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 import nn
from math import sqrt
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_add_div_0(in_out_ptr0, in_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
    x4 = xindex
    tmp0 = 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 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp10 & xmask, other=0.0
        )
    tmp12 = 0.1767766952966369
    tmp13 = tmp11 * tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp10, tmp13, tmp14)
    tmp16 = -1 + x1
    tmp17 = tmp16 >= tmp1
    tmp18 = tmp16 < tmp3
    tmp19 = tmp17 & tmp18
    tmp20 = tmp19 & tmp6
    tmp21 = tmp20 & tmp7
    tmp22 = tl.load(in_ptr0 + (-4 + x0 + 4 * x1 + 16 * x2), tmp21 & xmask,
        other=0.0)
    tmp23 = tmp22 * tmp12
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp21, tmp23, tmp24)
    tmp26 = tmp15 + tmp25
    tmp27 = -1 + x0
    tmp28 = tmp27 >= tmp1
    tmp29 = tmp27 < tmp3
    tmp30 = tmp8 & tmp28
    tmp31 = tmp30 & tmp29
    tmp32 = tl.load(in_ptr0 + (-1 + x0 + 4 * x1 + 16 * x2), tmp31 & xmask,
        other=0.0)
    tmp33 = tmp32 * tmp12
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp31, tmp33, tmp34)
    tmp36 = tmp26 + tmp35
    tmp37 = tmp19 & tmp28
    tmp38 = tmp37 & tmp29
    tmp39 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp38 & xmask,
        other=0.0)
    tmp40 = tmp39 * tmp12
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp36 + tmp42
    tmp44 = 0.25
    tmp45 = tmp43 * tmp44
    tl.store(in_out_ptr0 + x4, tmp45, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 121 % 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)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_div_0[grid(400)](buf1, primals_1, 400, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf2 = extern_kernels.convolution(primals_3, buf1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 11, 11), (484, 121, 11, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(1936)](buf3, primals_2, 1936,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf3, primals_3, buf1


class FusedUpsampleNew(nn.Module):

    def __init__(self, in_channel, out_channel, kernel_size, padding=0):
        super().__init__()
        weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
        bias = torch.zeros(out_channel)
        fan_in = in_channel * kernel_size * kernel_size
        self.multiplier = sqrt(2 / fan_in)
        self.weight = nn.Parameter(weight)
        self.bias = nn.Parameter(bias)
        self.pad = padding

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


Measured latency_ms: 0.033688