



from math import pow, sqrt






def      _A				(*UpperCamelCase		:      float )  ->bool:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		len(UpperCamelCase ) > 0 and all(value > 0.0 for value in values )
			return result






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      float )  ->float | ValueError:



			'''simple docstring'''







			return (
			    round(sqrt(molar_mass_a / molar_mass_a )      ,			6 )
			    if validate(UpperCamelCase      ,			UpperCamelCase )
			    else ValueError("""Input Error: Molar mass values must greater than 0.""" )
			)






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      float      ,			UpperCamelCase		:      float )  ->float | ValueError:



			'''simple docstring'''







			return (
			    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )      ,			6 )
			    if validate(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
			    else ValueError(
			        """Input Error: Molar mass and effusion rate values must greater than 0.""" )
			)






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      float      ,			UpperCamelCase		:      float )  ->float | ValueError:



			'''simple docstring'''







			return (
			    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )      ,			6 )
			    if validate(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
			    else ValueError(
			        """Input Error: Molar mass and effusion rate values must greater than 0.""" )
			)






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      float      ,			UpperCamelCase		:      float )  ->float | ValueError:



			'''simple docstring'''







			return (
			    round(molar_mass / pow(effusion_rate_a / effusion_rate_a      ,			2 )      ,			6 )
			    if validate(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
			    else ValueError(
			        """Input Error: Molar mass and effusion rate values must greater than 0.""" )
			)






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      float      ,			UpperCamelCase		:      float )  ->float | ValueError:



			'''simple docstring'''







			return (
			    round(pow(effusion_rate_a / effusion_rate_a      ,			2 ) / molar_mass      ,			6 )
			    if validate(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
			    else ValueError(
			        """Input Error: Molar mass and effusion rate values must greater than 0.""" )
			)








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



