

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, 101)]

def 							__SCREAMING_SNAKE_CASE							(				)   ->       int:
				_UpperCAmelCase       :		Optional[Any] 		=    os.path.dirname(os.path.realpath(__A ) )
				_UpperCAmelCase       :		Tuple 		=    os.path.join(__A  ,      "words.txt" )

				_UpperCAmelCase       :		int 		=    """"""
				with open(__A ) as f:
								_UpperCAmelCase       :		Union[str, Any] 		=    f.readline()

				_UpperCAmelCase       :		Tuple 		=    [word.strip("\"" ) for word in words.strip("\r\n" ).split("," )]
				_UpperCAmelCase       :		Optional[Any] 		=    [
				    word
				    for word in [sum(ord(__A ) - 64 for x in word ) for word in words]
				    if word in TRIANGULAR_NUMBERS
				]
				return len(__A )


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




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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"timesformer"


						def __init__(       self						,					A_=224						,					A_=16						,					A_=3						,					A_=8						,					A_=768						,					A_=12						,					A_=12						,					A_=3072						,					A_="gelu"						,					A_=0.0						,					A_=0.0						,					A_=0.02						,					A_=1e-6						,					A_=True						,					A_="divided_space_time"						,					A_=0						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    image_size
										_UpperCAmelCase       :		Tuple 		=    patch_size
										_UpperCAmelCase       :		Any 		=    num_channels
										_UpperCAmelCase       :		List[Any] 		=    num_frames

										_UpperCAmelCase       :		str 		=    hidden_size
										_UpperCAmelCase       :		Any 		=    num_hidden_layers
										_UpperCAmelCase       :		Dict 		=    num_attention_heads
										_UpperCAmelCase       :		Optional[int] 		=    intermediate_size
										_UpperCAmelCase       :		Union[str, Any] 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Tuple 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Union[str, Any] 		=    initializer_range
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_eps
										_UpperCAmelCase       :		str 		=    qkv_bias

										_UpperCAmelCase       :		Tuple 		=    attention_type
										_UpperCAmelCase       :		List[Any] 		=    drop_path_rate


