code: 
  
  
  
  import numpy as np
  import torch
  import torch.nn as nn
  from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
  
  from ...utils import logging
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  def 	UpperCamelCase			(     snake_case__ :					List[str] ,  snake_case__ :					str			)			->  Dict:
     UpperCamelCase					:						int					=			nn.functional.normalize(snake_case__			)
     UpperCamelCase					:						Union[str, Any]					=			nn.functional.normalize(snake_case__			)
     return torch.mm(snake_case__ ,  normalized_text_embeds.t()			)
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      int       =						CLIPConfig
  
         UpperCAmelCase__       :      Union[str, Any]       =						["CLIPEncoderLayer"]
         def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	List[str]:
            super().__init__(SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						int					=			CLIPVisionModel(config.vision_config )
            UpperCamelCase					:						int					=			nn.Linear(config.vision_config.hidden_size,    config.projection_dim,    bias=SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						List[Any]					=			nn.Parameter(torch.ones(17,    config.projection_dim ),    requires_grad=SCREAMING_SNAKE_CASE_ )
            UpperCamelCase					:						str					=			nn.Parameter(torch.ones(3,    config.projection_dim ),    requires_grad=SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						Dict					=			nn.Parameter(torch.ones(17 ),    requires_grad=SCREAMING_SNAKE_CASE_ )
            UpperCamelCase					:						Optional[Any]					=			nn.Parameter(torch.ones(3 ),    requires_grad=SCREAMING_SNAKE_CASE_ )
         @torch.no_grad()
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Union[str, Any]:
            UpperCamelCase					:						Optional[int]					=			self.vision_model(SCREAMING_SNAKE_CASE_ )[1]  # pooled_output
            UpperCamelCase					:						int					=			self.visual_projection(SCREAMING_SNAKE_CASE_ )
  
            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
            UpperCamelCase					:						Optional[Any]					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.special_care_embeds ).cpu().float().numpy()
            UpperCamelCase					:						Any					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.concept_embeds ).cpu().float().numpy()
  
            UpperCamelCase					:						str					=			[]
            UpperCamelCase					:						int					=			image_embeds.shape[0]
            for i in range(SCREAMING_SNAKE_CASE_ ):
               UpperCamelCase					:						Optional[int]					=			{'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
               UpperCamelCase					:						Optional[Any]					=			0.0
  
               for concept_idx in range(len(special_cos_dist[0] ) ):
                  UpperCamelCase					:						Union[str, Any]					=			special_cos_dist[i][concept_idx]
                  UpperCamelCase					:						Optional[Any]					=			self.special_care_embeds_weights[concept_idx].item()
                  UpperCamelCase					:						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]} )
                     UpperCamelCase					:						Dict					=			0.01
  
               for concept_idx in range(len(cos_dist[0] ) ):
                  UpperCamelCase					:						List[Any]					=			cos_dist[i][concept_idx]
                  UpperCamelCase					:						Optional[int]					=			self.concept_embeds_weights[concept_idx].item()
                  UpperCamelCase					:						Union[str, Any]					=			round(concept_cos - concept_threshold + adjustment,    3 )
                  if result_img["concept_scores"][concept_idx] > 0:
                     result_img["bad_concepts"].append(SCREAMING_SNAKE_CASE_ )
  
               result.append(SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						int					=			[len(res['bad_concepts'] ) > 0 for res in result]
  
            for idx, has_nsfw_concept in enumerate(SCREAMING_SNAKE_CASE_ ):
               if has_nsfw_concept:
                  if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) or torch.is_tensor(images[0] ):
                     UpperCamelCase					:						List[str]					=			torch.zeros_like(images[idx] )  # black image
                  else:
                     UpperCamelCase					:						int					=			np.zeros(images[idx].shape )  # black image
  
            if any(SCREAMING_SNAKE_CASE_ ):
               logger.warning(
                   'Potential NSFW content was detected in one or more images. A black image will be returned instead.'
                   ' Try again with a different prompt and/or seed.' )
  
            return images, has_nsfw_concepts
  
  
  
  
  
  
         @torch.no_grad()
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Optional[int]:
            UpperCamelCase					:						Union[str, Any]					=			self.vision_model(SCREAMING_SNAKE_CASE_ )[1]  # pooled_output
            UpperCamelCase					:						str					=			self.visual_projection(SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						str					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.special_care_embeds )
            UpperCamelCase					:						Any					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.concept_embeds )
  
            # increase this value to create a stronger `nsfw` filter
            # at the cost of increasing the possibility of filtering benign images
            UpperCamelCase					:						Dict					=			0.0
  
            UpperCamelCase					:						str					=			special_cos_dist - self.special_care_embeds_weights + adjustment
            # special_scores = special_scores.round(decimals=3)
            UpperCamelCase					:						List[str]					=			torch.any(special_scores > 0,    dim=1 )
            UpperCamelCase					:						List[Any]					=			special_care * 0.01
            UpperCamelCase					:						List[Any]					=			special_adjustment.unsqueeze(1 ).expand(-1,    cos_dist.shape[1] )
  
            UpperCamelCase					:						List[str]					=			(cos_dist - self.concept_embeds_weights) + special_adjustment
            # concept_scores = concept_scores.round(decimals=3)
            UpperCamelCase					:						Tuple					=			torch.any(concept_scores > 0,    dim=1 )
  
            UpperCamelCase					:						Any					=			0.0  # black image
  
            return images, has_nsfw_concepts
  
  
  
  
style_context: 
  
  
  
  import numpy as np
  import torch
  import torch.nn as nn
  from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
  
  from ...utils import logging
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  def 	UpperCamelCase			(     snake_case__ :					List[str] ,  snake_case__ :					str			)			->  Dict:
     UpperCamelCase					:						int					=			nn.functional.normalize(snake_case__			)
     UpperCamelCase					:						Union[str, Any]					=			nn.functional.normalize(snake_case__			)
     return torch.mm(snake_case__ ,  normalized_text_embeds.t()			)
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      int       =						CLIPConfig
  
         UpperCAmelCase__       :      Union[str, Any]       =						["CLIPEncoderLayer"]
         def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	List[str]:
            super().__init__(SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						int					=			CLIPVisionModel(config.vision_config )
            UpperCamelCase					:						int					=			nn.Linear(config.vision_config.hidden_size,    config.projection_dim,    bias=SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						List[Any]					=			nn.Parameter(torch.ones(17,    config.projection_dim ),    requires_grad=SCREAMING_SNAKE_CASE_ )
            UpperCamelCase					:						str					=			nn.Parameter(torch.ones(3,    config.projection_dim ),    requires_grad=SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						Dict					=			nn.Parameter(torch.ones(17 ),    requires_grad=SCREAMING_SNAKE_CASE_ )
            UpperCamelCase					:						Optional[Any]					=			nn.Parameter(torch.ones(3 ),    requires_grad=SCREAMING_SNAKE_CASE_ )
         @torch.no_grad()
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Union[str, Any]:
            UpperCamelCase					:						Optional[int]					=			self.vision_model(SCREAMING_SNAKE_CASE_ )[1]  # pooled_output
            UpperCamelCase					:						int					=			self.visual_projection(SCREAMING_SNAKE_CASE_ )
  
            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
            UpperCamelCase					:						Optional[Any]					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.special_care_embeds ).cpu().float().numpy()
            UpperCamelCase					:						Any					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.concept_embeds ).cpu().float().numpy()
  
            UpperCamelCase					:						str					=			[]
            UpperCamelCase					:						int					=			image_embeds.shape[0]
            for i in range(SCREAMING_SNAKE_CASE_ ):
               UpperCamelCase					:						Optional[int]					=			{'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
               UpperCamelCase					:						Optional[Any]					=			0.0
  
               for concept_idx in range(len(special_cos_dist[0] ) ):
                  UpperCamelCase					:						Union[str, Any]					=			special_cos_dist[i][concept_idx]
                  UpperCamelCase					:						Optional[Any]					=			self.special_care_embeds_weights[concept_idx].item()
                  UpperCamelCase					:						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]} )
                     UpperCamelCase					:						Dict					=			0.01
  
               for concept_idx in range(len(cos_dist[0] ) ):
                  UpperCamelCase					:						List[Any]					=			cos_dist[i][concept_idx]
                  UpperCamelCase					:						Optional[int]					=			self.concept_embeds_weights[concept_idx].item()
                  UpperCamelCase					:						Union[str, Any]					=			round(concept_cos - concept_threshold + adjustment,    3 )
                  if result_img["concept_scores"][concept_idx] > 0:
                     result_img["bad_concepts"].append(SCREAMING_SNAKE_CASE_ )
  
               result.append(SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						int					=			[len(res['bad_concepts'] ) > 0 for res in result]
  
            for idx, has_nsfw_concept in enumerate(SCREAMING_SNAKE_CASE_ ):
               if has_nsfw_concept:
                  if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) or torch.is_tensor(images[0] ):
                     UpperCamelCase					:						List[str]					=			torch.zeros_like(images[idx] )  # black image
                  else:
                     UpperCamelCase					:						int					=			np.zeros(images[idx].shape )  # black image
  
            if any(SCREAMING_SNAKE_CASE_ ):
               logger.warning(
                   'Potential NSFW content was detected in one or more images. A black image will be returned instead.'
                   ' Try again with a different prompt and/or seed.' )
  
            return images, has_nsfw_concepts
  
  
  
  
  
  
         @torch.no_grad()
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Optional[int]:
            UpperCamelCase					:						Union[str, Any]					=			self.vision_model(SCREAMING_SNAKE_CASE_ )[1]  # pooled_output
            UpperCamelCase					:						str					=			self.visual_projection(SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						str					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.special_care_embeds )
            UpperCamelCase					:						Any					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.concept_embeds )
  
            # increase this value to create a stronger `nsfw` filter
            # at the cost of increasing the possibility of filtering benign images
            UpperCamelCase					:						Dict					=			0.0
  
            UpperCamelCase					:						str					=			special_cos_dist - self.special_care_embeds_weights + adjustment
            # special_scores = special_scores.round(decimals=3)
            UpperCamelCase					:						List[str]					=			torch.any(special_scores > 0,    dim=1 )
            UpperCamelCase					:						List[Any]					=			special_care * 0.01
            UpperCamelCase					:						List[Any]					=			special_adjustment.unsqueeze(1 ).expand(-1,    cos_dist.shape[1] )
  
            UpperCamelCase					:						List[str]					=			(cos_dist - self.concept_embeds_weights) + special_adjustment
            # concept_scores = concept_scores.round(decimals=3)
            UpperCamelCase					:						Tuple					=			torch.any(concept_scores > 0,    dim=1 )
  
            UpperCamelCase					:						Any					=			0.0  # black image
  
            return images, has_nsfw_concepts
  
  
  
  