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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
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_per_fused__native_batch_norm_legit_mean_view_0(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp18 = tl.sum(tmp3, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp16 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp18 / tmp19
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp23, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp24, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_sigmoid_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 2
    x0 = xindex % 16
    x2 = xindex // 32
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * x2), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_sigmoid_3(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_mul_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 16
    x0 = xindex % 16
    x2 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_5(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr0 + x2, xmask)
    tmp9 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 * tmp7
    tmp10 = 0.0
    tmp11 = tmp9 + tmp10
    tmp12 = tmp8 + tmp11
    tl.store(in_out_ptr0 + x2, tmp12, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 32), (32, 1))
    assert_size_stride(primals_3, (32, 4), (4, 1))
    assert_size_stride(primals_4, (1, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_5, (1, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf1 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf3 = reinterpret_tensor(buf1, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf1
        buf5 = reinterpret_tensor(buf4, (4, 4), (4, 1), 0)
        del buf4
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_mean_view_0[grid(16)](buf3,
            buf5, primals_1, buf0, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf6 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf5, primals_2, out=buf6)
        del primals_2
        buf7 = buf6
        del buf6
        triton_poi_fused_sigmoid_1[grid(128)](buf7, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf7, primals_3, out=buf8)
        buf9 = torch.ops.aten._adaptive_avg_pool3d.default(primals_1, [1, 4, 4]
            )
        buf10 = buf9
        del buf9
        buf11 = torch.ops.aten.adaptive_max_pool3d.default(primals_1, [1, 4, 4]
            )
        buf12 = buf11[0]
        del buf11
        buf14 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_2[grid(128)](buf10, buf12, buf14, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf10
        del buf12
        buf15 = extern_kernels.convolution(buf14, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 1, 4, 4), (16, 16, 4, 1))
        buf16 = buf15
        del buf15
        triton_poi_fused_sigmoid_3[grid(64)](buf16, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf17 = extern_kernels.convolution(buf16, primals_5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 1, 4, 4), (16, 16, 4, 1))
        buf18 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_4[grid(256)](buf8, buf17, buf18, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf19 = extern_kernels.convolution(buf18, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 4, 4, 4), (64, 16, 4, 1))
        buf20 = extern_kernels.convolution(buf18, primals_7, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 4, 4, 4), (64, 16, 4, 1))
        buf21 = buf19
        del buf19
        triton_poi_fused_add_mul_5[grid(256)](buf21, primals_1, buf0, buf3,
            buf20, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf20
    return (buf21, primals_1, primals_4, primals_5, primals_6, primals_7,
        buf0, buf3, buf7, buf8, buf14, buf16, buf17, buf18,
        reinterpret_tensor(primals_3, (4, 32), (1, 4), 0),
        reinterpret_tensor(buf5, (4, 4), (1, 4), 0))


class AttentiveTrans2dNew(nn.Module):

    def __init__(self, num_features, hidden_channels=32):
        super(AttentiveTrans2dNew, self).__init__()
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.smooth_gamma = 1
        self.smooth_beta = 0
        self.matrix1 = nn.Parameter(torch.ones(num_features, hidden_channels))
        self.matrix2 = nn.Parameter(torch.ones(hidden_channels, num_features))
        self.sigmoid = nn.Sigmoid()
        self.conv1 = nn.Conv2d(2, 1, 3, padding=1, bias=False)
        self.conv2 = nn.Conv2d(1, 1, 3, padding=1, bias=False)
        self.conv3 = nn.Conv2d(num_features, num_features, 1, bias=False)
        self.conv4 = nn.Conv2d(num_features, num_features, 1, bias=False)
        self.IN_norm = nn.InstanceNorm2d(num_features, affine=False,
            track_running_stats=False)

    def forward(self, input_0):
        primals_2 = self.matrix1
        primals_3 = self.matrix2
        primals_4 = self.conv1.weight
        primals_5 = self.conv2.weight
        primals_6 = self.conv3.weight
        primals_7 = self.conv4.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.157086