






from math import sqrt




def      lowercase__      (     __snake_case    :							int = 1_000_000  ):


					'''simple docstring'''


					UpperCAmelCase_    :      int            =						0
					UpperCAmelCase_    :      int            =						0
					UpperCAmelCase_    :      int

					while num_cuboids <= limit:
										max_cuboid_size += 1
										for sum_shortest_sides in range(2     ,       2 * max_cuboid_size + 1  ):
															if sqrt(sum_shortest_sides**2 + max_cuboid_size**2  ).is_integer():
																				num_cuboids += (
																				    min(__snake_case     ,       sum_shortest_sides // 2  )
																				    - max(1     ,       sum_shortest_sides - max_cuboid_size  )
																				    + 1
																				)

					return max_cuboid_size


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










'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case							      =     {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     ['''DPTFeatureExtractor''']
		__snake_case							      =     ['''DPTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DPTForDepthEstimation''',
		    '''DPTForSemanticSegmentation''',
		    '''DPTModel''',
		    '''DPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		__snake_case							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)