


import os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE							    =							[int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]




def  a   ():
   __a					=  os.path.dirname(os.path.realpath(lowerCAmelCase__   )   )
   __a					=  os.path.join(lowerCAmelCase__		,				"""words.txt"""   )

   __a					=  """"""
   with open(lowerCAmelCase__   ) as f:
      __a					=  f.readline()

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


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







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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """luke"""


     def __init__(	self  ,       __A=50267  ,       __A=500000  ,       __A=768  ,       __A=256  ,       __A=12  ,       __A=12  ,       __A=3072  ,       __A="gelu"  ,       __A=0.1  ,       __A=0.1  ,       __A=512  ,       __A=2  ,       __A=0.02  ,       __A=1E-12  ,       __A=True  ,       __A=None  ,       __A=1  ,       __A=0  ,       __A=2  ,       **__A  ,       ):
        super().__init__(pad_token_id=__A  ,       bos_token_id=__A  ,       eos_token_id=__A  ,       **__A    )

        __a					=  vocab_size
        __a					=  entity_vocab_size
        __a					=  hidden_size
        __a					=  entity_emb_size
        __a					=  num_hidden_layers
        __a					=  num_attention_heads
        __a					=  hidden_act
        __a					=  intermediate_size
        __a					=  hidden_dropout_prob
        __a					=  attention_probs_dropout_prob
        __a					=  max_position_embeddings
        __a					=  type_vocab_size
        __a					=  initializer_range
        __a					=  layer_norm_eps
        __a					=  use_entity_aware_attention
        __a					=  classifier_dropout



