# 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_8/inductor_cache/ft/cftna6aefrj2tfodmmc3kcwp7a6w2rj3uw3zfu2aa7nsg6bgu3xb.py
# Topologically Sorted Source Nodes: [feat_2, regr, gt_regr, regr_loss], Original ATen: [aten.gather, aten.mul, aten.smooth_l1_loss]
# Source node to ATen node mapping:
#   feat_2 => gather
#   gt_regr => mul_1
#   regr => mul
#   regr_loss => abs_1, div, lt, mul_2, pow_1, sub, sub_1, sum_2, where
# Graph fragment:
#   %gather : [num_users=1] = call_function[target=torch.ops.aten.gather.default](args = (%view, 1, %expand), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%gather, %expand_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg3_1, %expand_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %abs_1 : [num_users=3] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%abs_1, 1.0), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_2, 1.0), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%abs_1, 0.5), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %div, %sub_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%where,), kwargs = {})
triton_per_fused_gather_mul_smooth_l1_loss_0 = async_compile.triton('triton_per_fused_gather_mul_smooth_l1_loss_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: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_gather_mul_smooth_l1_loss_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 3, '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_gather_mul_smooth_l1_loss_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, 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)
    r4 = (rindex // 4) % 16
    r0 = rindex % 4
    r2 = (rindex // 16) % 4
    r5 = (rindex // 16)
    r6 = rindex
    tmp0 = tl.load(in_ptr0 + (r4), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (r0 + (4*r5)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + (r6), None)
    tmp1 = tl.full([RBLOCK], 16, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 16), "index out of bounds: 0 <= tmp4 < 16")
    tmp6 = tl.load(in_ptr1 + ((16*r0) + (64*r2) + (tmp4 % 16)), None, eviction_policy='evict_last')
    tmp8 = tmp6 * tmp7
    tmp10 = tmp9 * tmp7
    tmp11 = tmp8 - tmp10
    tmp12 = tl_math.abs(tmp11)
    tmp13 = 1.0
    tmp14 = tmp12 < tmp13
    tmp15 = tmp12 * tmp12
    tmp16 = 0.5
    tmp17 = tmp15 * tmp16
    tmp18 = tmp17 * tmp13
    tmp19 = tmp12 - tmp16
    tmp20 = tl.where(tmp14, tmp18, tmp19)
    tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp21, 0))
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp23, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ob/cob2grz7alvzwl7r4j3xyhalhmisr6ugrqntlztlbaz72vi7aaxw.py
# Topologically Sorted Source Nodes: [num, add, regr_loss_1], Original ATen: [aten.sum, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   num => sum_1
#   regr_loss_1 => div_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%arg2_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 0.0001), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, %add), kwargs = {})
triton_per_fused_add_div_sum_1 = async_compile.triton('triton_per_fused_add_div_sum_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, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, '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_add_div_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp4 = tl.load(in_out_ptr0 + (0))
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, 1])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp6 = 0.0001
    tmp7 = tmp3 + tmp6
    tmp8 = tmp5 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp8, 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, 1))
    assert_size_stride(arg2_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [feat_2, regr, gt_regr, regr_loss], Original ATen: [aten.gather, aten.mul, aten.smooth_l1_loss]
        stream0 = get_raw_stream(0)
        triton_per_fused_gather_mul_smooth_l1_loss_0.run(arg1_1, arg0_1, arg2_1, arg3_1, buf0, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg3_1
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [num, add, regr_loss_1], Original ATen: [aten.sum, aten.add, aten.div]
        triton_per_fused_add_div_sum_1.run(buf2, arg2_1, 1, 64, grid=grid(1), stream=stream0)
        del arg2_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, 1), device='cuda:0', dtype=torch.int64)
    arg2_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    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.nn as nn
import torch.utils.data


def _gather_feat(feat, ind, mask=None):
    dim = feat.size(2)
    ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
    feat = feat.gather(1, ind)
    if mask is not None:
        mask = mask.unsqueeze(2).expand_as(feat)
        feat = feat[mask]
        feat = feat.view(-1, dim)
    return feat


def _tranpose_and_gather_feat(feat, ind):
    feat = feat.permute(0, 2, 3, 1).contiguous()
    feat = feat.view(feat.size(0), -1, feat.size(3))
    feat = _gather_feat(feat, ind)
    return feat


def _reg_loss(regr, gt_regr, mask):
    """ L1 regression loss
    Arguments:
      regr (batch x max_objects x dim)
      gt_regr (batch x max_objects x dim)
      mask (batch x max_objects)
  """
    num = mask.float().sum()
    mask = mask.unsqueeze(2).expand_as(gt_regr).float()
    regr = regr * mask
    gt_regr = gt_regr * mask
    regr_loss = nn.functional.smooth_l1_loss(regr, gt_regr, size_average=False)
    regr_loss = regr_loss / (num + 0.0001)
    return regr_loss


class RegLoss(nn.Module):
    """Regression loss for an output tensor
    Arguments:
      output (batch x dim x h x w)
      mask (batch x max_objects)
      ind (batch x max_objects)
      target (batch x max_objects x dim)
  """

    def __init__(self):
        super(RegLoss, self).__init__()

    def forward(self, output, mask, ind, target):
        pred = _tranpose_and_gather_feat(output, ind)
        loss = _reg_loss(pred, target, mask)
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4]), torch.ones([4,
        4], dtype=torch.int64), 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_per_fused_gather_mul_smooth_l1_loss_0(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, 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)
    r4 = rindex // 4 % 16
    r0 = rindex % 4
    r2 = rindex // 16 % 4
    r5 = rindex // 16
    r6 = rindex
    tmp0 = tl.load(in_ptr0 + r4, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (r0 + 4 * r5), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + r6, None)
    tmp1 = tl.full([RBLOCK], 16, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 16),
        'index out of bounds: 0 <= tmp4 < 16')
    tmp6 = tl.load(in_ptr1 + (16 * r0 + 64 * r2 + tmp4 % 16), None,
        eviction_policy='evict_last')
    tmp8 = tmp6 * tmp7
    tmp10 = tmp9 * tmp7
    tmp11 = tmp8 - tmp10
    tmp12 = tl_math.abs(tmp11)
    tmp13 = 1.0
    tmp14 = tmp12 < tmp13
    tmp15 = tmp12 * tmp12
    tmp16 = 0.5
    tmp17 = tmp15 * tmp16
    tmp18 = tmp17 * tmp13
    tmp19 = tmp12 - tmp16
    tmp20 = tl.where(tmp14, tmp18, tmp19)
    tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp21, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp23, None)


@triton.jit
def triton_per_fused_add_div_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp4 = tl.load(in_out_ptr0 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, 1])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp6 = 0.0001
    tmp7 = tmp3 + tmp6
    tmp8 = tmp5 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp8, 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, 1))
    assert_size_stride(arg2_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_gather_mul_smooth_l1_loss_0[grid(1)](arg1_1,
            arg0_1, arg2_1, arg3_1, buf0, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg3_1
        buf2 = buf0
        del buf0
        triton_per_fused_add_div_sum_1[grid(1)](buf2, arg2_1, 1, 64, XBLOCK
            =1, num_warps=2, num_stages=1)
        del arg2_1
    return buf2,


def _gather_feat(feat, ind, mask=None):
    dim = feat.size(2)
    ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
    feat = feat.gather(1, ind)
    if mask is not None:
        mask = mask.unsqueeze(2).expand_as(feat)
        feat = feat[mask]
        feat = feat.view(-1, dim)
    return feat


def _tranpose_and_gather_feat(feat, ind):
    feat = feat.permute(0, 2, 3, 1).contiguous()
    feat = feat.view(feat.size(0), -1, feat.size(3))
    feat = _gather_feat(feat, ind)
    return feat


def _reg_loss(regr, gt_regr, mask):
    """ L1 regression loss
    Arguments:
      regr (batch x max_objects x dim)
      gt_regr (batch x max_objects x dim)
      mask (batch x max_objects)
  """
    num = mask.float().sum()
    mask = mask.unsqueeze(2).expand_as(gt_regr).float()
    regr = regr * mask
    gt_regr = gt_regr * mask
    regr_loss = nn.functional.smooth_l1_loss(regr, gt_regr, size_average=False)
    regr_loss = regr_loss / (num + 0.0001)
    return regr_loss


class RegLossNew(nn.Module):
    """Regression loss for an output tensor
    Arguments:
      output (batch x dim x h x w)
      mask (batch x max_objects)
      ind (batch x max_objects)
      target (batch x max_objects x dim)
  """

    def __init__(self):
        super(RegLossNew, self).__init__()

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