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
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


@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_sub_var_0(in_ptr0, in_ptr1,
    in_ptr2, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + x4), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + x4), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + x4), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp1 - tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp2 - tmp9
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = tmp4 - tmp9
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp6 - tmp9
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp21 / tmp8
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = tmp10 / tmp25
    tmp28 = tmp26 * tmp27
    tmp30 = tmp28 + tmp29
    tl.store(out_ptr0 + x3, tmp30, 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((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_mean_mul_pow_sub_var_0[grid(256)](primals_1,
            primals_2, primals_3, buf0, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        del primals_3
    return buf0, primals_1


class CustomBatchNormAutogradNew(nn.Module):
    """
    This nn.module implements a custom version of the batch norm operation for MLPs.
    The operations called in self.forward track the history if the input tensors have the
    flag requires_grad set to True. The backward pass does not need to be implemented, it
    is dealt with by the automatic differentiation provided by PyTorch.
    """

    def __init__(self, n_neurons, eps=1e-05):
        """
        Initializes CustomBatchNormAutograd object.

        Args:
          n_neurons: int specifying the number of neurons
          eps: small float to be added to the variance for stability
        """
        super(CustomBatchNormAutogradNew, self).__init__()
        self.n_neurons = n_neurons
        self.eps = eps
        self.gamma = nn.Parameter(torch.ones(n_neurons))
        self.beta = nn.Parameter(torch.zeros(n_neurons))

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


Measured latency_ms: 0.015772