




'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}





class  __lowercase    (	_lowercase			):
   _a    						=   '''camembert'''







   def __init__(						self   ,				UpperCamelCase=3_0522   ,				UpperCamelCase=768   ,				UpperCamelCase=12   ,				UpperCamelCase=12   ,				UpperCamelCase=3072   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=512   ,				UpperCamelCase=2   ,				UpperCamelCase=0.02   ,				UpperCamelCase=1e-12   ,				UpperCamelCase=1   ,				UpperCamelCase=0   ,				UpperCamelCase=2   ,				UpperCamelCase="absolute"   ,				UpperCamelCase=True   ,				UpperCamelCase=None   ,				**UpperCamelCase   ,				)					->     List[str]:
     super().__init__(pad_token_id=A_   ,				bos_token_id=A_   ,				eos_token_id=A_   ,				**A_   )

     __a								=       vocab_size
     __a								=       hidden_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								=       position_embedding_type
     __a								=       use_cache
     __a								=       classifier_dropout





class  __lowercase    (	_lowercase			):







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
       __a								=       {0: 'batch', 1: 'choice', 2: 'sequence'}
     else:
       __a								=       {0: 'batch', 1: 'sequence'}
     return OrderedDict(
         [
             ('input_ids', dynamic_axis),
             ('attention_mask', dynamic_axis),
         ]   )





"""simple docstring"""


import os

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






def    _snake_case					(		):
	A     = os.path.dirname(os.path.realpath(snake_case__  )  )
	A     = os.path.join(snake_case__						,   'words.txt'  )

	A     = ''
	with open(snake_case__  ) as f:
		A     = f.readline()

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


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