




"""simple docstring"""



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

from ...utils import logging


SCREAMING_SNAKE_CASE							=			logging.get_logger(__name__)







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						Union[str, Any]:
 A__			=							nn.functional.normalize(lowercase_		)
 A__			=							nn.functional.normalize(lowercase_		)
 return torch.mm(lowercase_     ,					normalized_text_embeds.t()		)






class       UpperCAmelCase_    (		A_				):
    lowercase__		   = CLIPConfig

    lowercase__		   = ['''CLIPEncoderLayer''']




    def __init__(       self		:			List[Any]      ,					snake_case_		:			CLIPConfig ) ->				str:




     '''simple docstring'''

     super().__init__(snake_case_ )

     A__			=							CLIPVisionModel(config.vision_config )
     A__			=							nn.Linear(config.vision_config.hidden_size      ,					config.projection_dim      ,					bias=snake_case_ )

     A__			=							nn.Parameter(torch.ones(17      ,					config.projection_dim )      ,					requires_grad=snake_case_ )
     A__			=							nn.Parameter(torch.ones(3      ,					config.projection_dim )      ,					requires_grad=snake_case_ )

     A__			=							nn.Parameter(torch.ones(17 )      ,					requires_grad=snake_case_ )
     A__			=							nn.Parameter(torch.ones(3 )      ,					requires_grad=snake_case_ )




    @torch.no_grad()
    def 				__magic_name__				(       self		:			Union[str, Any]      ,					snake_case_		:			Tuple      ,					snake_case_		:			Any ) ->				Tuple:




     '''simple docstring'''

     A__			=							self.vision_model(snake_case_ )[1]  # pooled_output
     A__			=							self.visual_projection(snake_case_ )

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

     A__			=							[]
     A__			=							image_embeds.shape[0]
     for i in range(snake_case_ ):
      A__			=							{"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
      A__			=							0.0

      for concept_idx in range(len(special_cos_dist[0] ) ):
       A__			=							special_cos_dist[i][concept_idx]
       A__			=							self.special_care_embeds_weights[concept_idx].item()
       A__			=							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]} )
        A__			=							0.01

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

      result.append(snake_case_ )

     A__			=							[len(res["bad_concepts"] ) > 0 for res in result]

     for idx, has_nsfw_concept in enumerate(snake_case_ ):
      if has_nsfw_concept:
       if torch.is_tensor(snake_case_ ) or torch.is_tensor(images[0] ):
        A__			=							torch.zeros_like(images[idx] )  # black image
       else:
        A__			=							np.zeros(images[idx].shape )  # black image

     if any(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 				__magic_name__				(       self		:			str      ,					snake_case_		:			torch.FloatTensor      ,					snake_case_		:			torch.FloatTensor ) ->				Dict:




     '''simple docstring'''

     A__			=							self.vision_model(snake_case_ )[1]  # pooled_output
     A__			=							self.visual_projection(snake_case_ )

     A__			=							cosine_distance(snake_case_      ,					self.special_care_embeds )
     A__			=							cosine_distance(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
     A__			=							0.0

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

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

     A__			=							0.0  # black image

     return images, has_nsfw_concepts











"""simple docstring"""



def     _SCREAMING_SNAKE_CASE  (				lowercase_ = 10_00		)		->						int:
 A__			=							0
 A__			=							0
 while 1:
  num += 3
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
 return total


if __name__ == "__main__":
      print(f'{solution() = }')





