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

from ...utils import logging


_A		     =    logging.get_logger(__name__)




def  lowerCamelCase__ (  a__     :     str		,					a__     :     List[Any]   )						->		Optional[Any]:
       UpperCamelCase_											=				nn.functional.normalize(__UpperCAmelCase   )
       UpperCamelCase_											=				nn.functional.normalize(__UpperCAmelCase   )
       return torch.mm(__UpperCAmelCase		,					normalized_text_embeds.t()   )







class 						lowercase_				(				__lowerCamelCase ):
 A__  : List[Any]      					=			CLIPConfig

 A__  : Tuple      					=			["CLIPEncoderLayer"]

 def __init__(					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        super().__init__(__UpperCamelCase   )

        UpperCamelCase_											=				CLIPVisionModel(config.vision_config   )
        UpperCamelCase_											=				nn.Linear(config.vision_config.hidden_size   ,		config.projection_dim   ,		bias=__UpperCamelCase   )

        UpperCamelCase_											=				nn.Parameter(torch.ones(1_7   ,		config.projection_dim   )   ,		requires_grad=__UpperCamelCase   )
        UpperCamelCase_											=				nn.Parameter(torch.ones(3   ,		config.projection_dim   )   ,		requires_grad=__UpperCamelCase   )

        UpperCamelCase_											=				nn.Parameter(torch.ones(1_7   )   ,		requires_grad=__UpperCamelCase   )
        UpperCamelCase_											=				nn.Parameter(torch.ones(3   )   ,		requires_grad=__UpperCamelCase   )

 @torch.no_grad()
 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.vision_model(__UpperCamelCase   )[1]  # pooled_output
        UpperCamelCase_											=				self.visual_projection(__UpperCamelCase   )

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

        UpperCamelCase_											=				[]
        UpperCamelCase_											=				image_embeds.shape[0]
        for i in range(__UpperCamelCase   ):
               UpperCamelCase_											=				{'''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_											=				0.0

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

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

               result.append(__UpperCamelCase   )

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

        return images, has_nsfw_concepts

 @torch.no_grad()
 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.vision_model(__UpperCamelCase   )[1]  # pooled_output
        UpperCamelCase_											=				self.visual_projection(__UpperCamelCase   )

        UpperCamelCase_											=				cosine_distance(__UpperCamelCase   ,		self.special_care_embeds   )
        UpperCamelCase_											=				cosine_distance(__UpperCamelCase   ,		self.concept_embeds   )

        # increase this value to create a stronger `nsfw` filter
        # at the cost of increasing the possibility of filtering benign images
        UpperCamelCase_											=				0.0

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

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

        return images, has_nsfw_concepts










import random




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			Optional[int]      )      ->	tuple:
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      [], [], []
    for element in data:
        if element < pivot:
            less.append(__UpperCAmelCase      )
        elif element > pivot:
            greater.append(__UpperCAmelCase      )
        else:
            equal.append(__UpperCAmelCase      )
    return less, equal, greater





def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			int      )      ->	List[str]:
    # index = len(items) // 2 when trying to find the median
    #   (value of index when items is sorted)

    # invalid input
    if index >= len(__UpperCAmelCase      ) or index < 0:
        return None

    UpperCamelCase__							:				List[str]          =      items[random.randint(0		,   len(__UpperCAmelCase      ) - 1      )]
    UpperCamelCase__							:				List[Any]          =      0
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				int          =      _partition(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      len(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      len(__UpperCAmelCase      )

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(__UpperCAmelCase		,   __UpperCAmelCase      )
    # must be in larger
    else:
        return quick_select(__UpperCAmelCase		,   index - (m + count)      )

