


from __future__ import annotations

from math import ceil, floor, sqrt






def 							__lowercase     (				__lowerCAmelCase							:       int = 2_0_0_0_0_0_0     ):
							a__    =  [0]
							a__    =  42

							for idx in range(1      ,					ceil(sqrt(target * 2     ) * 1.1     )     ):
														triangle_numbers.append(triangle_numbers[-1] + idx     )

							# we want this to be as close as possible to target
							a__    =  0
							# the area corresponding to the grid that gives the product closest to target
							a__    =  0
							# an estimate of b, using the quadratic formula
							a__    =  42
							# the largest integer less than b_estimate
							a__    =  42
							# the largest integer less than b_estimate
							a__    =  42
							# the triangle number corresponding to b_floor
							a__    =  42
							# the triangle number corresponding to b_ceil
							a__    =  42

							for idx_a, triangle_a in enumerate(triangle_numbers[1:]      ,					1     ):
														a__    =  (-1 + sqrt(1 + 8 * target / triangle_a     )) / 2
														a__    =  floor(__lowerCAmelCase     )
														a__    =  ceil(__lowerCAmelCase     )
														a__    =  triangle_numbers[b_floor]
														a__    =  triangle_numbers[b_ceil]

														if abs(target - triangle_b_first_guess * triangle_a     ) < abs(
														    target - best_product     ):
																					a__    =  triangle_b_first_guess * triangle_a
																					a__    =  idx_a * b_floor

														if abs(target - triangle_b_second_guess * triangle_a     ) < abs(
														    target - best_product     ):
																					a__    =  triangle_b_second_guess * triangle_a
																					a__    =  idx_a * b_ceil

							return area


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








from typing import TYPE_CHECKING

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


snake_case							: Any      =   {
    '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Union[str, Any]      =   ['''MobileViTFeatureExtractor''']
						snake_case							: int      =   ['''MobileViTImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Dict      =   [
						    '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileViTForImageClassification''',
						    '''MobileViTForSemanticSegmentation''',
						    '''MobileViTModel''',
						    '''MobileViTPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Tuple      =   [
						    '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFMobileViTForImageClassification''',
						    '''TFMobileViTForSemanticSegmentation''',
						    '''TFMobileViTModel''',
						    '''TFMobileViTPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilevit import MobileViTFeatureExtractor
												from .image_processing_mobilevit import MobileViTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilevit import (
												    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileViTForImageClassification,
												    MobileViTForSemanticSegmentation,
												    MobileViTModel,
												    MobileViTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilevit import (
												    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileViTForImageClassification,
												    TFMobileViTForSemanticSegmentation,
												    TFMobileViTModel,
												    TFMobileViTPreTrainedModel,
												)


else:
						import sys

						snake_case							: Optional[int]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




