# 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/hk/chkpslg5m5ihvdto3wwfd7lir2sackpfx7tasffh776j65cyuh3u.py
# Topologically Sorted Source Nodes: [mul, adaptive_avg_pool2d, mul_1, adaptive_avg_pool2d_1, mul_2, adaptive_avg_pool2d_2, mul_3, adaptive_avg_pool2d_3, feature_matrix_2], Original ATen: [aten.mul, aten.mean, aten.cat]
# Source node to ATen node mapping:
#   adaptive_avg_pool2d => mean
#   adaptive_avg_pool2d_1 => mean_1
#   adaptive_avg_pool2d_2 => mean_2
#   adaptive_avg_pool2d_3 => mean_3
#   feature_matrix_2 => cat_2
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %slice_2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul, [-1, -2], True), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %slice_4), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul_1, [-1, -2], True), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %slice_6), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul_2, [-1, -2], True), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %slice_8), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul_3, [-1, -2], True), kwargs = {})
#   %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%cat_1, %view_3], 1), kwargs = {})
triton_per_fused_cat_mean_mul_0 = async_compile.triton('triton_per_fused_cat_mean_mul_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.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_cat_mean_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_cat_mean_mul_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr4, 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)
    r2 = rindex
    x3 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (r2 + (16*x3)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.load(in_ptr1 + (16 + r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
    tmp13 = tl.load(in_ptr1 + (32 + r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tl.load(in_ptr1 + (48 + r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp8 = tmp0 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp14 = tmp0 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp20 = tmp0 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.where(xmask, tmp21, 0)
    tmp24 = tl.sum(tmp23, 1)[:, None]
    tmp25 = 16.0
    tmp26 = tmp24 / tmp25
    tl.store(out_ptr4 + (x0 + (16*x1)), tmp26, xmask)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
    tl.store(out_ptr1 + (x3), tmp12, xmask)
    tl.store(out_ptr2 + (x3), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/r5/cr53h7usiewja26u2sea4u4jtduxgzde5otqe4joagw4acv3jppj.py
# Topologically Sorted Source Nodes: [feature_matrix_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   feature_matrix_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%cat, %view_2], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 3
    x0 = xindex % 4
    x2 = (xindex // 12)
    x3 = xindex % 12
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 1, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.load(in_ptr0 + (x0 + (4*x2)), tmp7 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = 16.0
    tmp10 = tmp8 / tmp9
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp7, tmp10, tmp11)
    tmp13 = tmp0 >= tmp5
    tmp14 = tmp13 & tmp4
    tmp15 = tl.load(in_ptr1 + (x0 + (4*x2)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp15 / tmp9
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp14, tmp16, tmp17)
    tmp19 = tl.where(tmp6, tmp12, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp4, tmp19, tmp20)
    tmp22 = tmp0 >= tmp3
    tmp23 = tl.full([1], 3, tl.int64)
    tmp24 = tmp0 < tmp23
    tmp25 = tl.load(in_ptr2 + (x0 + (4*x2)), tmp22 & xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp25 / tmp9
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp22, tmp26, tmp27)
    tmp29 = tl.where(tmp4, tmp21, tmp28)
    tl.store(out_ptr0 + (x3 + (16*x2)), tmp29, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 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 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf5 = reinterpret_tensor(buf6, (4, 1, 4), (16, 4, 1), 12)  # alias
        # Topologically Sorted Source Nodes: [mul, adaptive_avg_pool2d, mul_1, adaptive_avg_pool2d_1, mul_2, adaptive_avg_pool2d_2, mul_3, adaptive_avg_pool2d_3, feature_matrix_2], Original ATen: [aten.mul, aten.mean, aten.cat]
        stream0 = get_raw_stream(0)
        triton_per_fused_cat_mean_mul_0.run(arg0_1, arg1_1, buf0, buf1, buf2, buf5, 16, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
        buf4 = reinterpret_tensor(buf6, (4, 3, 4), (16, 4, 1), 0)  # alias
        # Topologically Sorted Source Nodes: [feature_matrix_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf0, buf1, buf2, buf4, 48, grid=grid(48), stream=stream0)
        del buf0
        del buf1
        del buf2
    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)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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 BAP(nn.Module):

    def __init__(self, pool='GAP'):
        super(BAP, self).__init__()
        assert pool in ['GAP', 'GMP']
        if pool == 'GAP':
            self.pool = nn.AdaptiveAvgPool2d(1)
        else:
            self.pool = nn.AdaptiveMaxPool2d(1)

    def forward(self, features, attentions):
        B = features.size(0)
        M = attentions.size(1)
        for i in range(M):
            AiF = self.pool(features * attentions[:, i:i + 1, ...]).view(B,
                1, -1)
            if i == 0:
                feature_matrix = AiF
            else:
                feature_matrix = torch.cat([feature_matrix, AiF], dim=1)
        return feature_matrix


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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
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_cat_mean_mul_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, out_ptr4, 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)
    r2 = rindex
    x3 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r2 + 64 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tl.load(in_ptr1 + (16 + r2 + 64 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp13 = tl.load(in_ptr1 + (32 + r2 + 64 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp19 = tl.load(in_ptr1 + (48 + r2 + 64 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp8 = tmp0 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp14 = tmp0 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp20 = tmp0 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.where(xmask, tmp21, 0)
    tmp24 = tl.sum(tmp23, 1)[:, None]
    tmp25 = 16.0
    tmp26 = tmp24 / tmp25
    tl.store(out_ptr4 + (x0 + 16 * x1), tmp26, xmask)
    tl.store(out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp12, xmask)
    tl.store(out_ptr2 + x3, tmp18, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 3
    x0 = xindex % 4
    x2 = xindex // 12
    x3 = xindex % 12
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 1, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.load(in_ptr0 + (x0 + 4 * x2), tmp7 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp9 = 16.0
    tmp10 = tmp8 / tmp9
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp7, tmp10, tmp11)
    tmp13 = tmp0 >= tmp5
    tmp14 = tmp13 & tmp4
    tmp15 = tl.load(in_ptr1 + (x0 + 4 * x2), tmp14 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp16 = tmp15 / tmp9
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp14, tmp16, tmp17)
    tmp19 = tl.where(tmp6, tmp12, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp4, tmp19, tmp20)
    tmp22 = tmp0 >= tmp3
    tl.full([1], 3, tl.int64)
    tmp25 = tl.load(in_ptr2 + (x0 + 4 * x2), tmp22 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp26 = tmp25 / tmp9
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp22, tmp26, tmp27)
    tmp29 = tl.where(tmp4, tmp21, tmp28)
    tl.store(out_ptr0 + (x3 + 16 * x2), tmp29, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 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 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf5 = reinterpret_tensor(buf6, (4, 1, 4), (16, 4, 1), 12)
        get_raw_stream(0)
        triton_per_fused_cat_mean_mul_0[grid(16)](arg0_1, arg1_1, buf0,
            buf1, buf2, buf5, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf4 = reinterpret_tensor(buf6, (4, 3, 4), (16, 4, 1), 0)
        triton_poi_fused_cat_1[grid(48)](buf0, buf1, buf2, buf4, 48, XBLOCK
            =64, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del buf2
    return buf6,


class BAPNew(nn.Module):

    def __init__(self, pool='GAP'):
        super(BAPNew, self).__init__()
        assert pool in ['GAP', 'GMP']
        if pool == 'GAP':
            self.pool = nn.AdaptiveAvgPool2d(1)
        else:
            self.pool = nn.AdaptiveMaxPool2d(1)

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
