import torch
from torch import device
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.triton_helpers import math as tl_math
import torch.nn as nn
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_reflection_pad2d_0(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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + x0) + -4 * tl_math
        .abs(-3 + x1) + 16 * x2), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_convolution_exp_mul_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = 0.5
    tmp7 = tmp2 * tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 * tmp9
    tmp11 = tmp5 + tmp10
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp11, xmask)
    tl.store(out_ptr1 + x3, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 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, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(256)](primals_3, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.
            float32, device=device(type='cuda', index=0), pin_memory=False)
        buf5 = buf4
        del buf4
        buf2 = buf1
        del buf1
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_convolution_exp_mul_1[grid(256)](buf2,
            primals_2, buf3, primals_5, buf5, buf6, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf3
        del primals_2
        del primals_5
    return buf6, reinterpret_tensor(buf7, (4, 64), (64, 1), 0
        ), reinterpret_tensor(buf2, (4, 64), (64, 1), 0
        ), primals_1, primals_4, buf0, buf2, buf5


class VariationalNew(nn.Module):

    def __init__(self, channels, filter=1, stride=1, padding=0, activation=
        nn.LeakyReLU):
        super(VariationalNew, self).__init__()
        self.mu_logit = nn.Conv2d(channels, channels, filter, stride,
            padding, padding_mode='reflect')
        self.sigma_logit = nn.Conv2d(channels, channels, filter, stride,
            padding, padding_mode='reflect')

    def forward(self, input_0):
        primals_1 = self.mu_logit.weight
        primals_2 = self.mu_logit.bias
        primals_4 = self.sigma_logit.weight
        primals_5 = self.sigma_logit.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.025545