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
import torch.nn.init
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):
    ynumel = 16
    xnumel = 16
    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]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(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]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_2(
    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 // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 3, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tmp11 = x0
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp12 * tmp2
    tmp14 = triton_helpers.maximum(tmp13, tmp4)
    tmp15 = tmp14.to(tl.int32)
    tmp16 = tl.load(in_ptr0 + (tmp15 + 4 * tmp10 + 16 * x2), None,
        eviction_policy='evict_last')
    tmp17 = tmp15 + tmp7
    tmp18 = triton_helpers.minimum(tmp17, tmp9)
    tmp19 = tl.load(in_ptr0 + (tmp18 + 4 * tmp10 + 16 * x2), None,
        eviction_policy='evict_last')
    tmp20 = tmp19 - tmp16
    tmp21 = tmp15.to(tl.float32)
    tmp22 = tmp14 - tmp21
    tmp23 = triton_helpers.maximum(tmp22, tmp4)
    tmp24 = triton_helpers.minimum(tmp23, tmp2)
    tmp25 = tmp20 * tmp24
    tmp26 = tmp16 + tmp25
    tmp27 = tl.load(in_ptr0 + (tmp15 + 4 * tmp6 + 16 * x2), None,
        eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (tmp18 + 4 * tmp6 + 16 * x2), None,
        eviction_policy='evict_last')
    tmp29 = tmp28 - tmp27
    tmp30 = tmp29 * tmp24
    tmp31 = tmp27 + tmp30
    tmp32 = tmp26 - tmp31
    tmp33 = tmp6.to(tl.float32)
    tmp34 = tmp5 - tmp33
    tmp35 = triton_helpers.maximum(tmp34, tmp4)
    tmp36 = triton_helpers.minimum(tmp35, tmp2)
    tmp37 = tmp32 * tmp36
    tmp38 = tmp31 + tmp37
    tl.store(in_out_ptr0 + x4, tmp38, None)


@triton.jit
def triton_poi_fused_add_convolution_3(in_out_ptr0, in_ptr0, in_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 256
    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]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 4096 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (x2 + 256 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x2 + 256 * y3), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_4(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    xnumel = 16
    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]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 256
    y1 = yindex // 256
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 256 * x2 + 4096 * y1), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 256, 4, 4), (4096, 16, 4, 1))
    assert_size_stride(primals_3, (256, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (256,), (1,))
    assert_size_stride(primals_5, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_6, (256,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(16, 16)](primals_1, buf0, 16, 16, XBLOCK=16,
            YBLOCK=16, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_1[grid(65536, 9)](primals_5, buf1, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_5
        buf2 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
            float32)
        buf3 = buf2
        del buf2
        triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_2[grid
            (16384)](buf3, primals_2, 16384, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        buf4 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf5 = buf4
        del buf4
        triton_poi_fused_add_convolution_3[grid(64, 256)](buf5, buf3,
            primals_4, 64, 256, XBLOCK=256, YBLOCK=1, num_warps=4, num_stages=1
            )
        del primals_4
        buf6 = extern_kernels.convolution(buf5, buf1, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf7 = buf3
        del buf3
        triton_poi_fused_convolution_4[grid(1024, 16)](buf6, primals_6,
            buf7, 1024, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del buf6
        del primals_6
    return buf7, buf0, primals_3, buf1, buf5


class _FPNUpNew(nn.Module):

    def __init__(self, num_input_features, skip_channel_adjust=True):
        super().__init__()
        self.conv_channel_adjust = nn.Conv2d(num_input_features, 256,
            kernel_size=1)
        self.conv_fusion = nn.Conv2d(256, 256, kernel_size=3, padding=1)

    def forward(self, input_0, input_1):
        primals_3 = self.conv_channel_adjust.weight
        primals_4 = self.conv_channel_adjust.bias
        primals_5 = self.conv_fusion.weight
        primals_6 = self.conv_fusion.bias
        primals_2 = input_0
        primals_1 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.025808