





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A					:				str				=			{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				List[Any]				=			["""AlbertTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			["""AlbertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				int				=			[
      """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AlbertForMaskedLM""",
      """AlbertForMultipleChoice""",
      """AlbertForPreTraining""",
      """AlbertForQuestionAnswering""",
      """AlbertForSequenceClassification""",
      """AlbertForTokenClassification""",
      """AlbertModel""",
      """AlbertPreTrainedModel""",
      """load_tf_weights_in_albert""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Optional[Any]				=			[
      """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFAlbertForMaskedLM""",
      """TFAlbertForMultipleChoice""",
      """TFAlbertForPreTraining""",
      """TFAlbertForQuestionAnswering""",
      """TFAlbertForSequenceClassification""",
      """TFAlbertForTokenClassification""",
      """TFAlbertMainLayer""",
      """TFAlbertModel""",
      """TFAlbertPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Union[str, Any]				=			[
      """FlaxAlbertForMaskedLM""",
      """FlaxAlbertForMultipleChoice""",
      """FlaxAlbertForPreTraining""",
      """FlaxAlbertForQuestionAnswering""",
      """FlaxAlbertForSequenceClassification""",
      """FlaxAlbertForTokenClassification""",
      """FlaxAlbertModel""",
      """FlaxAlbertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_albert import AlbertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_albert_fast import AlbertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_albert import (
        ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        AlbertForMaskedLM,
        AlbertForMultipleChoice,
        AlbertForPreTraining,
        AlbertForQuestionAnswering,
        AlbertForSequenceClassification,
        AlbertForTokenClassification,
        AlbertModel,
        AlbertPreTrainedModel,
        load_tf_weights_in_albert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_albert import (
        TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAlbertForMaskedLM,
        TFAlbertForMultipleChoice,
        TFAlbertForPreTraining,
        TFAlbertForQuestionAnswering,
        TFAlbertForSequenceClassification,
        TFAlbertForTokenClassification,
        TFAlbertMainLayer,
        TFAlbertModel,
        TFAlbertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_albert import (
        FlaxAlbertForMaskedLM,
        FlaxAlbertForMultipleChoice,
        FlaxAlbertForPreTraining,
        FlaxAlbertForQuestionAnswering,
        FlaxAlbertForSequenceClassification,
        FlaxAlbertForTokenClassification,
        FlaxAlbertModel,
        FlaxAlbertPreTrainedModel,
    )
else:
  import sys

  _A					:				Optional[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


import os

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




def 							a		(				):


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

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

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




