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
import torch.nn as nn
from torch.nn import Linear
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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4, 4), (4, 1))
    assert_size_stride(primals_15, (4,), (1,))
    assert_size_stride(primals_16, (4, 4), (4, 1))
    assert_size_stride(primals_17, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(256)](buf1, primals_2, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_relu_0[grid(256)](buf3, primals_5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        triton_poi_fused_relu_0[grid(256)](buf5, primals_7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf6)
        del primals_9
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(primals_10, (4, 4), (1, 
            4), 0), out=buf7)
        buf8 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf7
        buf16 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(256)](buf8,
            primals_11, buf16, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf9 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_12, (4, 4), (1, 4), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf9
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(256)](buf10,
            primals_13, buf15, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_13
        buf11 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf10, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_14, (4, 4), (1, 4), 0), out=buf11)
        buf12 = reinterpret_tensor(buf11, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf11
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(256)](buf12,
            primals_15, buf14, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_15
        buf13 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_17, reinterpret_tensor(buf12, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_16, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf13)
        del primals_17
    return (reinterpret_tensor(buf13, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        buf1, buf3, buf5, reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4,
        1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1,
        buf3, buf5, buf6, reinterpret_tensor(buf8, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf10, (64, 4), (4, 1), 0), reinterpret_tensor(
        buf12, (64, 4), (4, 1), 0), primals_16, buf14, primals_14, buf15,
        primals_12, buf16, primals_10, primals_8, primals_6, primals_4)


class AENew(nn.Module):

    def __init__(self, n_enc_1, n_enc_2, n_enc_3, n_dec_1, n_dec_2, n_dec_3,
        n_input, n_z):
        super(AENew, self).__init__()
        self.enc_1 = Linear(n_input, n_enc_1)
        self.enc_2 = Linear(n_enc_1, n_enc_2)
        self.enc_3 = Linear(n_enc_2, n_enc_3)
        self.z_layer = Linear(n_enc_3, n_z)
        self.dec_1 = Linear(n_z, n_dec_1)
        self.dec_2 = Linear(n_dec_1, n_dec_2)
        self.dec_3 = Linear(n_dec_2, n_dec_3)
        self.x_bar_layer = Linear(n_dec_3, n_input)

    def forward(self, input_0):
        primals_1 = self.enc_1.weight
        primals_2 = self.enc_1.bias
        primals_4 = self.enc_2.weight
        primals_5 = self.enc_2.bias
        primals_6 = self.enc_3.weight
        primals_7 = self.enc_3.bias
        primals_8 = self.z_layer.weight
        primals_9 = self.z_layer.bias
        primals_10 = self.dec_1.weight
        primals_11 = self.dec_1.bias
        primals_12 = self.dec_2.weight
        primals_13 = self.dec_2.bias
        primals_14 = self.dec_3.weight
        primals_15 = self.dec_3.bias
        primals_16 = self.x_bar_layer.weight
        primals_17 = self.x_bar_layer.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17])
        return output[0], output[1], output[2], output[3], output[4]


Measured latency_ms: 0.027484