# 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_8/inductor_cache/iy/ciynizvjkbcugcpyscud5dulsxcw4wgeelopvtt6czcxtsavlxaj.py
# Topologically Sorted Source Nodes: [d, d_1, d_2, sum_1, d2pos, e_pos, d_3, d_4, d_5, sum_2, d2neg, e_neg, add, d_pos, pow_3, loss, lt, sum_4], Original ATen: [aten.sub, aten.pow, aten.relu, aten.sum, aten.sqrt, aten.exp, aten.add, aten.div, aten.lt]
# Source node to ATen node mapping:
#   add => add
#   d => sub
#   d2neg => sqrt_1
#   d2pos => sqrt
#   d_1 => pow_1
#   d_2 => relu
#   d_3 => sub_1
#   d_4 => pow_2
#   d_5 => relu_1
#   d_pos => div
#   e_neg => exp_1
#   e_pos => exp
#   loss => sum_3
#   lt => lt
#   pow_3 => pow_3
#   sum_1 => sum_1
#   sum_2 => sum_2
#   sum_4 => sum_4
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%pow_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%relu, [-1]), kwargs = {})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_1,), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sqrt,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg2_1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
#   %relu_1 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%pow_2,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%relu_1, [-1]), kwargs = {})
#   %sqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_2,), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sqrt_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp, %exp_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %add), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%div, 2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_3,), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Tensor](args = (%sqrt, %sqrt_1), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%lt,), kwargs = {})
triton_per_fused_add_div_exp_lt_pow_relu_sqrt_sub_sum_0 = async_compile.triton('triton_per_fused_add_div_exp_lt_pow_relu_sqrt_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.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i64', 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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_exp_lt_pow_relu_sqrt_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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_exp_lt_pow_relu_sqrt_sub_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr2, out_ptr3, 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
    tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + (4*r0), None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr2 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr2 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.full([1, 1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp8 = tmp6 - tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = triton_helpers.maximum(tmp4, tmp9)
    tmp11 = tmp5 + tmp10
    tmp14 = tmp12 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = triton_helpers.maximum(tmp4, tmp15)
    tmp17 = tmp11 + tmp16
    tmp20 = tmp18 - tmp19
    tmp21 = tmp20 * tmp20
    tmp22 = triton_helpers.maximum(tmp4, tmp21)
    tmp23 = tmp17 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp26 = tmp0 - tmp25
    tmp27 = tmp26 * tmp26
    tmp28 = triton_helpers.maximum(tmp4, tmp27)
    tmp30 = tmp6 - tmp29
    tmp31 = tmp30 * tmp30
    tmp32 = triton_helpers.maximum(tmp4, tmp31)
    tmp33 = tmp28 + tmp32
    tmp35 = tmp12 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = triton_helpers.maximum(tmp4, tmp36)
    tmp38 = tmp33 + tmp37
    tmp40 = tmp18 - tmp39
    tmp41 = tmp40 * tmp40
    tmp42 = triton_helpers.maximum(tmp4, tmp41)
    tmp43 = tmp38 + tmp42
    tmp44 = libdevice.sqrt(tmp43)
    tmp45 = tl_math.exp(tmp24)
    tmp46 = tl_math.exp(tmp44)
    tmp47 = tmp45 + tmp46
    tmp48 = tmp45 / tmp47
    tmp49 = tmp48 * tmp48
    tmp50 = tl.broadcast_to(tmp49, [XBLOCK, RBLOCK])
    tmp52 = tl.sum(tmp50, 1)[:, None]
    tmp53 = tmp24 < tmp44
    tmp54 = tmp53.to(tl.int64)
    tmp55 = tl.broadcast_to(tmp54, [XBLOCK, RBLOCK])
    tmp57 = tl.sum(tmp55, 1)[:, None]
    tl.store(out_ptr2 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp52, None)
    tl.store(out_ptr3 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp57, 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)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = empty_strided_cuda((), (), torch.int64)
        # Topologically Sorted Source Nodes: [d, d_1, d_2, sum_1, d2pos, e_pos, d_3, d_4, d_5, sum_2, d2neg, e_neg, add, d_pos, pow_3, loss, lt, sum_4], Original ATen: [aten.sub, aten.pow, aten.relu, aten.sum, aten.sqrt, aten.exp, aten.add, aten.div, aten.lt]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_exp_lt_pow_relu_sqrt_sub_sum_0.run(arg0_1, arg1_1, arg2_1, buf2, buf3, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
    return (buf2, 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)
    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 softmaxtripletLoss(nn.Module):

    def __init__(self):
        super(softmaxtripletLoss, self).__init__()
        self.relu = nn.ReLU()

    def forward(self, anchor, pos, neg):
        anchor.size(0)
        d2pos = self.dist(anchor, pos)
        d2neg = self.dist(anchor, neg)
        e_pos = torch.exp(d2pos)
        e_neg = torch.exp(d2neg)
        d_pos = e_pos / (e_pos + e_neg)
        e_neg / (e_pos + e_neg)
        loss = torch.sum(d_pos ** 2)
        return loss, (d2pos < d2neg).sum()

    def dist(self, a, b):
        d = a - b
        d = d ** 2
        d = self.relu(d)
        return torch.sqrt(torch.sum(d, dim=-1))


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 [[], {}]

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_add_div_exp_lt_pow_relu_sqrt_sub_sum_0(in_ptr0,
    in_ptr1, in_ptr2, out_ptr2, out_ptr3, 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
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.full([1, 1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp8 = tmp6 - tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = triton_helpers.maximum(tmp4, tmp9)
    tmp11 = tmp5 + tmp10
    tmp14 = tmp12 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = triton_helpers.maximum(tmp4, tmp15)
    tmp17 = tmp11 + tmp16
    tmp20 = tmp18 - tmp19
    tmp21 = tmp20 * tmp20
    tmp22 = triton_helpers.maximum(tmp4, tmp21)
    tmp23 = tmp17 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp26 = tmp0 - tmp25
    tmp27 = tmp26 * tmp26
    tmp28 = triton_helpers.maximum(tmp4, tmp27)
    tmp30 = tmp6 - tmp29
    tmp31 = tmp30 * tmp30
    tmp32 = triton_helpers.maximum(tmp4, tmp31)
    tmp33 = tmp28 + tmp32
    tmp35 = tmp12 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = triton_helpers.maximum(tmp4, tmp36)
    tmp38 = tmp33 + tmp37
    tmp40 = tmp18 - tmp39
    tmp41 = tmp40 * tmp40
    tmp42 = triton_helpers.maximum(tmp4, tmp41)
    tmp43 = tmp38 + tmp42
    tmp44 = libdevice.sqrt(tmp43)
    tmp45 = tl_math.exp(tmp24)
    tmp46 = tl_math.exp(tmp44)
    tmp47 = tmp45 + tmp46
    tmp48 = tmp45 / tmp47
    tmp49 = tmp48 * tmp48
    tmp50 = tl.broadcast_to(tmp49, [XBLOCK, RBLOCK])
    tmp52 = tl.sum(tmp50, 1)[:, None]
    tmp53 = tmp24 < tmp44
    tmp54 = tmp53.to(tl.int64)
    tmp55 = tl.broadcast_to(tmp54, [XBLOCK, RBLOCK])
    tmp57 = tl.sum(tmp55, 1)[:, None]
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp52, None)
    tl.store(out_ptr3 + tl.full([XBLOCK, 1], 0, tl.int32), tmp57, 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)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = empty_strided_cuda((), (), torch.int64)
        get_raw_stream(0)
        triton_per_fused_add_div_exp_lt_pow_relu_sqrt_sub_sum_0[grid(1)](arg0_1
            , arg1_1, arg2_1, buf2, buf3, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf2, buf3


class softmaxtripletLossNew(nn.Module):

    def __init__(self):
        super(softmaxtripletLossNew, self).__init__()
        self.relu = nn.ReLU()

    def dist(self, a, b):
        d = a - b
        d = d ** 2
        d = self.relu(d)
        return torch.sqrt(torch.sum(d, dim=-1))

    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], output[1]
