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.init as init
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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 36
    x0 = xindex % 16
    x2 = xindex // 576
    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], 36, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 512 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-4 + x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = 0.0
    tmp13 = tmp11 > tmp12
    tmp14 = 0.2
    tmp15 = tmp11 * tmp14
    tmp16 = tl.where(tmp13, tmp11, tmp15)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp6, tmp16, tmp17)
    tmp19 = tl.where(tmp4, tmp5, tmp18)
    tl.store(out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4352
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 68
    x0 = xindex % 16
    x2 = xindex // 1088
    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
    tmp7 = tl.full([1], 36, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 512 * x2), tmp9 &
        xmask, other=0.0)
    tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tl.full([1], 68, tl.int64)
    tmp23 = tl.load(in_ptr3 + (x0 + 16 * (-36 + x1) + 512 * x2), tmp20 &
        xmask, other=0.0)
    tmp24 = tl.load(in_ptr4 + (-36 + x1), tmp20 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tmp25 > tmp13
    tmp27 = tmp25 * tmp15
    tmp28 = tl.where(tmp26, tmp25, tmp27)
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp20, tmp28, tmp29)
    tmp31 = tl.where(tmp9, tmp19, tmp30)
    tmp32 = tl.where(tmp4, tmp5, tmp31)
    tl.store(out_ptr0 + x3, tmp32, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 6400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 100
    x0 = xindex % 16
    x2 = xindex // 1600
    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
    tmp7 = tl.full([1], 36, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 512 * x2), tmp9 &
        xmask, other=0.0)
    tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tmp21 = tl.full([1], 68, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr3 + (x0 + 16 * (-36 + x1) + 512 * x2), tmp23 &
        xmask, other=0.0)
    tmp25 = tl.load(in_ptr4 + (-36 + x1), tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 > tmp13
    tmp28 = tmp26 * tmp15
    tmp29 = tl.where(tmp27, tmp26, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp23, tmp29, tmp30)
    tmp32 = tmp0 >= tmp21
    tl.full([1], 100, tl.int64)
    tmp35 = tl.load(in_ptr5 + (x0 + 16 * (-68 + x1) + 512 * x2), tmp32 &
        xmask, other=0.0)
    tmp36 = tl.load(in_ptr6 + (-68 + x1), tmp32 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp37 = tmp35 + tmp36
    tmp38 = tmp37 > tmp13
    tmp39 = tmp37 * tmp15
    tmp40 = tl.where(tmp38, tmp37, tmp39)
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp32, tmp40, tmp41)
    tmp43 = tl.where(tmp23, tmp31, tmp42)
    tmp44 = tl.where(tmp9, tmp19, tmp43)
    tmp45 = tl.where(tmp4, tmp5, tmp44)
    tl.store(out_ptr0 + x3, tmp45, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 8448
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 132
    x0 = xindex % 16
    x2 = xindex // 2112
    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
    tmp7 = tl.full([1], 36, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 512 * x2), tmp9 &
        xmask, other=0.0)
    tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tmp21 = tl.full([1], 68, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr3 + (x0 + 16 * (-36 + x1) + 512 * x2), tmp23 &
        xmask, other=0.0)
    tmp25 = tl.load(in_ptr4 + (-36 + x1), tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 > tmp13
    tmp28 = tmp26 * tmp15
    tmp29 = tl.where(tmp27, tmp26, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp23, tmp29, tmp30)
    tmp32 = tmp0 >= tmp21
    tmp33 = tl.full([1], 100, tl.int64)
    tmp34 = tmp0 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tl.load(in_ptr5 + (x0 + 16 * (-68 + x1) + 512 * x2), tmp35 &
        xmask, other=0.0)
    tmp37 = tl.load(in_ptr6 + (-68 + x1), tmp35 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp38 = tmp36 + tmp37
    tmp39 = tmp38 > tmp13
    tmp40 = tmp38 * tmp15
    tmp41 = tl.where(tmp39, tmp38, tmp40)
    tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype)
    tmp43 = tl.where(tmp35, tmp41, tmp42)
    tmp44 = tmp0 >= tmp33
    tl.full([1], 132, tl.int64)
    tmp47 = tl.load(in_ptr7 + (x0 + 16 * (-100 + x1) + 512 * x2), tmp44 &
        xmask, other=0.0)
    tmp48 = tl.load(in_ptr8 + (-100 + x1), tmp44 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp49 = tmp47 + tmp48
    tmp50 = tmp49 > tmp13
    tmp51 = tmp49 * tmp15
    tmp52 = tl.where(tmp50, tmp49, tmp51)
    tmp53 = tl.full(tmp52.shape, 0.0, tmp52.dtype)
    tmp54 = tl.where(tmp44, tmp52, tmp53)
    tmp55 = tl.where(tmp35, tmp43, tmp54)
    tmp56 = tl.where(tmp23, tmp31, tmp55)
    tmp57 = tl.where(tmp9, tmp19, tmp56)
    tmp58 = tl.where(tmp4, tmp5, tmp57)
    tl.store(out_ptr0 + x3, tmp58, xmask)


@triton.jit
def triton_poi_fused_convolution_4(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
    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')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5(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 % 32
    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 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(out_ptr0 + x3, tmp8, None)


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) = args
    args.clear()
    assert_size_stride(primals_1, (32, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (32, 36, 3, 3), (324, 9, 3, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (32, 68, 3, 3), (612, 9, 3, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (32, 100, 3, 3), (900, 9, 3, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (4, 132, 3, 3), (1188, 9, 3, 1))
    assert_size_stride(primals_11, (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, 32, 4, 4), (512, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 36, 4, 4), (576, 16, 4, 1), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(2304)](primals_3, buf0, primals_2, buf1,
            2304, XBLOCK=256, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 4, 4), (512, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 68, 4, 4), (1088, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_1[grid(4352)](primals_3, buf0, primals_2, buf2,
            primals_5, buf3, 4352, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 32, 4, 4), (512, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 100, 4, 4), (1600, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_2[grid(6400)](primals_3, buf0, primals_2, buf2,
            primals_5, buf4, primals_7, buf5, 6400, XBLOCK=256, num_warps=4,
            num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 32, 4, 4), (512, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 132, 4, 4), (2112, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_3[grid(8448)](primals_3, buf0, primals_2, buf2,
            primals_5, buf4, primals_7, buf6, primals_9, buf7, 8448, XBLOCK
            =128, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_4[grid(256)](buf9, primals_11, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(
            2048)](buf6, primals_9, buf10, 2048, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf6
        del primals_9
        buf11 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(
            2048)](buf4, primals_7, buf11, 2048, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf4
        del primals_7
        buf12 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(
            2048)](buf2, primals_5, buf12, 2048, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf2
        del primals_5
        buf13 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(
            2048)](buf0, primals_2, buf13, 2048, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf0
        del primals_2
    return (buf9, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, buf1, buf3, buf5, buf7, buf10, buf11, buf12, buf13)


def initialize_weights(net_l, scale=1):
    if not isinstance(net_l, list):
        net_l = [net_l]
    for net in net_l:
        for m in net.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias.data, 0.0)


def initialize_weights_xavier(net_l, scale=1):
    if not isinstance(net_l, list):
        net_l = [net_l]
    for net in net_l:
        for m in net.modules():
            if isinstance(m, nn.Conv2d):
                init.xavier_normal_(m.weight)
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                init.xavier_normal_(m.weight)
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias.data, 0.0)


class DenseBlockNew(nn.Module):

    def __init__(self, channel_in, channel_out, init='xavier', gc=32, bias=True
        ):
        super(DenseBlockNew, self).__init__()
        self.conv1 = nn.Conv2d(channel_in, gc, 3, 1, 1, bias=bias)
        self.conv2 = nn.Conv2d(channel_in + gc, gc, 3, 1, 1, bias=bias)
        self.conv3 = nn.Conv2d(channel_in + 2 * gc, gc, 3, 1, 1, bias=bias)
        self.conv4 = nn.Conv2d(channel_in + 3 * gc, gc, 3, 1, 1, bias=bias)
        self.conv5 = nn.Conv2d(channel_in + 4 * gc, channel_out, 3, 1, 1,
            bias=bias)
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
        if init == 'xavier':
            initialize_weights_xavier([self.conv1, self.conv2, self.conv3,
                self.conv4], 0.1)
        else:
            initialize_weights([self.conv1, self.conv2, self.conv3, self.
                conv4], 0.1)
        initialize_weights(self.conv5, 0)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.conv5.weight
        primals_11 = self.conv5.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])
        return output[0]


Measured latency_ms: 0.014012