"""simple docstring"""






from typing import TYPE_CHECKING

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


__lowerCAmelCase					: Union[str, Any]        ={
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: int        =[
           """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """RobertaForCausalLM""",
           """RobertaForMaskedLM""",
           """RobertaForMultipleChoice""",
           """RobertaForQuestionAnswering""",
           """RobertaForSequenceClassification""",
           """RobertaForTokenClassification""",
           """RobertaModel""",
           """RobertaPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: Optional[Any]        =[
           """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFRobertaForCausalLM""",
           """TFRobertaForMaskedLM""",
           """TFRobertaForMultipleChoice""",
           """TFRobertaForQuestionAnswering""",
           """TFRobertaForSequenceClassification""",
           """TFRobertaForTokenClassification""",
           """TFRobertaMainLayer""",
           """TFRobertaModel""",
           """TFRobertaPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: Optional[int]        =[
           """FlaxRobertaForCausalLM""",
           """FlaxRobertaForMaskedLM""",
           """FlaxRobertaForMultipleChoice""",
           """FlaxRobertaForQuestionAnswering""",
           """FlaxRobertaForSequenceClassification""",
           """FlaxRobertaForTokenClassification""",
           """FlaxRobertaModel""",
           """FlaxRobertaPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

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




"""simple docstring"""






def     UpperCAmelCase__ (       lowerCAmelCase__  :int	)				->					bool:
      '''simple docstring'''


      lowercase														=			(1 + 2_4 * n) ** 0.5
      return ((1 + root) / 6) % 1 == 0


def     UpperCAmelCase__ (       lowerCAmelCase__  :int = 5_0_0_0	)				->					int:
      '''simple docstring'''


      lowercase														=			[(i * (3 * i - 1)) // 2 for i in range(1      ,	lowerCAmelCase__	)]
      for i, pentagonal_i in enumerate(lowerCAmelCase__	):
            for j in range(lowerCAmelCase__      ,	len(lowerCAmelCase__	)	):
                  lowercase														=			pentagonal_nums[j]
                  lowercase														=			pentagonal_i + pentagonal_j
                  lowercase														=			pentagonal_j - pentagonal_i
                  if is_pentagonal(lowerCAmelCase__	) and is_pentagonal(lowerCAmelCase__	):
                        return b

      return -1


if __name__ == "__main__":
       print(F"""{solution() = }""")



