# 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_7/inductor_cache/bi/cbitwluw6amzzk2l4tcnovidjasx6v2ou4cmmzo6twgopcjgen27.py
# Topologically Sorted Source Nodes: [mean, std, add_1], Original ATen: [aten.mean, aten.std, aten.add]
# Source node to ATen node mapping:
#   add_1 => add_1
#   mean => mean
#   std => sqrt, var
# Graph fragment:
#   %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [1], True), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%primals_1, [1]), kwargs = {correction: 1.0, keepdim: True})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-06), kwargs = {})
triton_per_fused_add_mean_std_0 = async_compile.triton('triton_per_fused_add_mean_std_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.persistent_reduction(
    size_hints=[64, 64],
    reduction_hint=ReductionHint.OUTER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mean_std_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_add_mean_std_0(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 16
    x1 = (xindex // 16)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*r2) + (1024*x1)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp4 / tmp19
    tmp21 = 63.0
    tmp22 = tmp18 / tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-06
    tmp25 = tmp23 + tmp24
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x3), tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp25, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/p2/cp2sb4sx4v7ayyh4tpqrrvnnyvmyc426vakzlccrfjjkfpgy6emj.py
# Topologically Sorted Source Nodes: [mean_1], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   mean_1 => mean_1
# Graph fragment:
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view_2, [1], True), kwargs = {})
triton_per_fused_mean_1 = async_compile.triton('triton_per_fused_mean_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.persistent_reduction(
    size_hints=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_mean_1(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/a6/ca6mrxitdufdjl2fq6s3h5bk3bstkie6uxpoa2rwuq55e4j3oxdh.py
# Topologically Sorted Source Nodes: [mean_1, sub, scale_1, mean_2, bias_1, sub_2, mul, truediv, add_2], Original ATen: [aten.mean, aten.sub, aten.add, aten.mul, aten.div]
# Source node to ATen node mapping:
#   add_2 => add_2
#   bias_1 => sub_1
#   mean_1 => mean_1
#   mean_2 => mean_2
#   mul => mul
#   scale_1 => add
#   sub => sub
#   sub_2 => sub_2
#   truediv => div
# Graph fragment:
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view_2, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_2, %mean_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, 1), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view_5, [1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_5, %mean_2), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %sub_2), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add_1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %sub_1), kwargs = {})
triton_poi_fused_add_div_mean_mul_sub_2 = async_compile.triton('triton_poi_fused_add_div_mean_mul_sub_2', '''
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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '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, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_sub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_div_mean_mul_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = (xindex // 16)
    x2 = (xindex // 1024)
    x4 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (x3), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (x4), None)
    tmp8 = tl.load(in_ptr3 + (x0 + (16*x2)), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + (x0 + (16*x2)), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr5 + (x3), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + (x2), None, eviction_policy='evict_last')
    tmp2 = 64.0
    tmp3 = tmp1 / tmp2
    tmp4 = tmp0 - tmp3
    tmp5 = 1.0
    tmp6 = tmp4 + tmp5
    tmp9 = tmp7 - tmp8
    tmp10 = tmp6 * tmp9
    tmp12 = tmp10 / tmp11
    tmp15 = tmp14 / tmp2
    tmp16 = tmp13 - tmp15
    tmp17 = tmp12 + tmp16
    tl.store(out_ptr0 + (x4), tmp17, None)
''', 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, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 16, 4, 1), 0); del buf0  # reuse
        buf9 = reinterpret_tensor(buf3, (4, 1, 4, 4), (16, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [mean, std, add_1], Original ATen: [aten.mean, aten.std, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_mean_std_0.run(buf1, buf9, primals_1, 64, 64, grid=grid(64), stream=stream0)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_2
        del primals_3
        buf6 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [mean_1], Original ATen: [aten.mean]
        triton_per_fused_mean_1.run(buf5, buf6, 4, 64, grid=grid(4), stream=stream0)
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf7)
        del primals_5
        del primals_6
        buf8 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [mean_2], Original ATen: [aten.mean]
        triton_per_fused_mean_1.run(buf7, buf8, 4, 64, grid=grid(4), stream=stream0)
        buf10 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean_1, sub, scale_1, mean_2, bias_1, sub_2, mul, truediv, add_2], Original ATen: [aten.mean, aten.sub, aten.add, aten.mul, aten.div]
        triton_poi_fused_add_div_mean_mul_sub_2.run(buf5, buf6, primals_1, buf1, buf9, buf7, buf8, buf10, 4096, grid=grid(4096), stream=stream0)
        del buf5
        del buf6
        del buf7
        del buf8
    return (buf10, primals_1, buf1, reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), buf9, )


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, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = 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])
    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.jit
import torch.nn


class AdaptiveLayerNorm(nn.Module):

    def __init__(self, in_size, ada_size):
        super(AdaptiveLayerNorm, self).__init__()
        self.scale = nn.Linear(ada_size, in_size)
        self.bias = nn.Linear(ada_size, in_size)

    def forward(self, inputs, style):
        expand = [1] * (inputs.dim() - 2)
        mean = inputs.mean(dim=1, keepdim=True)
        std = inputs.std(dim=1, keepdim=True)
        scale = self.scale(style).view(style.size(0), -1, *expand)
        scale = scale - scale.mean(dim=1, keepdim=True) + 1
        bias = self.bias(style).view(style.size(0), -1, *expand)
        bias = bias - bias.mean(dim=1, keepdim=True)
        return scale * (inputs - mean) / (std + 1e-06) + bias


def get_inputs():
    return [torch.rand([4, 64, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'in_size': 4, 'ada_size': 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.jit
import torch.nn
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_per_fused_add_mean_std_0(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 16
    x1 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * r2 + 1024 * x1), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp4 / tmp19
    tmp21 = 63.0
    tmp22 = tmp18 / tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-06
    tmp25 = tmp23 + tmp24
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x3, tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp25, xmask)


@triton.jit
def triton_per_fused_mean_1(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_sub_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex // 16
    x2 = xindex // 1024
    x4 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x3, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + x4, None)
    tmp8 = tl.load(in_ptr3 + (x0 + 16 * x2), None, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr4 + (x0 + 16 * x2), None, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr5 + x3, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x2, None, eviction_policy='evict_last')
    tmp2 = 64.0
    tmp3 = tmp1 / tmp2
    tmp4 = tmp0 - tmp3
    tmp5 = 1.0
    tmp6 = tmp4 + tmp5
    tmp9 = tmp7 - tmp8
    tmp10 = tmp6 * tmp9
    tmp12 = tmp10 / tmp11
    tmp15 = tmp14 / tmp2
    tmp16 = tmp13 - tmp15
    tmp17 = tmp12 + tmp16
    tl.store(out_ptr0 + x4, tmp17, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 16, 4, 1), 0)
        del buf0
        buf9 = reinterpret_tensor(buf3, (4, 1, 4, 4), (16, 16, 4, 1), 0)
        del buf3
        get_raw_stream(0)
        triton_per_fused_add_mean_std_0[grid(64)](buf1, buf9, primals_1, 64,
            64, XBLOCK=32, num_warps=8, num_stages=1)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_4, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf5)
        del primals_2
        del primals_3
        buf6 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        triton_per_fused_mean_1[grid(4)](buf5, buf6, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(primals_4, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf7)
        del primals_5
        del primals_6
        buf8 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        triton_per_fused_mean_1[grid(4)](buf7, buf8, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf10 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.
            float32)
        triton_poi_fused_add_div_mean_mul_sub_2[grid(4096)](buf5, buf6,
            primals_1, buf1, buf9, buf7, buf8, buf10, 4096, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf5
        del buf6
        del buf7
        del buf8
    return buf10, primals_1, buf1, reinterpret_tensor(primals_4, (64, 4), (
        4, 1), 0), buf9


class AdaptiveLayerNormNew(nn.Module):

    def __init__(self, in_size, ada_size):
        super(AdaptiveLayerNormNew, self).__init__()
        self.scale = nn.Linear(ada_size, in_size)
        self.bias = nn.Linear(ada_size, in_size)

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