


'''simple docstring'''



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

from ...utils import logging


A__		: List[str]												=  logging.get_logger(__name__)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Tuple				,   UpperCAmelCase_   :    Dict							)					->		str:
     __lowerCamelCase       :     str        =		nn.functional.normalize(UpperCAmelCase_							)
     __lowerCamelCase       :     Union[str, Any]        =		nn.functional.normalize(UpperCAmelCase_							)
     return torch.mm(UpperCAmelCase_				,   normalized_text_embeds.t()							)





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Optional[int]												=      CLIPConfig

    lowerCamelCase  : Optional[int]												=      ['CLIPEncoderLayer']







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_     )    ->					Dict:
         super().__init__(SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Dict        =		CLIPVisionModel(config.vision_config     )
         __lowerCamelCase       :     int        =		nn.Linear(config.vision_config.hidden_size      ,					config.projection_dim      ,					bias=SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Optional[int]        =		nn.Parameter(torch.ones(17      ,					config.projection_dim     )      ,					requires_grad=SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Tuple        =		nn.Parameter(torch.ones(3      ,					config.projection_dim     )      ,					requires_grad=SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Union[str, Any]        =		nn.Parameter(torch.ones(17     )      ,					requires_grad=SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Dict        =		nn.Parameter(torch.ones(3     )      ,					requires_grad=SCREAMING_SNAKE_CASE_     )







    @torch.no_grad()
    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					Union[str, Any]:
         __lowerCamelCase       :     Optional[int]        =		self.vision_model(SCREAMING_SNAKE_CASE_     )[1]  # pooled_output
         __lowerCamelCase       :     str        =		self.visual_projection(SCREAMING_SNAKE_CASE_     )

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

         __lowerCamelCase       :     Tuple        =		[]
         __lowerCamelCase       :     Any        =		image_embeds.shape[0]
         for i in range(SCREAMING_SNAKE_CASE_     ):
              __lowerCamelCase       :     Dict        =		{'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       :     int        =		0.0

              for concept_idx in range(len(special_cos_dist[0]     )     ):
                   __lowerCamelCase       :     List[Any]        =		special_cos_dist[i][concept_idx]
                   __lowerCamelCase       :     Dict        =		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       :     Any        =		0.0_1

              for concept_idx in range(len(cos_dist[0]     )     ):
                   __lowerCamelCase       :     List[Any]        =		cos_dist[i][concept_idx]
                   __lowerCamelCase       :     List[Any]        =		self.concept_embeds_weights[concept_idx].item()
                   __lowerCamelCase       :     List[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_     )

         __lowerCamelCase       :     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]     ):
                        __lowerCamelCase       :     str        =		torch.zeros_like(images[idx]     )  # black image
                   else:
                        __lowerCamelCase       :     List[str]        =		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 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					Optional[int]:
         __lowerCamelCase       :     Optional[Any]        =		self.vision_model(SCREAMING_SNAKE_CASE_     )[1]  # pooled_output
         __lowerCamelCase       :     Any        =		self.visual_projection(SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Any        =		cosine_distance(SCREAMING_SNAKE_CASE_      ,					self.special_care_embeds     )
         __lowerCamelCase       :     int        =		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
         __lowerCamelCase       :     Any        =		0.0

         __lowerCamelCase       :     Any        =		special_cos_dist - self.special_care_embeds_weights + adjustment
         # special_scores = special_scores.round(decimals=3)
         __lowerCamelCase       :     str        =		torch.any(special_scores > 0      ,					dim=1     )
         __lowerCamelCase       :     Tuple        =		special_care * 0.0_1
         __lowerCamelCase       :     Dict        =		special_adjustment.unsqueeze(1     ).expand(-1      ,					cos_dist.shape[1]     )

         __lowerCamelCase       :     Optional[int]        =		(cos_dist - self.concept_embeds_weights) + special_adjustment
         # concept_scores = concept_scores.round(decimals=3)
         __lowerCamelCase       :     Optional[int]        =		torch.any(concept_scores > 0      ,					dim=1     )

         __lowerCamelCase       :     Union[str, Any]        =		0.0  # black image

         return images, has_nsfw_concepts










'''simple docstring'''



def       UpperCAmelCase__ (     UpperCAmelCase_   :    int = 4_00_00_00							)					->		int:
     __lowerCamelCase       :     int        =		1
     __lowerCamelCase       :     Union[str, Any]        =		2
     __lowerCamelCase       :     Optional[int]        =		0
     while j <= n:
          if j % 2 == 0:
               total += j
          __lowerCamelCase    ,	__lowerCamelCase       :     List[Any]        =		j, i + j

     return total


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






