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.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


@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 64
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tl.store(out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_reflection_pad2d_2(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x4 = xindex // 36
    x2 = xindex // 36 % 64
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x4),
        xmask, eviction_policy='evict_last').to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x4),
        xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = 0.01
    tmp5 = tmp3 * tmp4
    tmp6 = tl.where(tmp0, tmp3, tmp5)
    tl.store(out_ptr0 + x5, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 64
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 16, 4, 4), (256, 16, 4, 1))
    assert_size_stride(primals_2, (64, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 6, 6), (576, 36, 6, 1), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(2304)](primals_1, buf0, 
            2304, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_1[grid(4096)](buf1,
            primals_3, buf2, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch.
            float32)
        triton_poi_fused_convolution_leaky_relu_reflection_pad2d_2[grid(9216)](
            buf2, buf1, primals_3, buf3, 9216, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_3
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
        buf6 = buf1
        del buf1
        triton_poi_fused_convolution_leaky_relu_3[grid(4096)](buf4,
            primals_5, buf5, buf6, 4096, XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        del primals_5
    return buf6, primals_2, primals_4, buf0, buf2, buf3, buf5


class LocalNetNew(nn.Module):

    def __init__(self, in_channels=16, out_channels=64):
        """Double convolutional block

        :param in_channels:  number of input channels
        :param out_channels: number of output channels
        :returns: N/A
        :rtype: N/A

        """
        super(LocalNetNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, 3, 1, 0, 1)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, 1, 0, 1)
        self.lrelu = nn.LeakyReLU()
        self.refpad = nn.ReflectionPad2d(1)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.013854