import torch
import torch.nn as nn

BATCH_SIZE = 4096
HIDDEN_DIM = 4096
SHAPE = (BATCH_SIZE, HIDDEN_DIM)

# SAAF 初始参数
ALPHA_INIT = 1.0
BETA_INIT = 1.0

class SAAF(nn.Module):
    """
    Zhou Y, Li D, Huo S, Kung SY (2021) Shape autotuning activation function [Formula presented]. 
    Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.114534
    Formula: f(x) = (alpha * x) / (1 + exp(-beta * x))
    """
    def __init__(self, alpha_init=1.0, beta_init=1.0):
        super(SAAF, self).__init__()
        self.alpha = nn.Parameter(torch.tensor(alpha_init))
        self.beta = nn.Parameter(torch.tensor(beta_init))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.alpha * x * torch.sigmoid(self.beta * x)

class Model(nn.Module):
    def __init__(self, alpha_init=1.0, beta_init=1.0):
        super(Model, self).__init__()
        self.act = SAAF(alpha_init, beta_init)
    
    def forward(self, x):
        return self.act(x)

def get_inputs():
    input_tensor = torch.randn(SHAPE, dtype=torch.float32)
    return [input_tensor.contiguous()]

def get_init_inputs():
    return [ALPHA_INIT, BETA_INIT]