'''simple docstring'''




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


_a   :	Tuple 							=   logging.get_logger(__name__)

_a   :	List[str] 							=   {
    "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 		__A       (a__   ):
							snake_case       :Optional[Any]				    =					"pegasus"
							snake_case       :List[str]				    =					["past_key_values"]
							snake_case       :Union[str, Any]				    =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






							def __init__(      self				,     UpperCamelCase_=5_02_65				,     UpperCamelCase_=10_24				,     UpperCamelCase_=12				,     UpperCamelCase_=40_96				,     UpperCamelCase_=16				,     UpperCamelCase_=12				,     UpperCamelCase_=40_96				,     UpperCamelCase_=16				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=True				,     UpperCamelCase_=True				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=10_24				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=0				,     UpperCamelCase_=False				,     UpperCamelCase_=0				,     UpperCamelCase_=1				,     UpperCamelCase_=1				,     **UpperCamelCase_				,     ):
														__UpperCAmelCase  :				List[str]       				=		vocab_size
														__UpperCAmelCase  :				List[str]       				=		max_position_embeddings
														__UpperCAmelCase  :				Optional[Any]       				=		d_model
														__UpperCAmelCase  :				Union[str, Any]       				=		encoder_ffn_dim
														__UpperCAmelCase  :				int       				=		encoder_layers
														__UpperCAmelCase  :				str       				=		encoder_attention_heads
														__UpperCAmelCase  :				str       				=		decoder_ffn_dim
														__UpperCAmelCase  :				Tuple       				=		decoder_layers
														__UpperCAmelCase  :				Optional[int]       				=		decoder_attention_heads
														__UpperCAmelCase  :				List[str]       				=		dropout
														__UpperCAmelCase  :				Optional[int]       				=		attention_dropout
														__UpperCAmelCase  :				Optional[int]       				=		activation_dropout
														__UpperCAmelCase  :				Dict       				=		activation_function
														__UpperCAmelCase  :				int       				=		init_std
														__UpperCAmelCase  :				str       				=		encoder_layerdrop
														__UpperCAmelCase  :				int       				=		decoder_layerdrop
														__UpperCAmelCase  :				str       				=		use_cache
														__UpperCAmelCase  :				int       				=		encoder_layers
														__UpperCAmelCase  :				List[str]       				=		scale_embedding  # scale factor will be sqrt(d_model) if True
														super().__init__(
														    pad_token_id=lowerCamelCase_				,     eos_token_id=lowerCamelCase_				,     is_encoder_decoder=lowerCamelCase_				,     decoder_start_token_id=lowerCamelCase_				,     forced_eos_token_id=lowerCamelCase_				,     **lowerCamelCase_				,     )






							@property
							def 			_snake_case						(      self      ):
														return self.encoder_attention_heads







							@property
							def 			_snake_case						(      self      ):
														return self.d_model







'''simple docstring'''




def _lowercase	(      lowerCamelCase__ = 100						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		(n * (n + 1) // 2) ** 2
       __UpperCAmelCase  :				Any       				=		n * (n + 1) * (2 * n + 1) // 6
       return sum_cubes - sum_squares


if __name__ == "__main__":
   print(f"""{solution() = }""")






