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.functional as F
from torch import 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_poi_fused_div_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)
    tl.store(out_ptr1 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_max_min_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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')
    tmp4 = tl.load(in_ptr1 + x0, xmask)
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + 1)
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
    tmp19 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr1 + 2)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp28 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp31 = tl.load(in_ptr1 + 3)
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK])
    tmp1 = 2.0
    tmp2 = tmp0 * tmp1
    tmp3 = tmp1 - tmp2
    tmp7 = tmp4 == tmp6
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp3 * tmp8
    tmp11 = tmp10 * tmp1
    tmp12 = tmp1 - tmp11
    tmp15 = tmp4 == tmp14
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp12 * tmp16
    tmp18 = triton_helpers.maximum(tmp9, tmp17)
    tmp20 = tmp19 * tmp1
    tmp21 = tmp1 - tmp20
    tmp24 = tmp4 == tmp23
    tmp25 = tmp24.to(tl.float32)
    tmp26 = tmp21 * tmp25
    tmp27 = triton_helpers.maximum(tmp18, tmp26)
    tmp29 = tmp28 * tmp1
    tmp30 = tmp1 - tmp29
    tmp33 = tmp4 == tmp32
    tmp34 = tmp33.to(tl.float32)
    tmp35 = tmp30 * tmp34
    tmp36 = triton_helpers.maximum(tmp27, tmp35)
    tmp37 = tmp4 != tmp6
    tmp38 = tmp37.to(tl.float32)
    tmp39 = tmp3 * tmp38
    tmp40 = 99999999.0
    tmp41 = tmp8 * tmp40
    tmp42 = tmp39 + tmp41
    tmp43 = tmp4 != tmp14
    tmp44 = tmp43.to(tl.float32)
    tmp45 = tmp12 * tmp44
    tmp46 = tmp16 * tmp40
    tmp47 = tmp45 + tmp46
    tmp48 = triton_helpers.minimum(tmp42, tmp47)
    tmp49 = tmp4 != tmp23
    tmp50 = tmp49.to(tl.float32)
    tmp51 = tmp21 * tmp50
    tmp52 = tmp25 * tmp40
    tmp53 = tmp51 + tmp52
    tmp54 = triton_helpers.minimum(tmp48, tmp53)
    tmp55 = tmp4 != tmp32
    tmp56 = tmp55.to(tl.float32)
    tmp57 = tmp30 * tmp56
    tmp58 = tmp34 * tmp40
    tmp59 = tmp57 + tmp58
    tmp60 = triton_helpers.minimum(tmp54, tmp59)
    tl.store(out_ptr0 + x0, tmp36, xmask)
    tl.store(out_ptr1 + x0, tmp60, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(16)](arg0_1, buf0, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(buf1, (4, 4), (1, 4), 0),
            out=buf2)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4,), (1,), torch.float32)
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_max_min_1[grid(4)](buf2, arg1_1, buf3, buf4, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        del arg1_1
        del buf2
        buf5 = empty_strided_cuda((0,), (1,), torch.float32)
    return buf5, buf4, buf3


def cosine_dist(x, y):
    """Computes Cosine Distance."""
    x = F.normalize(x, dim=1)
    y = F.normalize(y, dim=1)
    dist = 2 - 2 * torch.mm(x, y.t())
    return dist


def euclidean_dist(x, y):
    """Computes Euclidean distance."""
    m, n = x.size(0), y.size(0)
    xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
    yy = torch.pow(x, 2).sum(1, keepdim=True).expand(m, m).t()
    dist = xx + yy - 2 * torch.matmul(x, y.t())
    dist = dist.clamp(min=1e-12).sqrt()
    return dist


def hard_example_mining(distance_matrix, pos_idxs, neg_idxs):
    """For each anchor, find the hardest positive and negative sample.

    Args:
        distance_matrix: pair wise distance between samples, shape [N, M]
        pos_idxs: positive index with shape [N, M]
        neg_idxs: negative index with shape [N, M]

    Returns:
        dist_ap: pytorch Variable, distance(anchor, positive); shape [N]
        dist_an: pytorch Variable, distance(anchor, negative); shape [N]
        p_inds: pytorch LongTensor, with shape [N];
            indices of selected hard positive samples; 0 <= p_inds[i] <= N - 1
        n_inds: pytorch LongTensor, with shape [N];
            indices of selected hard negative samples; 0 <= n_inds[i] <= N - 1

    Note:
        Only consider the case in which all targets have same num of samples,
        thus we can cope with all anchors in parallel.
    """
    assert len(distance_matrix.size()) == 2
    dist_ap, _ = torch.max(distance_matrix * pos_idxs, dim=1)
    dist_an, _ = torch.min(distance_matrix * neg_idxs + pos_idxs * 
        99999999.0, dim=1)
    return dist_ap, dist_an


def softmax_weights(dist, mask):
    max_v = torch.max(dist * mask, dim=1, keepdim=True)[0]
    difference = dist - max_v
    z = torch.sum(torch.exp(difference) * mask, dim=1, keepdim=True) + 1e-06
    weights = torch.exp(difference) * mask / z
    return weights


def weighted_example_mining(distance_matrix, pos_idxs, neg_idxs):
    """For each anchor, find the weighted positive and negative sample.

    Args:
        distance_matrix: pytorch Variable, pair wise distance between samples, shape [N, N]
        pos_idxs:positive index with shape [N, M]
        neg_idxs: negative index with shape [N, M]

    Returns:
        dist_ap: pytorch Variable, distance(anchor, positive); shape [N]
        dist_an: pytorch Variable, distance(anchor, negative); shape [N]
    """
    assert len(distance_matrix.size()) == 2
    dist_ap = distance_matrix * pos_idxs
    dist_an = distance_matrix * neg_idxs
    weights_ap = softmax_weights(dist_ap, pos_idxs)
    weights_an = softmax_weights(-dist_an, neg_idxs)
    dist_ap = torch.sum(dist_ap * weights_ap, dim=1)
    dist_an = torch.sum(dist_an * weights_an, dim=1)
    return dist_ap, dist_an


class TripletLossNew(nn.Module):
    """Computes Triplet loss.

    Args:
            normalize_features: Whether to normalize the features. Default = True
            margin: The value for margin. Default = None.
            hard_mining: Whether to use hard sample mining. Default = True.
    """

    def __init__(self, normalize_features: 'bool'=True, margin: 'float'=
        None, hard_mining: 'bool'=True):
        """Constructor method for TripletLoss."""
        super(TripletLossNew, self).__init__()
        self.normalize_features = normalize_features
        self.margin = margin
        self.hard_mining = hard_mining

    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: 0.038035