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.utils.data
import torch.nn as nn
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__native_batch_norm_legit_clone_0(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + 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 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 4
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (x1 + 4 * y0), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.1
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + (y0 + 4 * x1), tmp9, xmask & ymask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((1, 4, 1), (4, 1, 4), torch.float32)
        buf2 = empty_strided_cuda((1, 4, 1), (4, 1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__native_batch_norm_legit_clone_0[grid(4)](buf0,
            buf1, buf2, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (1, 4), torch.float32)
        triton_poi_fused_leaky_relu_1[grid(4, 4)](buf0, buf1, buf2, buf3, 4,
            4, XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1)
        del buf1
        del buf2
    return buf3, primals_2, buf0


class BatchNormBlock(nn.Module):

    def __init__(self, in_dim, use_bn, bn_momentum):
        """
        Initialize a batch normalization block. If network does not use batch normalization, replace with biases.
        :param in_dim: dimension input features
        :param use_bn: boolean indicating if we use Batch Norm
        :param bn_momentum: Batch norm momentum
        """
        super(BatchNormBlock, self).__init__()
        self.bn_momentum = bn_momentum
        self.use_bn = use_bn
        self.in_dim = in_dim
        if self.use_bn:
            self.batch_norm = nn.InstanceNorm1d(in_dim, momentum=bn_momentum)
        else:
            self.bias = Parameter(torch.zeros(in_dim, dtype=torch.float32),
                requires_grad=True)
        return

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

    def forward(self, x):
        if self.use_bn:
            x = x.unsqueeze(2)
            x = x.transpose(0, 2)
            x = self.batch_norm(x)
            x = x.transpose(0, 2)
            return x.squeeze()
        else:
            return x + self.bias

    def __repr__(self):
        return (
            'BatchNormBlock(in_feat: {:d}, momentum: {:.3f}, only_bias: {:s})'
            .format(self.in_dim, self.bn_momentum, str(not self.use_bn)))


class UnaryBlockNew(nn.Module):

    def __init__(self, in_dim, out_dim, use_bn, bn_momentum, no_relu=False):
        """
        Initialize a standard unary block with its ReLU and BatchNorm.
        :param in_dim: dimension input features
        :param out_dim: dimension input features
        :param use_bn: boolean indicating if we use Batch Norm
        :param bn_momentum: Batch norm momentum
        """
        super(UnaryBlockNew, self).__init__()
        self.bn_momentum = bn_momentum
        self.use_bn = use_bn
        self.no_relu = no_relu
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.mlp = nn.Linear(in_dim, out_dim, bias=False)
        self.batch_norm = BatchNormBlock(out_dim, self.use_bn, self.bn_momentum
            )
        if not no_relu:
            self.leaky_relu = nn.LeakyReLU(0.1)
        return

    def __repr__(self):
        return (
            'UnaryBlock(in_feat: {:d}, out_feat: {:d}, BN: {:s}, ReLU: {:s})'
            .format(self.in_dim, self.out_dim, str(self.use_bn), str(not
            self.no_relu)))

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


Measured latency_ms: 0.037872