		nn.Parameter(torch.ones(17							,    config.projection_dim				)							,    requires_grad=__magic_name__				)
						lowerCamelCase__ :      Union[str, Any]								=		nn.Parameter(torch.ones(3							,    config.projection_dim				)							,    requires_grad=__magic_name__				)

						lowerCamelCase__ :      Any								=		nn.Parameter(torch.ones(17				)							,    requires_grad=__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		nn.Parameter(torch.ones(3				)							,    requires_grad=__magic_name__				)





			@torch.no_grad()
			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Any								=		self.vision_model(__magic_name__				)[1]  # pooled_output
						lowerCamelCase__ :      Tuple								=		self.visual_projection(__magic_name__				)

						# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
						lowerCamelCase__ :      Any								=		cosine_distance(__magic_name__							,    self.special_care_embeds				).cpu().float().numpy()
						lowerCamelCase__ :      str								=		cosine_distance(__magic_name__							,    self.concept_embeds				).cpu().float().numpy()

						lowerCamelCase__ :      int								=		[]
						lowerCamelCase__ :      str								=		image_embeds.shape[0]
						for i in range(__magic_name__				):
									lowerCamelCase__ :      str								=		{"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}

									# increase this value to create a stronger `nfsw` filter
									# at the cost of increasing the possibility of filtering benign images
									lowerCamelCase__ :      Optional[Any]								=		0.0

									for concept_idx in range(len(special_cos_dist[0]				)				):
												lowerCamelCase__ :      List[str]								=		special_cos_dist[i][concept_idx]
												lowerCamelCase__ :      Tuple								=		self.special_care_embeds_weights[concept_idx].item()
												lowerCamelCase__ :      str								=		round(concept_cos - concept_threshold + adjustment							,    3				)
												if result_img["special_scores"][concept_idx] > 0:
															result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]}				)
															lowerCamelCase__ :      List[str]								=		0.01

									for concept_idx in range(len(cos_dist[0]				)				):
												lowerCamelCase__ :      str								=		cos_dist[i][concept_idx]
												lowerCamelCase__ :      Dict								=		self.concept_embeds_weights[concept_idx].item()
												lowerCamelCase__ :      int								=		round(concept_cos - concept_threshold + adjustment							,    3				)
												if result_img["concept_scores"][concept_idx] > 0:
															result_img["bad_concepts"].append(__magic_name__				)

									result.append(__magic_name__				)

						lowerCamelCase__ :      Tuple								=		[len(res["""bad_concepts"""]				) > 0 for res in result]

						return images, has_nsfw_concepts





			@torch.no_grad()
			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Union[str, Any]								=		self.vision_model(__magic_name__				)[1]  # pooled_output
						lowerCamelCase__ :      str								=		self.visual_projection(__magic_name__				)

						lowerCamelCase__ :      Optional[Any]								=		cosine_distance(__magic_name__							,    self.special_care_embeds				)
						lowerCamelCase__ :      Dict								=		cosine_distance(__magic_name__							,    self.concept_embeds				)

						# increase this value to create a stronger `nsfw` filter
						# at the cost of increasing the possibility of filtering benign images
						lowerCamelCase__ :      Any								=		0.0

						lowerCamelCase__ :      Dict								=		special_cos_dist - self.special_care_embeds_weights + adjustment
						# special_scores = special_scores.round(decimals=3)
						lowerCamelCase__ :      Union[str, Any]								=		torch.any(special_scores > 0							,    dim=1				)
						lowerCamelCase__ :      Optional[Any]								=		special_care * 0.01
						lowerCamelCase__ :      Dict								=		special_adjustment.unsqueeze(1				).expand(-1							,    cos_dist.shape[1]				)

						lowerCamelCase__ :      Tuple								=		(cos_dist - self.concept_embeds_weights) + special_adjustment
						# concept_scores = concept_scores.round(decimals=3)
						lowerCamelCase__ :      Any								=		torch.any(concept_scores > 0							,    dim=1				)

						return images, has_nsfw_concepts



