
"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase											=			logging.get_logger(__name__)

_lowercase											=			{
    '''google/pegasus-x-base''': '''https://huggingface.co/google/pegasus-x-base/resolve/main/config.json''',
    '''google/pegasus-x-large''': '''https://huggingface.co/google/pegasus-x-large/resolve/main/config.json''',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      str =      '''pegasus_x'''
						_lowerCamelCase:      str =      ['''past_key_values''']
						_lowerCamelCase:      Dict =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


						def __init__(	self       : str					,A_       : Dict=9_6103					,A_       : Union[str, Any]=1_6384					,A_       : str=16					,A_       : Dict=4096					,A_       : Optional[int]=16					,A_       : Union[str, Any]=16					,A_       : List[Any]=4096					,A_       : Dict=16					,A_       : Any=0.0					,A_       : int=0.0					,A_       : List[str]=True					,A_       : Optional[Any]=True					,A_       : int="gelu"					,A_       : List[str]=1024					,A_       : List[Any]=0.1					,A_       : Tuple=0.0					,A_       : List[Any]=0.0					,A_       : Optional[int]=0.02					,A_       : Union[str, Any]=0					,A_       : Union[str, Any]=True					,A_       : Dict=0					,A_       : Optional[Any]=1					,A_       : List[Any]=1					,A_       : Any=32					,A_       : str=512					,A_       : Any=True					,**A_       : List[Any]					,)       ->				Optional[int]:
							A  				=				vocab_size
							A  				=				max_position_embeddings
							A  				=				d_model
							A  				=				encoder_ffn_dim
							A  				=				encoder_layers
							A  				=				encoder_attention_heads
							A  				=				decoder_ffn_dim
							A  				=				decoder_layers
							A  				=				decoder_attention_heads
							A  				=				dropout
							A  				=				attention_dropout
							A  				=				activation_dropout
							A  				=				activation_function
							A  				=				init_std
							A  				=				encoder_layerdrop
							A  				=				decoder_layerdrop
							A  				=				use_cache
							A  				=				encoder_layers
							A  				=				scale_embedding  # scale factor will be sqrt(d_model) if True

							A  				=				num_global_tokens
							A  				=				block_size
							A  				=				stagger_local_blocks

							super().__init__(
							    pad_token_id=A_					,eos_token_id=A_					,is_encoder_decoder=A_					,decoder_start_token_id=A_					,forced_eos_token_id=A_					,**A_					,)


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				int:
							return self.encoder_attention_heads




						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				int:
							return self.d_model

"""simple docstring"""


from __future__ import annotations






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

	A  				=				len(snake_case__       )
	A  				=				list(range(snake_case__       )       )
	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(snake_case__       ) > rec_preference.index(snake_case__       ):
				A  				=				donor
				A  				=				recipient
				unmatched_donors.append(snake_case__       )
				unmatched_donors.remove(snake_case__       )
		else:
			A  				=				donor
			A  				=				recipient
			unmatched_donors.remove(snake_case__       )
	return donor_record