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
import torch.nn as nn
import torch.optim
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_bernoulli_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = float('nan')
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_div_mul_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = 1.25
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 * tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1, 
            1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 16, 4, 4, 4), (1024, 64, 16, 4, 1))
        buf2 = empty_strided_cuda((1, 16, 1, 1, 1), (16, 1, 1, 1, 1), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_bernoulli_0[grid(16)](buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        torch.ops.aten.bernoulli_.float(buf2, 0.8)
        buf4 = buf0
        del buf0
        triton_poi_fused_div_mul_1[grid(1024)](buf4, primals_2, buf2, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return reinterpret_tensor(buf4, (16, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, reinterpret_tensor(primals_3, (1, 4, 4, 4, 4), (256, 
        64, 16, 4, 1), 0), buf2


class InitConvNew(nn.Module):

    def __init__(self, in_channels=4, out_channels=16, dropout=0.2):
        super(InitConvNew, self).__init__()
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=3,
            padding=1)
        self.dropout = dropout

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


Measured latency_ms: 0.014923