code: 
  
  
  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 typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__				:    List[str]         =    {
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   SCREAMING_SNAKE_CASE__				:    int         =    [
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   SCREAMING_SNAKE_CASE__				:    Dict         =    [
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )
  
  else:
   import sys
  
   SCREAMING_SNAKE_CASE__				:    List[Any]         =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  