# 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_7/inductor_cache/bn/cbnw4uewpv7yyudtpcdoczlgvlxmpcnzej45i7ca2geg5hg5zbwg.py
# Topologically Sorted Source Nodes: [dist, clamp, dist_1, eq, mat_sim, sub, mul, add_1, sort, mul_1, add_2, sort_1], Original ATen: [aten.add, aten.clamp, aten.sqrt, aten.eq, aten._to_copy, aten.rsub, aten.mul, aten.sort]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_2 => add_2
#   clamp => clamp_min
#   dist => add
#   dist_1 => sqrt
#   eq => eq
#   mat_sim => convert_element_type
#   mul => mul
#   mul_1 => mul_1
#   sort => sort
#   sort_1 => sort_1
#   sub => sub
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand, %permute), kwargs = {})
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %add), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_tensor, 1e-12), kwargs = {})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%clamp_min,), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%expand_2, %permute_2), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %convert_element_type), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, -9999999.0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, %mul), kwargs = {})
#   %sort : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%add_1, 1, True), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, 9999999.0), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, %mul_1), kwargs = {})
#   %sort_1 : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%add_2, 1), kwargs = {})
triton_per_fused__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_0 = async_compile.triton('triton_per_fused__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_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.persistent_reduction(
    size_hints=[4, 4],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 11, '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}
)
@triton.jit
def triton_per_fused__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 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, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + (4*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (4*r1), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + (r1 + (4*x0)), xmask, other=0.0)
    tmp29 = tl.load(in_ptr1 + (x0 + (4*r1)), xmask, other=0.0)
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp13 = tmp12 * tmp12
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp23 = tmp11 + tmp22
    tmp24 = tmp0 + tmp23
    tmp25 = 1e-12
    tmp26 = triton_helpers.maximum(tmp24, tmp25)
    tmp27 = libdevice.sqrt(tmp26)
    tmp30 = tmp28 == tmp29
    tmp31 = tmp30.to(tl.float32)
    tmp32 = 1.0
    tmp33 = tmp32 - tmp31
    tmp34 = -9999999.0
    tmp35 = tmp33 * tmp34
    tmp36 = tmp27 + tmp35
    tmp37 = r1
    tmp38 = tmp37.to(tl.int16)
    tmp39 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK])
    tmp40 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
    tmp41, tmp42, = triton_helpers.sort_with_index(tmp39, tmp40, None, 1, stable=False, descending=True)
    tmp43 = 9999999.0
    tmp44 = tmp31 * tmp43
    tmp45 = tmp27 + tmp44
    tmp46 = tl.broadcast_to(tmp45, [XBLOCK, RBLOCK])
    tmp47, tmp48, = triton_helpers.sort_with_index(tmp46, tmp40, None, 1, stable=False, descending=False)
    tl.store(in_out_ptr0 + (r1 + (4*x0)), tmp24, xmask)
    tl.store(out_ptr0 + (r1 + (4*x0)), tmp41, xmask)
    tl.store(out_ptr1 + (r1 + (4*x0)), tmp47, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/e3/ce32pujiahet4pqxynqfc4b7tbaczqldo5uzescozpvluutc4mbw.py
# Topologically Sorted Source Nodes: [loss], Original ATen: [aten.neg, aten.sub, aten.mul, aten.add, aten.clamp_min, aten.mean]
# Source node to ATen node mapping:
#   loss => add_3, clamp_min_1, full_default, mean, mul_2, sub_1
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4], -1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_1, %select), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%full_default, %sub_1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Scalar](args = (%mul_2, 4.0), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_3, 0), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%clamp_min_1,), kwargs = {})
triton_per_fused_add_clamp_min_mean_mul_neg_sub_1 = async_compile.triton('triton_per_fused_add_clamp_min_mean_mul_neg_sub_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_clamp_min_mean_mul_neg_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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}
)
@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):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = 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 = 4.0
    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 = tmp11 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp12, None)
''', 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, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf0)
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dist, clamp, dist_1, eq, mat_sim, sub, mul, add_1, sort, mul_1, add_2, sort_1], Original ATen: [aten.add, aten.clamp, aten.sqrt, aten.eq, aten._to_copy, aten.rsub, aten.mul, aten.sort]
        stream0 = get_raw_stream(0)
        triton_per_fused__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_0.run(buf1, arg0_1, arg1_1, buf2, buf4, 4, 4, grid=grid(4), stream=stream0)
        del arg0_1
        del arg1_1
        del buf1
        buf6 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [loss], Original ATen: [aten.neg, aten.sub, aten.mul, aten.add, aten.clamp_min, aten.mean]
        triton_per_fused_add_clamp_min_mean_mul_neg_sub_1.run(buf7, buf4, buf2, 1, 4, grid=grid(1), stream=stream0)
        del buf2
        del buf4
    return (buf7, )


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, 4), (4, 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
from torch import nn
import torch.nn.functional as F
from torch.optim.lr_scheduler import *


def _batch_hard(mat_distance, mat_similarity, indice=False):
    sorted_mat_distance, positive_indices = torch.sort(mat_distance + -
        9999999.0 * (1 - mat_similarity), dim=1, descending=True)
    hard_p = sorted_mat_distance[:, 0]
    hard_p_indice = positive_indices[:, 0]
    sorted_mat_distance, negative_indices = torch.sort(mat_distance + 
        9999999.0 * mat_similarity, dim=1, descending=False)
    hard_n = sorted_mat_distance[:, 0]
    hard_n_indice = negative_indices[:, 0]
    if indice:
        return hard_p, hard_n, hard_p_indice, hard_n_indice
    return hard_p, hard_n


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


class TripletLoss(nn.Module):
    """
	Compute Triplet loss augmented with Batch Hard
	Details can be seen in 'In defense of the Triplet Loss for Person Re-Identification'
	"""

    def __init__(self, margin, normalize_feature=False):
        super(TripletLoss, self).__init__()
        self.margin = margin
        self.normalize_feature = normalize_feature
        self.margin_loss = nn.MarginRankingLoss(margin=margin)

    def forward(self, emb, label):
        if self.normalize_feature:
            emb = F.normalize(emb)
        mat_dist = euclidean_dist(emb, emb)
        assert mat_dist.size(0) == mat_dist.size(1)
        N = mat_dist.size(0)
        mat_sim = label.expand(N, N).eq(label.expand(N, N).t()).float()
        dist_ap, dist_an = _batch_hard(mat_dist, mat_sim)
        assert dist_an.size(0) == dist_ap.size(0)
        y = torch.ones_like(dist_ap)
        loss = self.margin_loss(dist_an, dist_ap, y)
        return loss


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


def get_init_inputs():
    return [[], {'margin': 4}]

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
from torch import nn
from torch.optim.lr_scheduler 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__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_0(in_out_ptr0,
    in_ptr0, in_ptr1, 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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + 4 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + (r1 + 4 * x0), xmask, other=0.0)
    tmp29 = tl.load(in_ptr1 + (x0 + 4 * r1), xmask, other=0.0)
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp13 = tmp12 * tmp12
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp23 = tmp11 + tmp22
    tmp24 = tmp0 + tmp23
    tmp25 = 1e-12
    tmp26 = triton_helpers.maximum(tmp24, tmp25)
    tmp27 = libdevice.sqrt(tmp26)
    tmp30 = tmp28 == tmp29
    tmp31 = tmp30.to(tl.float32)
    tmp32 = 1.0
    tmp33 = tmp32 - tmp31
    tmp34 = -9999999.0
    tmp35 = tmp33 * tmp34
    tmp36 = tmp27 + tmp35
    tmp37 = r1
    tmp38 = tmp37.to(tl.int16)
    tmp39 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK])
    tmp40 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
    tmp41, _tmp42 = triton_helpers.sort_with_index(tmp39, tmp40, None, 1,
        stable=False, descending=True)
    tmp43 = 9999999.0
    tmp44 = tmp31 * tmp43
    tmp45 = tmp27 + tmp44
    tmp46 = tl.broadcast_to(tmp45, [XBLOCK, RBLOCK])
    tmp47, _tmp48 = triton_helpers.sort_with_index(tmp46, tmp40, None, 1,
        stable=False, descending=False)
    tl.store(in_out_ptr0 + (r1 + 4 * x0), tmp24, xmask)
    tl.store(out_ptr0 + (r1 + 4 * x0), tmp41, xmask)
    tl.store(out_ptr1 + (r1 + 4 * x0), tmp47, 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 = 4.0
    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 = tmp11 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp12, None)


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, 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(arg0_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_0[grid(4)](
            buf1, arg0_1, arg1_1, buf2, buf4, 4, 4, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_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 _batch_hard(mat_distance, mat_similarity, indice=False):
    sorted_mat_distance, positive_indices = torch.sort(mat_distance + -
        9999999.0 * (1 - mat_similarity), dim=1, descending=True)
    hard_p = sorted_mat_distance[:, 0]
    hard_p_indice = positive_indices[:, 0]
    sorted_mat_distance, negative_indices = torch.sort(mat_distance + 
        9999999.0 * mat_similarity, dim=1, descending=False)
    hard_n = sorted_mat_distance[:, 0]
    hard_n_indice = negative_indices[:, 0]
    if indice:
        return hard_p, hard_n, hard_p_indice, hard_n_indice
    return hard_p, hard_n


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


class TripletLossNew(nn.Module):
    """
	Compute Triplet loss augmented with Batch Hard
	Details can be seen in 'In defense of the Triplet Loss for Person Re-Identification'
	"""

    def __init__(self, margin, normalize_feature=False):
        super(TripletLossNew, self).__init__()
        self.margin = margin
        self.normalize_feature = normalize_feature
        self.margin_loss = nn.MarginRankingLoss(margin=margin)

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