





import itertools




def 			lowerCAmelCase_			(				UpperCamelCase_ = 1000000	)  ->		int:
       UpperCamelCase_ 							=	[1]

       for i in itertools.count(len(_A	)	):
              UpperCamelCase_ 							=	0
              for j in itertools.count(1	):
                     UpperCamelCase_ 							=	-1 if j % 2 == 0 else +1
                     UpperCamelCase_ 							=	(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(_A	)

       return 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(f'''{solution() = }''')




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__			:  Dict					     =     {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Union[str, Any]					     =     [
      """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """HubertForCTC""",
      """HubertForSequenceClassification""",
      """HubertModel""",
      """HubertPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  List[Any]					     =     [
      """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFHubertForCTC""",
      """TFHubertModel""",
      """TFHubertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_hubert import (
        HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        HubertForCTC,
        HubertForSequenceClassification,
        HubertModel,
        HubertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_hubert import (
        TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFHubertForCTC,
        TFHubertModel,
        TFHubertPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase__			:  str					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




