code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  def       a__						(		snake_case__				:       list[list[int]]	,      snake_case__				:       list[list[int]]    ):
    assert len(snake_case__    ) == len(snake_case__    )
  
    _UpperCAmelCase		:       List[str]       =    len(snake_case__    )
    _UpperCAmelCase		:       Tuple       =    list(range(snake_case__    )    )
    _UpperCAmelCase		:       Tuple       =    [-1] * n  # who the donor has donated to
    _UpperCAmelCase		:       List[Any]       =    [-1] * n  # who the recipient has received from
    _UpperCAmelCase		:       List[str]       =    [0] * n
  
    while unmatched_donors:
      _UpperCAmelCase		:       Any       =    unmatched_donors[0]
      _UpperCAmelCase		:       Union[str, Any]       =    donor_pref[donor]
      _UpperCAmelCase		:       List[str]       =    donor_preference[num_donations[donor]]
      num_donations[donor] += 1
      _UpperCAmelCase		:       List[Any]       =    recipient_pref[recipient]
      _UpperCAmelCase		:       List[Any]       =    rec_record[recipient]
  
      if prev_donor != -1:
        if rec_preference.index(snake_case__    ) > rec_preference.index(snake_case__    ):
          _UpperCAmelCase		:       List[Any]       =    donor
          _UpperCAmelCase		:       Union[str, Any]       =    recipient
          unmatched_donors.append(snake_case__    )
          unmatched_donors.remove(snake_case__    )
      else:
        _UpperCAmelCase		:       int       =    donor
        _UpperCAmelCase		:       Optional[Any]       =    recipient
        unmatched_donors.remove(snake_case__    )
    return donor_record
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  @dataclass
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
       __SCREAMING_SNAKE_CASE    				=						{
           '''sld_warmup_steps''': 1_5,
           '''sld_guidance_scale''': 2_0,
           '''sld_threshold''': 0.0,
           '''sld_momentum_scale''': 0.0,
           '''sld_mom_beta''': 0.0,
       }
       __SCREAMING_SNAKE_CASE    				=						{
           '''sld_warmup_steps''': 1_0,
           '''sld_guidance_scale''': 1_0_0_0,
           '''sld_threshold''': 0.01,
           '''sld_momentum_scale''': 0.3,
           '''sld_mom_beta''': 0.4,
       }
       __SCREAMING_SNAKE_CASE    				=						{
           '''sld_warmup_steps''': 7,
           '''sld_guidance_scale''': 2_0_0_0,
           '''sld_threshold''': 0.025,
           '''sld_momentum_scale''': 0.5,
           '''sld_mom_beta''': 0.7,
       }
       __SCREAMING_SNAKE_CASE    				=						{
           '''sld_warmup_steps''': 0,
           '''sld_guidance_scale''': 5_0_0_0,
           '''sld_threshold''': 1.0,
           '''sld_momentum_scale''': 0.5,
           '''sld_mom_beta''': 0.7,
       }
  
  
  @dataclass
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
       __SCREAMING_SNAKE_CASE    				=						42
       __SCREAMING_SNAKE_CASE    				=						42
       __SCREAMING_SNAKE_CASE    				=						42
       __SCREAMING_SNAKE_CASE    				=						42
  
  
  if is_transformers_available() and is_torch_available():
   from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
   from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  