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.nn.functional as F
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_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
    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_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tl.store(out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0).to(tl.int1)
    tmp10 = tl.load(in_ptr2 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp11 = tl.load(in_ptr3 + (-4 + x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.2
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp6, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp5, tmp17)
    tl.store(out_ptr0 + x3, tmp18, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 12
    x0 = xindex % 16
    x2 = xindex // 192
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 8, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 128 * x2), tmp4 & xmask, other=0.0
        )
    tmp6 = tmp0 >= tmp3
    tl.full([1], 12, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0).to(tl.int1)
    tmp10 = tl.load(in_ptr2 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp11 = tl.load(in_ptr3 + (-8 + x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.2
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp6, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp5, tmp17)
    tl.store(out_ptr0 + x3, tmp18, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 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, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(256)](buf0,
            primals_2, buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_1[grid(512)](primals_3, buf1, buf0, primals_2,
            buf2, 512, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(1, 1), 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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_0[grid(256)](buf3,
            primals_5, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_2[grid(768)](buf2, buf4, buf3, primals_5, buf5,
            768, XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
        del primals_5
    return buf5, primals_1, primals_3, primals_4, buf1, buf2, buf4


class ConvLeakyRelu2d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=3, padding=1,
        stride=1, dilation=1, groups=1):
        super(ConvLeakyRelu2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=
            kernel_size, padding=padding, stride=stride, dilation=dilation,
            groups=groups)

    def forward(self, x):
        return F.leaky_relu(self.conv(x), negative_slope=0.2)


class DenseBlockNew(nn.Module):

    def __init__(self, channels):
        super(DenseBlockNew, self).__init__()
        self.conv1 = ConvLeakyRelu2d(channels, channels)
        self.conv2 = ConvLeakyRelu2d(2 * channels, channels)

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


Measured latency_ms: 0.029679