



'''simple docstring'''





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

from ...utils import logging


A			:   Optional[int]			=      logging.get_logger(__name__)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							List[str]						  =nn.functional.normalize(lowerCamelCase_							)
   snake_case   :							Any						  =nn.functional.normalize(lowerCamelCase_							)
   return torch.mm(lowerCamelCase_					,						normalized_text_embeds.t()							)





class 							lowerCAmelCase_		(	a_						):
    __UpperCAmelCase							=	CLIPConfig

    __UpperCAmelCase							=	['CLIPEncoderLayer']





    def __init__(    self    :    Optional[Any],    _snake_case    :    CLIPConfig     ):



       '''simple docstring'''







       super().__init__(_snake_case     )

       snake_case   :							str						  =CLIPVisionModel(config.vision_config     )
       snake_case   :							str						  =nn.Linear(config.vision_config.hidden_size,    config.projection_dim,    bias=_snake_case     )

       snake_case   :							Union[str, Any]						  =nn.Parameter(torch.ones(17,    config.projection_dim     ),    requires_grad=_snake_case     )
       snake_case   :							Union[str, Any]						  =nn.Parameter(torch.ones(3,    config.projection_dim     ),    requires_grad=_snake_case     )

       snake_case   :							List[str]						  =nn.Parameter(torch.ones(17     ),    requires_grad=_snake_case     )
       snake_case   :							Optional[Any]						  =nn.Parameter(torch.ones(3     ),    requires_grad=_snake_case     )





    @torch.no_grad()
    def 					__snake_case       (    self    :    Tuple,    _snake_case    :    int,    _snake_case    :    Dict     ):



       '''simple docstring'''







       snake_case   :							List[str]						  =self.vision_model(_snake_case     )[1]  # pooled_output
       snake_case   :							Optional[Any]						  =self.visual_projection(_snake_case     )

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

       snake_case   :							int						  =[]
       snake_case   :							int						  =image_embeds.shape[0]
       for i in range(_snake_case     ):
          snake_case   :							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
          snake_case   :							List[Any]						  =0.0

          for concept_idx in range(len(special_cos_dist[0]     )     ):
             snake_case   :							List[Any]						  =special_cos_dist[i][concept_idx]
             snake_case   :							Optional[Any]						  =self.special_care_embeds_weights[concept_idx].item()
             snake_case   :							List[Any]						  =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]}     )
                snake_case   :							Dict						  =0.01

          for concept_idx in range(len(cos_dist[0]     )     ):
             snake_case   :							Tuple						  =cos_dist[i][concept_idx]
             snake_case   :							int						  =self.concept_embeds_weights[concept_idx].item()
             snake_case   :							Dict						  =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     )

       snake_case   :							str						  =[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]     ):
                snake_case   :							str						  =torch.zeros_like(images[idx]     )  # black image
             else:
                snake_case   :							Any						  =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 					__snake_case       (    self    :    Optional[int],    _snake_case    :    torch.FloatTensor,    _snake_case    :    torch.FloatTensor     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =self.vision_model(_snake_case     )[1]  # pooled_output
       snake_case   :							str						  =self.visual_projection(_snake_case     )

       snake_case   :							Optional[int]						  =cosine_distance(_snake_case,    self.special_care_embeds     )
       snake_case   :							Union[str, Any]						  =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
       snake_case   :							int						  =0.0

       snake_case   :							Any						  =special_cos_dist - self.special_care_embeds_weights + adjustment
       # special_scores = special_scores.round(decimals=3)
       snake_case   :							Dict						  =torch.any(special_scores > 0,    dim=1     )
       snake_case   :							Optional[int]						  =special_care * 0.01
       snake_case   :							List[str]						  =special_adjustment.unsqueeze(1     ).expand(-1,    cos_dist.shape[1]     )

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

       snake_case   :							str						  =0.0  # black image

       return images, has_nsfw_concepts










'''simple docstring'''





from __future__ import annotations

import random
import string





class 							lowerCAmelCase_		:





    def __init__(    self    :    Union[str, Any],    _snake_case    :    str | None = None     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =passcode or self.__passcode_creator()
       snake_case   :							List[str]						  =self.__make_key_list()
       snake_case   :							Dict						  =self.__make_shift_key()





    def __str__(    self    :    List[Any]     ):



       '''simple docstring'''







       return "".join(self.__passcode     )





    def 					__snake_case       (    self    :    int,    _snake_case    :    list[int]     ):



       '''simple docstring'''







       for i in range(1,    len(_snake_case     ),    2     ):
          iterlist[i] *= -1
       return iterlist





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							int						  =string.ascii_letters + string.digits
       snake_case   :							Union[str, Any]						  =[random.choice(_snake_case     ) for _ in range(random.randint(10,    20     )     )]
       return password





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							Dict						  =(
           string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
       )

       snake_case   :							Any						  =[]

       # creates points known as breakpoints to break the key_list_options at those
       # points and pivot each substring
       snake_case   :							Optional[int]						  =sorted(set(self.__passcode     )     )
       snake_case   :							list[str]						  =[]

       # algorithm for creating a new shuffled list, keys_l, out of key_list_options
       for i in key_list_options:
          temp_list.extend(_snake_case     )

          # checking breakpoints at which to pivot temporary sublist and add it into
          # keys_l
          if i in breakpoints or i == key_list_options[-1]:
             keys_l.extend(temp_list[::-1]     )
             temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
       return keys_l





    def 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       snake_case   :							str						  =sum(self.__neg_pos([ord(_snake_case     ) for x in self.__passcode]     )     )
       return num if num > 0 else len(self.__passcode     )





    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =''''''

       # decoding shift like Caesar cipher algorithm implementing negative shift or
       # reverse shift or left shift
       for i in encoded_message:
          snake_case   :							Optional[int]						  =self.__key_list.index(_snake_case     )
          decoded_message += self.__key_list[
              (position - self.__shift_key) % -len(self.__key_list     )
          ]

       return decoded_message






    def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							int						  =''''''

       # encoding shift like Caesar cipher algorithm implementing positive shift or
       # forward shift or right shift
       for i in plaintext:
          snake_case   :							int						  =self.__key_list.index(_snake_case     )
          encoded_message += self.__key_list[
              (position + self.__shift_key) % len(self.__key_list     )
          ]

       return encoded_message




def 		_a   (  lowerCamelCase_ = "Hello, this is a modified Caesar cipher"							):
   snake_case   :							List[Any]						  =ShuffledShiftCipher()
   return cipa.decrypt(cipa.encrypt(lowerCamelCase_							)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





