






from typing import TYPE_CHECKING

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


UpperCamelCase__						:      str								=					{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      List[Any]								=					["""DebertaV2TokenizerFast"""]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      Union[str, Any]								=					[
				    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFDebertaV2ForMaskedLM""",
				    """TFDebertaV2ForQuestionAnswering""",
				    """TFDebertaV2ForSequenceClassification""",
				    """TFDebertaV2ForTokenClassification""",
				    """TFDebertaV2Model""",
				    """TFDebertaV2PreTrainedModel""",
				]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      Optional[Any]								=					[
				    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """DebertaV2ForMaskedLM""",
				    """DebertaV2ForMultipleChoice""",
				    """DebertaV2ForQuestionAnswering""",
				    """DebertaV2ForSequenceClassification""",
				    """DebertaV2ForTokenClassification""",
				    """DebertaV2Model""",
				    """DebertaV2PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_deberta_va import (
				    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DebertaVaConfig,
				    DebertaVaOnnxConfig,
				)
				from .tokenization_deberta_va import DebertaVaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta_va import (
								    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaVaForMaskedLM,
								    TFDebertaVaForQuestionAnswering,
								    TFDebertaVaForSequenceClassification,
								    TFDebertaVaForTokenClassification,
								    TFDebertaVaModel,
								    TFDebertaVaPreTrainedModel,
								)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta_va import (
								    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaVaForMaskedLM,
								    DebertaVaForMultipleChoice,
								    DebertaVaForQuestionAnswering,
								    DebertaVaForSequenceClassification,
								    DebertaVaForTokenClassification,
								    DebertaVaModel,
								    DebertaVaPreTrainedModel,
								)

else:
				import sys

				UpperCamelCase__						:      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())





