# 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_7/inductor_cache/vl/cvlhiduy67xn6o4lhuk4xvwnthj72fhj5ocfqllkw3dbrthiez3v.py
# Topologically Sorted Source Nodes: [truediv, round_1, lof_tag_avg_gather_img, round_2, sub, tag, pull, mul_1], Original ATen: [aten.div, aten.round, aten.mul, aten.sub, aten.pow, aten.mean]
# Source node to ATen node mapping:
#   lof_tag_avg_gather_img => mul
#   mul_1 => mul_1
#   pull => mean
#   round_1 => round_1
#   round_2 => round_2
#   sub => sub
#   tag => pow_1
#   truediv => div
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, 4), kwargs = {})
#   %round_1 : [num_users=1] = call_function[target=torch.ops.aten.round.default](args = (%div,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%round_1, 4), kwargs = {})
#   %round_2 : [num_users=1] = call_function[target=torch.ops.aten.round.default](args = (%mul,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %round_2), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, 4), kwargs = {})
triton_per_fused_div_mean_mul_pow_round_sub_0 = async_compile.triton('triton_per_fused_div_mean_mul_pow_round_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.persistent_reduction(
    size_hints=[1, 256],
    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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_mean_mul_pow_round_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, '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_div_mean_mul_pow_round_sub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp2 = 0.25
    tmp3 = tmp1 * tmp2
    tmp4 = libdevice.nearbyint(tmp3)
    tmp5 = 4.0
    tmp6 = tmp4 * tmp5
    tmp7 = libdevice.nearbyint(tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = tl.broadcast_to(tmp9, [RBLOCK])
    tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0))
    tmp13 = 256.0
    tmp14 = tmp12 / tmp13
    tmp15 = tmp14 * tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wh/cwh6c5rozspxfwkgtxpck2blxytwzmnff7c2kuwonwucgfcqkexw.py
# Topologically Sorted Source Nodes: [dist, abs_1, dist_1, dist_2, dist_3, push, mul_2], Original ATen: [aten.sub, aten.abs, aten.rsub, aten.relu, aten.index, aten.mean, aten.mul]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   dist => sub_1
#   dist_1 => sub_2
#   dist_2 => relu
#   dist_3 => index
#   mul_2 => mul_2
#   push => mean_1
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (8, %abs_1), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub_2,), kwargs = {})
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%relu, [%arg3_1]), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%index,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean_1, 4), kwargs = {})
triton_per_fused_abs_index_mean_mul_relu_rsub_sub_1 = async_compile.triton('triton_per_fused_abs_index_mean_mul_relu_rsub_sub_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=[1, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_index_mean_mul_relu_rsub_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, '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_abs_index_mean_mul_relu_rsub_sub_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 1024
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = (rindex // 256)
    r3 = rindex % 256
    r0 = rindex % 64
    tmp0 = tl.load(in_ptr0 + (r2), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (r3), None, eviction_policy='evict_last')
    tmp1 = tl.full([RBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4), "index out of bounds: 0 <= tmp4 < 4")
    tmp7 = tl.load(in_ptr1 + (r0 + (64*tmp4)), None)
    tmp8 = tmp6 - tmp7
    tmp9 = tl_math.abs(tmp8)
    tmp10 = 8.0
    tmp11 = tmp10 - tmp9
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = 1024.0
    tmp18 = tmp16 / tmp17
    tmp19 = 4.0
    tmp20 = tmp18 * tmp19
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp20, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [truediv, round_1, lof_tag_avg_gather_img, round_2, sub, tag, pull, mul_1], Original ATen: [aten.div, aten.round, aten.mul, aten.sub, aten.pow, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_div_mean_mul_pow_round_sub_0.run(buf2, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [dist, abs_1, dist_1, dist_2, dist_3, push, mul_2], Original ATen: [aten.sub, aten.abs, aten.rsub, aten.relu, aten.index, aten.mean, aten.mul]
        triton_per_fused_abs_index_mean_mul_relu_rsub_sub_1.run(buf3, arg3_1, arg2_1, 1, 1024, grid=grid(1), stream=stream0)
        del arg2_1
        del arg3_1
    return (buf2, buf3, )


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)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_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.utils.data
from torch import nn


class AELoss(nn.Module):

    def __init__(self, pull_factor, push_factor, distance, margin_push):
        super(AELoss, self).__init__()
        self.pull_factor = pull_factor
        self.push_factor = push_factor
        self.distance = distance
        self.margin_push = margin_push

    def forward(self, lof_tag_img, lof_tag_avg_img, lof_tag_avg_gather_img,
        mask, centerness_img=None):
        lof_tag_avg_gather_img = torch.round(lof_tag_avg_gather_img / self.
            distance) * self.distance
        tag = torch.pow(lof_tag_img - torch.round(lof_tag_avg_gather_img), 2)
        dist = lof_tag_avg_img.unsqueeze(0) - lof_tag_avg_img.unsqueeze(1)
        dist = self.distance + self.margin_push - torch.abs(dist)
        dist = nn.functional.relu(dist, inplace=True)
        dist = dist[mask]
        if centerness_img is not None:
            pull = (tag * centerness_img).sum() / centerness_img.sum()
            push = torch.zeros_like(pull)
            if mask.any():
                push = dist.sum() / mask.sum().float()
        else:
            pull = tag.mean()
            push = dist.mean()
        return self.pull_factor * pull, self.push_factor * push


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
        [4, 4, 4, 4]), torch.ones([4], dtype=torch.int64)]


def get_init_inputs():
    return [[], {'pull_factor': 4, 'push_factor': 4, 'distance': 4,
        'margin_push': 4}]

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 libdevice, math as tl_math
import torch.utils.data
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_div_mean_mul_pow_round_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = 0.25
    tmp3 = tmp1 * tmp2
    tmp4 = libdevice.nearbyint(tmp3)
    tmp5 = 4.0
    tmp6 = tmp4 * tmp5
    tmp7 = libdevice.nearbyint(tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = tl.broadcast_to(tmp9, [RBLOCK])
    tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0))
    tmp13 = 256.0
    tmp14 = tmp12 / tmp13
    tmp15 = tmp14 * tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp15, None)


@triton.jit
def triton_per_fused_abs_index_mean_mul_relu_rsub_sub_1(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex // 256
    r3 = rindex % 256
    r0 = rindex % 64
    tmp0 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
    tmp1 = tl.full([RBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4),
        'index out of bounds: 0 <= tmp4 < 4')
    tmp7 = tl.load(in_ptr1 + (r0 + 64 * tmp4), None)
    tmp8 = tmp6 - tmp7
    tmp9 = tl_math.abs(tmp8)
    tmp10 = 8.0
    tmp11 = tmp10 - tmp9
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = 1024.0
    tmp18 = tmp16 / tmp17
    tmp19 = 4.0
    tmp20 = tmp18 * tmp19
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp20, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_div_mean_mul_pow_round_sub_0[grid(1)](buf2, arg1_1,
            arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1
        del buf1
        triton_per_fused_abs_index_mean_mul_relu_rsub_sub_1[grid(1)](buf3,
            arg3_1, arg2_1, 1, 1024, num_warps=8, num_stages=1)
        del arg2_1
        del arg3_1
    return buf2, buf3


class AELossNew(nn.Module):

    def __init__(self, pull_factor, push_factor, distance, margin_push):
        super(AELossNew, self).__init__()
        self.pull_factor = pull_factor
        self.push_factor = push_factor
        self.distance = distance
        self.margin_push = margin_push

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0], output[1]
