# 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/7v/c7vka2znqtp5ayxhjpvmqdtv6nlabfgsnkhlpsokjs2z7tviw3qs.py
# Topologically Sorted Source Nodes: [xm_mean, xm_centered, mul, mean_1, xm_std_rev, xm_norm, mul_1, xm_scaled], Original ATen: [aten.mean, aten.sub, aten.mul, aten.rsqrt, aten.add]
# Source node to ATen node mapping:
#   mean_1 => mean_1
#   mul => mul
#   mul_1 => mul_1
#   xm_centered => sub
#   xm_mean => mean
#   xm_norm => sub_1
#   xm_scaled => add
#   xm_std_rev => rsqrt
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [2], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %mean), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %sub), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul, [2], True), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%mean_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %unsqueeze), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %unsqueeze_1), kwargs = {})
triton_poi_fused_add_mean_mul_rsqrt_sub_0 = async_compile.triton('triton_poi_fused_add_mean_mul_rsqrt_sub_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_mul_rsqrt_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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_mean_mul_rsqrt_sub_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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)
    tmp8 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp9 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (x2), xmask)
    tmp13 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 / tmp1
    tmp3 = tmp0 - tmp2
    tmp4 = tmp3 * tmp3
    tmp5 = tmp4 / tmp1
    tmp6 = libdevice.rsqrt(tmp5)
    tmp7 = tmp3 - tmp6
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 * tmp10
    tmp14 = tmp12 + tmp13
    tmp15 = tmp11 + tmp14
    tl.store(in_out_ptr0 + (x2), tmp15, 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 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [xm_mean, xm_centered, mul, mean_1, xm_std_rev, xm_norm, mul_1, xm_scaled], Original ATen: [aten.mean, aten.sub, aten.mul, aten.rsqrt, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mean_mul_rsqrt_sub_0.run(buf2, primals_6, primals_2, buf1, primals_5, 16, grid=grid(16), stream=stream0)
        del buf1
        del primals_2
        del primals_5
    return (reinterpret_tensor(buf2, (4, 4), (4, 1), 0), primals_3, primals_6, )


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)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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


class AdaIN(torch.nn.Module):

    def __init__(self, channels_in, channels_out, norm=True):
        super(AdaIN, self).__init__()
        self.channels_in = channels_in
        self.channels_out = channels_out
        self.norm = norm
        self.affine_scale = torch.nn.Linear(channels_in, channels_out, bias
            =True)
        self.affine_bias = torch.nn.Linear(channels_in, channels_out, bias=True
            )

    def forward(self, x, w):
        ys = self.affine_scale(w)
        yb = self.affine_bias(w)
        ys = torch.unsqueeze(ys, -1)
        yb = torch.unsqueeze(yb, -1)
        xm = torch.reshape(x, shape=(x.shape[0], x.shape[1], -1))
        if self.norm:
            xm_mean = torch.mean(xm, dim=2, keepdims=True)
            xm_centered = xm - xm_mean
            xm_std_rev = torch.rsqrt(torch.mean(torch.mul(xm_centered,
                xm_centered), dim=2, keepdims=True))
            xm_norm = xm_centered - xm_std_rev
        else:
            xm_norm = xm
        xm_scaled = xm_norm * ys + yb
        return torch.reshape(xm_scaled, x.shape)


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


def get_init_inputs():
    return [[], {'channels_in': 4, 'channels_out': 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
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_add_mean_mul_rsqrt_sub_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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)
    tmp8 = tl.load(in_out_ptr0 + x2, xmask)
    tmp9 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + x2, xmask)
    tmp13 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 / tmp1
    tmp3 = tmp0 - tmp2
    tmp4 = tmp3 * tmp3
    tmp5 = tmp4 / tmp1
    tmp6 = libdevice.rsqrt(tmp5)
    tmp7 = tmp3 - tmp6
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 * tmp10
    tmp14 = tmp12 + tmp13
    tmp15 = tmp11 + tmp14
    tl.store(in_out_ptr0 + x2, tmp15, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_4, (4, 4),
            (1, 4), 0), out=buf1)
        del primals_4
        buf2 = reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_mean_mul_rsqrt_sub_0[grid(16)](buf2, primals_6,
            primals_2, buf1, primals_5, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf1
        del primals_2
        del primals_5
    return reinterpret_tensor(buf2, (4, 4), (4, 1), 0), primals_3, primals_6


class AdaINNew(torch.nn.Module):

    def __init__(self, channels_in, channels_out, norm=True):
        super(AdaINNew, self).__init__()
        self.channels_in = channels_in
        self.channels_out = channels_out
        self.norm = norm
        self.affine_scale = torch.nn.Linear(channels_in, channels_out, bias
            =True)
        self.affine_bias = torch.nn.Linear(channels_in, channels_out, bias=True
            )

    def forward(self, input_0, input_1):
        primals_1 = self.affine_scale.weight
        primals_2 = self.affine_scale.bias
        primals_3 = self.affine_bias.weight
        primals_5 = self.affine_bias.bias
        primals_4 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
