






'''simple docstring'''





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


a_		:						Any             =    logging.get_logger(__name__)

a_		:						Optional[int]             =    {
    '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 		_snake_case  (  A__					):
			_lowercase		:    Tuple			     =							'''pegasus'''
			_lowercase		:    Optional[Any]			     =							['''past_key_values''']
			_lowercase		:    Union[str, Any]			     =							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






			def __init__(       self		,					a=5_0265		,					a=1024		,					a=12		,					a=4096		,					a=16		,					a=12		,					a=4096		,					a=16		,					a=0.0		,					a=0.0		,					a=True		,					a=True		,					a="gelu"		,					a=1024		,					a=0.1		,					a=0.0		,					a=0.0		,					a=0.02		,					a=0		,					a=False		,					a=0		,					a=1		,					a=1		,					**a		,					)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= vocab_size
						SCREAMING_SNAKE_CASE						= max_position_embeddings
						SCREAMING_SNAKE_CASE						= d_model
						SCREAMING_SNAKE_CASE						= encoder_ffn_dim
						SCREAMING_SNAKE_CASE						= encoder_layers
						SCREAMING_SNAKE_CASE						= encoder_attention_heads
						SCREAMING_SNAKE_CASE						= decoder_ffn_dim
						SCREAMING_SNAKE_CASE						= decoder_layers
						SCREAMING_SNAKE_CASE						= decoder_attention_heads
						SCREAMING_SNAKE_CASE						= dropout
						SCREAMING_SNAKE_CASE						= attention_dropout
						SCREAMING_SNAKE_CASE						= activation_dropout
						SCREAMING_SNAKE_CASE						= activation_function
						SCREAMING_SNAKE_CASE						= init_std
						SCREAMING_SNAKE_CASE						= encoder_layerdrop
						SCREAMING_SNAKE_CASE						= decoder_layerdrop
						SCREAMING_SNAKE_CASE						= use_cache
						SCREAMING_SNAKE_CASE						= encoder_layers
						SCREAMING_SNAKE_CASE						= scale_embedding  # scale factor will be sqrt(d_model) if True
						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)      ->       int:
						return self.encoder_attention_heads







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












import os

# Precomputes a list of the 100 first triangular numbers
a_		:						str             =    [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= os.path.dirname(os.path.realpath(_UpperCAmelCase))
			SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       'words.txt')

			SCREAMING_SNAKE_CASE						= ''
			with open(_UpperCAmelCase) as f:
						SCREAMING_SNAKE_CASE						= f.readline()

			SCREAMING_SNAKE_CASE						= [word.strip('"') for word in words.strip('\r\n').split(',')]
			SCREAMING_SNAKE_CASE						= [
			    word
			    for word in [sum(ord(_UpperCAmelCase) - 64 for x in word) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase)


if __name__ == "__main__":
				print(solution())




