# AOT ID: ['0_inference']
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_1/inductor_cache/m5/cm56w2t2bg3fyy263ufo23366ui2nrrj7ct7k5ith6xxt7byyrfi.py
# Topologically Sorted Source Nodes: [abs_1, max_1, max_2], Original ATen: [aten.abs, aten.max]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   max_1 => max_1
#   max_2 => max_2
# Graph fragment:
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%abs_1, 0), kwargs = {})
#   %max_2 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%getitem, 1), kwargs = {})
triton_poi_fused_abs_max_0 = async_compile.triton('triton_poi_fused_abs_max_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_max_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_abs_max_0(in_ptr0, out_ptr0, 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')
    tmp2 = tl.load(in_ptr0 + (16 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (32 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (48 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (17 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (33 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (49 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (18 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (34 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (50 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (19 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr0 + (35 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr0 + (51 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tl_math.abs(tmp0)
    tmp3 = tl_math.abs(tmp2)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp6 = tl_math.abs(tmp5)
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tl_math.abs(tmp8)
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tl_math.abs(tmp11)
    tmp14 = tl_math.abs(tmp13)
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp17 = tl_math.abs(tmp16)
    tmp18 = triton_helpers.maximum(tmp15, tmp17)
    tmp20 = tl_math.abs(tmp19)
    tmp21 = triton_helpers.maximum(tmp18, tmp20)
    tmp22 = triton_helpers.maximum(tmp10, tmp21)
    tmp24 = tl_math.abs(tmp23)
    tmp26 = tl_math.abs(tmp25)
    tmp27 = triton_helpers.maximum(tmp24, tmp26)
    tmp29 = tl_math.abs(tmp28)
    tmp30 = triton_helpers.maximum(tmp27, tmp29)
    tmp32 = tl_math.abs(tmp31)
    tmp33 = triton_helpers.maximum(tmp30, tmp32)
    tmp34 = triton_helpers.maximum(tmp22, tmp33)
    tmp36 = tl_math.abs(tmp35)
    tmp38 = tl_math.abs(tmp37)
    tmp39 = triton_helpers.maximum(tmp36, tmp38)
    tmp41 = tl_math.abs(tmp40)
    tmp42 = triton_helpers.maximum(tmp39, tmp41)
    tmp44 = tl_math.abs(tmp43)
    tmp45 = triton_helpers.maximum(tmp42, tmp44)
    tmp46 = triton_helpers.maximum(tmp34, tmp45)
    tl.store(out_ptr0 + (x0), tmp46, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/im/cimrjwjy6ikyz6r55vyb52pdsz5v4kcxofvkvzks6ege7cowcaad.py
# Topologically Sorted Source Nodes: [divider_2, x], Original ATen: [aten.repeat, aten.div]
# Source node to ATen node mapping:
#   divider_2 => repeat
#   x => div
# Graph fragment:
#   %repeat : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%unsqueeze_1, [4, 1, 4]), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %repeat), kwargs = {})
triton_poi_fused_div_repeat_1 = async_compile.triton('triton_poi_fused_div_repeat_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_repeat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_div_repeat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = 1e-05
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 / tmp3
    tl.store(out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [abs_1, max_1, max_2], Original ATen: [aten.abs, aten.max]
        stream0 = get_raw_stream(0)
        triton_poi_fused_abs_max_0.run(arg0_1, buf0, 4, grid=grid(4), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [divider_2, x], Original ATen: [aten.repeat, aten.div]
        triton_poi_fused_div_repeat_1.run(arg0_1, buf0, buf1, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del buf0
    return (buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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


class ChannelNorm(nn.Module):

    def __init__(self):
        super(ChannelNorm, self).__init__()

    def forward(self, x):
        divider = torch.max(torch.max(torch.abs(x), dim=0)[0], dim=1)[0
            ] + 1e-05
        divider = divider.unsqueeze(0).unsqueeze(2)
        divider = divider.repeat(x.size(0), 1, x.size(2))
        x = x / divider
        return x


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


def get_init_inputs():
    return [[], {}]

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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
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_abs_max_0(in_ptr0, out_ptr0, 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')
    tmp2 = tl.load(in_ptr0 + (16 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (32 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr0 + (48 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr0 + (17 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr0 + (33 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (49 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr0 + (18 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (34 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp31 = tl.load(in_ptr0 + (50 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp35 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp37 = tl.load(in_ptr0 + (19 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr0 + (35 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp43 = tl.load(in_ptr0 + (51 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl_math.abs(tmp0)
    tmp3 = tl_math.abs(tmp2)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp6 = tl_math.abs(tmp5)
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tl_math.abs(tmp8)
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tl_math.abs(tmp11)
    tmp14 = tl_math.abs(tmp13)
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp17 = tl_math.abs(tmp16)
    tmp18 = triton_helpers.maximum(tmp15, tmp17)
    tmp20 = tl_math.abs(tmp19)
    tmp21 = triton_helpers.maximum(tmp18, tmp20)
    tmp22 = triton_helpers.maximum(tmp10, tmp21)
    tmp24 = tl_math.abs(tmp23)
    tmp26 = tl_math.abs(tmp25)
    tmp27 = triton_helpers.maximum(tmp24, tmp26)
    tmp29 = tl_math.abs(tmp28)
    tmp30 = triton_helpers.maximum(tmp27, tmp29)
    tmp32 = tl_math.abs(tmp31)
    tmp33 = triton_helpers.maximum(tmp30, tmp32)
    tmp34 = triton_helpers.maximum(tmp22, tmp33)
    tmp36 = tl_math.abs(tmp35)
    tmp38 = tl_math.abs(tmp37)
    tmp39 = triton_helpers.maximum(tmp36, tmp38)
    tmp41 = tl_math.abs(tmp40)
    tmp42 = triton_helpers.maximum(tmp39, tmp41)
    tmp44 = tl_math.abs(tmp43)
    tmp45 = triton_helpers.maximum(tmp42, tmp44)
    tmp46 = triton_helpers.maximum(tmp34, tmp45)
    tl.store(out_ptr0 + x0, tmp46, xmask)


@triton.jit
def triton_poi_fused_div_repeat_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = 1e-05
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 / tmp3
    tl.store(out_ptr0 + x3, tmp4, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_abs_max_0[grid(4)](arg0_1, buf0, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_div_repeat_1[grid(64)](arg0_1, buf0, buf1, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del buf0
    return buf1,


class ChannelNormNew(nn.Module):

    def __init__(self):
        super(ChannelNormNew, self).__init__()

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
