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
from torch._inductor.runtime.triton_helpers import math as tl_math
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_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__log_softmax_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x4 = xindex % 6
    x5 = xindex // 2
    x1 = xindex // 2 % 3
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x4, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + 2 * x5, None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + 2 * x1, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 2 * x5), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 2 * x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.maximum(tmp5, tmp8)
    tmp10 = tmp2 - tmp9
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused__log_softmax__log_softmax_backward_data_2(in_ptr0,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x1 = xindex // 2
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + 2 * x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 2 * x1), None, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp6 = tl_math.log(tmp5)
    tmp7 = tmp0 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tl.store(out_ptr0 + x2, tmp7, None)
    tl.store(out_ptr1 + x2, tmp8, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (6, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_2, (6,), (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, 6, 64, 64), (24576, 1, 384, 6))
        buf2 = empty_strided_cuda((4, 64, 64, 3, 2), (24576, 384, 6, 2, 1),
            torch.float32)
        triton_poi_fused__log_softmax_1[grid(98304)](buf1, primals_2, buf2,
            98304, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf3 = reinterpret_tensor(buf1, (4, 64, 64, 3, 2), (24576, 384, 6, 
            2, 1), 0)
        del buf1
        buf4 = empty_strided_cuda((4, 64, 64, 3, 2), (24576, 384, 6, 2, 1),
            torch.float32)
        triton_poi_fused__log_softmax__log_softmax_backward_data_2[grid(98304)
            ](buf2, buf3, buf4, 98304, XBLOCK=512, num_warps=8, num_stages=1)
        del buf2
    return reinterpret_tensor(buf3, (4, 12288, 2), (24576, 2, 1), 0
        ), primals_1, buf0, buf4


class ClassHeadNew(nn.Module):

    def __init__(self, inchannels=512, num_anchors=3):
        super(ClassHeadNew, self).__init__()
        self.num_anchors = num_anchors
        self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
            kernel_size=(1, 1), stride=1, padding=0)
        self.output_act = nn.LogSoftmax(dim=-1)

    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: 0.154073