# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/6s/c6sstbvcita246hkfqwdeatnmsh3e6vlcncrzcwlsoqg7dmxvabp.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   out => add_1, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%addmm, [1]), kwargs = {correction: 0, keepdim: True})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
triton_poi_fused_native_layer_norm_0 = async_compile.triton('triton_poi_fused_native_layer_norm_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/34/c34yi37wflkzqt637htf5qtm5snxs5ncmmpmnqod5krfe2gk5ezp.py
# Topologically Sorted Source Nodes: [gain, out, mul, out_1, x_1], Original ATen: [aten.add, aten.native_layer_norm, aten.mul, aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   gain => add
#   mul => mul_1
#   out => add_1, mul, rsqrt, sub, var_mean
#   out_1 => add_2
#   x_1 => gt, mul_2, where
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%addmm_1, 1.0), kwargs = {})
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_8), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%addmm, [1]), kwargs = {correction: 0, keepdim: True})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add), kwargs = {})
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %add_tensor), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add_2, %mul_2), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where, 0), kwargs = {})
triton_poi_fused_add_leaky_relu_leaky_relu_backward_mul_native_layer_norm_1 = async_compile.triton('triton_poi_fused_add_leaky_relu_leaky_relu_backward_mul_native_layer_norm_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i1', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_leaky_relu_leaky_relu_backward_mul_native_layer_norm_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.addmm]
        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)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        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)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0.run(buf0, buf3, buf4, 4, grid=grid(4), stream=stream0)
        buf5 = buf2; del buf2  # reuse
        buf6 = buf5; del buf5  # reuse
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [gain, out, mul, out_1, x_1], Original ATen: [aten.add, aten.native_layer_norm, aten.mul, aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_add_leaky_relu_leaky_relu_backward_mul_native_layer_norm_1.run(buf6, buf0, buf3, buf4, buf1, primals_8, buf7, 16, grid=grid(16), stream=stream0)
        del buf3
        del buf4
        del primals_8
    return (buf6, primals_3, primals_6, buf0, buf1, buf7, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.nn.functional as F


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 CLNLayer(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, x, style):
        x = self.linear1(x)
        x = self.cln1(x, style)
        x = self.act1(x)
        return x


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'in_dim': 4, 'out_dim': 4, 'style_dim': 4}]

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]
