# 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_4/inductor_cache/p2/cp2qgb7aaybhhtbkhkhvtqsmgrwhpwcnf5r2r3ro24naatm2jr4o.py
# Topologically Sorted Source Nodes: [sub_1, sub_2, ap, sub_3, sub_4, ag, add, overlap, sub_5, union, ious, ious_1, log, neg], Original ATen: [aten.sub, aten.mul, aten.add, aten.div, aten.clamp, aten.log, aten.neg]
# Source node to ATen node mapping:
#   add => add
#   ag => mul_2
#   ap => mul_1
#   ious => div
#   ious_1 => clamp_min_1
#   log => log
#   neg => neg
#   overlap => mul
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   sub_4 => sub_4
#   sub_5 => sub_5
#   union => add_1
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_2, %select_3), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_4, %select_5), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %sub_2), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_6, %select_7), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_8, %select_9), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %sub_4), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select, %select_1), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mul), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_5, 1e-08), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add_1), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%div, 1e-08), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_min_1,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%log,), kwargs = {})
triton_poi_fused_add_clamp_div_log_mul_neg_sub_0 = async_compile.triton('triton_poi_fused_add_clamp_div_log_mul_neg_sub_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: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_div_log_mul_neg_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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_add_clamp_div_log_mul_neg_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 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.minimum(tmp0, tmp1)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp2 - tmp5
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp11 - tmp14
    tmp16 = triton_helpers.maximum(tmp15, tmp7)
    tmp17 = tmp8 * tmp16
    tmp18 = tmp0 - tmp3
    tmp19 = tmp9 - tmp12
    tmp20 = tmp18 * tmp19
    tmp21 = tmp1 - tmp4
    tmp22 = tmp10 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp20 + tmp23
    tmp25 = tmp24 - tmp17
    tmp26 = 1e-08
    tmp27 = tmp25 + tmp26
    tmp28 = tmp17 / tmp27
    tmp29 = triton_helpers.maximum(tmp28, tmp26)
    tmp30 = tl_math.log(tmp29)
    tmp31 = -tmp30
    tl.store(in_out_ptr0 + (x0), tmp31, 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)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub_1, sub_2, ap, sub_3, sub_4, ag, add, overlap, sub_5, union, ious, ious_1, log, neg], Original ATen: [aten.sub, aten.mul, aten.add, aten.div, aten.clamp, aten.log, aten.neg]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_clamp_div_log_mul_neg_sub_0.run(buf2, arg0_1, arg1_1, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf2, )


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
import torch.utils.data


class IoULoss(nn.Module):
    """Some Information about IoULoss"""

    def forward(self, preds: 'torch.Tensor', targets: 'torch.Tensor', eps:
        'float'=1e-08) ->torch.Tensor:
        """IoU Loss

        Args:
            preds (torch.Tensor): [x1, y1, x2, y2] predictions [*, 4]
            targets (torch.Tensor): [x1, y1, x2, y2] targets [*, 4]

        Returns:
            torch.Tensor: [-log(iou)] [*]
        """
        lt = torch.max(preds[..., :2], targets[..., :2])
        rb = torch.min(preds[..., 2:], targets[..., 2:])
        wh = (rb - lt).clamp(min=0)
        overlap = wh[..., 0] * wh[..., 1]
        ap = (preds[..., 2] - preds[..., 0]) * (preds[..., 3] - preds[..., 1])
        ag = (targets[..., 2] - targets[..., 0]) * (targets[..., 3] -
            targets[..., 1])
        union = ap + ag - overlap + eps
        ious = overlap / union
        ious = torch.clamp(ious, min=eps)
        return -ious.log()


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


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

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
from torch._inductor.runtime.triton_helpers import math as tl_math
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_add_clamp_div_log_mul_neg_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 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = triton_helpers.minimum(tmp0, tmp1)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp2 - tmp5
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp11 - tmp14
    tmp16 = triton_helpers.maximum(tmp15, tmp7)
    tmp17 = tmp8 * tmp16
    tmp18 = tmp0 - tmp3
    tmp19 = tmp9 - tmp12
    tmp20 = tmp18 * tmp19
    tmp21 = tmp1 - tmp4
    tmp22 = tmp10 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp20 + tmp23
    tmp25 = tmp24 - tmp17
    tmp26 = 1e-08
    tmp27 = tmp25 + tmp26
    tmp28 = tmp17 / tmp27
    tmp29 = triton_helpers.maximum(tmp28, tmp26)
    tmp30 = tl_math.log(tmp29)
    tmp31 = -tmp30
    tl.store(in_out_ptr0 + x0, tmp31, 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)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_clamp_div_log_mul_neg_sub_0[grid(64)](buf2,
            arg0_1, arg1_1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class IoULossNew(nn.Module):
    """Some Information about IoULoss"""

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