


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:  Optional[Any]			=						{
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase					:  Dict			=						[
		    """UperNetForSemanticSegmentation""",
		    """UperNetPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

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








def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = 100_0000 )			->     int:
    lowerCamelCase__    :      int					 =		limit + 1
    lowerCamelCase__    :      Optional[Any]					 =		[0] * limit
    for first_term in range(1						,	_UpperCAmelCase ):
        for n in range(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[Any]					 =		first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    lowerCamelCase__    :      List[str]					 =		sum(1 for x in frequency[1:limit] if x == 10 )

    return count


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





