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
import torch.nn.parallel
import torch.utils.data
import torch.utils
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_poi_fused_add_min_mul_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (5 * x0 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (5 + 16 * x1), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask)
    tmp13 = tl.load(in_ptr0 + (10 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (15 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask)
    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)
    tl.store(out_ptr0 + x2, tmp24, xmask)


@triton.jit
def triton_poi_fused_add_min_mul_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (5 * x0 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + 16 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (5 + 16 * x1), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr2 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (10 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr2 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (15 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr2 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 - tmp5
    tmp8 = tmp0 + tmp7
    tmp10 = tmp9 * tmp4
    tmp11 = tmp8 - tmp10
    tmp12 = triton_helpers.minimum(tmp6, tmp11)
    tmp14 = tmp0 + tmp13
    tmp16 = tmp15 * tmp4
    tmp17 = tmp14 - tmp16
    tmp18 = triton_helpers.minimum(tmp12, tmp17)
    tmp20 = tmp0 + tmp19
    tmp22 = tmp21 * tmp4
    tmp23 = tmp20 - tmp22
    tmp24 = triton_helpers.minimum(tmp18, tmp23)
    tl.store(out_ptr0 + x2, tmp24, xmask)


@triton.jit
def triton_poi_fused_add_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp9 = tmp7 + tmp8
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tmp6 + tmp13
    tl.store(out_ptr0 + x0, tmp14, xmask)


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
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_min_mul_sub_0[grid(16)](buf0, buf1, buf2, buf3,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_min_mul_sub_1[grid(16)](buf0, buf1, buf2, buf4,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del buf2
        buf5 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_add_sum_2[grid(4)](buf3, buf4, buf5, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del buf3
        del buf4
    return buf5,


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.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))
        if self.use_cuda:
            dtype = torch.LongTensor
        else:
            dtype = torch.LongTensor
        diag_ind_x = torch.arange(0, num_points_x).type(dtype)
        diag_ind_y = torch.arange(0, num_points_y).type(dtype)
        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: 0.062969