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
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 6 % 5
    x0 = xindex % 6
    x2 = xindex // 30
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = -1 + x0
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 >= tmp4
    tmp6 = tmp3 < tmp1
    tmp7 = tmp2 & tmp5
    tmp8 = tmp7 & tmp6
    tmp9 = tl.load(in_ptr0 + (-1 + x0 + 4 * x1 + 16 * x2), tmp8 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 5 % 6
    x0 = xindex % 5
    x2 = xindex // 30
    x3 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x0
    tmp6 = tmp5 < tmp3
    tmp7 = tmp2 & tmp4
    tmp8 = tmp7 & tmp6
    tmp9 = tl.load(in_ptr0 + (-4 + x0 + 4 * x1 + 16 * x2), tmp8 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_2(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 5 % 5
    x0 = xindex % 5
    x2 = xindex // 25
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_stack_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2 % 4
    x2 = xindex // 8 % 8
    x5 = xindex // 64
    x3 = xindex // 64 % 4
    x6 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x1 + 4 * (x2 % 2)
    tmp7 = tl.full([1], 4, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = tmp8 & tmp4
    tmp10 = tl.load(in_ptr0 + (4 * (x2 // 2) + 16 * x5 + (x1 + 4 * (x2 % 2)
        )), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tl.load(in_ptr1 + x3, tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp5 >= tmp7
    tl.full([1], 8, tl.int64)
    tmp18 = tmp15 & tmp4
    tmp19 = tl.load(in_ptr2 + (4 * (x2 // 2) + 16 * x5 + (-4 + x1 + 4 * (x2 %
        2))), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tl.load(in_ptr3 + x3, tmp18 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp18, tmp21, tmp22)
    tmp24 = tl.where(tmp8, tmp14, tmp23)
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp4, tmp24, tmp25)
    tmp27 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp30 = tmp8 & tmp27
    tmp31 = tl.load(in_ptr4 + (4 * (x2 // 2) + 16 * x5 + (x1 + 4 * (x2 % 2)
        )), tmp30 & xmask, eviction_policy='evict_last', other=0.0)
    tmp32 = tl.load(in_ptr5 + x3, tmp30 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp33 = tmp31 + tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp30, tmp33, tmp34)
    tmp36 = tmp15 & tmp27
    tmp37 = tl.load(in_ptr6 + (4 * (x2 // 2) + 16 * x5 + (-4 + x1 + 4 * (x2 %
        2))), tmp36 & xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = tl.load(in_ptr7 + x3, tmp36 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp39 = tmp37 + tmp38
    tmp40 = tl.full(tmp39.shape, 0.0, tmp39.dtype)
    tmp41 = tl.where(tmp36, tmp39, tmp40)
    tmp42 = tl.where(tmp8, tmp35, tmp41)
    tmp43 = tl.full(tmp42.shape, 0.0, tmp42.dtype)
    tmp44 = tl.where(tmp27, tmp42, tmp43)
    tmp45 = tl.where(tmp4, tmp26, tmp44)
    tl.store(out_ptr0 + x6, tmp45, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = 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, 4, 2, 3), (24, 6, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 3, 2), (24, 6, 2, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 2, 2), (16, 4, 2, 1))
    assert_size_stride(primals_9, (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, 5, 6), (120, 30, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(480)](primals_3, buf1, 480,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 4, 6, 5), (120, 30, 5, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_1[grid(480)](primals_3, buf3, 480,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_2[grid(400)](primals_3, buf5, 400,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 4, 8, 4, 2), (256, 64, 8, 2, 1),
            torch.float32)
        triton_poi_fused_stack_3[grid(1024)](buf0, primals_2, buf2,
            primals_5, buf4, primals_7, buf6, primals_9, buf7, 1024, XBLOCK
            =128, num_warps=4, num_stages=1)
        del buf0
        del buf2
        del buf4
        del buf6
        del primals_2
        del primals_5
        del primals_7
        del primals_9
    return reinterpret_tensor(buf7, (4, 4, 8, 8), (256, 64, 8, 1), 0
        ), primals_1, primals_3, primals_4, primals_6, primals_8, buf1, buf3, buf5


def get_incoming_shape(incoming):
    size = incoming.size()
    return [size[0], size[1], size[2], size[3]]


def interleave(tensors, axis):
    old_shape = get_incoming_shape(tensors[0])[1:]
    new_shape = [-1] + old_shape
    new_shape[axis] *= len(tensors)
    stacked = torch.stack(tensors, axis + 1)
    reshaped = stacked.view(new_shape)
    return reshaped


class UnpoolingAsConvolutionNew(nn.Module):

    def __init__(self, inplanes, planes):
        super(UnpoolingAsConvolutionNew, self).__init__()
        self.conv_A = nn.Conv2d(in_channels=inplanes, out_channels=planes,
            kernel_size=(3, 3), stride=1, padding=1)
        self.conv_B = nn.Conv2d(in_channels=inplanes, out_channels=planes,
            kernel_size=(2, 3), stride=1, padding=0)
        self.conv_C = nn.Conv2d(in_channels=inplanes, out_channels=planes,
            kernel_size=(3, 2), stride=1, padding=0)
        self.conv_D = nn.Conv2d(in_channels=inplanes, out_channels=planes,
            kernel_size=(2, 2), stride=1, padding=0)

    def forward(self, input_0):
        primals_1 = self.conv_A.weight
        primals_2 = self.conv_A.bias
        primals_4 = self.conv_B.weight
        primals_5 = self.conv_B.bias
        primals_6 = self.conv_C.weight
        primals_7 = self.conv_C.bias
        primals_8 = self.conv_D.weight
        primals_9 = self.conv_D.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])
        return output[0]


Measured latency_ms: 0.035309