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._inductor.runtime.triton_helpers import libdevice
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_sub_0(in_out_ptr0
    , in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    x0 = xindex
    r1 = rindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + 4 * r1, None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_out_ptr0 + (r1 + 4 * x0), xmask, other=0.0)
    tmp30 = tl.load(in_ptr2 + (x0 + 4 * r1), xmask, other=0.0)
    tmp31 = tl.load(in_ptr3 + (r1 + 4 * x0), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 * tmp11
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 + tmp21
    tmp24 = 2.0
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 - tmp25
    tmp27 = 1e-12
    tmp28 = triton_helpers.maximum(tmp26, tmp27)
    tmp29 = libdevice.sqrt(tmp28)
    tmp32 = tmp30 == tmp31
    tmp33 = tmp32.to(tl.float32)
    tmp34 = 1.0
    tmp35 = tmp34 - tmp33
    tmp36 = -10000000.0
    tmp37 = tmp35 * tmp36
    tmp38 = tmp29 + tmp37
    tmp39 = r1
    tmp40 = tmp39.to(tl.int16)
    tmp41 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
    tmp42 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43, _tmp44 = triton_helpers.sort_with_index(tmp41, tmp42, None, 1,
        stable=False, descending=True)
    tmp45 = 10000000.0
    tmp46 = tmp33 * tmp45
    tmp47 = tmp29 + tmp46
    tmp48 = tl.broadcast_to(tmp47, [XBLOCK, RBLOCK])
    tmp49, _tmp50 = triton_helpers.sort_with_index(tmp48, tmp42, None, 1,
        stable=False, descending=False)
    tl.store(in_out_ptr0 + (r1 + 4 * x0), tmp26, xmask)
    tl.store(out_ptr0 + (r1 + 4 * x0), tmp43, xmask)
    tl.store(out_ptr1 + (r1 + 4 * x0), tmp49, xmask)


@triton.jit
def triton_per_fused_add_clamp_min_mean_mul_neg_sub_1(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = -1.0
    tmp4 = tmp3 * tmp2
    tmp5 = 0.3
    tmp6 = tmp4 + tmp5
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp13, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    assert_size_stride(arg3_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg0_1, reinterpret_tensor(arg1_1, (4, 4), (1, 4),
            0), out=buf0)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_sub_0[grid(4)
            ](buf1, arg0_1, arg1_1, arg2_1, arg3_1, buf2, buf4, 4, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
        del buf1
        buf6 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf6
        del buf6
        triton_per_fused_add_clamp_min_mean_mul_neg_sub_1[grid(1)](buf7,
            buf4, buf2, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
        del buf4
    return buf7,


def hard_examples_mining(dist_mat, identity_mat, return_idxes=False):
    """Select hard positives and hard negatives according to `In defense of the Triplet Loss for Person
    Re-Identification (ICCV 2017) <https://arxiv.org/pdf/1703.07737v2.pdf>`_

    Args:
        dist_mat (tensor): pairwise distance matrix between two sets of features
        identity_mat (tensor): a matrix of shape :math:`(N, M)`. If two images :math:`P[i]` of set :math:`P` and
            :math:`Q[j]` of set :math:`Q` come from the same person, then :math:`identity\\_mat[i, j] = 1`,
            otherwise :math:`identity\\_mat[i, j] = 0`
        return_idxes (bool, optional): if True, also return indexes of hard examples. Default: False
    """
    sorted_dist_mat, sorted_idxes = torch.sort(dist_mat + -10000000.0 * (1 -
        identity_mat), dim=1, descending=True)
    dist_ap = sorted_dist_mat[:, 0]
    hard_positive_idxes = sorted_idxes[:, 0]
    sorted_dist_mat, sorted_idxes = torch.sort(dist_mat + 10000000.0 *
        identity_mat, dim=1, descending=False)
    dist_an = sorted_dist_mat[:, 0]
    hard_negative_idxes = sorted_idxes[:, 0]
    if return_idxes:
        return dist_ap, dist_an, hard_positive_idxes, hard_negative_idxes
    return dist_ap, dist_an


def pairwise_euclidean_distance(x, y):
    """Compute pairwise euclidean distance between two sets of features"""
    m, n = x.size(0), y.size(0)
    dist_mat = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n) + torch.pow(y,
        2).sum(1, keepdim=True).expand(n, m).t() - 2 * torch.matmul(x, y.t())
    dist_mat = dist_mat.clamp(min=1e-12).sqrt()
    return dist_mat


class TripletLossXBMNew(nn.Module):
    """Triplet loss augmented with batch hard from `In defense of the Triplet Loss for Person Re-Identification
    (ICCV 2017) <https://arxiv.org/pdf/1703.07737v2.pdf>`_. The only difference from triplet loss lies in that
    both features from current mini batch and external storage (XBM) are involved.

    Args:
        margin (float, optional): margin of triplet loss. Default: 0.3
        normalize_feature (bool, optional): if True, normalize features into unit norm first before computing loss.
            Default: False

    Inputs:
        - f (tensor): features of current mini batch, :math:`f`
        - labels (tensor): identity labels for current mini batch, :math:`labels`
        - xbm_f (tensor): features collected from XBM, :math:`xbm\\_f`
        - xbm_labels (tensor): corresponding identity labels of xbm_f, :math:`xbm\\_labels`

    Shape:
        - f: :math:`(minibatch, F)`, where :math:`F` is the feature dimension
        - labels: :math:`(minibatch, )`
        - xbm_f: :math:`(minibatch, F)`
        - xbm_labels: :math:`(minibatch, )`
    """

    def __init__(self, margin=0.3, normalize_feature=False):
        super(TripletLossXBMNew, self).__init__()
        self.margin = margin
        self.normalize_feature = normalize_feature
        self.ranking_loss = nn.MarginRankingLoss(margin=margin)

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


Measured latency_ms: 0.01389