# 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/v7/cv7duh3vx2zbvzgykpxoo3zrorbpe24v6abbzgskhj2f2lhowmlt.py
# Topologically Sorted Source Nodes: [add, mul, P, min_1, min_2, loss_1, loss_2, add_1], Original ATen: [aten.add, aten.mul, aten.sub, aten.min, aten.sum]
# Source node to ATen node mapping:
#   P => sub
#   add => add
#   add_1 => add_1
#   loss_1 => sum_1
#   loss_2 => sum_2
#   min_1 => min_1
#   min_2 => min_2
#   mul => mul
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%permute_4, %expand_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm_2, 2), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mul), kwargs = {})
#   %min_1 : [num_users=1] = call_function[target=torch.ops.aten.min.dim](args = (%sub, 1), kwargs = {})
#   %min_2 : [num_users=1] = call_function[target=torch.ops.aten.min.dim](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%getitem,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%getitem_2,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_2), kwargs = {})
triton_per_fused_add_min_mul_sub_sum_0 = async_compile.triton('triton_per_fused_add_min_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, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_min_mul_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 18, 'num_reduction': 2, '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_min_mul_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    r0 = rindex % 4
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (16*r1), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + ((5*r0) + (16*r1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (r0 + (16*r1)), None)
    tmp7 = tl.load(in_ptr0 + (5 + (16*r1)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + r0 + (16*r1)), None)
    tmp13 = tl.load(in_ptr0 + (10 + (16*r1)), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (8 + r0 + (16*r1)), None)
    tmp19 = tl.load(in_ptr0 + (15 + (16*r1)), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + (12 + r0 + (16*r1)), None)
    tmp28 = tl.load(in_ptr0 + ((5*r0) + (16*r1)), None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr1 + (16*r1), None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr2 + (4*r2), None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr1 + (5 + (16*r1)), None, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr2 + (1 + (4*r2)), None, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr1 + (10 + (16*r1)), None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr2 + (2 + (4*r2)), None, eviction_policy='evict_last')
    tmp46 = tl.load(in_ptr1 + (15 + (16*r1)), None, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr2 + (3 + (4*r2)), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 - tmp5
    tmp8 = tmp7 + tmp1
    tmp10 = tmp9 * tmp4
    tmp11 = tmp8 - tmp10
    tmp12 = triton_helpers.minimum(tmp6, tmp11)
    tmp14 = tmp13 + tmp1
    tmp16 = tmp15 * tmp4
    tmp17 = tmp14 - tmp16
    tmp18 = triton_helpers.minimum(tmp12, tmp17)
    tmp20 = tmp19 + tmp1
    tmp22 = tmp21 * tmp4
    tmp23 = tmp20 - tmp22
    tmp24 = triton_helpers.minimum(tmp18, tmp23)
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.sum(tmp25, 1)[:, None]
    tmp30 = tmp28 + tmp29
    tmp32 = tmp31 * tmp4
    tmp33 = tmp30 - tmp32
    tmp35 = tmp28 + tmp34
    tmp37 = tmp36 * tmp4
    tmp38 = tmp35 - tmp37
    tmp39 = triton_helpers.minimum(tmp33, tmp38)
    tmp41 = tmp28 + tmp40
    tmp43 = tmp42 * tmp4
    tmp44 = tmp41 - tmp43
    tmp45 = triton_helpers.minimum(tmp39, tmp44)
    tmp47 = tmp28 + tmp46
    tmp49 = tmp48 * tmp4
    tmp50 = tmp47 - tmp49
    tmp51 = triton_helpers.minimum(tmp45, tmp50)
    tmp52 = tl.broadcast_to(tmp51, [XBLOCK, RBLOCK])
    tmp54 = tl.sum(tmp52, 1)[:, None]
    tmp55 = tmp27 + tmp54
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp55, 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)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xx], Original ATen: [aten.bmm]
        extern_kernels.bmm(arg0_1, reinterpret_tensor(arg0_1, (4, 4, 4), (16, 1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [yy], Original ATen: [aten.bmm]
        extern_kernels.bmm(arg1_1, reinterpret_tensor(arg1_1, (4, 4, 4), (16, 1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [zz], Original ATen: [aten.bmm]
        extern_kernels.bmm(arg0_1, reinterpret_tensor(arg1_1, (4, 4, 4), (16, 1, 4), 0), out=buf2)
        del arg0_1
        del arg1_1
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [add, mul, P, min_1, min_2, loss_1, loss_2, add_1], Original ATen: [aten.add, aten.mul, aten.sub, aten.min, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_min_mul_sub_sum_0.run(buf7, buf0, buf1, buf2, 1, 16, grid=grid(1), stream=stream0)
        del buf0
        del buf1
        del buf2
    return (buf7, )


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 as nn


class ChamferLoss(nn.Module):
    """
    Torch implementation of chamferLoss for n-dimensional geometries
    """

    def __init__(self):
        self.init__ = super(ChamferLoss, self).__init__()
        self.use_cuda = torch.cuda.is_available()

    def batch_pairwise_dist(self, x, y):
        _bs, num_points_x, _points_dim = x.size()
        _, num_points_y, _ = y.size()
        xx = torch.bmm(x, x.transpose(2, 1))
        yy = torch.bmm(y, y.transpose(2, 1))
        zz = torch.bmm(x, y.transpose(2, 1))
        diag_ind_x = torch.arange(0, num_points_x)
        diag_ind_y = torch.arange(0, num_points_y)
        if x.get_device() != -1:
            diag_ind_x = diag_ind_x
            diag_ind_y = diag_ind_y
        rx = xx[:, diag_ind_x, diag_ind_x].unsqueeze(1).expand_as(zz.
            transpose(2, 1))
        ry = yy[:, diag_ind_y, diag_ind_y].unsqueeze(1).expand_as(zz)
        P = rx.transpose(2, 1) + ry - 2 * zz
        return P

    def forward(self, preds, gts):
        P = self.batch_pairwise_dist(gts, preds)
        mins, _ = torch.min(P, 1)
        loss_1 = torch.sum(mins)
        mins, _ = torch.min(P, 2)
        loss_2 = torch.sum(mins)
        return loss_1 + loss_2


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_add_min_mul_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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
    r0 = rindex % 4
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + 16 * r1, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (5 * r0 + 16 * r1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (r0 + 16 * r1), None)
    tmp7 = tl.load(in_ptr0 + (5 + 16 * r1), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + r0 + 16 * r1), None)
    tmp13 = tl.load(in_ptr0 + (10 + 16 * r1), None, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr2 + (8 + r0 + 16 * r1), None)
    tmp19 = tl.load(in_ptr0 + (15 + 16 * r1), None, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr2 + (12 + r0 + 16 * r1), None)
    tmp28 = tl.load(in_ptr0 + (5 * r0 + 16 * r1), None, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr1 + 16 * r1, None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr2 + 4 * r2, None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr1 + (5 + 16 * r1), None, eviction_policy='evict_last'
        )
    tmp36 = tl.load(in_ptr2 + (1 + 4 * r2), None, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr1 + (10 + 16 * r1), None, eviction_policy=
        'evict_last')
    tmp42 = tl.load(in_ptr2 + (2 + 4 * r2), None, eviction_policy='evict_last')
    tmp46 = tl.load(in_ptr1 + (15 + 16 * r1), None, eviction_policy=
        'evict_last')
    tmp48 = tl.load(in_ptr2 + (3 + 4 * r2), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 - tmp5
    tmp8 = tmp7 + tmp1
    tmp10 = tmp9 * tmp4
    tmp11 = tmp8 - tmp10
    tmp12 = triton_helpers.minimum(tmp6, tmp11)
    tmp14 = tmp13 + tmp1
    tmp16 = tmp15 * tmp4
    tmp17 = tmp14 - tmp16
    tmp18 = triton_helpers.minimum(tmp12, tmp17)
    tmp20 = tmp19 + tmp1
    tmp22 = tmp21 * tmp4
    tmp23 = tmp20 - tmp22
    tmp24 = triton_helpers.minimum(tmp18, tmp23)
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.sum(tmp25, 1)[:, None]
    tmp30 = tmp28 + tmp29
    tmp32 = tmp31 * tmp4
    tmp33 = tmp30 - tmp32
    tmp35 = tmp28 + tmp34
    tmp37 = tmp36 * tmp4
    tmp38 = tmp35 - tmp37
    tmp39 = triton_helpers.minimum(tmp33, tmp38)
    tmp41 = tmp28 + tmp40
    tmp43 = tmp42 * tmp4
    tmp44 = tmp41 - tmp43
    tmp45 = triton_helpers.minimum(tmp39, tmp44)
    tmp47 = tmp28 + tmp46
    tmp49 = tmp48 * tmp4
    tmp50 = tmp47 - tmp49
    tmp51 = triton_helpers.minimum(tmp45, tmp50)
    tmp52 = tl.broadcast_to(tmp51, [XBLOCK, RBLOCK])
    tmp54 = tl.sum(tmp52, 1)[:, None]
    tmp55 = tmp27 + tmp54
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp55, 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)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(arg0_1, reinterpret_tensor(arg0_1, (4, 4, 4), (
            16, 1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(arg1_1, reinterpret_tensor(arg1_1, (4, 4, 4), (
            16, 1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(arg0_1, reinterpret_tensor(arg1_1, (4, 4, 4), (
            16, 1, 4), 0), out=buf2)
        del arg0_1
        del arg1_1
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf5
        del buf5
        get_raw_stream(0)
        triton_per_fused_add_min_mul_sub_sum_0[grid(1)](buf7, buf0, buf1,
            buf2, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
        del buf2
    return buf7,


class ChamferLossNew(nn.Module):
    """
    Torch implementation of chamferLoss for n-dimensional geometries
    """

    def __init__(self):
        self.init__ = super(ChamferLossNew, self).__init__()
        self.use_cuda = torch.cuda.is_available()

    def batch_pairwise_dist(self, x, y):
        _bs, num_points_x, _points_dim = x.size()
        _, num_points_y, _ = y.size()
        xx = torch.bmm(x, x.transpose(2, 1))
        yy = torch.bmm(y, y.transpose(2, 1))
        zz = torch.bmm(x, y.transpose(2, 1))
        diag_ind_x = torch.arange(0, num_points_x)
        diag_ind_y = torch.arange(0, num_points_y)
        if x.get_device() != -1:
            diag_ind_x = diag_ind_x
            diag_ind_y = diag_ind_y
        rx = xx[:, diag_ind_x, diag_ind_x].unsqueeze(1).expand_as(zz.
            transpose(2, 1))
        ry = yy[:, diag_ind_y, diag_ind_y].unsqueeze(1).expand_as(zz)
        P = rx.transpose(2, 1) + ry - 2 * zz
        return P

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