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
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
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


def import_flatten_impl():
    global flatten_impl, unflatten_impl, imported_flatten_impl
    try:
        flatten_impl = apex_C.flatten
        unflatten_impl = apex_C.unflatten
    except ImportError:
        None
        flatten_impl = torch._utils._flatten_dense_tensors
        unflatten_impl = torch._utils._unflatten_dense_tensors
    imported_flatten_impl = True


def flatten(bucket):
    if not imported_flatten_impl:
        import_flatten_impl()
    return flatten_impl(bucket)


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]


Measured latency_ms: 0.038139