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
from functools import partial
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_avg_pool2d_convolution_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-06
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 5
    x0 = xindex % 4
    x2 = xindex // 20
    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], 5, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x0 + 16 * x2 + (-1 + x1)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = 0.5
    tmp11 = tmp9 * tmp10
    tmp12 = 0.7071067811865476
    tmp13 = tmp9 * tmp12
    tmp14 = libdevice.erf(tmp13)
    tmp15 = 1.0
    tmp16 = tmp14 + tmp15
    tmp17 = tmp11 * tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp6, tmp17, tmp18)
    tmp20 = tl.where(tmp4, tmp5, tmp19)
    tl.store(out_ptr0 + x3, tmp20, 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, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4, 4), (64, 16, 4, 1), 0), primals_1, stride=(1, 1), padding
            =(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_avg_pool2d_convolution_0[grid(64)](buf1, primals_2,
            buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(16)](buf2, buf3, buf4, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        triton_poi_fused_native_layer_norm_2[grid(64)](buf2, buf3, buf4,
            primals_4, primals_5, buf5, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf3
        del buf4
        buf6 = empty_strided_cuda((4, 5, 4), (20, 4, 1), torch.float32)
        triton_poi_fused_cat_3[grid(80)](primals_6, buf5, buf6, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf5
        del primals_6
    return buf6, primals_1, primals_4, primals_5, reinterpret_tensor(primals_3,
        (1, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf1, (4, 4, 4
        ), (16, 4, 1), 0), buf2


class FCUDownNew(nn.Module):
    """ CNN feature maps -> Transformer patch embeddings
    """

    def __init__(self, inplanes, outplanes, dw_stride, act_layer=nn.GELU,
        norm_layer=partial(nn.LayerNorm, eps=1e-06)):
        super(FCUDownNew, self).__init__()
        self.dw_stride = dw_stride
        self.conv_project = nn.Conv2d(inplanes, outplanes, kernel_size=1,
            stride=1, padding=0)
        self.sample_pooling = nn.AvgPool2d(kernel_size=dw_stride, stride=
            dw_stride)
        self.ln = norm_layer(outplanes)
        self.act = act_layer()

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


Measured latency_ms: 0.059154