# 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_4/inductor_cache/zj/czjfayfcarkuoamzfcxjw3kmfgj6fl22etyizczrjgbqk5bwokbq.py
# Topologically Sorted Source Nodes: [x, y], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   x => div
#   y => div_1
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %expand), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %expand_1), kwargs = {})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qz/cqzob3vprd66msbqxksowaomykxxjuerzonuix4auynyweifzuqd.py
# Topologically Sorted Source Nodes: [max_1, min_1], Original ATen: [aten.max, aten.min]
# Source node to ATen node mapping:
#   max_1 => getitem
#   min_1 => getitem_2
# Graph fragment:
#   %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%max_1, 0), kwargs = {})
#   %getitem_2 : [num_users=1] = call_function[target=operator.getitem](args = (%min_1, 0), kwargs = {})
triton_poi_fused_max_min_1 = async_compile.triton('triton_poi_fused_max_min_1', '''
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.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_min_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@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)
''', 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, 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)
        # Topologically Sorted Source Nodes: [x, y], Original ATen: [aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_0.run(arg0_1, buf0, buf1, 16, grid=grid(16), stream=stream0)
        del arg0_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, mm], Original ATen: [aten.div, aten.mm]
        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)
        # Topologically Sorted Source Nodes: [max_1, min_1], Original ATen: [aten.max, aten.min]
        triton_poi_fused_max_min_1.run(buf2, arg1_1, buf3, buf4, 4, grid=grid(4), stream=stream0)
        del arg1_1
        del buf2
        buf5 = empty_strided_cuda((0, ), (1, ), torch.float32)
    return (buf5, buf4, buf3, )


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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 1), (1, 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.functional as F
import torch.nn as nn


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 TripletLoss(nn.Module):
    """Computes Triplet loss."""

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

        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.
        """
        super(TripletLoss, self).__init__()
        self.normalize_features = normalize_features
        self.margin = margin
        self.hard_mining = hard_mining

    def forward(self, embedding: 'torch.Tensor', targets: 'torch.Tensor'
        ) ->torch.Tensor:
        """Forward Method.

        Args:
            embedding: The output of the network.
            targets: The targets.

        Returns:
            The computed Triplet Loss.
        """
        distance_matrix = cosine_dist(embedding, embedding
            ) if self.normalize_features else euclidean_dist(embedding,
            embedding)
        n = distance_matrix.size(0)
        pos_idxs = targets.view(n, 1).expand(n, n).eq(targets.view(n, 1).
            expand(n, n).t()).float()
        neg_idxs = targets.view(n, 1).expand(n, n).ne(targets.view(n, 1).
            expand(n, n).t()).float()
        if self.hard_mining:
            dist_ap, dist_an = hard_example_mining(distance_matrix=
                distance_matrix, pos_idxs=pos_idxs, neg_idxs=neg_idxs)
        else:
            dist_ap, dist_an = weighted_example_mining(distance_matrix=
                distance_matrix, pos_idxs=pos_idxs, neg_idxs=neg_idxs)
        y = dist_an.new().resize_as_(dist_an).fill_(1)
        if self.margin is not None and self.margin > 0:
            loss = F.margin_ranking_loss(dist_an, dist_ap, y, margin=self.
                margin)
        else:
            loss = F.soft_margin_loss(dist_an - dist_ap, y)
            if loss == float('Inf'):
                loss = F.margin_ranking_loss(dist_an, dist_ap, y, margin=0.3)
        return loss


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([4, 1])]


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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.functional as F
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_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."""

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

        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.
        """
        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]
