# 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_7/inductor_cache/bx/cbxo3ysebjxo7frqqfuqagvyggzebou7r7gl4zlptrlb2nihr64v.py
# Topologically Sorted Source Nodes: [raw_features, pooling_features], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
# Source node to ATen node mapping:
#   pooling_features => mul_1
#   raw_features => clamp_min, div_1, pow_1, pow_2, sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_4, 2.0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_2, 1e-12), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_4, %expand), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, 100.0), kwargs = {})
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0 = async_compile.triton('triton_poi_fused_clamp_min_div_linalg_vector_norm_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = 0.0625
    tmp2 = tmp0 * tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = tmp3 < tmp2
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp2 < tmp3
    tmp7 = tmp6.to(tl.int8)
    tmp8 = tmp5 - tmp7
    tmp9 = tmp8.to(tmp2.dtype)
    tmp10 = tl_math.abs(tmp2)
    tmp11 = 1e-12
    tmp12 = tmp10 + tmp11
    tmp13 = libdevice.sqrt(tmp12)
    tmp14 = tmp9 * tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tmp17 = triton_helpers.maximum(tmp16, tmp11)
    tmp18 = tmp14 / tmp17
    tmp19 = 100.0
    tmp20 = tmp18 * tmp19
    tl.store(in_out_ptr0 + (x0), tmp20, 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, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [phi_I], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(arg0_1, (4, 16, 4), (64, 1, 16), 0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = reinterpret_tensor(buf0, (4, 16, 1, 1), (16, 1, 1, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [raw_features, pooling_features], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0.run(buf1, 64, grid=grid(64), stream=stream0)
    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, 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, **kwargs):
        super(BAP, self).__init__()

    def forward(self, feature_maps, attention_maps):
        feature_shape = feature_maps.size()
        attention_shape = attention_maps.size()
        phi_I = torch.einsum('imjk,injk->imn', (attention_maps, feature_maps))
        phi_I = torch.div(phi_I, attention_shape[1] * attention_shape[2])
        phi_I = torch.mul(torch.sign(phi_I), torch.sqrt(torch.abs(phi_I) + 
            1e-12))
        phi_I = phi_I.view(feature_shape[0], -1, 1, 1)
        raw_features = torch.nn.functional.normalize(phi_I, dim=-1)
        pooling_features = raw_features * 100.0
        return pooling_features


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


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

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 libdevice, 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_poi_fused_clamp_min_div_linalg_vector_norm_mul_0(in_out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 0.0625
    tmp2 = tmp0 * tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = tmp3 < tmp2
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp2 < tmp3
    tmp7 = tmp6.to(tl.int8)
    tmp8 = tmp5 - tmp7
    tmp9 = tmp8.to(tmp2.dtype)
    tmp10 = tl_math.abs(tmp2)
    tmp11 = 1e-12
    tmp12 = tmp10 + tmp11
    tmp13 = libdevice.sqrt(tmp12)
    tmp14 = tmp9 * tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tmp17 = triton_helpers.maximum(tmp16, tmp11)
    tmp18 = tmp14 / tmp17
    tmp19 = 100.0
    tmp20 = tmp18 * tmp19
    tl.store(in_out_ptr0 + x0, tmp20, 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, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 4, 16), (64, 16, 
            1), 0), reinterpret_tensor(arg0_1, (4, 16, 4), (64, 1, 16), 0),
            out=buf0)
        del arg0_1
        del arg1_1
        buf1 = reinterpret_tensor(buf0, (4, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0[grid(64)](buf1,
            64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf1,


class BAPNew(nn.Module):

    def __init__(self, **kwargs):
        super(BAPNew, self).__init__()

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