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]


Measured latency_ms: 0.03467