import torch
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_mean_var_0(in_ptr0, out_ptr0, out_ptr1, 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_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x0), 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 = 3.0
    tmp21 = tmp19 / tmp20
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp21, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_sqrt_sub_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, 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
    x4 = xindex % 64
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1e-05
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tmp2 / tmp6
    tmp9 = tmp7 * tmp8
    tmp11 = tmp9 + tmp10
    tl.store(out_ptr0 + x3, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_var_0[grid(64)](primals_1, buf0, buf1, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_sqrt_sub_1[grid(256)](primals_1, buf0,
            buf1, primals_2, primals_3, buf2, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        del primals_3
    return buf2, buf1, buf0, primals_1, buf0, buf1


class VirtualBatchNormNew(nn.Module):
    """
    Applies Virtual Batch Normalization over a 4D input (a mini-batch
    of 2D inputs with additional channel dimension) as described in
    paper `Improved Techniques for Training GANs`:
    https://arxiv.org/abs/1606.03498

    .. math::

        y = \\frac{x - \\mathrm{E}[x_\\text{ref}]}{ \\sqrt{\\mathrm{Var}[x_\\text{ref}] + \\epsilon}} * \\gamma + \\beta

    VirtualBatchNorm requires two forward passes. First one is to
    calculate mean and variance over a reference batch and second
    is to calculate the actual output.

    Args:

        num_features: :math:`C` from an expected input of size
            :math:`(N, C, H, W)`
        eps: a value added to the denominator for numerical stability.
            Default: 1e-5
    """

    def __init__(self, num_features, eps=1e-05):
        super(VirtualBatchNormNew, self).__init__()
        self.num_features = num_features
        self.eps = eps
        self.mean = None
        self.var = None
        self.weight = nn.parameter.Parameter(torch.Tensor(num_features))
        self.bias = nn.parameter.Parameter(torch.Tensor(num_features))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.ones_(self.weight)
        nn.init.zeros_(self.bias)

    def normalize(self, x):
        y = (x - self.mean) / torch.sqrt(self.var + self.eps
            ) * self.weight.view(1, self.num_features, 1, 1) + self.bias.view(
            1, self.num_features, 1, 1)
        return y

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


Measured latency_ms: 0.031549