# 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/rs/crsnywfgmmsx4owavufb4dscuwupmbyjscwr76bbv6xspf54k5jh.py
# Topologically Sorted Source Nodes: [mean_1, mean_2, mean_3], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   mean_1 => add
#   mean_2 => add_1
#   mean_3 => add_2
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select, %select_1), kwargs = {})
#   %select_scatter_default : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%primals_1, %add, 3, 0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_2, %select_4), kwargs = {})
#   %select_scatter_default_1 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default, %add_1, 3, 0), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_5, %select_7), kwargs = {})
#   %select_scatter_default_2 : [num_users=5] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_1, %add_2, 3, 0), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_add_0', 'mutated_arg_names': [], '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_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp4 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (x2), xmask)
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = tmp1 == tmp1
    tmp6 = tmp4 + tmp5
    tmp7 = tl.where(tmp3, tmp6, tmp4)
    tmp8 = tl.full([1], 2, tl.int32)
    tmp9 = tmp8 == tmp1
    tmp11 = tl.where(tmp9, tmp6, tmp10)
    tmp12 = tmp7 + tmp11
    tmp13 = tl.where(tmp3, tmp12, tmp7)
    tmp14 = tl.full([1], 3, tl.int32)
    tmp15 = tmp14 == tmp1
    tmp17 = tl.where(tmp15, tmp6, tmp16)
    tmp18 = tl.where(tmp15, tmp12, tmp17)
    tmp19 = tmp13 + tmp18
    tmp21 = tl.where(tmp2, tmp6, tmp20)
    tmp22 = tl.where(tmp2, tmp12, tmp21)
    tmp23 = tl.where(tmp2, tmp19, tmp22)
    tl.store(out_ptr0 + (x2), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bj/cbjpy7gm4gjedatir5qy7auyeeo7oqzof6u47err2ci3ozaq5n6z.py
# Topologically Sorted Source Nodes: [mean_4, sub, pow_1, mean_6, mean_7, mean_8], Original ATen: [aten.div, aten.sub, aten.pow, aten.add]
# Source node to ATen node mapping:
#   mean_4 => div
#   mean_6 => add_3
#   mean_7 => add_4
#   mean_8 => add_5
#   pow_1 => pow_1
#   sub => sub
# Graph fragment:
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%unsqueeze_1, 4), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_scatter_default_2, %div), kwargs = {})
#   %pow_1 : [num_users=3] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_10, %select_11), kwargs = {})
#   %select_scatter_default_3 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%pow_1, %add_3, 3, 0), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_12, %select_14), kwargs = {})
#   %select_scatter_default_4 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_3, %add_4, 3, 0), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_15, %select_17), kwargs = {})
#   %select_scatter_default_5 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_4, %add_5, 3, 0), kwargs = {})
triton_poi_fused_add_div_pow_sub_1 = async_compile.triton('triton_poi_fused_add_div_pow_sub_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_add_div_pow_sub_1', 'mutated_arg_names': [], '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_div_pow_sub_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp4 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (x2), xmask)
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = tmp1 == tmp1
    tmp5 = 0.25
    tmp6 = tmp4 * tmp5
    tmp7 = tmp4 - tmp6
    tmp8 = tmp7 * tmp7
    tmp10 = tmp9 - tmp6
    tmp11 = tmp10 * tmp10
    tmp12 = tmp8 + tmp11
    tmp13 = tl.where(tmp3, tmp12, tmp8)
    tmp14 = tl.full([1], 2, tl.int32)
    tmp15 = tmp14 == tmp1
    tmp17 = tmp16 - tmp6
    tmp18 = tmp17 * tmp17
    tmp19 = tl.where(tmp15, tmp12, tmp18)
    tmp20 = tmp13 + tmp19
    tmp21 = tl.where(tmp3, tmp20, tmp13)
    tmp22 = tl.full([1], 3, tl.int32)
    tmp23 = tmp22 == tmp1
    tmp25 = tmp24 - tmp6
    tmp26 = tmp25 * tmp25
    tmp27 = tl.where(tmp23, tmp12, tmp26)
    tmp28 = tl.where(tmp23, tmp20, tmp27)
    tmp29 = tmp21 + tmp28
    tmp31 = tmp30 - tmp6
    tmp32 = tmp31 * tmp31
    tmp33 = tl.where(tmp2, tmp12, tmp32)
    tmp34 = tl.where(tmp2, tmp20, tmp33)
    tmp35 = tl.where(tmp2, tmp29, tmp34)
    tl.store(out_ptr0 + (x2), tmp35, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bh/cbhh24rywaugwnpzje6fccgvksyqpmthcmbtrwr5wsew7niz2cal.py
# Topologically Sorted Source Nodes: [truediv_1, add, std], Original ATen: [aten.div, aten.add, aten.sqrt]
# Source node to ATen node mapping:
#   add => add_6
#   std => sqrt
#   truediv_1 => div_1
# Graph fragment:
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%unsqueeze_3, 4), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_1, 1e-08), kwargs = {})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_6,), kwargs = {})
triton_poi_fused_add_div_sqrt_2 = async_compile.triton('triton_poi_fused_add_div_sqrt_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_add_div_sqrt_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_sqrt_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = 1e-08
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.sqrt(tmp4)
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xp/cxpikcr4kkuns4utqmqad22uw6hnlhsakqxh4hpiv244g4luz6so.py
# Topologically Sorted Source Nodes: [sub_1, x, mul, add_1], Original ATen: [aten.sub, aten.div, aten.mul, aten.add]
# Source node to ATen node mapping:
#   add_1 => add_7
#   mul => mul
#   sub_1 => sub_1
#   x => div_2
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_scatter_default_2, %expand), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_1, %expand_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_2, %expand_2), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %expand_3), kwargs = {})
#   %copy_ : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%primals_1, %select_scatter_default_2), kwargs = {})
triton_poi_fused_add_div_mul_sub_3 = async_compile.triton('triton_poi_fused_add_div_mul_sub_3', '''
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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mul_sub_3', 'mutated_arg_names': ['out_ptr1'], '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_div_mul_sub_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x5 = (xindex // 4)
    x2 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x5), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x5), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
    tmp2 = 0.25
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 - tmp3
    tmp6 = tmp4 / tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + (x4), tmp10, xmask)
    tl.store(out_ptr1 + (x4), tmp0, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean_1, mean_2, mean_3], Original ATen: [aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean_4, sub, pow_1, mean_6, mean_7, mean_8], Original ATen: [aten.div, aten.sub, aten.pow, aten.add]
        triton_poi_fused_add_div_pow_sub_1.run(buf0, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [truediv_1, add, std], Original ATen: [aten.div, aten.add, aten.sqrt]
        triton_poi_fused_add_div_sqrt_2.run(buf1, buf2, 64, grid=grid(64), stream=stream0)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub_1, x, mul, add_1], Original ATen: [aten.sub, aten.div, aten.mul, aten.add]
        triton_poi_fused_add_div_mul_sub_3.run(buf0, buf2, primals_2, primals_3, buf3, primals_1, 256, grid=grid(256), stream=stream0)
        del primals_1
        del primals_2
        del primals_3
    return (buf3, buf0, buf2, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
from torch.autograd import Variable


def my_mean(x):
    f = x.shape[-1]
    mean = x[..., 0]
    for i in range(1, f):
        mean += x[..., i]
    return mean[..., None] / f


class tLNv2(nn.Module):

    def __init__(self, dimension, eps=1e-08, trainable=True):
        super(tLNv2, self).__init__()
        self.eps = eps
        if trainable:
            self.gain = nn.Parameter(torch.ones(1, dimension, 1, 1))
            self.bias = nn.Parameter(torch.zeros(1, dimension, 1, 1))
        else:
            self.gain = Variable(torch.ones(1, dimension, 1, 1),
                requires_grad=False)
            self.bias = Variable(torch.zeros(1, dimension, 1, 1),
                requires_grad=False)

    def forward(self, inp):
        inp.size(0)
        mean = my_mean(inp)
        std = torch.sqrt(my_mean((inp - mean) ** 2) + self.eps)
        x = (inp - mean.expand_as(inp)) / std.expand_as(inp)
        return x * self.gain.expand_as(x).type(x.type()) + self.bias.expand_as(
            x).type(x.type())


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


def get_init_inputs():
    return [[], {'dimension': 4}]

import torch
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
from torch.autograd import Variable
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp4 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = tmp1 == tmp1
    tmp6 = tmp4 + tmp5
    tmp7 = tl.where(tmp3, tmp6, tmp4)
    tmp8 = tl.full([1], 2, tl.int32)
    tmp9 = tmp8 == tmp1
    tmp11 = tl.where(tmp9, tmp6, tmp10)
    tmp12 = tmp7 + tmp11
    tmp13 = tl.where(tmp3, tmp12, tmp7)
    tmp14 = tl.full([1], 3, tl.int32)
    tmp15 = tmp14 == tmp1
    tmp17 = tl.where(tmp15, tmp6, tmp16)
    tmp18 = tl.where(tmp15, tmp12, tmp17)
    tmp19 = tmp13 + tmp18
    tmp21 = tl.where(tmp2, tmp6, tmp20)
    tmp22 = tl.where(tmp2, tmp12, tmp21)
    tmp23 = tl.where(tmp2, tmp19, tmp22)
    tl.store(out_ptr0 + x2, tmp23, xmask)


@triton.jit
def triton_poi_fused_add_div_pow_sub_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp4 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp30 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = tmp1 == tmp1
    tmp5 = 0.25
    tmp6 = tmp4 * tmp5
    tmp7 = tmp4 - tmp6
    tmp8 = tmp7 * tmp7
    tmp10 = tmp9 - tmp6
    tmp11 = tmp10 * tmp10
    tmp12 = tmp8 + tmp11
    tmp13 = tl.where(tmp3, tmp12, tmp8)
    tmp14 = tl.full([1], 2, tl.int32)
    tmp15 = tmp14 == tmp1
    tmp17 = tmp16 - tmp6
    tmp18 = tmp17 * tmp17
    tmp19 = tl.where(tmp15, tmp12, tmp18)
    tmp20 = tmp13 + tmp19
    tmp21 = tl.where(tmp3, tmp20, tmp13)
    tmp22 = tl.full([1], 3, tl.int32)
    tmp23 = tmp22 == tmp1
    tmp25 = tmp24 - tmp6
    tmp26 = tmp25 * tmp25
    tmp27 = tl.where(tmp23, tmp12, tmp26)
    tmp28 = tl.where(tmp23, tmp20, tmp27)
    tmp29 = tmp21 + tmp28
    tmp31 = tmp30 - tmp6
    tmp32 = tmp31 * tmp31
    tmp33 = tl.where(tmp2, tmp12, tmp32)
    tmp34 = tl.where(tmp2, tmp20, tmp33)
    tmp35 = tl.where(tmp2, tmp29, tmp34)
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused_add_div_sqrt_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    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 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = 1e-08
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.sqrt(tmp4)
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_sub_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x5 = xindex // 4
    x2 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x5, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp2 = 0.25
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 - tmp3
    tmp6 = tmp4 / tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x4, tmp10, xmask)
    tl.store(out_ptr1 + x4, tmp0, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](primals_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_pow_sub_1[grid(256)](buf0, buf1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_add_div_sqrt_2[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = buf1
        del buf1
        triton_poi_fused_add_div_mul_sub_3[grid(256)](buf0, buf2, primals_2,
            primals_3, buf3, primals_1, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_1
        del primals_2
        del primals_3
    return buf3, buf0, buf2


def my_mean(x):
    f = x.shape[-1]
    mean = x[..., 0]
    for i in range(1, f):
        mean += x[..., i]
    return mean[..., None] / f


class tLNv2New(nn.Module):

    def __init__(self, dimension, eps=1e-08, trainable=True):
        super(tLNv2New, self).__init__()
        self.eps = eps
        if trainable:
            self.gain = nn.Parameter(torch.ones(1, dimension, 1, 1))
            self.bias = nn.Parameter(torch.zeros(1, dimension, 1, 1))
        else:
            self.gain = Variable(torch.ones(1, dimension, 1, 1),
                requires_grad=False)
            self.bias = Variable(torch.zeros(1, dimension, 1, 1),
                requires_grad=False)

    def forward(self, input_0):
        primals_2 = self.gain
        primals_3 = self.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
