






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__						:      List[Any]								=					{"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      Optional[Any]								=					[
				    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """QDQBertForMaskedLM""",
				    """QDQBertForMultipleChoice""",
				    """QDQBertForNextSentencePrediction""",
				    """QDQBertForQuestionAnswering""",
				    """QDQBertForSequenceClassification""",
				    """QDQBertForTokenClassification""",
				    """QDQBertLayer""",
				    """QDQBertLMHeadModel""",
				    """QDQBertModel""",
				    """QDQBertPreTrainedModel""",
				    """load_tf_weights_in_qdqbert""",
				]


if TYPE_CHECKING:
				from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_qdqbert import (
								    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    QDQBertForMaskedLM,
								    QDQBertForMultipleChoice,
								    QDQBertForNextSentencePrediction,
								    QDQBertForQuestionAnswering,
								    QDQBertForSequenceClassification,
								    QDQBertForTokenClassification,
								    QDQBertLayer,
								    QDQBertLMHeadModel,
								    QDQBertModel,
								    QDQBertPreTrainedModel,
								    load_tf_weights_in_qdqbert,
								)


else:
				import sys

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













UpperCamelCase__						:      Dict								=					10**8





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  bool:


	"""simple docstring"""



	if n % 1_0 == 0:
		return False
	a		=			str(snake_case_  )
	return s == s[::-1]





def   SCREAMING_SNAKE_CASE__						(   )   ->  int:


	"""simple docstring"""



	a		=			set()
	a		=			1
	a		=			5
	while sum_squares < LIMIT:
		a		=			first_square + 1
		while sum_squares < LIMIT:
			if is_palindrome(snake_case_  ):
				answer.add(snake_case_  )
			last_square += 1
			sum_squares += last_square**2
		first_square += 1
		a		=			first_square**2 + (first_square + 1) ** 2

	return sum(snake_case_  )


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





