


A__	:      Optional[Any]				=						10**8



def     UpperCAmelCase__      (			UpperCAmelCase_					:       int							)  ->	bool:
     if n % 10 == 0:
          return False
     __lowerCamelCase     :			Tuple              =	str(UpperCAmelCase_							)
     return s == s[::-1]



def     UpperCAmelCase__      (			)  ->	int:
     __lowerCamelCase     :			Dict              =	set()
     __lowerCamelCase     :			Optional[int]              =	1
     __lowerCamelCase     :			int              =	5
     while sum_squares < LIMIT:
          __lowerCamelCase     :			int              =	first_square + 1
          while sum_squares < LIMIT:
               if is_palindrome(UpperCAmelCase_							):
                    answer.add(UpperCAmelCase_							)
               last_square += 1
               sum_squares += last_square**2
          first_square += 1
          __lowerCamelCase     :			int              =	first_square**2 + (first_square + 1) ** 2

     return sum(UpperCAmelCase_							)


if __name__ == "__main__":
      print(solution())










'''simple docstring'''



from typing import TYPE_CHECKING

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


A__	:      Optional[Any]				=						{
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__	:      Optional[Any]				=						[
          """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """XLMProphetNetDecoder""",
          """XLMProphetNetEncoder""",
          """XLMProphetNetForCausalLM""",
          """XLMProphetNetForConditionalGeneration""",
          """XLMProphetNetModel""",
          """XLMProphetNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

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






