from __future__ import annotations

from math import ceil, floor, sqrt

def    SCREAMING_SNAKE_CASE_     (						snake_case__ = 2_0_0_0_0_0_0							)     ->	Optional[int]:
							lowerCAmelCase			=		[0]
							lowerCAmelCase			=		4_2

							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
							lowerCAmelCase			=		0
							# the area corresponding to the grid that gives the product closest to target
							lowerCAmelCase			=		0
							# an estimate of b, using the quadratic formula
							lowerCAmelCase			=		4_2
							# the largest integer less than b_estimate
							lowerCAmelCase			=		4_2
							# the largest integer less than b_estimate
							lowerCAmelCase			=		4_2
							# the triangle number corresponding to b_floor
							lowerCAmelCase			=		4_2
							# the triangle number corresponding to b_ceil
							lowerCAmelCase			=		4_2

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

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

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

							return area


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








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'glpn'



 def __init__(self ,   A=3 ,   A=4 ,   A=[2, 2, 2, 2] ,   A=[8, 4, 2, 1] ,   A=[32, 64, 160, 256] ,   A=[7, 3, 3, 3] ,   A=[4, 2, 2, 2] ,   A=[1, 2, 5, 8] ,   A=[4, 4, 4, 4] ,   A="gelu" ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=0.1 ,   A=1E-6 ,   A=64 ,   A=10 ,   A=-1 ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(**A       )

      _a      =  num_channels
      _a      =  num_encoder_blocks
      _a      =  depths
      _a      =  sr_ratios
      _a      =  hidden_sizes
      _a      =  patch_sizes
      _a      =  strides
      _a      =  mlp_ratios
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  initializer_range
      _a      =  drop_path_rate
      _a      =  layer_norm_eps
      _a      =  decoder_hidden_size
      _a      =  max_depth
      _a      =  head_in_index



