# 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_1/inductor_cache/yg/cygooswl5gkxugqq2ejgag2vtcqhtumn2j3notsgzty3xoxbrq4v.py
# Topologically Sorted Source Nodes: [adaptive_avg_pool2d], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   adaptive_avg_pool2d => mean
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [-1, -2], True), kwargs = {})
triton_per_fused_mean_0 = async_compile.triton('triton_per_fused_mean_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=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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_per_fused_mean_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/xr/cxrbkvclbwezn4blkxettbfalyh5q4bsuxhlcnje4eedoho3alx4.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_2 => relu
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_3), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_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=[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_relu_1', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_1(in_out_ptr0, in_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_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tl.store(in_out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ko/ckohiwbe7h5vj3bnohaai4ijfjs7dx5rmbls3kqi5c4urq6zevlx.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.log, aten.neg, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   x_4 => add, exp, log, neg, sum_1
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_5), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%exponential,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%log,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_tensor, %neg), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 1), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_add_log_neg_2 = async_compile.triton('triton_poi_fused__softmax_add_log_neg_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_log_neg_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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__softmax_add_log_neg_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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 + (2*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + (2*x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + (2*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (1))
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK])
    tmp14 = tl.load(in_ptr2 + (1 + (2*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tmp0 + tmp2
    tmp5 = tl_math.log(tmp4)
    tmp6 = -tmp5
    tmp7 = tmp3 + tmp6
    tmp8 = 1.0
    tmp9 = tmp7 * tmp8
    tmp13 = tmp10 + tmp12
    tmp15 = tl_math.log(tmp14)
    tmp16 = -tmp15
    tmp17 = tmp13 + tmp16
    tmp18 = tmp17 * tmp8
    tmp19 = triton_helpers.maximum(tmp9, tmp18)
    tmp20 = tmp9 - tmp19
    tmp21 = tmp20 * tmp8
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp18 - tmp19
    tmp24 = tmp23 * tmp8
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp22 + tmp25
    tl.store(out_ptr0 + (x0), tmp19, xmask)
    tl.store(out_ptr1 + (x0), tmp26, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/xs/cxsglt6gmdff5j7ipvbbuw5en4f7klmbz2bb4pdkfh5ioui7je36.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.log, aten.neg, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   x_4 => add, div_1, exp, log, neg, sum_1
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_5), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%exponential,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%log,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_tensor, %neg), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 1), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=4] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_add_log_neg_3 = async_compile.triton('triton_poi_fused__softmax_add_log_neg_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=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_log_neg_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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__softmax_add_log_neg_3(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    x1 = (xindex // 2)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp9 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tl_math.log(tmp3)
    tmp5 = -tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = 1.0
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tmp10 * tmp7
    tmp12 = tl_math.exp(tmp11)
    tmp14 = tmp12 / tmp13
    tl.store(in_out_ptr0 + (x2), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/dq/cdq4543nna3f7j45hcsu6apn5rnmsiiztgvx2kvajrzuce4na3zw.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.max, aten.scatter, aten.sub, aten.add]
# Source node to ATen node mapping:
#   x_4 => add_1, max_1, scatter_upon_const_tensor, sub_1
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%div_1, -1, True), kwargs = {})
#   %scatter_upon_const_tensor : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.scatter_upon_const_tensor](args = (), kwargs = {shape: [4, 2], background_val: 0, dtype: torch.float32, dim: -1, selector: %getitem_1, val: 1.0})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%scatter_upon_const_tensor, %div_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, %div_1), kwargs = {})
triton_poi_fused_add_max_scatter_sub_4 = async_compile.triton('triton_poi_fused_add_max_scatter_sub_4', '''
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=[8], 
    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_add_max_scatter_sub_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_add_max_scatter_sub_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 2)
    x0 = xindex % 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2*x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x1)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (x2), xmask)
    tmp2 = tmp0 > tmp1
    tmp3 = tmp0 == tmp1
    tmp4 = tmp0 != tmp0
    tmp5 = tmp1 != tmp1
    tmp6 = tmp4 > tmp5
    tmp7 = tmp2 | tmp6
    tmp8 = tmp4 & tmp5
    tmp9 = tmp3 | tmp8
    tmp10 = tl.full([1], 0, tl.int64)
    tmp11 = tl.full([1], 1, tl.int64)
    tmp12 = tmp10 < tmp11
    tmp13 = tmp9 & tmp12
    tmp14 = tmp7 | tmp13
    tmp15 = tl.where(tmp14, tmp0, tmp1)
    tmp16 = tl.where(tmp14, tmp10, tmp11)
    tmp17 = x0
    tmp18 = tmp16 == tmp17
    tmp19 = 1.0
    tmp20 = 0.0
    tmp21 = tl.where(tmp18, tmp19, tmp20)
    tmp23 = tmp21 - tmp22
    tmp24 = tmp23 + tmp22
    tl.store(out_ptr0 + (x2), tmp24, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (2, 1), (1, 1))
    assert_size_stride(primals_5, (2, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [adaptive_avg_pool2d], Original ATen: [aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_mean_0.run(buf1, primals_1, 16, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), out=buf2)
        del primals_2
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu]
        triton_poi_fused_relu_1.run(buf3, primals_3, 4, grid=grid(4), stream=stream0)
        del primals_3
        buf4 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf3, reinterpret_tensor(primals_4, (1, 2), (1, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.exponential]
        buf6 = torch.ops.aten.exponential.default(buf5)
        del buf5
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf9 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.log, aten.neg, aten.add, aten._softmax]
        triton_poi_fused__softmax_add_log_neg_2.run(buf4, primals_5, buf7, buf8, buf9, 4, grid=grid(4), stream=stream0)
        buf10 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.log, aten.neg, aten.add, aten._softmax]
        triton_poi_fused__softmax_add_log_neg_3.run(buf10, primals_5, buf7, buf8, buf9, 8, grid=grid(8), stream=stream0)
        del buf8
        del buf9
        del primals_5
        buf11 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.max, aten.scatter, aten.sub, aten.add]
        triton_poi_fused_add_max_scatter_sub_4.run(buf10, buf11, 8, grid=grid(8), stream=stream0)
    return (buf11, reinterpret_tensor(buf1, (4, 4), (4, 1), 0), buf3, buf10, primals_4, )


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), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((2, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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.nn.functional as F


class Attention_SEblock(nn.Module):

    def __init__(self, channels, reduction, temperature):
        super(Attention_SEblock, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Linear(channels, channels // reduction)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(channels // reduction, 2)
        self.fc2.bias.data[0] = 0.1
        self.fc2.bias.data[1] = 2
        self.temperature = temperature
        self.channels = channels

    def forward(self, x):
        x = self.avg_pool(x).view(-1, self.channels)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = F.gumbel_softmax(x, tau=1, hard=True)
        return x


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


def get_init_inputs():
    return [[], {'channels': 4, 'reduction': 4, 'temperature': 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 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_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_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tl.store(in_out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused__softmax_add_log_neg_2(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, 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 + 2 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + 2 * x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 2 * x0), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr1 + 1)
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK])
    tmp14 = tl.load(in_ptr2 + (1 + 2 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp0 + tmp2
    tmp5 = tl_math.log(tmp4)
    tmp6 = -tmp5
    tmp7 = tmp3 + tmp6
    tmp8 = 1.0
    tmp9 = tmp7 * tmp8
    tmp13 = tmp10 + tmp12
    tmp15 = tl_math.log(tmp14)
    tmp16 = -tmp15
    tmp17 = tmp13 + tmp16
    tmp18 = tmp17 * tmp8
    tmp19 = triton_helpers.maximum(tmp9, tmp18)
    tmp20 = tmp9 - tmp19
    tmp21 = tmp20 * tmp8
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp18 - tmp19
    tmp24 = tmp23 * tmp8
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp22 + tmp25
    tl.store(out_ptr0 + x0, tmp19, xmask)
    tl.store(out_ptr1 + x0, tmp26, xmask)


@triton.jit
def triton_poi_fused__softmax_add_log_neg_3(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    x1 = xindex // 2
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp9 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tl_math.log(tmp3)
    tmp5 = -tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = 1.0
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tmp10 * tmp7
    tmp12 = tl_math.exp(tmp11)
    tmp14 = tmp12 / tmp13
    tl.store(in_out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused_add_max_scatter_sub_4(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2
    x0 = xindex % 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + x2, xmask)
    tmp2 = tmp0 > tmp1
    tmp3 = tmp0 == tmp1
    tmp4 = tmp0 != tmp0
    tmp5 = tmp1 != tmp1
    tmp6 = tmp4 > tmp5
    tmp7 = tmp2 | tmp6
    tmp8 = tmp4 & tmp5
    tmp9 = tmp3 | tmp8
    tmp10 = tl.full([1], 0, tl.int64)
    tmp11 = tl.full([1], 1, tl.int64)
    tmp12 = tmp10 < tmp11
    tmp13 = tmp9 & tmp12
    tmp14 = tmp7 | tmp13
    tl.where(tmp14, tmp0, tmp1)
    tmp16 = tl.where(tmp14, tmp10, tmp11)
    tmp17 = x0
    tmp18 = tmp16 == tmp17
    tmp19 = 1.0
    tmp20 = 0.0
    tmp21 = tl.where(tmp18, tmp19, tmp20)
    tmp23 = tmp21 - tmp22
    tmp24 = tmp23 + tmp22
    tl.store(out_ptr0 + x2, tmp24, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (2, 1), (1, 1))
    assert_size_stride(primals_5, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), out=buf2)
        del primals_2
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(4)](buf3, primals_3, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_4, (1, 2), (1, 1
            ), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        buf6 = torch.ops.aten.exponential.default(buf5)
        del buf5
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf9 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused__softmax_add_log_neg_2[grid(4)](buf4, primals_5,
            buf7, buf8, buf9, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf10 = buf4
        del buf4
        triton_poi_fused__softmax_add_log_neg_3[grid(8)](buf10, primals_5,
            buf7, buf8, buf9, 8, XBLOCK=8, num_warps=1, num_stages=1)
        del buf8
        del buf9
        del primals_5
        buf11 = buf7
        del buf7
        triton_poi_fused_add_max_scatter_sub_4[grid(8)](buf10, buf11, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
    return buf11, reinterpret_tensor(buf1, (4, 4), (4, 1), 0
        ), buf3, buf10, primals_4


class Attention_SEblockNew(nn.Module):

    def __init__(self, channels, reduction, temperature):
        super(Attention_SEblockNew, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Linear(channels, channels // reduction)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(channels // reduction, 2)
        self.fc2.bias.data[0] = 0.1
        self.fc2.bias.data[1] = 2
        self.temperature = temperature
        self.channels = channels

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
