# 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/3k/c3kek72pwrcrumjnefj2rvdl74nxdxgux5tdgy2ov2z6zasezvm7.py
# Topologically Sorted Source Nodes: [mul, add, x, mul_1, add_1, x_1, mse_loss, s1, mul_3, mul_2, add_2, x_2, mse_loss_1, s2, pow_1, loss], Original ATen: [aten.mul, aten.add, aten.div, aten.mse_loss, aten.sum, aten.pow]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   loss => mul_4
#   mse_loss => mean, pow_7, sub
#   mse_loss_1 => mean_1, pow_8, sub_1
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   pow_1 => pow_9
#   s1 => sum_4
#   s2 => sum_5
#   x => div
#   x_1 => div_1
#   x_2 => div_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select, 1.0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand, 1e-12), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_1, 1.0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand_1, 1e-12), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %div_1), kwargs = {})
#   %pow_7 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_7,), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mean,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_4, 1.0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_2, 1.0), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand_2, 1e-12), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_2, %add_2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %div_2), kwargs = {})
#   %pow_8 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_8,), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mean_1,), kwargs = {})
#   %pow_9 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_5, -1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %pow_9), kwargs = {})
triton_per_fused_add_div_mse_loss_mul_pow_sum_0 = async_compile.triton('triton_per_fused_add_div_mse_loss_mul_pow_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.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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mse_loss_mul_pow_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 15, '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_div_mse_loss_mul_pow_sum_0(in_out_ptr0, in_ptr0, 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)
    r2 = rindex
    r1 = (rindex // 4)
    tmp0 = tl.load(in_ptr0 + (r2), None)
    tmp3 = tl.load(in_ptr0 + (4*r1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (64 + r2), None)
    tmp20 = tl.load(in_ptr0 + (64 + (4*r1)), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (65 + (4*r1)), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (66 + (4*r1)), None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (67 + (4*r1)), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr0 + (128 + r2), None)
    tmp41 = tl.load(in_ptr0 + (128 + (4*r1)), None, eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr0 + (129 + (4*r1)), None, eviction_policy='evict_last')
    tmp46 = tl.load(in_ptr0 + (130 + (4*r1)), None, eviction_policy='evict_last')
    tmp49 = tl.load(in_ptr0 + (131 + (4*r1)), None, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = libdevice.sqrt(tmp13)
    tmp15 = 1e-12
    tmp16 = tmp14 + tmp15
    tmp17 = tmp2 / tmp16
    tmp19 = tmp18 * tmp1
    tmp21 = tmp20 * tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp29 = tmp28 * tmp28
    tmp30 = tmp27 + tmp29
    tmp31 = libdevice.sqrt(tmp30)
    tmp32 = tmp31 + tmp15
    tmp33 = tmp19 / tmp32
    tmp34 = tmp17 - tmp33
    tmp35 = tmp34 * tmp34
    tmp36 = tl.broadcast_to(tmp35, [XBLOCK, RBLOCK])
    tmp38 = tl.sum(tmp36, 1)[:, None]
    tmp40 = tmp39 * tmp1
    tmp42 = tmp41 * tmp41
    tmp44 = tmp43 * tmp43
    tmp45 = tmp42 + tmp44
    tmp47 = tmp46 * tmp46
    tmp48 = tmp45 + tmp47
    tmp50 = tmp49 * tmp49
    tmp51 = tmp48 + tmp50
    tmp52 = libdevice.sqrt(tmp51)
    tmp53 = tmp52 + tmp15
    tmp54 = tmp40 / tmp53
    tmp55 = tmp17 - tmp54
    tmp56 = tmp55 * tmp55
    tmp57 = tl.broadcast_to(tmp56, [XBLOCK, RBLOCK])
    tmp59 = tl.sum(tmp57, 1)[:, None]
    tmp60 = 64.0
    tmp61 = tmp38 / tmp60
    tmp62 = tmp61 * tmp1
    tmp63 = tmp59 / tmp60
    tmp64 = tl.full([1, 1], 1, tl.int32)
    tmp65 = tmp64 / tmp63
    tmp66 = tmp62 * tmp65
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp66, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [mul, add, x, mul_1, add_1, x_1, mse_loss, s1, mul_3, mul_2, add_2, x_2, mse_loss_1, s2, pow_1, loss], Original ATen: [aten.mul, aten.add, aten.div, aten.mse_loss, aten.sum, aten.pow]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mse_loss_mul_pow_sum_0.run(buf3, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
    return (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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_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


def normalize(x, axis=-1):
    """Normalizing to unit length along the specified dimension.
    Args:
      x: pytorch Variable
    Returns:
      x: pytorch Variable, same shape as input
    """
    x = 1.0 * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12)
    return x


class triplet_my_loss(nn.Module):

    def __init__(self, margin=1.0, use_gpu=True):
        super(triplet_my_loss, self).__init__()
        self.use_gpu = use_gpu
        self.margin = margin
        self.mse = nn.MSELoss()

    def forward(self, inputs):
        """
        Args:
            inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
            targets: ground truth labels with shape (num_classes)
        """
        p = inputs[0]
        p1 = inputs[1]
        n1 = inputs[2]
        anchor = normalize(p, axis=-1)
        positive = normalize(p1, axis=-1)
        negative = normalize(n1, axis=-1)
        s1 = torch.sum(self.mse(anchor, positive))
        s2 = torch.sum(self.mse(anchor, negative))
        loss = torch.mul(torch.mul(s1, self.margin), torch.pow(s2, -1))
        return loss


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


def get_init_inputs():
    return [[], {}]

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.triton_helpers import libdevice
from torch import 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_add_div_mse_loss_mul_pow_sum_0(in_out_ptr0, in_ptr0,
    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)
    r2 = rindex
    r1 = rindex // 4
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp3 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (64 + r2), None)
    tmp20 = tl.load(in_ptr0 + (64 + 4 * r1), None, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr0 + (65 + 4 * r1), None, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr0 + (66 + 4 * r1), None, eviction_policy='evict_last'
        )
    tmp28 = tl.load(in_ptr0 + (67 + 4 * r1), None, eviction_policy='evict_last'
        )
    tmp39 = tl.load(in_ptr0 + (128 + r2), None)
    tmp41 = tl.load(in_ptr0 + (128 + 4 * r1), None, eviction_policy=
        'evict_last')
    tmp43 = tl.load(in_ptr0 + (129 + 4 * r1), None, eviction_policy=
        'evict_last')
    tmp46 = tl.load(in_ptr0 + (130 + 4 * r1), None, eviction_policy=
        'evict_last')
    tmp49 = tl.load(in_ptr0 + (131 + 4 * r1), None, eviction_policy=
        'evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = libdevice.sqrt(tmp13)
    tmp15 = 1e-12
    tmp16 = tmp14 + tmp15
    tmp17 = tmp2 / tmp16
    tmp19 = tmp18 * tmp1
    tmp21 = tmp20 * tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp29 = tmp28 * tmp28
    tmp30 = tmp27 + tmp29
    tmp31 = libdevice.sqrt(tmp30)
    tmp32 = tmp31 + tmp15
    tmp33 = tmp19 / tmp32
    tmp34 = tmp17 - tmp33
    tmp35 = tmp34 * tmp34
    tmp36 = tl.broadcast_to(tmp35, [XBLOCK, RBLOCK])
    tmp38 = tl.sum(tmp36, 1)[:, None]
    tmp40 = tmp39 * tmp1
    tmp42 = tmp41 * tmp41
    tmp44 = tmp43 * tmp43
    tmp45 = tmp42 + tmp44
    tmp47 = tmp46 * tmp46
    tmp48 = tmp45 + tmp47
    tmp50 = tmp49 * tmp49
    tmp51 = tmp48 + tmp50
    tmp52 = libdevice.sqrt(tmp51)
    tmp53 = tmp52 + tmp15
    tmp54 = tmp40 / tmp53
    tmp55 = tmp17 - tmp54
    tmp56 = tmp55 * tmp55
    tmp57 = tl.broadcast_to(tmp56, [XBLOCK, RBLOCK])
    tmp59 = tl.sum(tmp57, 1)[:, None]
    tmp60 = 64.0
    tmp61 = tmp38 / tmp60
    tmp62 = tmp61 * tmp1
    tmp63 = tmp59 / tmp60
    tmp64 = tl.full([1, 1], 1, tl.int32)
    tmp65 = tmp64 / tmp63
    tmp66 = tmp62 * tmp65
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp66, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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)
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_div_mse_loss_mul_pow_sum_0[grid(1)](buf3,
            arg0_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf3,


def normalize(x, axis=-1):
    """Normalizing to unit length along the specified dimension.
    Args:
      x: pytorch Variable
    Returns:
      x: pytorch Variable, same shape as input
    """
    x = 1.0 * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12)
    return x


class triplet_my_lossNew(nn.Module):

    def __init__(self, margin=1.0, use_gpu=True):
        super(triplet_my_lossNew, self).__init__()
        self.use_gpu = use_gpu
        self.margin = margin
        self.mse = nn.MSELoss()

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