




import itertools

def   UpperCamelCase__   (    UpperCAmelCase_ = 1000000			)						->							int:




       '''simple docstring'''






       _lowercase					:				Union[str, Any]           =			[1]

       for i in itertools.count(len(UpperCAmelCase_			)			):
              _lowercase					:				List[Any]           =			0
              for j in itertools.count(1			):
                     _lowercase					:				Dict           =			-1 if j % 2 == 0 else +1
                     _lowercase					:				Union[str, Any]           =			(j * j * 3 - j) // 2
                     if index > i:
                            break
                     item += partitions[i - index] * sign
                     item %= number
                     index += j
                     if index > i:
                            break
                     item += partitions[i - index] * sign
                     item %= number

              if item == 0:
                     return i
              partitions.append(UpperCAmelCase_			)

       return 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(F"""{solution() = }""")



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__				       =			{
    'configuration_wav2vec2_conformer': [
        'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Wav2Vec2ConformerConfig',
    ],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			[
       'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Wav2Vec2ConformerForAudioFrameClassification',
       'Wav2Vec2ConformerForCTC',
       'Wav2Vec2ConformerForPreTraining',
       'Wav2Vec2ConformerForSequenceClassification',
       'Wav2Vec2ConformerForXVector',
       'Wav2Vec2ConformerModel',
       'Wav2Vec2ConformerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_wavaveca_conformer import (
       WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       WavaVecaConformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca_conformer import (
          WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaConformerForAudioFrameClassification,
          WavaVecaConformerForCTC,
          WavaVecaConformerForPreTraining,
          WavaVecaConformerForSequenceClassification,
          WavaVecaConformerForXVector,
          WavaVecaConformerModel,
          WavaVecaConformerPreTrainedModel,
      )

else:
   import sys

   UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)