



from math import factorial
class 				__A					:





			def __init__(self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      int								=		real
						if isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      List[Any]								=		[1] * rank
						else:
									lowerCamelCase__ :      Tuple								=		rank





			def __repr__(self				):
						return (
						    f"{self.real}+"
						    f"{'+'.join(str(__magic_name__				)+'E'+str(n+1				)for n,dual in enumerate(self.duals				)				)}"
						)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		self.duals.copy()
						while cur[-1] == 0:
									cur.pop(-1				)
						return Dual(self.real							,    __magic_name__				)





			def __add__(self							,    __magic_name__				):
						if not isinstance(__magic_name__							,    __magic_name__				):
									return Dual(self.real + other							,    self.duals				)
						lowerCamelCase__ :      Any								=		self.duals.copy()
						lowerCamelCase__ :      List[str]								=		other.duals.copy()
						if len(__magic_name__				) > len(__magic_name__				):
									o_dual.extend([1] * (len(__magic_name__				) - len(__magic_name__				))				)
						elif len(__magic_name__				) < len(__magic_name__				):
									s_dual.extend([1] * (len(__magic_name__				) - len(__magic_name__				))				)
						lowerCamelCase__ :      Optional[Any]								=		[]
						for i in range(len(__magic_name__				)				):
									new_duals.append(s_dual[i] + o_dual[i]				)
						return Dual(self.real + other.real							,    __magic_name__				)

			UpperCamelCase					:Union[str, Any]     =			__add__





			def __sub__(self							,    __magic_name__				):
						return self + other * -1





			def __mul__(self							,    __magic_name__				):
						if not isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Tuple								=		[]
									for i in self.duals:
												new_duals.append(i * other				)
									return Dual(self.real * other							,    __magic_name__				)
						lowerCamelCase__ :      List[str]								=		[0] * (len(self.duals				) + len(other.duals				) + 1)
						for i, item in enumerate(self.duals				):
									for j, jtem in enumerate(other.duals				):
												new_duals[i + j + 1] += item * jtem
						for k in range(len(self.duals				)				):
									new_duals[k] += self.duals[k] * other.real
						for index in range(len(other.duals				)				):
									new_duals[index] += other.duals[index] * self.real
						return Dual(self.real * other.real							,    __magic_name__				)

			UpperCamelCase					:Tuple     =			__mul__





			def __truediv__(self							,    __magic_name__				):
						if not isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Optional[int]								=		[]
									for i in self.duals:
												new_duals.append(i / other				)
									return Dual(self.real / other							,    __magic_name__				)
						raise ValueError





			def __floordiv__(self							,    __magic_name__				):
						if not isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Optional[int]								=		[]
									for i in self.duals:
												new_duals.append(i // other				)
									return Dual(self.real // other							,    __magic_name__				)
						raise ValueError





			def __pow__(self							,    __magic_name__				):
						if n < 0 or isinstance(__magic_name__							,    __magic_name__				):
									raise ValueError("""power must be a positive integer"""				)
						if n == 0:
									return 1
						if n == 1:
									return self
						lowerCamelCase__ :      Optional[int]								=		self
						for _ in range(n - 1				):
									x *= self
						return x







def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[int] )  ->Any:



			'''simple docstring'''







			if not callable(UpperCamelCase ):
						raise ValueError("""differentiate() requires a function as input for func""" )
			if not isinstance(UpperCamelCase      ,			(float, int) ):
						raise ValueError("""differentiate() requires a float as input for position""" )
			if not isinstance(UpperCamelCase      ,			UpperCamelCase ):
						raise ValueError("""differentiate() requires an int as input for order""" )
			lowerCamelCase__ :      str								=		Dual(UpperCamelCase      ,			1 )
			lowerCamelCase__ :      Dict								=		func(UpperCamelCase )
			if order == 0:
						return result.real
			return result.duals[order - 1] * factorial(UpperCamelCase )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






				def      _A				(UpperCamelCase		:      List[Any] )  ->Optional[Any]:



							'''simple docstring'''







							return y**2 * y**4
				print(differentiate(f, 9, 2))








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



