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


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16 % 6
    x3 = xindex // 96
    x4 = xindex % 16
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x5 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + 16 * x2 + 64 * x3), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = x1
    tmp11 = tmp10.to(tl.float32)
    tmp12 = 0.3333333333333333
    tmp13 = tmp11 * tmp12
    tmp14 = 2.0
    tmp15 = tmp13 * tmp14
    tmp16 = 1.0
    tmp17 = tmp15 - tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tl.full([1], 6, tl.int64)
    tmp23 = x0
    tmp24 = tmp23.to(tl.float32)
    tmp25 = tmp24 * tmp12
    tmp26 = tmp25 * tmp14
    tmp27 = tmp26 - tmp16
    tmp28 = tl.full(tmp27.shape, 0.0, tmp27.dtype)
    tmp29 = tl.where(tmp20, tmp27, tmp28)
    tmp30 = tl.where(tmp9, tmp19, tmp29)
    tmp31 = tl.where(tmp4, tmp5, tmp30)
    tl.store(out_ptr0 + x5, tmp31, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, 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, 6, 4, 4), (96, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 6, 4, 4), (96, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(384)](primals_1, buf0, 384, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1, 1), (4, 1, 1, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(16)](buf2, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
    return buf2, primals_2, buf0


class AddCoords(nn.Module):

    def __init__(self, with_r=False):
        super().__init__()
        self.with_r = with_r

    def forward(self, input_tensor):
        """
        Args:
            input_tensor: shape(batch, channel, x_dim, y_dim)
        """
        batch_size, _, x_dim, y_dim = input_tensor.size()
        xx_channel = torch.arange(x_dim).repeat(1, y_dim, 1)
        yy_channel = torch.arange(y_dim).repeat(1, x_dim, 1).transpose(1, 2)
        xx_channel = xx_channel.float() / (x_dim - 1)
        yy_channel = yy_channel.float() / (y_dim - 1)
        xx_channel = xx_channel * 2 - 1
        yy_channel = yy_channel * 2 - 1
        xx_channel = xx_channel.repeat(batch_size, 1, 1, 1).transpose(2, 3)
        yy_channel = yy_channel.repeat(batch_size, 1, 1, 1).transpose(2, 3)
        ret = torch.cat([input_tensor, xx_channel.type_as(input_tensor),
            yy_channel.type_as(input_tensor)], dim=1)
        if self.with_r:
            rr = torch.sqrt(torch.pow(xx_channel.type_as(input_tensor) - 
                0.5, 2) + torch.pow(yy_channel.type_as(input_tensor) - 0.5, 2))
            ret = torch.cat([ret, rr], dim=1)
        return ret


class CoordConvNew(nn.Module):

    def __init__(self, in_channels, out_channels, with_r=False, **kwargs):
        super().__init__()
        self.addcoords = AddCoords(with_r=with_r)
        in_size = in_channels + 2
        if with_r:
            in_size += 1
        self.conv = nn.Conv2d(in_size, out_channels, **kwargs)

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


Measured latency_ms: 0.033674