# 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_0/inductor_cache/vn/cvn7neyecxwucvxwix3iwvixzuf4n42a24h7glkmj7uusonposlh.py
# Topologically Sorted Source Nodes: [add, mul, dist_mat, clamp, dist_mat_1, eq, identity_mat, sub_1, mul_1, add_1, sort, mul_2, add_2, sort_1], Original ATen: [aten.add, aten.mul, aten.sub, aten.clamp, aten.sqrt, aten.eq, aten._to_copy, aten.rsub, aten.sort]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   clamp => clamp_min
#   dist_mat => sub
#   dist_mat_1 => sqrt
#   eq => eq
#   identity_mat => convert_element_type
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   sort => sort
#   sort_1 => sort_1
#   sub_1 => sub_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand, %permute), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mul), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 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 = (%permute_2, %expand_3), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %convert_element_type), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, -10000000.0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, %mul_1), kwargs = {})
#   %sort : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%add_1, 1, True), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, 10000000.0), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, %mul_2), 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_sub_0 = async_compile.triton('triton_per_fused__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_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: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_sub_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_sub_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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)
    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)
''', device_str='cuda')


# kernel path: runs/run_shard_0/inductor_cache/ft/cft3mvcb27x3qu2aa7eszgtcgn7kayjq33cnvtx3ey4sz5yyvzsq.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_3, sub_2
# 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_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_1, %select), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%full_default, %sub_2), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Scalar](args = (%mul_3, 0.3), 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 = 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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(arg0_1, reinterpret_tensor(arg1_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: [add, mul, dist_mat, clamp, dist_mat_1, eq, identity_mat, sub_1, mul_1, add_1, sort, mul_2, add_2, sort_1], Original ATen: [aten.add, aten.mul, aten.sub, aten.clamp, aten.sqrt, aten.eq, aten._to_copy, aten.rsub, aten.sort]
        stream0 = get_raw_stream(0)
        triton_per_fused__to_copy_add_clamp_eq_mul_rsub_sort_sqrt_sub_0.run(buf1, arg0_1, arg1_1, arg2_1, arg3_1, buf2, buf4, 4, 4, grid=grid(4), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_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)
    arg2_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_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._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]
