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
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_unfold_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_per_fused_native_layer_norm_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, 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)
    r3 = rindex
    x0 = xindex % 2
    x1 = xindex // 2 % 2
    x2 = xindex // 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 4 * (r3 // 2 % 2) + 8 * x1 + 16 * (
        r3 // 4) + 64 * x2 + r3 % 2), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    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]
    tmp17 = 16.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp0 - tmp10
    tmp23 = tmp22 * tmp21
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 + tmp26
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x4, tmp21, xmask)
    tl.store(out_ptr1 + (r3 + 16 * x4), tmp27, xmask)
    tl.store(out_ptr0 + x4, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (16,), (1,))
    assert_size_stride(primals_4, (8, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 2, 4, 2, 2), (64, 8, 2, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_unfold_0[grid(256)](primals_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 2, 2, 1), (4, 2, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 2, 2, 1), (4, 2, 1, 16), torch.float32)
        buf4 = reinterpret_tensor(buf2, (4, 2, 2, 1), (4, 2, 1, 1), 0)
        del buf2
        buf5 = empty_strided_cuda((4, 2, 2, 16), (64, 32, 16, 1), torch.float32
            )
        triton_per_fused_native_layer_norm_1[grid(16)](buf4, buf0,
            primals_2, primals_3, buf1, buf5, 16, 16, XBLOCK=8, num_warps=2,
            num_stages=1)
        del primals_2
        del primals_3
        buf6 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_4, (16, 8), (1, 16), 0), out=buf6)
    return reinterpret_tensor(buf6, (4, 8, 2, 2), (32, 1, 16, 8), 0
        ), buf0, buf1, buf4, reinterpret_tensor(buf5, (16, 16), (16, 1), 0
        ), primals_4


def bchw_to_bhwc(input: 'torch.Tensor') ->torch.Tensor:
    """
    Permutes a tensor to the shape [batch size, height, width, channels]
    :param input: (torch.Tensor) Input tensor of the shape [batch size, height, width, channels]
    :return: (torch.Tensor) Output tensor of the shape [batch size, height, width, channels]
    """
    return input.permute(0, 2, 3, 1)


def bhwc_to_bchw(input: 'torch.Tensor') ->torch.Tensor:
    """
    Permutes a tensor to the shape [batch size, channels, height, width]
    :param input: (torch.Tensor) Input tensor of the shape [batch size, height, width, channels]
    :return: (torch.Tensor) Output tensor of the shape [batch size, channels, height, width]
    """
    return input.permute(0, 3, 1, 2)


class PatchMergingNew(nn.Module):
    """
    This class implements the patch merging approach which is essential a strided convolution with normalization before
    """

    def __init__(self, in_channels: 'int') ->None:
        """
        Constructor method
        :param in_channels: (int) Number of input channels
        """
        super(PatchMergingNew, self).__init__()
        self.normalization: 'nn.Module' = nn.LayerNorm(normalized_shape=4 *
            in_channels)
        self.linear_mapping: 'nn.Module' = nn.Linear(in_features=4 *
            in_channels, out_features=2 * in_channels, bias=False)

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


Measured latency_ms: 0.040921