# 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/fm/cfmcfdixueprgzfohxmib2ohsf3liakuhjyb3oh25nk5stvi3w7m.py
# Topologically Sorted Source Nodes: [sub, diff, pow_1, out, add, d_p, add_2, sub_1, diff_1, pow_3, out_1, add_1, d_n, sub_2, dist_hinge, loss], Original ATen: [aten.sub, aten.abs, aten.pow, aten.sum, aten.add, aten.clamp, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   d_n => pow_4
#   d_p => pow_2
#   diff => abs_1
#   diff_1 => abs_2
#   dist_hinge => clamp_min
#   loss => mean
#   out => sum_1
#   out_1 => sum_2
#   pow_1 => pow_1
#   pow_3 => pow_3
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 0.0001), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add, 0.5), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_2, 4), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg2_1), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_1,), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_2, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [1]), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, 0.0001), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add_1, 0.5), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %pow_4), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_2, 0), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%clamp_min,), kwargs = {})
triton_per_fused_abs_add_clamp_mean_pow_sub_sum_0 = async_compile.triton('triton_per_fused_abs_add_clamp_mean_pow_sub_sum_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=[1, 64],
    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, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_clamp_mean_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 12, '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_abs_add_clamp_mean_pow_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp5 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp6 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp11 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp12 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp17 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp18 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp28 = tl.load(in_ptr2 + (r0 + (64*r1)), None)
    tmp32 = tl.load(in_ptr2 + (16 + r0 + (64*r1)), None)
    tmp37 = tl.load(in_ptr2 + (32 + r0 + (64*r1)), None)
    tmp42 = tl.load(in_ptr2 + (48 + r0 + (64*r1)), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tmp3 * tmp3
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.abs(tmp7)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp4 + tmp9
    tmp13 = tmp11 - tmp12
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp19 = tmp17 - tmp18
    tmp20 = tl_math.abs(tmp19)
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp23 = 0.0001
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = 4.0
    tmp27 = tmp25 + tmp26
    tmp29 = tmp0 - tmp28
    tmp30 = tl_math.abs(tmp29)
    tmp31 = tmp30 * tmp30
    tmp33 = tmp5 - tmp32
    tmp34 = tl_math.abs(tmp33)
    tmp35 = tmp34 * tmp34
    tmp36 = tmp31 + tmp35
    tmp38 = tmp11 - tmp37
    tmp39 = tl_math.abs(tmp38)
    tmp40 = tmp39 * tmp39
    tmp41 = tmp36 + tmp40
    tmp43 = tmp17 - tmp42
    tmp44 = tl_math.abs(tmp43)
    tmp45 = tmp44 * tmp44
    tmp46 = tmp41 + tmp45
    tmp47 = tmp46 + tmp23
    tmp48 = libdevice.sqrt(tmp47)
    tmp49 = tmp27 - tmp48
    tmp50 = 0.0
    tmp51 = triton_helpers.maximum(tmp49, tmp50)
    tmp52 = tl.broadcast_to(tmp51, [XBLOCK, RBLOCK])
    tmp54 = tl.sum(tmp52, 1)[:, None]
    tmp55 = 64.0
    tmp56 = tmp54 / tmp55
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp56, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub, diff, pow_1, out, add, d_p, add_2, sub_1, diff_1, pow_3, out_1, add_1, d_n, sub_2, dist_hinge, loss], Original ATen: [aten.sub, aten.abs, aten.pow, aten.sum, aten.add, aten.clamp, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_add_clamp_mean_pow_sub_sum_0.run(buf2, arg0_1, arg1_1, arg2_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
    return (buf2, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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 PairwiseDistance(nn.Module):
    """class for calculating distance

    Arguments:
        nn {[type]} -- [description]
    """

    def __init__(self, smooth=0.0001):
        """Initializer

        Arguments:
            smooth {int} -- [description]
        """
        super(PairwiseDistance, self).__init__()
        self.smooth = smooth

    def forward(self, x1, x2):
        """x1, x2 represent input data

        Arguments:
            x1 {[type]} -- [description]
            x2 {[type]} -- [description]

        Returns:
            [type] -- [description]
        """
        assert x1.size() == x2.size()
        diff = torch.abs(x1 - x2)
        out = torch.pow(diff, 2).sum(dim=1)
        return torch.pow(out + self.smooth, 0.5)


class TripletMarginLoss(nn.Module):
    """Triplet loss

    Arguments:
        nn {[type]} -- [description]
    """

    def __init__(self, margin):
        super(TripletMarginLoss, self).__init__()
        self.margin = margin
        self.pdist = PairwiseDistance()

    def forward(self, anchor, positive, negative):
        d_p = self.pdist(anchor, positive)
        d_n = self.pdist(anchor, negative)
        dist_hinge = torch.clamp(self.margin + d_p - d_n, min=0)
        loss = torch.mean(dist_hinge)
        return loss


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


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

import torch
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, math as tl_math
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


@triton.jit
def triton_per_fused_abs_add_clamp_mean_pow_sub_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp6 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp11 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp12 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp17 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp28 = tl.load(in_ptr2 + (r0 + 64 * r1), None)
    tmp32 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
    tmp37 = tl.load(in_ptr2 + (32 + r0 + 64 * r1), None)
    tmp42 = tl.load(in_ptr2 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tmp3 * tmp3
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.abs(tmp7)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp4 + tmp9
    tmp13 = tmp11 - tmp12
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp19 = tmp17 - tmp18
    tmp20 = tl_math.abs(tmp19)
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp23 = 0.0001
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = 4.0
    tmp27 = tmp25 + tmp26
    tmp29 = tmp0 - tmp28
    tmp30 = tl_math.abs(tmp29)
    tmp31 = tmp30 * tmp30
    tmp33 = tmp5 - tmp32
    tmp34 = tl_math.abs(tmp33)
    tmp35 = tmp34 * tmp34
    tmp36 = tmp31 + tmp35
    tmp38 = tmp11 - tmp37
    tmp39 = tl_math.abs(tmp38)
    tmp40 = tmp39 * tmp39
    tmp41 = tmp36 + tmp40
    tmp43 = tmp17 - tmp42
    tmp44 = tl_math.abs(tmp43)
    tmp45 = tmp44 * tmp44
    tmp46 = tmp41 + tmp45
    tmp47 = tmp46 + tmp23
    tmp48 = libdevice.sqrt(tmp47)
    tmp49 = tmp27 - tmp48
    tmp50 = 0.0
    tmp51 = triton_helpers.maximum(tmp49, tmp50)
    tmp52 = tl.broadcast_to(tmp51, [XBLOCK, RBLOCK])
    tmp54 = tl.sum(tmp52, 1)[:, None]
    tmp55 = 64.0
    tmp56 = tmp54 / tmp55
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp56, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_abs_add_clamp_mean_pow_sub_sum_0[grid(1)](buf2,
            arg0_1, arg1_1, arg2_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf2,


class PairwiseDistance(nn.Module):
    """class for calculating distance

    Arguments:
        nn {[type]} -- [description]
    """

    def __init__(self, smooth=0.0001):
        """Initializer

        Arguments:
            smooth {int} -- [description]
        """
        super(PairwiseDistance, self).__init__()
        self.smooth = smooth

    def forward(self, x1, x2):
        """x1, x2 represent input data

        Arguments:
            x1 {[type]} -- [description]
            x2 {[type]} -- [description]

        Returns:
            [type] -- [description]
        """
        assert x1.size() == x2.size()
        diff = torch.abs(x1 - x2)
        out = torch.pow(diff, 2).sum(dim=1)
        return torch.pow(out + self.smooth, 0.5)


class TripletMarginLossNew(nn.Module):
    """Triplet loss

    Arguments:
        nn {[type]} -- [description]
    """

    def __init__(self, margin):
        super(TripletMarginLossNew, self).__init__()
        self.margin = margin
        self.pdist = PairwiseDistance()

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
