

"""simple docstring"""



import os

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





def  _snake_case     (    ):
					A__										=  os.path.dirname(os.path.realpath(UpperCamelCase__			)			)
					A__										=  os.path.join(UpperCamelCase__					,   """words.txt"""			)

					A__										=  """"""
					with open(UpperCamelCase__			) as f:
										A__										=  f.readline()

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









"""simple docstring"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_					:	List[str]							  =						{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_					:	List[Any]							  =						[
     'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MraForMaskedLM',
     'MraForMultipleChoice',
     'MraForQuestionAnswering',
     'MraForSequenceClassification',
     'MraForTokenClassification',
     'MraLayer',
     'MraModel',
     'MraPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 SCREAMING_SNAKE_CASE_					:	List[str]							  =						_LazyModule(__name__, globals()['__file__'], _import_structure)






