# 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_1/inductor_cache/fe/cfec2i3l2y26ao3a5ssf6yxutkraofhncql2ii4axnlcoila7roz.py
# Topologically Sorted Source Nodes: [interpolate, lava_loss_per_img, sum_1, sum_2, mul_1, loss], Original ATen: [aten._to_copy, aten.arange, aten.add, aten.mul, aten.sub, aten.clamp, aten._unsafe_index, aten.sum, aten.div]
# Source node to ATen node mapping:
#   interpolate => _unsafe_index, _unsafe_index_1, _unsafe_index_2, _unsafe_index_3, add_2, add_4, add_5, add_6, clamp_max_2, clamp_max_3, clamp_min_1, clamp_min_2, clamp_min_3, convert_element_type_1, convert_element_type_2, convert_element_type_3, iota_1, mul_1, mul_2, mul_3, mul_4, sub_1, sub_2, sub_3, sub_4, sub_5, sub_6
#   lava_loss_per_img => mul_5
#   loss => div
#   mul_1 => mul_6
#   sum_1 => sum_1
#   sum_2 => sum_2
# Graph fragment:
#   %convert_element_type_1 : [num_users=4] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view, torch.int64), kwargs = {})
#   %iota_1 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota_1, torch.float32), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_2, 0.5), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, 1.0), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, 0.5), kwargs = {})
#   %clamp_min_1 : [num_users=2] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_1, 0.0), kwargs = {})
#   %convert_element_type_3 : [num_users=4] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%clamp_min_1, torch.int64), kwargs = {})
#   %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%unsqueeze, [None, None, %clamp_max, %clamp_max_1]), kwargs = {})
#   %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%unsqueeze, [None, None, %clamp_max, %convert_element_type_3]), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_3, %_unsafe_index_2), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min_1, %convert_element_type_3), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_2, 0.0), kwargs = {})
#   %clamp_max_2 : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 1.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %clamp_max_2), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_2, %mul_3), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%unsqueeze, [None, None, %convert_element_type_1, %clamp_max_1]), kwargs = {})
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%unsqueeze, [None, None, %convert_element_type_1, %convert_element_type_3]), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_1, %_unsafe_index), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %clamp_max_2), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index, %mul_2), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_5, %add_4), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %convert_element_type_1), kwargs = {})
#   %clamp_min_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_5, 0.0), kwargs = {})
#   %clamp_max_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_3, 1.0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %clamp_max_3), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_4, %mul_4), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze, %arg0_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_5,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%arg0_1,), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, 4), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %mul_6), kwargs = {})
triton_per_fused__to_copy__unsafe_index_add_arange_clamp_div_mul_sub_sum_0 = async_compile.triton('triton_per_fused__to_copy__unsafe_index_add_arange_clamp_div_mul_sub_sum_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, 64],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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__to_copy__unsafe_index_add_arange_clamp_div_mul_sub_sum_0', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__to_copy__unsafe_index_add_arange_clamp_div_mul_sub_sum_0(in_out_ptr1, in_ptr0, in_ptr1, 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)
    r1 = (rindex // 4) % 4
    r0 = rindex % 4
    r2 = (rindex // 16)
    r3 = rindex
    tmp44 = tl.load(in_ptr1 + (r3), None)
    tmp0 = r1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp5 - tmp2
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.int32)
    tmp10 = tl.full([1, 1], 1, tl.int64)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1, 1], 3, tl.int64)
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tmp14 = r0
    tmp15 = tmp14.to(tl.float32)
    tmp16 = tmp15 + tmp2
    tmp17 = tmp16 * tmp4
    tmp18 = tmp17 - tmp2
    tmp19 = triton_helpers.maximum(tmp18, tmp7)
    tmp20 = tmp19.to(tl.int32)
    tmp21 = tmp20 + tmp10
    tmp22 = triton_helpers.minimum(tmp21, tmp12)
    tmp23 = tl.load(in_ptr0 + (tmp22 + (4*tmp13) + (16*r2)), None, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (tmp20 + (4*tmp13) + (16*r2)), None, eviction_policy='evict_last')
    tmp25 = tmp23 - tmp24
    tmp26 = tmp20.to(tl.float32)
    tmp27 = tmp19 - tmp26
    tmp28 = triton_helpers.maximum(tmp27, tmp7)
    tmp29 = triton_helpers.minimum(tmp28, tmp4)
    tmp30 = tmp25 * tmp29
    tmp31 = tmp24 + tmp30
    tmp32 = tl.load(in_ptr0 + (tmp20 + (4*tmp9) + (16*r2)), None, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (tmp22 + (4*tmp9) + (16*r2)), None, eviction_policy='evict_last')
    tmp34 = tmp33 - tmp32
    tmp35 = tmp34 * tmp29
    tmp36 = tmp32 + tmp35
    tmp37 = tmp31 - tmp36
    tmp38 = tmp9.to(tl.float32)
    tmp39 = tmp8 - tmp38
    tmp40 = triton_helpers.maximum(tmp39, tmp7)
    tmp41 = triton_helpers.minimum(tmp40, tmp4)
    tmp42 = tmp37 * tmp41
    tmp43 = tmp36 + tmp42
    tmp45 = tmp43 * tmp44
    tmp46 = tl.broadcast_to(tmp45, [XBLOCK, RBLOCK])
    tmp48 = tl.sum(tmp46, 1)[:, None]
    tmp49 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp51 = tl.sum(tmp49, 1)[:, None]
    tmp52 = 4.0
    tmp53 = tmp51 * tmp52
    tmp54 = tmp48 / tmp53
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp54, None)
''', 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), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [interpolate, lava_loss_per_img, sum_1, sum_2, mul_1, loss], Original ATen: [aten._to_copy, aten.arange, aten.add, aten.mul, aten.sub, aten.clamp, aten._unsafe_index, aten.sum, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused__to_copy__unsafe_index_add_arange_clamp_div_mul_sub_sum_0.run(buf5, arg1_1, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf5, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4), (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.functional as F
import torch.nn as nn


class LavaLoss(nn.Module):
    """
    Depth gradient Loss for instance segmentation
    """

    def __init__(self):
        super(LavaLoss, self).__init__()
        pass

    def forward(self, seg_masks, gradient_map):
        gt_size = gradient_map.shape[1:]
        seg_masks = F.interpolate(seg_masks.unsqueeze(0), size=gt_size,
            mode='bilinear').squeeze(0)
        lava_loss_per_img = seg_masks.mul(gradient_map)
        loss = lava_loss_per_img.sum() / (gradient_map.sum() * seg_masks.
            shape[0])
        return loss


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


@triton.jit
def triton_per_fused__to_copy__unsafe_index_add_arange_clamp_div_mul_sub_sum_0(
    in_out_ptr1, in_ptr0, in_ptr1, 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)
    r1 = rindex // 4 % 4
    r0 = rindex % 4
    r2 = rindex // 16
    r3 = rindex
    tmp44 = tl.load(in_ptr1 + r3, None)
    tmp0 = r1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp5 - tmp2
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.int32)
    tmp10 = tl.full([1, 1], 1, tl.int64)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1, 1], 3, tl.int64)
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tmp14 = r0
    tmp15 = tmp14.to(tl.float32)
    tmp16 = tmp15 + tmp2
    tmp17 = tmp16 * tmp4
    tmp18 = tmp17 - tmp2
    tmp19 = triton_helpers.maximum(tmp18, tmp7)
    tmp20 = tmp19.to(tl.int32)
    tmp21 = tmp20 + tmp10
    tmp22 = triton_helpers.minimum(tmp21, tmp12)
    tmp23 = tl.load(in_ptr0 + (tmp22 + 4 * tmp13 + 16 * r2), None,
        eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (tmp20 + 4 * tmp13 + 16 * r2), None,
        eviction_policy='evict_last')
    tmp25 = tmp23 - tmp24
    tmp26 = tmp20.to(tl.float32)
    tmp27 = tmp19 - tmp26
    tmp28 = triton_helpers.maximum(tmp27, tmp7)
    tmp29 = triton_helpers.minimum(tmp28, tmp4)
    tmp30 = tmp25 * tmp29
    tmp31 = tmp24 + tmp30
    tmp32 = tl.load(in_ptr0 + (tmp20 + 4 * tmp9 + 16 * r2), None,
        eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (tmp22 + 4 * tmp9 + 16 * r2), None,
        eviction_policy='evict_last')
    tmp34 = tmp33 - tmp32
    tmp35 = tmp34 * tmp29
    tmp36 = tmp32 + tmp35
    tmp37 = tmp31 - tmp36
    tmp38 = tmp9.to(tl.float32)
    tmp39 = tmp8 - tmp38
    tmp40 = triton_helpers.maximum(tmp39, tmp7)
    tmp41 = triton_helpers.minimum(tmp40, tmp4)
    tmp42 = tmp37 * tmp41
    tmp43 = tmp36 + tmp42
    tmp45 = tmp43 * tmp44
    tmp46 = tl.broadcast_to(tmp45, [XBLOCK, RBLOCK])
    tmp48 = tl.sum(tmp46, 1)[:, None]
    tmp49 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp51 = tl.sum(tmp49, 1)[:, None]
    tmp52 = 4.0
    tmp53 = tmp51 * tmp52
    tmp54 = tmp48 / tmp53
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp54, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf3
        del buf3
        get_raw_stream(0)
        triton_per_fused__to_copy__unsafe_index_add_arange_clamp_div_mul_sub_sum_0[
            grid(1)](buf5, arg1_1, arg0_1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf5,


class LavaLossNew(nn.Module):
    """
    Depth gradient Loss for instance segmentation
    """

    def __init__(self):
        super(LavaLossNew, self).__init__()
        pass

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