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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused__to_copy_add_div_lt_maximum_minimum_mul_pow_prod_rsub_sub_0(
    in_out_ptr0, in_ptr0, in_ptr1, 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_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tmp5 + tmp7
    tmp9 = triton_helpers.minimum(tmp4, tmp8)
    tmp10 = tmp0 - tmp3
    tmp11 = tmp5 - tmp7
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp13 = tmp9 - tmp12
    tmp16 = tmp15 * tmp2
    tmp17 = tmp14 + tmp16
    tmp20 = tmp19 * tmp2
    tmp21 = tmp18 + tmp20
    tmp22 = triton_helpers.minimum(tmp17, tmp21)
    tmp23 = tmp14 - tmp16
    tmp24 = tmp18 - tmp20
    tmp25 = triton_helpers.maximum(tmp23, tmp24)
    tmp26 = tmp22 - tmp25
    tmp27 = tmp13 * tmp26
    tmp28 = tmp12 < tmp9
    tmp29 = tmp28.to(tl.float32)
    tmp30 = tmp25 < tmp22
    tmp31 = tmp30.to(tl.float32)
    tmp32 = tmp29 * tmp31
    tmp33 = tmp27 * tmp32
    tmp34 = tmp1 * tmp15
    tmp35 = tmp6 * tmp19
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 - tmp33
    tmp38 = 1e-16
    tmp39 = tmp37 + tmp38
    tmp40 = tmp33 / tmp39
    tmp41 = tmp40 * tmp40
    tmp42 = 1.0
    tmp43 = tmp42 - tmp41
    tl.store(in_out_ptr0 + x0, tmp43, 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((64,), (1,), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused__to_copy_add_div_lt_maximum_minimum_mul_pow_prod_rsub_sub_0[
            grid(64)](buf1, arg0_1, arg1_1, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class IOUlossNew(nn.Module):

    def __init__(self, reduction='none', loss_type='iou'):
        super(IOUlossNew, self).__init__()
        self.reduction = reduction
        self.loss_type = loss_type

    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: 0.01479