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
import torch.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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = yindex // 512
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 512 * x2 + 2097152 * y1), tmp0, None)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 480
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 120
    y1 = yindex // 120
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 120 * x2 + 491520 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (120, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_2, (120,), (1,))
    assert_size_stride(primals_3, (4, 512, 64, 64), (2097152, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 512, 64, 64), (2097152, 1, 32768, 512
            ), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(2048, 4096)](primals_3, buf0, 2048, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 120, 64, 64), (491520, 1, 7680, 120))
        buf2 = empty_strided_cuda((4, 120, 64, 64), (491520, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_convolution_1[grid(480, 4096)](buf1, primals_2,
            buf2, 480, 4096, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del buf1
        del primals_2
    return buf2, primals_1, buf0


class LandmarkHeadNew(nn.Module):

    def __init__(self, inchannels=512, num_anchors=3):
        super(LandmarkHeadNew, self).__init__()
        self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 40, kernel_size=
            (1, 1), stride=1, padding=0)

    def forward(self, input_0):
        primals_1 = self.conv1x1.weight
        primals_2 = self.conv1x1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.151545