


'''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









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





