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 libdevice
import torch.nn as nn
from typing import Optional
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):
    ynumel = 2304
    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
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 256 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 768 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 12
    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 % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask)


@triton.jit
def triton_per_fused_convolution_native_layer_norm_2(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel
    ):
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + 768 * x0), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + r1, rmask, eviction_policy='evict_last', other=0.0
        )
    tmp26 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp28 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tl.where(rmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp10 = tl.full([1], 768, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = tl.where(rmask, tmp15, 0)
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp19 = 768.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tl.store(in_out_ptr0 + (r1 + 768 * x0), tmp2, rmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp23, None)
    tl.store(out_ptr1 + (r1 + 768 * x0), tmp29, rmask)
    tl.store(out_ptr0 + x0, tmp12, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (768, 3, 16, 16), (768, 256, 16, 1))
    assert_size_stride(primals_2, (768,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (768,), (1,))
    assert_size_stride(primals_5, (768,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((768, 3, 16, 16), (768, 1, 48, 3), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(2304, 256)](primals_1, buf0, 2304, 256,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
            .float32)
        triton_poi_fused_1[grid(12, 4096)](primals_3, buf1, 12, 4096,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(16, 16),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 768, 4, 4), (12288, 1, 3072, 768))
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 16, 1), (16, 1, 64), torch.float32)
        buf7 = reinterpret_tensor(buf5, (4, 16, 1), (16, 1, 1), 0)
        del buf5
        buf8 = empty_strided_cuda((4, 16, 768), (12288, 768, 1), torch.float32)
        triton_per_fused_convolution_native_layer_norm_2[grid(64)](buf3,
            buf7, primals_2, primals_4, primals_5, buf4, buf8, 64, 768,
            num_warps=8, num_stages=1)
        del primals_2
        del primals_5
    return buf8, buf0, buf1, primals_4, buf3, buf4, buf7


class PatchEmbedNew(nn.Module):

    def __init__(self, img_size: 'int'=224, patch_size: 'int'=16, stride:
        'int'=None, in_channels: 'int'=3, embed_dim: 'int'=768, multi_conv:
        'bool'=False, norm_layer: 'Optional'=nn.LayerNorm):
        super(PatchEmbedNew, self).__init__()
        assert img_size % patch_size == 0, 'Argument `img_size` should be factor of argument `patch_size`'
        self.grid_size = img_size // patch_size
        self.patch_size = patch_size
        self.num_patches = self.grid_size ** 2
        if stride is None:
            stride = patch_size
        if multi_conv:
            if patch_size == 12:
                self.proj = nn.Sequential(nn.Conv2d(in_channels=in_channels,
                    out_channels=embed_dim // 4, kernel_size=7, stride=4,
                    padding=3), nn.ReLU(inplace=True), nn.Conv2d(
                    in_channels=embed_dim // 4, out_channels=embed_dim // 2,
                    kernel_size=3, stride=3), nn.ReLU(inplace=True), nn.
                    Conv2d(in_channels=embed_dim // 2, out_channels=
                    embed_dim, kernel_size=3, stride=1, padding=1))
            elif patch_size == 16:
                self.proj = nn.Sequential(nn.Conv2d(in_channels, embed_dim //
                    4, kernel_size=7, stride=4, padding=3), nn.ReLU(inplace
                    =True), nn.Conv2d(in_channels=embed_dim // 4,
                    out_channels=embed_dim // 2, kernel_size=3, stride=2,
                    padding=1), nn.ReLU(inplace=True), nn.Conv2d(
                    in_channels=embed_dim // 2, out_channels=embed_dim,
                    kernel_size=3, stride=2, padding=1))
        else:
            self.proj = nn.Conv2d(in_channels=in_channels, out_channels=
                embed_dim, kernel_size=patch_size, stride=stride)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = norm_layer

    def forward(self, input_0):
        primals_1 = self.proj.weight
        primals_2 = self.proj.bias
        primals_4 = self.norm.weight
        primals_5 = self.norm.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.013952