code: 
  
  
  
  #
  # Sort large text files in a minimum amount of memory
  #
  import argparse
  import os
  
  
  
  class        lowerCAmelCase_	:
         UpperCAmelCase__       :      Tuple       =						"block_{0}.dat"
         def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	int:
            UpperCamelCase					:						int					=			filename
            UpperCamelCase					:						Optional[Any]					=			[]
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	int:
            UpperCamelCase					:						Dict					=			self.BLOCK_FILENAME_FORMAT.format(SCREAMING_SNAKE_CASE_ )
            with open(SCREAMING_SNAKE_CASE_,    'w' ) as file:
               file.write(SCREAMING_SNAKE_CASE_ )
            self.block_filenames.append(SCREAMING_SNAKE_CASE_ )
         def       snake_case_ (    self )  ->	str:
            return self.block_filenames
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=None )  ->	int:
            UpperCamelCase					:						Any					=			0
            with open(self.filename ) as file:
               while True:
                  UpperCamelCase					:						Union[str, Any]					=			file.readlines(SCREAMING_SNAKE_CASE_ )
  
                  if lines == []:
                     break
  
                  if sort_key is None:
                     lines.sort()
                  else:
                     lines.sort(key=SCREAMING_SNAKE_CASE_ )
  
                  self.write_block(''.join(SCREAMING_SNAKE_CASE_ ),    SCREAMING_SNAKE_CASE_ )
                  i += 1
  
  
  
  
  
  
         def       snake_case_ (    self )  ->	List[str]:
            map(os.remove,    self.block_filenames )
  
  
  
  
  
  
  class        lowerCAmelCase_	:
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	Optional[int]:
            UpperCamelCase					:						Tuple					=			-1
            UpperCamelCase					:						int					=			None
  
            for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
               if min_str is None or choices[i] < min_str:
                  UpperCamelCase					:						Tuple					=			i
  
            return min_index
  
  
  
  
  
  
  class        lowerCAmelCase_	:
         def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	Tuple:
            UpperCamelCase					:						Union[str, Any]					=			files
            UpperCamelCase					:						Optional[int]					=			set()
            UpperCamelCase					:						Optional[Any]					=			len(SCREAMING_SNAKE_CASE_ )
            UpperCamelCase					:						str					=			{i: None for i in range(self.num_buffers )}
         def       snake_case_ (    self )  ->	Optional[int]:
            return {
                i: self.buffers[i] for i in range(self.num_buffers ) if i not in self.empty
            }
         def       snake_case_ (    self )  ->	Union[str, Any]:
            for i in range(self.num_buffers ):
               if self.buffers[i] is None and i not in self.empty:
                  UpperCamelCase					:						List[str]					=			self.files[i].readline()
  
                  if self.buffers[i] == "":
                     self.empty.add(SCREAMING_SNAKE_CASE_ )
                     self.files[i].close()
  
            if len(self.empty ) == self.num_buffers:
               return False
  
            return True
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	List[str]:
            UpperCamelCase					:						str					=			self.buffers[index]
            UpperCamelCase					:						Optional[Any]					=			None
  
            return value
  
  
  
  
  
  
  class        lowerCAmelCase_	:
         def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	Optional[Any]:
            UpperCamelCase					:						List[str]					=			merge_strategy
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Any:
            UpperCamelCase					:						Any					=			FilesArray(self.get_file_handles(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ) )
            with open(SCREAMING_SNAKE_CASE_,    'w',    SCREAMING_SNAKE_CASE_ ) as outfile:
               while buffers.refresh():
                  UpperCamelCase					:						Tuple					=			self.merge_strategy.select(buffers.get_dict() )
                  outfile.write(buffers.unshift(SCREAMING_SNAKE_CASE_ ) )
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Any:
            UpperCamelCase					:						List[Any]					=			{}
  
            for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
               UpperCamelCase					:						List[str]					=			open(filenames[i],    'r',    SCREAMING_SNAKE_CASE_ )  # noqa: UP015
  
            return files
  
  
  
  
  
  
  class        lowerCAmelCase_	:
         def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	Dict:
            UpperCamelCase					:						Any					=			block_size
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=None )  ->	int:
            UpperCamelCase					:						Optional[Any]					=			self.get_number_blocks(SCREAMING_SNAKE_CASE_,    self.block_size )
            UpperCamelCase					:						Any					=			FileSplitter(SCREAMING_SNAKE_CASE_ )
            splitter.split(self.block_size,    SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						List[Any]					=			FileMerger(NWayMerge() )
            UpperCamelCase					:						Optional[int]					=			self.block_size / (num_blocks + 1)
            merger.merge(splitter.get_block_filenames(),    filename + '.out',    SCREAMING_SNAKE_CASE_ )
  
            splitter.cleanup()
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	List[Any]:
            return (os.stat(SCREAMING_SNAKE_CASE_ ).st_size / block_size) + 1
  
  
  
  
  
  
  def 	UpperCamelCase			(     snake_case__ :					List[str]			)			->  str:
     if string[-1].lower() == "k":
        return int(string[:-1]			) * 1024
     elif string[-1].lower() == "m":
        return int(string[:-1]			) * 1024 * 1024
     elif string[-1].lower() == "g":
        return int(string[:-1]			) * 1024 * 1024 * 1024
     else:
        return int(snake_case__			)
  def 	UpperCamelCase			(     )			->  str:
     UpperCamelCase					:						str					=			argparse.ArgumentParser()
     parser.add_argument(
         '-m' ,  '--mem' ,  help='amount of memory to use for sorting' ,  default='100M'			)
     parser.add_argument(
         'filename' ,  metavar='<filename>' ,  nargs=1 ,  help='name of file to sort'			)
     UpperCamelCase					:						int					=			parser.parse_args()
  
     UpperCamelCase					:						Optional[Any]					=			ExternalSort(parse_memory(args.mem			)			)
     sorter.sort(args.filename[0]			)
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
style_context: 
  
  
  
  import numpy as np
  import torch
  import torch.nn as nn
  from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
  
  from ...utils import logging
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  def 	UpperCamelCase			(     snake_case__ :					List[str] ,  snake_case__ :					str			)			->  Dict:
     UpperCamelCase					:						int					=			nn.functional.normalize(snake_case__			)
     UpperCamelCase					:						Union[str, Any]					=			nn.functional.normalize(snake_case__			)
     return torch.mm(snake_case__ ,  normalized_text_embeds.t()			)
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      int       =						CLIPConfig
  
         UpperCAmelCase__       :      Union[str, Any]       =						["CLIPEncoderLayer"]
         def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	List[str]:
            super().__init__(SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						int					=			CLIPVisionModel(config.vision_config )
            UpperCamelCase					:						int					=			nn.Linear(config.vision_config.hidden_size,    config.projection_dim,    bias=SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						List[Any]					=			nn.Parameter(torch.ones(17,    config.projection_dim ),    requires_grad=SCREAMING_SNAKE_CASE_ )
            UpperCamelCase					:						str					=			nn.Parameter(torch.ones(3,    config.projection_dim ),    requires_grad=SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						Dict					=			nn.Parameter(torch.ones(17 ),    requires_grad=SCREAMING_SNAKE_CASE_ )
            UpperCamelCase					:						Optional[Any]					=			nn.Parameter(torch.ones(3 ),    requires_grad=SCREAMING_SNAKE_CASE_ )
         @torch.no_grad()
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Union[str, Any]:
            UpperCamelCase					:						Optional[int]					=			self.vision_model(SCREAMING_SNAKE_CASE_ )[1]  # pooled_output
            UpperCamelCase					:						int					=			self.visual_projection(SCREAMING_SNAKE_CASE_ )
  
            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
            UpperCamelCase					:						Optional[Any]					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.special_care_embeds ).cpu().float().numpy()
            UpperCamelCase					:						Any					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.concept_embeds ).cpu().float().numpy()
  
            UpperCamelCase					:						str					=			[]
            UpperCamelCase					:						int					=			image_embeds.shape[0]
            for i in range(SCREAMING_SNAKE_CASE_ ):
               UpperCamelCase					:						Optional[int]					=			{'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					:						Optional[Any]					=			0.0
  
               for concept_idx in range(len(special_cos_dist[0] ) ):
                  UpperCamelCase					:						Union[str, Any]					=			special_cos_dist[i][concept_idx]
                  UpperCamelCase					:						Optional[Any]					=			self.special_care_embeds_weights[concept_idx].item()
                  UpperCamelCase					:						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]} )
                     UpperCamelCase					:						Dict					=			0.01
  
               for concept_idx in range(len(cos_dist[0] ) ):
                  UpperCamelCase					:						List[Any]					=			cos_dist[i][concept_idx]
                  UpperCamelCase					:						Optional[int]					=			self.concept_embeds_weights[concept_idx].item()
                  UpperCamelCase					:						Union[str, 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_ )
  
            UpperCamelCase					:						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] ):
                     UpperCamelCase					:						List[str]					=			torch.zeros_like(images[idx] )  # black image
                  else:
                     UpperCamelCase					:						int					=			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       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Optional[int]:
            UpperCamelCase					:						Union[str, Any]					=			self.vision_model(SCREAMING_SNAKE_CASE_ )[1]  # pooled_output
            UpperCamelCase					:						str					=			self.visual_projection(SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						str					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.special_care_embeds )
            UpperCamelCase					:						Any					=			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
            UpperCamelCase					:						Dict					=			0.0
  
            UpperCamelCase					:						str					=			special_cos_dist - self.special_care_embeds_weights + adjustment
            # special_scores = special_scores.round(decimals=3)
            UpperCamelCase					:						List[str]					=			torch.any(special_scores > 0,    dim=1 )
            UpperCamelCase					:						List[Any]					=			special_care * 0.01
            UpperCamelCase					:						List[Any]					=			special_adjustment.unsqueeze(1 ).expand(-1,    cos_dist.shape[1] )
  
            UpperCamelCase					:						List[str]					=			(cos_dist - self.concept_embeds_weights) + special_adjustment
            # concept_scores = concept_scores.round(decimals=3)
            UpperCamelCase					:						Tuple					=			torch.any(concept_scores > 0,    dim=1 )
  
            UpperCamelCase					:						Any					=			0.0  # black image
  
            return images, has_nsfw_concepts
  
  
  
  