



'''simple docstring'''







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__      ):






			@register_to_config
			def __init__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							float      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							bool = False      ,       ):




					"""simple docstring"""







					super().__init__()

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      nn.Linear(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       bias=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      nn.Embedding(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:int										=      False

					SCREAMING_SNAKE_CASE_						:Tuple										=      nn.Dropout(p=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:int										=      TaConfig(
					    d_model=SCREAMING_SNAKE_CASE      ,       num_heads=SCREAMING_SNAKE_CASE      ,       d_kv=SCREAMING_SNAKE_CASE      ,       d_ff=SCREAMING_SNAKE_CASE      ,       feed_forward_proj=SCREAMING_SNAKE_CASE      ,       dropout_rate=SCREAMING_SNAKE_CASE      ,       is_decoder=SCREAMING_SNAKE_CASE      ,       is_encoder_decoder=SCREAMING_SNAKE_CASE      ,       )
					SCREAMING_SNAKE_CASE_						:Dict										=      nn.ModuleList()
					for lyr_num in range(SCREAMING_SNAKE_CASE	):
							SCREAMING_SNAKE_CASE_						:List[str]										=      TaBlock(SCREAMING_SNAKE_CASE	)
							self.encoders.append(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      TaLayerNorm(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      nn.Dropout(p=SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.input_proj(SCREAMING_SNAKE_CASE	)

					# terminal relative positional encodings
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      encoder_inputs.shape[1]
					SCREAMING_SNAKE_CASE_						:List[str]										=      torch.arange(SCREAMING_SNAKE_CASE      ,       device=encoder_inputs.device	)

					SCREAMING_SNAKE_CASE_						:Dict										=      encoder_inputs_mask.sum(-1	)
					SCREAMING_SNAKE_CASE_						:Any										=      torch.roll(input_positions.unsqueeze(0	)      ,       tuple(seq_lens.tolist()	)      ,       dims=0	)
					x += self.position_encoding(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:int										=      self.dropout_pre(SCREAMING_SNAKE_CASE	)

					# inverted the attention mask
					SCREAMING_SNAKE_CASE_						:str										=      encoder_inputs.size()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.get_extended_attention_mask(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					for lyr in self.encoders:
							SCREAMING_SNAKE_CASE_						:Any										=      lyr(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)[0]
					SCREAMING_SNAKE_CASE_						:str										=      self.layer_norm(SCREAMING_SNAKE_CASE	)

					return self.dropout_post(SCREAMING_SNAKE_CASE	), encoder_inputs_mask



"""simple docstring"""



from __future__ import annotations





def 			_snake_case  (     UpperCAmelCase_	:       list[list[int]]       , UpperCAmelCase_	:       list[list[int]]      ):
     assert len(UpperCAmelCase_      ) == len(UpperCAmelCase_      )

     A__				      =	len(UpperCAmelCase_      )
     A__				      =	list(range(UpperCAmelCase_      )      )
     A__				      =	[-1] * n  # who the donor has donated to
     A__				      =	[-1] * n  # who the recipient has received from
     A__				      =	[0] * n

     while unmatched_donors:
          A__				      =	unmatched_donors[0]
          A__				      =	donor_pref[donor]
          A__				      =	donor_preference[num_donations[donor]]
          num_donations[donor] += 1
          A__				      =	recipient_pref[recipient]
          A__				      =	rec_record[recipient]

          if prev_donor != -1:
               if rec_preference.index(UpperCAmelCase_      ) > rec_preference.index(UpperCAmelCase_      ):
                    A__				      =	donor
                    A__				      =	recipient
                    unmatched_donors.append(UpperCAmelCase_      )
                    unmatched_donors.remove(UpperCAmelCase_      )
          else:
               A__				      =	donor
               A__				      =	recipient
               unmatched_donors.remove(UpperCAmelCase_      )
     return donor_record






