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
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from typing import *
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):

    def __init__(self):
        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.shape
        _, 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]


Measured latency_ms: 0.036138