# 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_1/inductor_cache/es/ces3dv7vvtgywgajbgybagdw4thvfu4es6eezxvodbdh5rwootwo.py
# Topologically Sorted Source Nodes: [add, pos, sub, dist_hinge, mean, loss], Original ATen: [aten.add, aten.repeat, aten.sub, aten.clamp, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   dist_hinge => clamp_min
#   loss => mean_1
#   mean => mean
#   pos => repeat
#   sub => sub
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_3, 4), kwargs = {})
#   %repeat : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%unsqueeze, [1, 3]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %repeat), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 0.0), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%clamp_min, [1]), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mean,), kwargs = {})
triton_per_fused_add_clamp_mean_repeat_sub_0 = async_compile.triton('triton_per_fused_add_clamp_mean_repeat_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=[1, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_clamp_mean_repeat_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mean_repeat_sub_0(in_out_ptr0, in_ptr0, 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 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp1 = 4.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp8 = tmp7 + tmp1
    tmp9 = tmp8 - tmp3
    tmp10 = triton_helpers.maximum(tmp9, tmp5)
    tmp11 = tmp6 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp13 - tmp3
    tmp15 = triton_helpers.maximum(tmp14, tmp5)
    tmp16 = tmp11 + tmp15
    tmp17 = 3.0
    tmp18 = tmp16 / tmp17
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
    tmp21 = tl.sum(tmp19, 1)[:, None]
    tmp22 = tmp21 / tmp1
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp22, 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, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [add, pos, sub, dist_hinge, mean, loss], Original ATen: [aten.add, aten.repeat, aten.sub, aten.clamp, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_mean_repeat_sub_0.run(buf1, arg0_1, 1, 4, grid=grid(1), stream=stream0)
        del arg0_1
    return (buf1, )


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)
    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)

from torch.autograd import Function
import torch


class PairwiseDistance(Function):

    def __init__(self, p):
        super(PairwiseDistance, self).__init__()
        self.norm = p

    def forward(self, x1, x2):
        assert x1.size() == x2.size()
        eps = 0.0001 / x1.size(1)
        diff = torch.abs(x1 - x2)
        out = torch.pow(diff, self.norm).sum(dim=1)
        return torch.pow(out + eps, 1.0 / self.norm)


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

    def __init__(self, margin):
        super(TripletMarginLoss, self).__init__()
        self.margin = margin
        self.pdist = PairwiseDistance(2)

    def forward(self, repr):
        queue_len_plus = repr.shape[-1]
        pos = repr[:, 0].unsqueeze(-1).repeat(1, queue_len_plus - 1)
        neg = repr[:, 1:]
        dist_hinge = torch.clamp(self.margin + neg - pos, min=0.0)
        loss = torch.mean(dist_hinge, 1).mean()
        return loss


def get_inputs():
    return [torch.rand([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.autograd import Function
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_clamp_mean_repeat_sub_0(in_out_ptr0, in_ptr0,
    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 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = 4.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp8 = tmp7 + tmp1
    tmp9 = tmp8 - tmp3
    tmp10 = triton_helpers.maximum(tmp9, tmp5)
    tmp11 = tmp6 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp13 - tmp3
    tmp15 = triton_helpers.maximum(tmp14, tmp5)
    tmp16 = tmp11 + tmp15
    tmp17 = 3.0
    tmp18 = tmp16 / tmp17
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
    tmp21 = tl.sum(tmp19, 1)[:, None]
    tmp22 = tmp21 / tmp1
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp22, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_clamp_mean_repeat_sub_0[grid(1)](buf1, arg0_1,
            1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf1,


class PairwiseDistance(Function):

    def __init__(self, p):
        super(PairwiseDistance, self).__init__()
        self.norm = p

    def forward(self, x1, x2):
        assert x1.size() == x2.size()
        eps = 0.0001 / x1.size(1)
        diff = torch.abs(x1 - x2)
        out = torch.pow(diff, self.norm).sum(dim=1)
        return torch.pow(out + eps, 1.0 / self.norm)


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

    def __init__(self, margin):
        super(TripletMarginLossNew, self).__init__()
        self.margin = margin
        self.pdist = PairwiseDistance(2)

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