

import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowercase_   							=  ''
lowercase_   							=  ''
lowercase_   							=  ''
lowercase_   							=  1  # (0 is vertical, 1 is horizontal)
def 	a (     )       -> None:





      """simple docstring"""



      _lowercase			,			_lowercase      			=get_dataset(A__ ,  A__	)
      print('Processing...'	)
      _lowercase			,			_lowercase			,			_lowercase      			=update_image_and_anno(A__ ,  A__ ,  A__	)

      for index, image in enumerate(A__	):
            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            _lowercase      			=random_chars(32	)
            _lowercase      			=paths[index].split(os.sep	)[-1].rsplit('.' ,  1	)[0]
            _lowercase      			=F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
            cva.imwrite(F'''/{file_root}.jpg''' ,  A__ ,  [cva.IMWRITE_JPEG_QUALITY, 85]	)
            print(F'''Success {index+1}/{len(A__	)} with {file_name}'''	)
            _lowercase      			=[]
            for anno in new_annos[index]:
                  _lowercase      			=F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
                  annos_list.append(A__	)
            with open(F'''/{file_root}.txt''' ,  'w'	) as outfile:
                  outfile.write('\n'.join(line for line in annos_list	)	)
def 	a (     A__      :  str ,  A__      :  str	)       -> tuple[list, list]:





      """simple docstring"""



      _lowercase      			=[]
      _lowercase      			=[]
      for label_file in glob.glob(os.path.join(A__ ,  '*.txt'	)	):
            _lowercase      			=label_file.split(os.sep	)[-1].rsplit('.' ,  1	)[0]
            with open(A__	) as in_file:
                  _lowercase      			=in_file.readlines()
            _lowercase      			=os.path.join(A__ ,  F'''{label_name}.jpg'''	)

            _lowercase      			=[]
            for obj_list in obj_lists:
                  _lowercase      			=obj_list.rstrip('\n'	).split(' '	)
                  boxes.append(
                      [
                          int(obj[0]	),
                          float(obj[1]	),
                          float(obj[2]	),
                          float(obj[3]	),
                          float(obj[4]	),
                      ]	)
            if not boxes:
                  continue
            img_paths.append(A__	)
            labels.append(A__	)
      return img_paths, labels
def 	a (     A__      :  list ,  A__      :  list ,  A__      :  int = 1	)       -> tuple[list, list, list]:





      """simple docstring"""



      _lowercase      			=[]
      _lowercase      			=[]
      _lowercase      			=[]
      for idx in range(len(A__	)	):
            _lowercase      			=[]
            _lowercase      			=img_list[idx]
            path_list.append(A__	)
            _lowercase      			=anno_list[idx]
            _lowercase      			=cva.imread(A__	)
            if flip_type == 1:
                  _lowercase      			=cva.flip(A__ ,  A__	)
                  for bbox in img_annos:
                        _lowercase      			=1 - bbox[1]
                        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]	)
            elif flip_type == 0:
                  _lowercase      			=cva.flip(A__ ,  A__	)
                  for bbox in img_annos:
                        _lowercase      			=1 - bbox[2]
                        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]	)
            new_annos_lists.append(A__	)
            new_imgs_list.append(A__	)
      return new_imgs_list, new_annos_lists, path_list
def 	a (     A__      :  int = 32	)       -> str:





      """simple docstring"""



      assert number_char > 1, "The number of character should greater than 1"
      _lowercase      			=ascii_lowercase + digits
      return "".join(random.choice(A__	) for _ in range(A__	)	)


if __name__ == "__main__":
  main()
  print('DONE ✅')





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

from ...utils import logging


lowercase_   							=  logging.get_logger(__name__)
def 	a (     A__      :  Optional[int] ,  A__      :  Union[str, Any]	)       -> int:





      """simple docstring"""



      _lowercase      			=nn.functional.normalize(A__	)
      _lowercase      			=nn.functional.normalize(A__	)
      return torch.mm(A__ ,  normalized_text_embeds.t()	)







class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    CLIPConfig

  _a		     =    ["""CLIPEncoderLayer"""]




  def __init__(							self				,  lowerCAmelCase   )    ->					Union[str, Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase   )

        _lowercase      			=CLIPVisionModel(config.vision_config   )
        _lowercase      			=nn.Linear(config.vision_config.hidden_size				,  config.projection_dim				,  bias=lowerCAmelCase   )

        _lowercase      			=nn.Parameter(torch.ones(17				,  config.projection_dim   )				,  requires_grad=lowerCAmelCase   )
        _lowercase      			=nn.Parameter(torch.ones(3				,  config.projection_dim   )				,  requires_grad=lowerCAmelCase   )

        _lowercase      			=nn.Parameter(torch.ones(17   )				,  requires_grad=lowerCAmelCase   )
        _lowercase      			=nn.Parameter(torch.ones(3   )				,  requires_grad=lowerCAmelCase   )




  @torch.no_grad()
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.vision_model(lowerCAmelCase   )[1]  # pooled_output
        _lowercase      			=self.visual_projection(lowerCAmelCase   )

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

        _lowercase      			=[]
        _lowercase      			=image_embeds.shape[0]
        for i in range(lowerCAmelCase   ):
              _lowercase      			={'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
              _lowercase      			=0.0

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

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

              result.append(lowerCAmelCase   )

        _lowercase      			=[len(res['bad_concepts']   ) > 0 for res in result]

        return images, has_nsfw_concepts




  @torch.no_grad()
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=self.vision_model(lowerCAmelCase   )[1]  # pooled_output
        _lowercase      			=self.visual_projection(lowerCAmelCase   )

        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.special_care_embeds   )
        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.concept_embeds   )

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

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

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

        return images, has_nsfw_concepts


