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.nn import Module
import torch.utils.data
from torch.nn import init
from torch.nn.parameter import Parameter
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_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1e-06
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 / tmp5
    tl.store(out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr1 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_sub_0[grid(256)](primals_2, primals_1,
            primals_3, buf0, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
    return buf0, buf1, primals_3, buf0


class StandardizeNew(Module):
    """
    Applies (element-wise) standardization with trainable translation parameter μ and scale parameter σ, i.e. computes
    (x - μ) / σ where '/' is applied element-wise.

    Args:
        in_features: size of each input sample
        out_features: size of each output sample
        bias: If set to False, the layer will not learn a translation parameter μ.
            Default: ``True``

    Attributes:
        mu: the learnable translation parameter μ.
        std: the learnable scale parameter σ.
    """
    __constants__ = ['mu']

    def __init__(self, in_features, bias=True, eps=1e-06):
        super(StandardizeNew, self).__init__()
        self.in_features = in_features
        self.out_features = in_features
        self.eps = eps
        self.std = Parameter(torch.Tensor(in_features))
        if bias:
            self.mu = Parameter(torch.Tensor(in_features))
        else:
            self.register_parameter('mu', None)
        self.reset_parameters()

    def reset_parameters(self):
        init.constant_(self.std, 1)
        if self.mu is not None:
            init.constant_(self.mu, 0)

    def extra_repr(self):
        return 'in_features={}, out_features={}, bias={}'.format(self.
            in_features, self.out_features, self.mu is not None)

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


Measured latency_ms: 0.017702