# 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/6b/c6bixyrttikkg53cqwagks4mvhumeai6tdkva5oiiuq4gbnhahnc.py
# Topologically Sorted Source Nodes: [diag_2, mul, scores_1, sort_1], Original ATen: [aten.diag_embed, aten.mul, aten.sub, aten.sort]
# Source node to ATen node mapping:
#   diag_2 => eq, full_default, iota, where
#   mul => mul
#   scores_1 => sub
#   sort_1 => sort_1
# Graph fragment:
#   %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%iota, %unsqueeze_1), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %permute_1, %full_default), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, 2), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mm, %mul), kwargs = {})
#   %sort_1 : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%sub, 1, True), kwargs = {})
triton_per_fused_diag_embed_mul_sort_sub_0 = async_compile.triton('triton_per_fused_diag_embed_mul_sort_sub_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: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_diag_embed_mul_sort_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_diag_embed_mul_sort_sub_0(in_ptr0, 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_ptr0 + (r1 + (4*x0)), xmask, other=0.0)
    tmp4 = tl.load(in_ptr0 + (5*r1), None, eviction_policy='evict_last')
    tmp1 = r1
    tmp2 = x0
    tmp3 = tmp1 == tmp2
    tmp5 = 0.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp1.to(tl.int16)
    tmp11 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13, tmp14, = triton_helpers.sort_with_index(tmp11, tmp12, None, 1, stable=False, descending=True)
    tl.store(out_ptr0 + (r1 + (4*x0)), tmp9, xmask)
    tl.store(out_ptr1 + (r1 + (4*x0)), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/bs/cbsxmmi256ux5vrzlxdpdqr5wob5stbqcvv56dyd2e4mzqbamq5p.py
# Topologically Sorted Source Nodes: [sort], Original ATen: [aten.sort]
# Source node to ATen node mapping:
#   sort => sort
# Graph fragment:
#   %sort : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%sub, 0, True), kwargs = {})
triton_per_fused_sort_1 = async_compile.triton('triton_per_fused_sort_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=[4, 4],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_sort_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_sort_1(in_ptr0, out_ptr0, 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_ptr0 + (x0 + (4*r1)), xmask, other=0.0)
    tmp1 = r1
    tmp2 = tmp1.to(tl.int16)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5, tmp6, = triton_helpers.sort_with_index(tmp3, tmp4, None, 1, stable=False, descending=True)
    tl.store(out_ptr0 + (x0 + (4*r1)), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wk/cwkjmujyv2r6zzrwcajoo66qsgvmg3m4hwbrrzlhogm6ow5ra2cv.py
# Topologically Sorted Source Nodes: [add, clamp, neg_cap, add_1, clamp_1, neg_img, loss], Original ATen: [aten.add, aten.clamp, aten.sum]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   clamp => clamp_min
#   clamp_1 => clamp_min_1
#   loss => add_2
#   neg_cap => sum_1
#   neg_img => sum_2
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_1, %expand), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%clamp_min,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_4, %expand_1), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_1, 0), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%clamp_min_1,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_2), kwargs = {})
triton_per_fused_add_clamp_sum_2 = async_compile.triton('triton_per_fused_add_clamp_sum_2', '''
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: '*fp32', 4: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_clamp_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 2, '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_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (5*r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (4*r0), None, eviction_policy='evict_last')
    tmp2 = 0.2
    tmp3 = tmp2 - tmp1
    tmp4 = tmp0 + tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp11 = tmp10 + tmp3
    tmp12 = triton_helpers.maximum(tmp11, tmp5)
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp16 = tmp9 + tmp15
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp16, 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: [scores], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [diag_2, mul, scores_1, sort_1], Original ATen: [aten.diag_embed, aten.mul, aten.sub, aten.sort]
        stream0 = get_raw_stream(0)
        triton_per_fused_diag_embed_mul_sort_sub_0.run(buf0, buf1, buf4, 4, 4, grid=grid(4), stream=stream0)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sort], Original ATen: [aten.sort]
        triton_per_fused_sort_1.run(buf1, buf2, 4, 4, grid=grid(4), stream=stream0)
        del buf1
        buf6 = empty_strided_cuda((), (), torch.float32)
        buf8 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [add, clamp, neg_cap, add_1, clamp_1, neg_img, loss], Original ATen: [aten.add, aten.clamp, aten.sum]
        triton_per_fused_add_clamp_sum_2.run(buf8, buf2, buf0, buf4, 1, 4, grid=grid(1), stream=stream0)
        del buf0
        del buf2
        del buf4
    return (buf8, )


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
import torch.nn as nn


class HardNegativeContrastiveLoss(nn.Module):

    def __init__(self, nmax=1, margin=0.2):
        super(HardNegativeContrastiveLoss, self).__init__()
        self.margin = margin
        self.nmax = nmax

    def forward(self, imgs, caps):
        scores = torch.mm(imgs, caps.t())
        diag = scores.diag()
        scores = scores - 2 * torch.diag(scores.diag())
        sorted_cap, _ = torch.sort(scores, 0, descending=True)
        sorted_img, _ = torch.sort(scores, 1, descending=True)
        max_c = sorted_cap[:self.nmax, :]
        max_i = sorted_img[:, :self.nmax]
        neg_cap = torch.sum(torch.clamp(max_c + (self.margin - diag).view(1,
            -1).expand_as(max_c), min=0))
        neg_img = torch.sum(torch.clamp(max_i + (self.margin - diag).view(-
            1, 1).expand_as(max_i), min=0))
        loss = neg_cap + neg_img
        return loss


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


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
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_per_fused_diag_embed_mul_sort_sub_0(in_ptr0, 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_ptr0 + (r1 + 4 * x0), xmask, other=0.0)
    tmp4 = tl.load(in_ptr0 + 5 * r1, None, eviction_policy='evict_last')
    tmp1 = r1
    tmp2 = x0
    tmp3 = tmp1 == tmp2
    tmp5 = 0.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp1.to(tl.int16)
    tmp11 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13, _tmp14 = triton_helpers.sort_with_index(tmp11, tmp12, None, 1,
        stable=False, descending=True)
    tl.store(out_ptr0 + (r1 + 4 * x0), tmp9, xmask)
    tl.store(out_ptr1 + (r1 + 4 * x0), tmp13, xmask)


@triton.jit
def triton_per_fused_sort_1(in_ptr0, out_ptr0, 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_ptr0 + (x0 + 4 * r1), xmask, other=0.0)
    tmp1 = r1
    tmp2 = tmp1.to(tl.int16)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5, _tmp6 = triton_helpers.sort_with_index(tmp3, tmp4, None, 1,
        stable=False, descending=True)
    tl.store(out_ptr0 + (x0 + 4 * r1), tmp5, xmask)


@triton.jit
def triton_per_fused_add_clamp_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    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 + r0, None)
    tmp1 = tl.load(in_ptr1 + 5 * r0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp2 = 0.2
    tmp3 = tmp2 - tmp1
    tmp4 = tmp0 + tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp11 = tmp10 + tmp3
    tmp12 = triton_helpers.maximum(tmp11, tmp5)
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp16 = tmp9 + tmp15
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, 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(arg1_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4),
            0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = 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_diag_embed_mul_sort_sub_0[grid(4)](buf0, buf1,
            buf4, 4, 4, XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_per_fused_sort_1[grid(4)](buf1, buf2, 4, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf1
        buf6 = empty_strided_cuda((), (), torch.float32)
        buf8 = buf6
        del buf6
        triton_per_fused_add_clamp_sum_2[grid(1)](buf8, buf2, buf0, buf4, 1,
            4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf2
        del buf4
    return buf8,


class HardNegativeContrastiveLossNew(nn.Module):

    def __init__(self, nmax=1, margin=0.2):
        super(HardNegativeContrastiveLossNew, self).__init__()
        self.margin = margin
        self.nmax = nmax

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