

"""simple docstring"""




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


_lowercase   :	List[str]												=						logging.get_logger(__name__)

_lowercase   :	Any												=						{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Union[str, Any]					=					"pegasus"
	a__		: Optional[int]					=					["past_key_values"]
	a__		: str					=					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



	def __init__(  self     :       Optional[int]			,    _lowercase     :       Tuple=5_02_65			,    _lowercase     :       Dict=10_24			,    _lowercase     :       int=12			,    _lowercase     :       Any=40_96			,    _lowercase     :       Tuple=16			,    _lowercase     :       List[str]=12			,    _lowercase     :       List[Any]=40_96			,    _lowercase     :       List[str]=16			,    _lowercase     :       Any=0.0			,    _lowercase     :       int=0.0			,    _lowercase     :       str=True			,    _lowercase     :       Union[str, Any]=True			,    _lowercase     :       Tuple="gelu"			,    _lowercase     :       Any=10_24			,    _lowercase     :       Optional[int]=0.1			,    _lowercase     :       Tuple=0.0			,    _lowercase     :       Tuple=0.0			,    _lowercase     :       Union[str, Any]=0.02			,    _lowercase     :       Tuple=0			,    _lowercase     :       str=False			,    _lowercase     :       Tuple=0			,    _lowercase     :       Any=1			,    _lowercase     :       List[Any]=1			,    **_lowercase     :       Dict			,    ):
		__UpperCAmelCase      		= vocab_size
		__UpperCAmelCase      		= max_position_embeddings
		__UpperCAmelCase      		= d_model
		__UpperCAmelCase      		= encoder_ffn_dim
		__UpperCAmelCase      		= encoder_layers
		__UpperCAmelCase      		= encoder_attention_heads
		__UpperCAmelCase      		= decoder_ffn_dim
		__UpperCAmelCase      		= decoder_layers
		__UpperCAmelCase      		= decoder_attention_heads
		__UpperCAmelCase      		= dropout
		__UpperCAmelCase      		= attention_dropout
		__UpperCAmelCase      		= activation_dropout
		__UpperCAmelCase      		= activation_function
		__UpperCAmelCase      		= init_std
		__UpperCAmelCase      		= encoder_layerdrop
		__UpperCAmelCase      		= decoder_layerdrop
		__UpperCAmelCase      		= use_cache
		__UpperCAmelCase      		= encoder_layers
		__UpperCAmelCase      		= scale_embedding  # scale factor will be sqrt(d_model) if True
		super().__init__(
		    pad_token_id=_lowercase			,    eos_token_id=_lowercase			,    is_encoder_decoder=_lowercase			,    decoder_start_token_id=_lowercase			,    forced_eos_token_id=_lowercase			,    **_lowercase			,    )



	@property
	def        a  (  self     :       Any						):
		return self.encoder_attention_heads



	@property
	def        a  (  self     :       List[Any]						):
		return self.d_model





'''simple docstring'''





from __future__ import annotations

_UpperCamelCase											=		10

def 			a_	(				_lowerCAmelCase			) -> list[int]:
  __lowerCamelCase			:			str       			=					1
  __lowerCamelCase			:			Union[str, Any]       			=					max(_lowerCAmelCase			)
  while placement <= max_digit:
    # declare and initialize empty buckets
    __lowerCamelCase			:			list[list]       			=					[[] for _ in range(_lowerCAmelCase			)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      __lowerCamelCase			:			List[str]       			=					int((i / placement) % RADIX			)
      buckets[tmp].append(_lowerCAmelCase			)
    # put each buckets' contents into list_of_ints
    __lowerCamelCase			:			Dict       			=					0
    for b in range(_lowerCAmelCase			):
      for i in buckets[b]:
        __lowerCamelCase			:			List[str]       			=					i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
   import doctest

   doctest.testmod()






