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
import torch.nn.functional as F
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_layer_norm_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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    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_add_leaky_relu_leaky_relu_backward_mul_native_layer_norm_1(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x2, xmask)
    tmp9 = tl.load(in_out_ptr0 + x2, xmask)
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 * tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = tmp8 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tmp17 > tmp13
    tl.store(in_out_ptr0 + x2, tmp17, xmask)
    tl.store(out_ptr0 + x2, tmp18, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
            primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, primals_6, reinterpret_tensor(
            primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_6, reinterpret_tensor(primals_7, (4, 4),
            (1, 4), 0), out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf4 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(4)](buf0, buf3, buf4, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf5 = buf2
        del buf2
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_add_leaky_relu_leaky_relu_backward_mul_native_layer_norm_1[
            grid(16)](buf6, buf0, buf3, buf4, buf1, primals_8, buf7, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf3
        del buf4
        del primals_8
    return buf6, primals_3, primals_6, buf0, buf1, buf7


class CLN(nn.Module):

    def __init__(self, in_dim, use_style_fc=False, style_dim=None,
        which_linear=nn.Linear, spectral_norm=False, eps=1e-05, **kwargs):
        super(CLN, self).__init__()
        self.in_dim = in_dim
        self.use_style_fc = use_style_fc
        self.style_dim = style_dim
        self.spectral_norm = spectral_norm
        if use_style_fc:
            self.gain = which_linear(style_dim, in_dim)
            self.bias = which_linear(style_dim, in_dim)
            if spectral_norm:
                self.gain = nn.utils.spectral_norm(self.gain)
                self.bias = nn.utils.spectral_norm(self.bias)
        else:
            self.style_dim = in_dim * 2
        self.eps = eps
        pass

    def forward(self, x, style):
        """
    Calculate class-conditional gains and biases.

    :param x: (b, c) or (b, n, c)
    :param style: (b, c)
    :return:
    """
        if self.use_style_fc:
            gain = self.gain(style) + 1.0
            bias = self.bias(style)
        else:
            style = rearrange(style, 'b (n c) -> b n c', n=2)
            gain, bias = style.unbind(dim=1)
            gain = gain + 1.0
        if x.dim() == 3:
            gain = rearrange(gain, 'b c -> b 1 c')
            bias = rearrange(bias, 'b c -> b 1 c')
        elif x.dim() == 2:
            pass
        else:
            assert 0
        out = F.layer_norm(x, normalized_shape=(self.in_dim,), weight=None,
            bias=None, eps=self.eps)
        out = out * gain + bias
        return out

    def __repr__(self):
        s = (
            f'{self.__class__.__name__}(in_dim={self.in_dim}, style_dim={self.style_dim})'
            )
        return s


class CLNLayerNew(nn.Module):

    def __repr__(self):
        return f'{self.__class__.__name__}({self.repr})'

    def __init__(self, in_dim, out_dim, style_dim):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.style_dim = style_dim
        self.repr = (
            f'in_dim={in_dim}, out_dim={out_dim}, style_dim={style_dim}')
        self.linear1 = nn.Linear(in_dim, out_dim)
        self.cln1 = CLN(in_dim=out_dim, use_style_fc=True, style_dim=style_dim)
        self.style_dim = self.cln1.style_dim
        self.act1 = nn.LeakyReLU(0.2, inplace=True)
        pass

    def forward(self, input_0, input_1):
        primals_1 = self.linear1.weight
        primals_2 = self.linear1.bias
        primals_3 = self.cln1.gain.weight
        primals_5 = self.cln1.gain.bias
        primals_4 = self.cln1.bias.weight
        primals_8 = self.cln1.bias.bias
        primals_6 = input_0
        primals_7 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.050251