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 import triton_helpers
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__to_copy__unsafe_index_add_arange_clamp_mul_sub_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
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex // 64
    x7 = xindex % 64
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp5 - tmp2
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.int32)
    tmp10 = tl.full([1], 1, tl.int64)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tmp14 = x0
    tmp15 = tmp14.to(tl.float32)
    tmp16 = tmp15 + tmp2
    tmp17 = tmp16 * tmp4
    tmp18 = tmp17 - tmp2
    tmp19 = triton_helpers.maximum(tmp18, tmp7)
    tmp20 = tmp19.to(tl.int32)
    tmp21 = tmp20 + tmp10
    tmp22 = triton_helpers.minimum(tmp21, tmp12)
    tmp23 = tl.load(in_ptr0 + (tmp22 + 4 * tmp13 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (tmp20 + 4 * tmp13 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp25 = tmp23 - tmp24
    tmp26 = tmp20.to(tl.float32)
    tmp27 = tmp19 - tmp26
    tmp28 = triton_helpers.maximum(tmp27, tmp7)
    tmp29 = triton_helpers.minimum(tmp28, tmp4)
    tmp30 = tmp25 * tmp29
    tmp31 = tmp24 + tmp30
    tmp32 = tl.load(in_ptr0 + (tmp20 + 4 * tmp9 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (tmp22 + 4 * tmp9 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp34 = tmp33 - tmp32
    tmp35 = tmp34 * tmp29
    tmp36 = tmp32 + tmp35
    tmp37 = tmp31 - tmp36
    tmp38 = tmp9.to(tl.float32)
    tmp39 = tmp8 - tmp38
    tmp40 = triton_helpers.maximum(tmp39, tmp7)
    tmp41 = triton_helpers.minimum(tmp40, tmp4)
    tmp42 = tmp37 * tmp41
    tmp43 = tmp36 + tmp42
    tl.store(out_ptr0 + (x7 + 128 * x4), tmp43, xmask)


@triton.jit
def triton_poi_fused_cat_1(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
    x2 = xindex
    x0 = xindex % 64
    x1 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tl.store(out_ptr0 + (x0 + 128 * x1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_2(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_3(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_4(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 = 0.01
    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_convolution_leaky_relu_5(in_ptr0, in_ptr1, 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_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, 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, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 8, 1, 1), (8, 1, 1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 12, 1, 1), (12, 1, 1, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_10, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf4 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf4, (4, 4, 4, 4), (128, 16, 4, 1), 0)
        get_raw_stream(0)
        triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0[grid
            (256)](primals_1, buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf3 = reinterpret_tensor(buf4, (4, 4, 4, 4), (128, 16, 4, 1), 64)
        triton_poi_fused_cat_1[grid(256)](primals_2, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_2
        buf5 = extern_kernels.convolution(buf4, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_2[grid(256)](buf6, primals_4, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf7 = extern_kernels.convolution(buf6, primals_5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_3[grid(256)](buf7,
            primals_6, buf8, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_4[grid(768)](buf4, buf8, buf7, primals_6, buf9,
            768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
        buf10 = extern_kernels.convolution(buf9, primals_7, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 4, 4, 4), (64, 16, 4, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_2[grid(256)](buf11, primals_8, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_8
        buf12 = extern_kernels.convolution(buf11, primals_9, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf14 = buf7
        del buf7
        triton_poi_fused_convolution_leaky_relu_5[grid(256)](buf12,
            primals_10, buf13, buf14, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf12
        del primals_10
    return (buf14, primals_3, primals_5, primals_7, primals_9, buf4, buf6,
        buf8, buf9, buf11, buf13)


class DenseNet2D_up_block_concatNew(nn.Module):

    def __init__(self, skip_channels, input_channels, output_channels,
        up_stride, dropout=False, prob=0):
        super(DenseNet2D_up_block_concatNew, self).__init__()
        self.conv11 = nn.Conv2d(skip_channels + input_channels,
            output_channels, kernel_size=(1, 1), padding=(0, 0))
        self.conv12 = nn.Conv2d(output_channels, output_channels,
            kernel_size=(3, 3), padding=(1, 1))
        self.conv21 = nn.Conv2d(skip_channels + input_channels +
            output_channels, output_channels, kernel_size=(1, 1), padding=(
            0, 0))
        self.conv22 = nn.Conv2d(output_channels, output_channels,
            kernel_size=(3, 3), padding=(1, 1))
        self.relu = nn.LeakyReLU()
        self.up_stride = up_stride
        self.dropout = dropout
        self.dropout1 = nn.Dropout(p=prob)
        self.dropout2 = nn.Dropout(p=prob)

    def forward(self, input_0, input_1):
        primals_3 = self.conv11.weight
        primals_4 = self.conv11.bias
        primals_5 = self.conv12.weight
        primals_6 = self.conv12.bias
        primals_7 = self.conv21.weight
        primals_8 = self.conv21.bias
        primals_9 = self.conv22.weight
        primals_10 = self.conv22.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.132635