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


@triton.jit
def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0(
    in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x2 = xindex // 256
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 7, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tmp13 = x0
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp14 + tmp2
    tmp16 = tmp15 * tmp2
    tmp17 = tmp16 - tmp2
    tmp18 = triton_helpers.maximum(tmp17, tmp6)
    tmp19 = tmp18.to(tl.int32)
    tmp20 = tmp19 + tmp9
    tmp21 = triton_helpers.minimum(tmp20, tmp11)
    tmp22 = tl.load(in_ptr0 + (tmp21 + 8 * tmp12 + 64 * x2), None,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (tmp19 + 8 * tmp12 + 64 * x2), None,
        eviction_policy='evict_last')
    tmp24 = tmp22 - tmp23
    tmp25 = tmp19.to(tl.float32)
    tmp26 = tmp18 - tmp25
    tmp27 = triton_helpers.maximum(tmp26, tmp6)
    tmp28 = 1.0
    tmp29 = triton_helpers.minimum(tmp27, tmp28)
    tmp30 = tmp24 * tmp29
    tmp31 = tmp23 + tmp30
    tmp32 = tl.load(in_ptr0 + (tmp19 + 8 * tmp8 + 64 * x2), None,
        eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (tmp21 + 8 * tmp8 + 64 * x2), None,
        eviction_policy='evict_last')
    tmp34 = tmp33 - tmp32
    tmp35 = tmp34 * tmp29
    tmp36 = tmp32 + tmp35
    tmp37 = tmp31 - tmp36
    tmp38 = tmp8.to(tl.float32)
    tmp39 = tmp7 - tmp38
    tmp40 = triton_helpers.maximum(tmp39, tmp6)
    tmp41 = triton_helpers.minimum(tmp40, tmp28)
    tmp42 = tmp37 * tmp41
    tmp43 = tmp36 + tmp42
    tl.store(in_out_ptr0 + x4, tmp43, None)


@triton.jit
def triton_poi_fused_add_convolution_1(in_out_ptr0, in_ptr0, in_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 // 256 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 16, 16), (1024, 256, 16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1), torch
            .float32)
        buf1 = buf0
        del buf0
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0[grid
            (4096)](buf2, primals_1, 4096, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_1
        buf3 = extern_kernels.convolution(buf2, primals_2, 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, 16, 16), (1024, 256, 16, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_add_convolution_1[grid(4096)](buf4, primals_3,
            primals_4, 4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        del primals_4
    return buf4, primals_2, buf2


class vggUpconvNew(nn.Module):
    """Some Information about vggUpconv"""

    def __init__(self, in_ch, out_ch, upsample=True):
        super(vggUpconvNew, self).__init__()
        if upsample:
            self.upsample = nn.Upsample(scale_factor=2, mode='bilinear')
        else:
            self.upsample = nn.Conv2d(in_ch, in_ch, 3, 1, 1)
        self.conv1 = nn.Conv2d(in_ch, out_ch, 3, 1, 1)

    def forward(self, input_0, input_1):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.015251