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
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
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_poi_fused_convolution_2(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 % 768
    y1 = yindex // 768
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 768 * x2 + 12288 * 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 = 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))
    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 = empty_strided_cuda((4, 768, 4, 4), (12288, 16, 4, 1), torch.
            float32)
        triton_poi_fused_convolution_2[grid(3072, 16)](buf2, primals_2,
            buf3, 3072, 16, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del buf2
        del primals_2
    return reinterpret_tensor(buf3, (4, 16, 768), (12288, 1, 16), 0
        ), buf0, buf1


class PatchEmbeddingNew(nn.Module):
    """PatchEmdedding class
    Args:
        image_size(int): size of the image. assume that image shape is square
        in_channels(int): input channel of the image, 3 for RGB color channel
        embed_size(int): output channel size. This is the latent vector size.
                         and is constant throughout the transformer
        patch_size(int): size of the patch

    Attributes:
        n_patches(int): calculate the number of patches.
        patcher: convert image into patches. Basically a convolution layer with
                 kernel size and stride as of the patch size
    """

    def __init__(self, image_size=224, in_channels=3, embed_size=768,
        patch_size=16):
        super(PatchEmbeddingNew, self).__init__()
        self.n_patches = (image_size // patch_size) ** 2
        self.patcher = nn.Conv2d(in_channels, embed_size, patch_size,
            patch_size)

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


Measured latency_ms: 0.058439