






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


_UpperCamelCase   :						Tuple     						= logging.get_logger(__name__)

_UpperCamelCase   :						int     						= {
    """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 __a   (   snake_case_				):
  __lowercase    :    List[str]						=					'pegasus_x'
  __lowercase    :    int						=					['past_key_values']
  __lowercase    :    Tuple						=					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



  def __init__(self  :		Union[str, Any]				,			lowercase_  :		str=96_103				,			lowercase_  :		Dict=16_384				,			lowercase_  :		str=16				,			lowercase_  :		Optional[Any]=4_096				,			lowercase_  :		Optional[Any]=16				,			lowercase_  :		Tuple=16				,			lowercase_  :		str=4_096				,			lowercase_  :		Tuple=16				,			lowercase_  :		Union[str, Any]=0.0				,			lowercase_  :		List[str]=0.0				,			lowercase_  :		List[str]=True				,			lowercase_  :		List[str]=True				,			lowercase_  :		Tuple="gelu"				,			lowercase_  :		Any=1_024				,			lowercase_  :		List[Any]=0.1				,			lowercase_  :		Optional[Any]=0.0				,			lowercase_  :		List[str]=0.0				,			lowercase_  :		str=0.02				,			lowercase_  :		Union[str, Any]=0				,			lowercase_  :		Dict=True				,			lowercase_  :		Union[str, Any]=0				,			lowercase_  :		int=1				,			lowercase_  :		Dict=1				,			lowercase_  :		List[Any]=32				,			lowercase_  :		int=512				,			lowercase_  :		Optional[int]=True				,			**lowercase_  :		Union[str, Any]				,			)		->							Optional[int]:
   __A	:       str       =	vocab_size
   __A	:       str       =	max_position_embeddings
   __A	:       Optional[Any]       =	d_model
   __A	:       List[Any]       =	encoder_ffn_dim
   __A	:       List[str]       =	encoder_layers
   __A	:       Any       =	encoder_attention_heads
   __A	:       List[Any]       =	decoder_ffn_dim
   __A	:       Union[str, Any]       =	decoder_layers
   __A	:       Union[str, Any]       =	decoder_attention_heads
   __A	:       Optional[int]       =	dropout
   __A	:       List[Any]       =	attention_dropout
   __A	:       List[str]       =	activation_dropout
   __A	:       str       =	activation_function
   __A	:       Tuple       =	init_std
   __A	:       Union[str, Any]       =	encoder_layerdrop
   __A	:       Any       =	decoder_layerdrop
   __A	:       Tuple       =	use_cache
   __A	:       Dict       =	encoder_layers
   __A	:       str       =	scale_embedding  # scale factor will be sqrt(d_model) if True

   __A	:       Union[str, Any]       =	num_global_tokens
   __A	:       List[str]       =	block_size
   __A	:       Optional[Any]       =	stagger_local_blocks

   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  :		Optional[int]   )		->							int:
   return self.encoder_attention_heads



  @property
  def 						A_				(self  :		Optional[Any]   )		->							int:
   return self.d_model













"""simple docstring"""


import math




def 				a (     UpperCamelCase_							:				int		)  ->		list[int]:
 if not isinstance(UpperCamelCase_     ,	UpperCamelCase_		):
  __A	:       Any       =	F"Input value of [number={number}] must be an integer"
  raise TypeError(UpperCamelCase_		)
 if number < 1:
  __A	:       int       =	F"Input value of [number={number}] must be a positive integer"
  raise ValueError(UpperCamelCase_		)
 __A	:       Optional[Any]       =	[number]
 while number != 1:
  if number % 2 == 0:
   __A	:       Optional[int]       =	math.floor(math.sqrt(UpperCamelCase_		)		)
  else:
   __A	:       List[str]       =	math.floor(
       math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		)		)
  sequence.append(UpperCamelCase_		)
 return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()





