import torch
import torch.nn as nn

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

BETA_INIT = 1.0
GAMMA_INIT = 1.0

class TanhSoft2(nn.Module):
    '''
    TanhSoft—Dynamic Trainable Activation Functions for Faster Learning and Better Performance
    https://ieeexplore.ieee.org/document/9514829
    Formula: f(x) = x * tanh(beta * exp(gamma * x))
    '''
    def __init__(self, beta_init=1.0, gamma_init=1.0):
        super(TanhSoft2, self).__init__()
        self.beta = nn.Parameter(torch.tensor(beta_init))
        self.gamma = nn.Parameter(torch.tensor(gamma_init))
        self.clamp_val = 80.0

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        clamped_gx = torch.clamp(self.gamma * x, max=self.clamp_val)
        inner = self.beta * torch.exp(clamped_gx)
        return x * torch.tanh(inner)

class Model(nn.Module):
    def __init__(self, beta_init=1.0, gamma_init=1.0):
        super(Model, self).__init__()
        self.act = TanhSoft2(beta_init, gamma_init)
    
    def forward(self, x):
        return self.act(x)

def get_inputs():
    input_tensor = torch.randn(SHAPE, dtype=torch.float32) * 5.0
    return [input_tensor.contiguous()]

def get_init_inputs():
    return [BETA_INIT, GAMMA_INIT]