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
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):
    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 % 192
    y1 = yindex // 192
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 192 * x2 + 1728 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 768
    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
    y3 = yindex
    y0 = yindex % 192
    y1 = yindex // 192
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 192 * x2 + 786432 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_2(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 % 384
    y1 = yindex // 384
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 384 * x2 + 3456 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(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_convolution_relu_4(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 384
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_add_convolution_relu_6(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 256
    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
    y3 = yindex
    y0 = yindex % 4096
    y1 = yindex // 4096
    tmp0 = tl.load(in_ptr0 + (x2 + 256 * y3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + 256 * y3), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x2 + 256 * y3), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr5 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp11 = tl.full([1, 1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tl.store(out_ptr0 + (y0 + 4096 * x2 + 1048576 * y1), tmp12, xmask)


@triton.jit
def triton_poi_fused_threshold_backward_7(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 % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy=
        'evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 <= tmp1
    tl.store(out_ptr0 + (y0 + 256 * x2 + 1048576 * y1), tmp2, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (384, 192, 3, 3), (1728, 9, 3, 1))
    assert_size_stride(primals_2, (384,), (1,))
    assert_size_stride(primals_3, (4, 192, 64, 64), (786432, 4096, 64, 1))
    assert_size_stride(primals_4, (256, 384, 3, 3), (3456, 9, 3, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (256, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_9, (256,), (1,))
    assert_size_stride(primals_10, (256, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_11, (256,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((384, 192, 3, 3), (1728, 1, 576, 192),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(73728, 9)](primals_1, buf0, 73728, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 192, 64, 64), (786432, 1, 12288, 192),
            torch.float32)
        triton_poi_fused_1[grid(768, 4096)](primals_3, buf1, 768, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((256, 384, 3, 3), (3456, 1, 1152, 384),
            torch.float32)
        triton_poi_fused_2[grid(98304, 9)](primals_4, buf2, 98304, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_3[grid(65536, 9)](primals_6, buf3, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf4 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 384, 64, 64), (1572864, 1, 24576, 384))
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 384, 64, 64), (1572864, 1, 24576, 384
            ), torch.float32)
        triton_poi_fused_convolution_relu_4[grid(6291456)](buf5, primals_2,
            buf6, 6291456, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf7 = extern_kernels.convolution(buf6, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf8 = buf7
        del buf7
        buf9 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256
            ), torch.float32)
        triton_poi_fused_convolution_relu_5[grid(4194304)](buf8, primals_5,
            buf9, 4194304, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf10 = extern_kernels.convolution(buf9, buf3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf11 = extern_kernels.convolution(buf8, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf12 = extern_kernels.convolution(buf5, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf13 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_add_convolution_relu_6[grid(16384, 256)](buf10,
            primals_7, buf11, primals_9, buf12, primals_11, buf13, 16384, 
            256, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del buf10
        del buf11
        del buf12
        del primals_11
        del primals_7
        del primals_9
        buf14 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 
            256), torch.bool)
        triton_poi_fused_threshold_backward_7[grid(1024, 4096)](buf13,
            buf14, 1024, 4096, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
    return (buf13, buf0, buf1, buf2, buf3, primals_8, primals_10, buf5,
        buf6, buf8, buf9, buf14)


class Fusion_featureNew(nn.Module):

    def __init__(self):
        super(Fusion_featureNew, self).__init__()
        self.conv3 = nn.Conv2d(192, 384, kernel_size=3, padding=1)
        self.conv3_1x1 = nn.Conv2d(384, 256, kernel_size=1, padding=0)
        self.conv4 = nn.Conv2d(384, 256, kernel_size=3, padding=1)
        self.conv4_1x1 = nn.Conv2d(256, 256, kernel_size=1, padding=0)
        self.conv5 = nn.Conv2d(256, 256, kernel_size=3, padding=1)

    def forward(self, input_0):
        primals_1 = self.conv3.weight
        primals_2 = self.conv3.bias
        primals_10 = self.conv3_1x1.weight
        primals_5 = self.conv3_1x1.bias
        primals_4 = self.conv4.weight
        primals_7 = self.conv4.bias
        primals_8 = self.conv4_1x1.weight
        primals_9 = self.conv4_1x1.bias
        primals_6 = self.conv5.weight
        primals_11 = self.conv5.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]


Measured latency_ms: 0.027116