




import os

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





def        __UpperCAmelCase						(							)       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Tuple					  =      os.path.dirname(os.path.realpath(lowerCamelCase_  )  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      os.path.join(lowerCamelCase_  ,							'words.txt'  )

			SCREAMING_SNAKE_CASE_   : Dict					  =      ''
			with open(lowerCamelCase_  ) as f:
						SCREAMING_SNAKE_CASE_   : Any					  =      f.readline()

			SCREAMING_SNAKE_CASE_   : Dict					  =      [word.strip('"'  ) for word in words.strip('\r\n'  ).split(','  )]
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      [
			    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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__ :   Optional[int]						=							{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Tuple						=							['''BloomTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Any						=							[
						    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BloomForCausalLM''',
						    '''BloomModel''',
						    '''BloomPreTrainedModel''',
						    '''BloomForSequenceClassification''',
						    '''BloomForTokenClassification''',
						    '''BloomForQuestionAnswering''',
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :   Tuple						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



