



from math import isqrt, loga

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		[True] * max_number
				for i in range(2				,     isqrt(max_number - 1    ) + 1    ):
								if is_prime[i]:
												for j in range(i**2				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ):
																__a       =		False

				return [i for i in range(2				,     _SCREAMING_SNAKE_CASE    ) if is_prime[i]]

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int = 80_0800				,     _SCREAMING_SNAKE_CASE       :			int = 80_0800    ):
				"""simple docstring"""







				__a       =		degree * loga(_SCREAMING_SNAKE_CASE    )
				__a       =		int(_SCREAMING_SNAKE_CASE    )
				__a       =		calculate_prime_numbers(_SCREAMING_SNAKE_CASE    )

				__a       =		0
				__a       =		0
				__a       =		len(_SCREAMING_SNAKE_CASE    ) - 1
				while left < right:
								while (
								    prime_numbers[right] * loga(prime_numbers[left]    )
								    + prime_numbers[left] * loga(prime_numbers[right]    )
								    > upper_bound
								):
												right -= 1
								hybrid_integers_count += right - left
								left += 1

				return hybrid_integers_count


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





from typing import TYPE_CHECKING

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


lowerCamelCase__			= {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NezhaForNextSentencePrediction""",
						    """NezhaForMaskedLM""",
						    """NezhaForPreTraining""",
						    """NezhaForMultipleChoice""",
						    """NezhaForQuestionAnswering""",
						    """NezhaForSequenceClassification""",
						    """NezhaForTokenClassification""",
						    """NezhaModel""",
						    """NezhaPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
