# 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_4/inductor_cache/72/c72xsm4j2ydy65cd6s2dqcf73hjvkqbi2lraxbqgd3vljynafcgn.py
# Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   max_1 => max_1
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%view, 1), kwargs = {})
triton_per_fused_max_0 = async_compile.triton('triton_per_fused_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.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_max_0', '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_max_0(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.full([1, 1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, float("-inf"))
    tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/yh/cyhmw3wd3ttwv3gwn5pdrawto4dhpm5r565oikeygp4rncuwxbr2.py
# Topologically Sorted Source Nodes: [batch, truediv, exp, pad], Original ATen: [aten.relu, aten.div, aten.exp, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   batch => relu
#   exp => exp
#   pad => constant_pad_nd
#   truediv => div
# Graph fragment:
#   %relu : [num_users=4] = call_function[target=torch.ops.aten.relu.default](args = (%arg0_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%relu, %view_1), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div,), kwargs = {})
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%exp, [1, 1, 1, 1], 1.0), kwargs = {})
triton_poi_fused_constant_pad_nd_div_exp_relu_1 = async_compile.triton('triton_poi_fused_constant_pad_nd_div_exp_relu_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=[1024], 
    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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_div_exp_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_constant_pad_nd_div_exp_relu_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 6) % 6
    x0 = xindex % 6
    x4 = (xindex // 36)
    x3 = (xindex // 144)
    x6 = xindex
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-1) + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-5) + x0 + (4*x1) + (16*x4)), tmp10 & xmask, other=0.0)
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.load(in_ptr1 + (x3), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 / tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tl.full(tmp16.shape, 1.0, tmp16.dtype)
    tmp18 = tl.where(tmp10, tmp16, tmp17)
    tl.store(out_ptr0 + (x6), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/av/cavduzqn2kcecsivfbdoiquab64kxakphvxa2ir5glbkv2uxr56x.py
# Topologically Sorted Source Nodes: [batch, truediv, exp, pad, avg_pool2d], Original ATen: [aten.relu, aten.div, aten.exp, aten.constant_pad_nd, aten.avg_pool2d]
# Source node to ATen node mapping:
#   avg_pool2d => avg_pool2d
#   batch => relu
#   exp => exp
#   pad => constant_pad_nd
#   truediv => div
# Graph fragment:
#   %relu : [num_users=4] = call_function[target=torch.ops.aten.relu.default](args = (%arg0_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%relu, %view_1), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div,), kwargs = {})
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%exp, [1, 1, 1, 1], 1.0), kwargs = {})
#   %avg_pool2d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%constant_pad_nd, [3, 3], [1, 1]), kwargs = {})
triton_poi_fused_avg_pool2d_constant_pad_nd_div_exp_relu_2 = async_compile.triton('triton_poi_fused_avg_pool2d_constant_pad_nd_div_exp_relu_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=[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_avg_pool2d_constant_pad_nd_div_exp_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_avg_pool2d_constant_pad_nd_div_exp_relu_2(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) % 4
    x2 = (xindex // 16)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (6*x1) + (36*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + (6*x1) + (36*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (2 + x0 + (6*x1) + (36*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (6 + x0 + (6*x1) + (36*x2)), xmask)
    tmp7 = tl.load(in_ptr0 + (7 + x0 + (6*x1) + (36*x2)), xmask)
    tmp9 = tl.load(in_ptr0 + (8 + x0 + (6*x1) + (36*x2)), xmask)
    tmp11 = tl.load(in_ptr0 + (12 + x0 + (6*x1) + (36*x2)), xmask)
    tmp13 = tl.load(in_ptr0 + (13 + x0 + (6*x1) + (36*x2)), xmask)
    tmp15 = tl.load(in_ptr0 + (14 + x0 + (6*x1) + (36*x2)), xmask)
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp8 = tmp7 + tmp6
    tmp10 = tmp9 + tmp8
    tmp12 = tmp11 + tmp10
    tmp14 = tmp13 + tmp12
    tmp16 = tmp15 + tmp14
    tmp17 = 0.1111111111111111
    tmp18 = tmp16 * tmp17
    tl.store(out_ptr0 + (x3), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6v/c6v2dq4j27mrmzkudmd2l2ookfsao2o6sqtsagdumpcc33lwpchn.py
# Topologically Sorted Source Nodes: [batch, truediv, exp, sum_exp, local_max_score, depth_wise_max_score, all_scores, max_3, sum_1, score_1], Original ATen: [aten.relu, aten.div, aten.exp, aten.mul, aten.max, aten.sum]
# Source node to ATen node mapping:
#   all_scores => mul_1
#   batch => relu
#   depth_wise_max_score => div_2
#   exp => exp
#   local_max_score => div_1
#   max_3 => max_3
#   score_1 => div_3
#   sum_1 => sum_1
#   sum_exp => mul
#   truediv => div
# Graph fragment:
#   %relu : [num_users=4] = call_function[target=torch.ops.aten.relu.default](args = (%arg0_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%relu, %view_1), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%avg_pool2d, 9), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %mul), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%relu, %unsqueeze), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, %div_2), kwargs = {})
#   %max_3 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul_1, 1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_2, [1]), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%getitem_4, %view_3), kwargs = {})
triton_per_fused_div_exp_max_mul_relu_sum_3 = async_compile.triton('triton_per_fused_div_exp_max_mul_relu_sum_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_exp_max_mul_relu_sum_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_div_exp_max_mul_relu_sum_3(in_ptr0, in_ptr1, in_ptr2, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    tmp3 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (r1 + (64*x0)), xmask, other=0.0)
    tmp10 = tl.load(in_ptr0 + (16 + r1 + (64*x0)), xmask, other=0.0)
    tmp13 = tl.load(in_ptr0 + (32 + r1 + (64*x0)), xmask, other=0.0)
    tmp16 = tl.load(in_ptr0 + (48 + r1 + (64*x0)), xmask, other=0.0)
    tmp23 = tl.load(in_ptr2 + (16 + r1 + (64*x0)), xmask, other=0.0)
    tmp31 = tl.load(in_ptr2 + (32 + r1 + (64*x0)), xmask, other=0.0)
    tmp39 = tl.load(in_ptr2 + (48 + r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = tmp2 / tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = 9.0
    tmp8 = tmp6 * tmp7
    tmp9 = tmp5 / tmp8
    tmp11 = triton_helpers.maximum(tmp1, tmp10)
    tmp12 = triton_helpers.maximum(tmp2, tmp11)
    tmp14 = triton_helpers.maximum(tmp1, tmp13)
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp17 = triton_helpers.maximum(tmp1, tmp16)
    tmp18 = triton_helpers.maximum(tmp15, tmp17)
    tmp19 = tmp2 / tmp18
    tmp20 = tmp9 * tmp19
    tmp21 = tmp11 / tmp3
    tmp22 = tl_math.exp(tmp21)
    tmp24 = tmp23 * tmp7
    tmp25 = tmp22 / tmp24
    tmp26 = tmp11 / tmp18
    tmp27 = tmp25 * tmp26
    tmp28 = triton_helpers.maximum(tmp20, tmp27)
    tmp29 = tmp14 / tmp3
    tmp30 = tl_math.exp(tmp29)
    tmp32 = tmp31 * tmp7
    tmp33 = tmp30 / tmp32
    tmp34 = tmp14 / tmp18
    tmp35 = tmp33 * tmp34
    tmp36 = triton_helpers.maximum(tmp28, tmp35)
    tmp37 = tmp17 / tmp3
    tmp38 = tl_math.exp(tmp37)
    tmp40 = tmp39 * tmp7
    tmp41 = tmp38 / tmp40
    tmp42 = tmp17 / tmp18
    tmp43 = tmp41 * tmp42
    tmp44 = triton_helpers.maximum(tmp36, tmp43)
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp47 = tl.where(xmask, tmp45, 0)
    tmp48 = tl.sum(tmp47, 1)[:, None]
    tmp49 = tmp44 / tmp48
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp49, 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, 4), (64, 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: [max_1], Original ATen: [aten.max]
        stream0 = get_raw_stream(0)
        triton_per_fused_max_0.run(arg0_1, buf0, 4, 64, grid=grid(4), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch, truediv, exp, pad], Original ATen: [aten.relu, aten.div, aten.exp, aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_div_exp_relu_1.run(arg0_1, buf0, buf2, 576, grid=grid(576), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch, truediv, exp, pad, avg_pool2d], Original ATen: [aten.relu, aten.div, aten.exp, aten.constant_pad_nd, aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_constant_pad_nd_div_exp_relu_2.run(buf2, buf3, 256, grid=grid(256), stream=stream0)
        del buf2
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch, truediv, exp, sum_exp, local_max_score, depth_wise_max_score, all_scores, max_3, sum_1, score_1], Original ATen: [aten.relu, aten.div, aten.exp, aten.mul, aten.max, aten.sum]
        triton_per_fused_div_exp_max_mul_relu_sum_3.run(arg0_1, buf0, buf3, buf6, 4, 16, grid=grid(4), stream=stream0)
        del arg0_1
        del buf0
        del buf3
    return (buf6, )


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, 4), (64, 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.functional as F
import torch.nn as nn


class SoftDetectionModule(nn.Module):

    def __init__(self, soft_local_max_size=3):
        super(SoftDetectionModule, self).__init__()
        self.soft_local_max_size = soft_local_max_size
        self.pad = self.soft_local_max_size // 2

    def forward(self, batch):
        b = batch.size(0)
        batch = F.relu(batch)
        max_per_sample = torch.max(batch.view(b, -1), dim=1)[0]
        exp = torch.exp(batch / max_per_sample.view(b, 1, 1, 1))
        sum_exp = self.soft_local_max_size ** 2 * F.avg_pool2d(F.pad(exp, [
            self.pad] * 4, mode='constant', value=1.0), self.
            soft_local_max_size, stride=1)
        local_max_score = exp / sum_exp
        depth_wise_max = torch.max(batch, dim=1)[0]
        depth_wise_max_score = batch / depth_wise_max.unsqueeze(1)
        all_scores = local_max_score * depth_wise_max_score
        score = torch.max(all_scores, dim=1)[0]
        score = score / torch.sum(score.view(b, -1), dim=1).view(b, 1, 1)
        return score


def get_inputs():
    return [torch.rand([4, 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_per_fused_max_0(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.full([1, 1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, float('-inf'))
    tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_div_exp_relu_1(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 6 % 6
    x0 = xindex % 6
    x4 = xindex // 36
    x3 = xindex // 144
    x6 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x4), tmp10 & xmask,
        other=0.0)
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.load(in_ptr1 + x3, tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp13 / tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tl.full(tmp16.shape, 1.0, tmp16.dtype)
    tmp18 = tl.where(tmp10, tmp16, tmp17)
    tl.store(out_ptr0 + x6, tmp18, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_constant_pad_nd_div_exp_relu_2(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 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 6 * x1 + 36 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + 6 * x1 + 36 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (2 + x0 + 6 * x1 + 36 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (6 + x0 + 6 * x1 + 36 * x2), xmask)
    tmp7 = tl.load(in_ptr0 + (7 + x0 + 6 * x1 + 36 * x2), xmask)
    tmp9 = tl.load(in_ptr0 + (8 + x0 + 6 * x1 + 36 * x2), xmask)
    tmp11 = tl.load(in_ptr0 + (12 + x0 + 6 * x1 + 36 * x2), xmask)
    tmp13 = tl.load(in_ptr0 + (13 + x0 + 6 * x1 + 36 * x2), xmask)
    tmp15 = tl.load(in_ptr0 + (14 + x0 + 6 * x1 + 36 * x2), xmask)
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp8 = tmp7 + tmp6
    tmp10 = tmp9 + tmp8
    tmp12 = tmp11 + tmp10
    tmp14 = tmp13 + tmp12
    tmp16 = tmp15 + tmp14
    tmp17 = 0.1111111111111111
    tmp18 = tmp16 * tmp17
    tl.store(out_ptr0 + x3, tmp18, xmask)


@triton.jit
def triton_per_fused_div_exp_max_mul_relu_sum_3(in_ptr0, in_ptr1, in_ptr2,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    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)
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (r1 + 64 * x0), xmask, other=0.0)
    tmp10 = tl.load(in_ptr0 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp13 = tl.load(in_ptr0 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp16 = tl.load(in_ptr0 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp23 = tl.load(in_ptr2 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp31 = tl.load(in_ptr2 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp39 = tl.load(in_ptr2 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = tmp2 / tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = 9.0
    tmp8 = tmp6 * tmp7
    tmp9 = tmp5 / tmp8
    tmp11 = triton_helpers.maximum(tmp1, tmp10)
    tmp12 = triton_helpers.maximum(tmp2, tmp11)
    tmp14 = triton_helpers.maximum(tmp1, tmp13)
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp17 = triton_helpers.maximum(tmp1, tmp16)
    tmp18 = triton_helpers.maximum(tmp15, tmp17)
    tmp19 = tmp2 / tmp18
    tmp20 = tmp9 * tmp19
    tmp21 = tmp11 / tmp3
    tmp22 = tl_math.exp(tmp21)
    tmp24 = tmp23 * tmp7
    tmp25 = tmp22 / tmp24
    tmp26 = tmp11 / tmp18
    tmp27 = tmp25 * tmp26
    tmp28 = triton_helpers.maximum(tmp20, tmp27)
    tmp29 = tmp14 / tmp3
    tmp30 = tl_math.exp(tmp29)
    tmp32 = tmp31 * tmp7
    tmp33 = tmp30 / tmp32
    tmp34 = tmp14 / tmp18
    tmp35 = tmp33 * tmp34
    tmp36 = triton_helpers.maximum(tmp28, tmp35)
    tmp37 = tmp17 / tmp3
    tmp38 = tl_math.exp(tmp37)
    tmp40 = tmp39 * tmp7
    tmp41 = tmp38 / tmp40
    tmp42 = tmp17 / tmp18
    tmp43 = tmp41 * tmp42
    tmp44 = triton_helpers.maximum(tmp36, tmp43)
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp47 = tl.where(xmask, tmp45, 0)
    tmp48 = tl.sum(tmp47, 1)[:, None]
    tmp49 = tmp44 / tmp48
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp49, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 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_per_fused_max_0[grid(4)](arg0_1, buf0, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_div_exp_relu_1[grid(576)](arg0_1,
            buf0, buf2, 576, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_avg_pool2d_constant_pad_nd_div_exp_relu_2[grid(256)](
            buf2, buf3, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf2
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_per_fused_div_exp_max_mul_relu_sum_3[grid(4)](arg0_1, buf0,
            buf3, buf6, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del buf0
        del buf3
    return buf6,


class SoftDetectionModuleNew(nn.Module):

    def __init__(self, soft_local_max_size=3):
        super(SoftDetectionModuleNew, self).__init__()
        self.soft_local_max_size = soft_local_max_size
        self.pad = self.soft_local_max_size // 2

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