



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class 							a_  (		nn.Linear				):

						def __init__(    self	:List[Any]   ,						_lowercase	:int   ,						_lowercase	:int   ,						_lowercase	:bool = True   ,						_lowercase	:str = "constant"   ,						_lowercase	:float = 0.0   ,						_lowercase	:str = "topK"   ,						)		->					str:
												super(_lowercase   ,						self).__init__(in_features=_lowercase   ,						out_features=_lowercase   ,						bias=_lowercase)
												assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
												UpperCAmelCase_     							=     pruning_method

												if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
																		UpperCAmelCase_     							=     mask_scale
																		UpperCAmelCase_     							=     mask_init
																		UpperCAmelCase_     							=     nn.Parameter(torch.empty(self.weight.size()))
																		self.init_mask()

						def 	__a       (    self	:List[str])		->					List[Any]:
												if self.mask_init == "constant":
																		init.constant_(self.mask_scores   ,						val=self.mask_scale)
												elif self.mask_init == "uniform":
																		init.uniform_(self.mask_scores   ,						a=-self.mask_scale   ,						b=self.mask_scale)
												elif self.mask_init == "kaiming":
																		init.kaiming_uniform_(self.mask_scores   ,						a=math.sqrt(5))




						def 	__a       (    self	:List[Any]   ,						_lowercase	:torch.tensor   ,						_lowercase	:float)		->					Dict:
												# Get the mask
												if self.pruning_method == "topK":
																		UpperCAmelCase_     							=     TopKBinarizer.apply(self.mask_scores   ,						_lowercase)
												elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCAmelCase_     							=     '''sigmoied''' in self.pruning_method
																		UpperCAmelCase_     							=     ThresholdBinarizer.apply(self.mask_scores   ,						_lowercase   ,						_lowercase)
												elif self.pruning_method == "magnitude":
																		UpperCAmelCase_     							=     MagnitudeBinarizer.apply(self.weight   ,						_lowercase)
												elif self.pruning_method == "l0":
																		UpperCAmelCase_							,      UpperCAmelCase_							,      UpperCAmelCase_     							=     -0.1, 1.1, 2 / 3
																		if self.training:
																								UpperCAmelCase_     							=     torch.zeros_like(self.mask_scores).uniform_().clamp(0.0_001   ,						0.9_999)
																								UpperCAmelCase_     							=     torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b)
																		else:
																								UpperCAmelCase_     							=     torch.sigmoid(self.mask_scores)
																		UpperCAmelCase_     							=     s * (r - l) + l
																		UpperCAmelCase_     							=     s_bar.clamp(min=0.0   ,						max=1.0)
												# Mask weights with computed mask
												UpperCAmelCase_     							=     mask * self.weight
												# Compute output (linear layer) with masked weights
												return nn.functional.linear(_lowercase   ,						_lowercase   ,						self.bias)










from __future__ import annotations

from pathlib import Path







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						return pointa[0] * pointa[1] - pointa[1] * pointa[0]







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       bool:



						'''simple docstring'''






						UpperCAmelCase_     							=     (xa, ya)
						UpperCAmelCase_     							=     (xa - xa, ya - ya)
						UpperCAmelCase_     							=     (xa - xa, ya - ya)
						UpperCAmelCase_     							=     -vector_product(__UpperCAmelCase							,					__UpperCAmelCase	) / vector_product(
						    __UpperCAmelCase							,					__UpperCAmelCase	)
						UpperCAmelCase_     							=     +vector_product(__UpperCAmelCase							,					__UpperCAmelCase	) / vector_product(
						    __UpperCAmelCase							,					__UpperCAmelCase	)

						return a > 0 and b > 0 and a + b < 1







def 				A			(		__UpperCAmelCase = "p102_triangles.txt"	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     Path(__UpperCAmelCase	).parent.joinpath(__UpperCAmelCase	).read_text(encoding='''utf-8'''	)

						UpperCAmelCase_     							=     []
						for line in data.strip().split('''\n'''	):
												triangles.append([int(__UpperCAmelCase	) for number in line.split(''','''	)]	)

						UpperCAmelCase_     							=     0
						UpperCAmelCase_     							=     42

						for triangle in triangles:
												ret += contains_origin(*__UpperCAmelCase	)

						return ret


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





