import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch as tr
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_clamp_ge_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp0 >= tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_div_exp_neg_pow_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0 * tmp0
    tmp2 = -tmp1
    tmp3 = 0.25
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.exp(tmp4)
    tl.store(out_ptr0 + x0, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (16, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        buf4 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_clamp_ge_0[grid(1024)](buf0, buf1, buf4, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 16), (16, 1), 0),
            reinterpret_tensor(primals_3, (16, 16), (1, 16), 0), out=buf2)
        buf3 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_div_exp_neg_pow_1[grid(1024)](buf2, buf3, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
    return buf3, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 16), (16, 1), 0
        ), buf2, buf3, primals_3, buf4


class quadexp(nn.Module):

    def __init__(self, sigma=2.0):
        super(quadexp, self).__init__()
        self.sigma = sigma

    def forward(self, x):
        return tr.exp(-x ** 2 / self.sigma ** 2)


class NoisyLinear(nn.Linear):

    def __init__(self, in_features, out_features, noise_level=1.0,
        noise_decay=0.1, bias=False):
        super(NoisyLinear, self).__init__(in_features, out_features, bias=bias)
        self.noise_level = noise_level
        self.register_buffer('epsilon_weight', tr.zeros(out_features,
            in_features))
        if bias:
            self.register_buffer('epsilon_bias', tr.zeros(out_features))
        self.noisy_mode = False
        self.noise_decay = noise_decay

    def update_noise_level(self):
        self.noise_level = self.noise_decay * self.noise_level

    def set_noisy_mode(self, is_noisy):
        self.noisy_mode = is_noisy

    def forward(self, input):
        if self.noisy_mode:
            tr.randn(self.epsilon_weight.size(), out=self.epsilon_weight)
            bias = self.bias
            if bias is not None:
                tr.randn(self.epsilon_bias.size(), out=self.epsilon_bias)
                bias = bias + self.noise_level * Variable(self.epsilon_bias,
                    requires_grad=False)
            self.noisy_mode = False
            return F.linear(input, self.weight + self.noise_level *
                Variable(self.epsilon_weight, requires_grad=False), bias)
        else:
            return F.linear(input, self.weight, self.bias)

    def add_noise(self):
        tr.randn(self.epsilon_weight.size(), out=self.epsilon_weight)
        self.weight.data += self.noise_level * Variable(self.epsilon_weight,
            requires_grad=False)
        bias = self.bias
        if bias is not None:
            tr.randn(self.epsilon_bias.size(), out=self.epsilon_bias)
            self.bias.data += self.noise_level * Variable(self.epsilon_bias,
                requires_grad=False)


class NoisyOneHiddenLayerNew(nn.Module):

    def __init__(self, d_int, H, d_out, n_particles, non_linearity=quadexp(
        ), noise_level=1.0, noise_decay=0.1, bias=False):
        super(NoisyOneHiddenLayerNew, self).__init__()
        self.linear1 = NoisyLinear(d_int, H * n_particles, noise_level=
            noise_level, noise_decay=noise_decay, bias=bias)
        self.linear2 = NoisyLinear(H * n_particles, n_particles * d_out,
            noise_level=noise_level, noise_decay=noise_decay, bias=bias)
        self.non_linearity = non_linearity
        self.n_particles = n_particles
        self.d_out = d_out

    def set_noisy_mode(self, is_noisy):
        self.linear1.set_noisy_mode(is_noisy)
        self.linear2.set_noisy_mode(is_noisy)

    def update_noise_level(self):
        self.linear1.update_noise_level()
        self.linear2.update_noise_level()

    def weights_init(self, center, std):
        self.linear1.weights_init(center, std)
        self.linear2.weights_init(center, std)

    def add_noise(self):
        self.linear1.add_noise()
        self.linear2.add_noise()

    def forward(self, input_0):
        primals_1 = self.linear1.weight
        primals_3 = self.linear2.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.013829