

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


UpperCAmelCase__     = logging.get_logger(__name__)

UpperCAmelCase__     = {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  '''roc_bert'''



			def __init__(		self     :						List[Any]				,	A     :						List[str]=3_05_22				,	A     :						str=7_68				,	A     :						Dict=12				,	A     :						str=12				,	A     :						int=30_72				,	A     :						List[str]="gelu"				,	A     :						Union[str, Any]=0.1				,	A     :						List[Any]=0.1				,	A     :						str=5_12				,	A     :						Optional[int]=2				,	A     :						Union[str, Any]=0.0_2				,	A     :						Any=1E-12				,	A     :						Any=True				,	A     :						List[str]=0				,	A     :						Optional[int]="absolute"				,	A     :						Tuple=None				,	A     :						Union[str, Any]=True				,	A     :						Dict=True				,	A     :						Union[str, Any]=7_68				,	A     :						Any=9_10				,	A     :						int=5_12				,	A     :						List[Any]=2_48_58				,	A     :						Union[str, Any]=True				,	**A     :						List[str]				,	)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     vocab_size
						_UpperCAmelCase	 =     max_position_embeddings
						_UpperCAmelCase	 =     hidden_size
						_UpperCAmelCase	 =     num_hidden_layers
						_UpperCAmelCase	 =     num_attention_heads
						_UpperCAmelCase	 =     intermediate_size
						_UpperCAmelCase	 =     hidden_act
						_UpperCAmelCase	 =     hidden_dropout_prob
						_UpperCAmelCase	 =     attention_probs_dropout_prob
						_UpperCAmelCase	 =     initializer_range
						_UpperCAmelCase	 =     type_vocab_size
						_UpperCAmelCase	 =     layer_norm_eps
						_UpperCAmelCase	 =     use_cache
						_UpperCAmelCase	 =     enable_pronunciation
						_UpperCAmelCase	 =     enable_shape
						_UpperCAmelCase	 =     pronunciation_embed_dim
						_UpperCAmelCase	 =     pronunciation_vocab_size
						_UpperCAmelCase	 =     shape_embed_dim
						_UpperCAmelCase	 =     shape_vocab_size
						_UpperCAmelCase	 =     concat_input
						_UpperCAmelCase	 =     position_embedding_type
						_UpperCAmelCase	 =     classifier_dropout
						super().__init__(pad_token_id=A				,	**A)





import os

# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__     = [int(0.5 * n * (n + 1)) for n in range(1, 101)]





def 							A			(  )       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     os.path.dirname(os.path.realpath(_UpperCAmelCase					)					)
			_UpperCAmelCase	 =     os.path.join(_UpperCAmelCase							,     'words.txt'					)

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

			_UpperCAmelCase	 =     [word.strip('"'					) for word in words.strip('\r\n'					).split(','					)]
			_UpperCAmelCase	 =     [
			    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())
