code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  def 					A_			(		snake_case_     :       list[list[int]]						,snake_case_     :       list[list[int]]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      assert len(snake_case_    ) == len(snake_case_    )
  
      UpperCamelCase				:   Dict          =							len(snake_case_    )
      UpperCamelCase				:   List[Any]          =							list(range(snake_case_    )    )
      UpperCamelCase				:   Optional[Any]          =							[-1] * n  # who the donor has donated to
      UpperCamelCase				:   Optional[Any]          =							[-1] * n  # who the recipient has received from
      UpperCamelCase				:   Optional[Any]          =							[0] * n
  
      while unmatched_donors:
          UpperCamelCase				:   str          =							unmatched_donors[0]
          UpperCamelCase				:   Tuple          =							donor_pref[donor]
          UpperCamelCase				:   Any          =							donor_preference[num_donations[donor]]
          num_donations[donor] += 1
          UpperCamelCase				:   Optional[Any]          =							recipient_pref[recipient]
          UpperCamelCase				:   Tuple          =							rec_record[recipient]
  
          if prev_donor != -1:
              if rec_preference.index(snake_case_    ) > rec_preference.index(snake_case_    ):
                  UpperCamelCase				:   Optional[Any]          =							donor
                  UpperCamelCase				:   Any          =							recipient
                  unmatched_donors.append(snake_case_    )
                  unmatched_donors.remove(snake_case_    )
          else:
              UpperCamelCase				:   Tuple          =							donor
              UpperCamelCase				:   Union[str, Any]          =							recipient
              unmatched_donors.remove(snake_case_    )
      return donor_record
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from ...utils import is_transformers_available
  from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
  
  
  if is_transformers_available():
    from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
  
  
  
  
  