# 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_7/inductor_cache/jz/cjzoc6xisenudem73wv6jtmndx35ok7u4hfkm7ipl6rjq5za2ecw.py
# Topologically Sorted Source Nodes: [m, sigmoid, m_1, sum_1], Original ATen: [aten.convolution, aten.sigmoid, aten.exp, aten.sum]
# Source node to ATen node mapping:
#   m => convolution
#   m_1 => exp
#   sigmoid => sigmoid
#   sum_1 => sum_1
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sigmoid,), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2, 3], True), kwargs = {})
triton_per_fused_convolution_exp_sigmoid_sum_0 = async_compile.triton('triton_per_fused_convolution_exp_sigmoid_sum_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, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_convolution_exp_sigmoid_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_exp_sigmoid_sum_0(in_out_ptr0, in_ptr0, out_ptr0, 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_out_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tl.store(in_out_ptr0 + (r1 + (16*x0)), tmp3, xmask)
    tl.store(out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/u7/cu7tuonvj2l2bybmuzk4zjpmuntxy47o26ta565v5zj47gq5o2pu.py
# Topologically Sorted Source Nodes: [sigmoid, m_1, x, x_1, x_2], Original ATen: [aten.sigmoid, aten.exp, aten.div, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   m_1 => exp
#   sigmoid => sigmoid
#   x => div
#   x_1 => mul
#   x_2 => sum_2
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sigmoid,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %primals_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2, 3], True), kwargs = {})
triton_per_fused_div_exp_mul_sigmoid_sum_1 = async_compile.triton('triton_per_fused_div_exp_mul_sigmoid_sum_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.persistent_reduction(
    size_hints=[16, 16],
    reduction_hint=ReductionHint.DEFAULT,
    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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_exp_mul_sigmoid_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_mul_sigmoid_sum_1(in_ptr0, in_ptr1, in_ptr2, out_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)
    r2 = rindex
    x1 = (xindex // 4)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + (16*x1)), xmask, eviction_policy='evict_last', other=0.0)
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (r2 + (16*x3)), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 / tmp3
    tmp6 = tmp4 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [m], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [m, sigmoid, m_1, sum_1], Original ATen: [aten.convolution, aten.sigmoid, aten.exp, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_convolution_exp_sigmoid_sum_0.run(buf1, primals_3, buf2, 4, 16, grid=grid(4), stream=stream0)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, m_1, x, x_1, x_2], Original ATen: [aten.sigmoid, aten.exp, aten.div, aten.mul, aten.sum]
        triton_per_fused_div_exp_mul_sigmoid_sum_1.run(buf1, buf2, primals_1, buf3, 16, 16, grid=grid(16), stream=stream0)
    return (buf3, primals_1, primals_2, buf1, buf2, )


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, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
from torch import nn


class GlobalWeightedAvgPool2d(nn.Module):
    """
    Global Weighted Average Pooling from paper "Global Weighted Average
    Pooling Bridges Pixel-level Localization and Image-level Classification"
    """

    def __init__(self, features: 'int', flatten=False):
        super().__init__()
        self.conv = nn.Conv2d(features, 1, kernel_size=1, bias=True)
        self.flatten = flatten

    def fscore(self, x):
        m = self.conv(x)
        m = m.sigmoid().exp()
        return m

    def norm(self, x: 'torch.Tensor'):
        return x / x.sum(dim=[2, 3], keepdim=True)

    def forward(self, x):
        input_x = x
        x = self.fscore(x)
        x = self.norm(x)
        x = x * input_x
        x = x.sum(dim=[2, 3], keepdim=not self.flatten)
        return x


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


def get_init_inputs():
    return [[], {'features': 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.triton_helpers import math as tl_math
from torch import 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_convolution_exp_sigmoid_sum_0(in_out_ptr0, in_ptr0,
    out_ptr0, 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_out_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tl.store(in_out_ptr0 + (r1 + 16 * x0), tmp3, xmask)
    tl.store(out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_per_fused_div_exp_mul_sigmoid_sum_1(in_ptr0, in_ptr1, in_ptr2,
    out_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)
    r2 = rindex
    x1 = xindex // 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 16 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (r2 + 16 * x3), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 / tmp3
    tmp6 = tmp4 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 1, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_convolution_exp_sigmoid_sum_0[grid(4)](buf1,
            primals_3, buf2, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_per_fused_div_exp_mul_sigmoid_sum_1[grid(16)](buf1, buf2,
            primals_1, buf3, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
    return buf3, primals_1, primals_2, buf1, buf2


class GlobalWeightedAvgPool2dNew(nn.Module):
    """
    Global Weighted Average Pooling from paper "Global Weighted Average
    Pooling Bridges Pixel-level Localization and Image-level Classification"
    """

    def __init__(self, features: 'int', flatten=False):
        super().__init__()
        self.conv = nn.Conv2d(features, 1, kernel_size=1, bias=True)
        self.flatten = flatten

    def fscore(self, x):
        m = self.conv(x)
        m = m.sigmoid().exp()
        return m

    def norm(self, x: 'torch.Tensor'):
        return x / x.sum(dim=[2, 3], keepdim=True)

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