# 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/qd/cqdfud7hfeqnmonz4fwgzv6mzxfxb3gsgns7bgjcslwhyyutfiy5.py
# Topologically Sorted Source Nodes: [sub_2, M, sub_3, add_1, dist_diff, mul_2, sum_1, sum_2, truediv], Original ATen: [aten.rsub, aten.mul, aten.sub, aten.add, aten.relu, aten.sum, aten.div]
# Source node to ATen node mapping:
#   M => mul_1
#   add_1 => add_1
#   dist_diff => relu
#   mul_2 => mul_2
#   sub_2 => sub_2
#   sub_3 => sub_3
#   sum_1 => sum_1
#   sum_2 => sum_2
#   truediv => div
# Graph fragment:
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %expand_5), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_4, %sub_2), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand_3, %permute_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_3, 1.0), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %relu), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_2,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %sum_2), kwargs = {})
triton_per_fused_add_div_mul_relu_rsub_sub_sum_0 = async_compile.triton('triton_per_fused_add_div_mul_relu_rsub_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: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mul_relu_rsub_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 2, '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_div_mul_relu_rsub_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, 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 % 4
    r2 = (rindex // 16)
    r1 = (rindex // 4) % 4
    r5 = (rindex // 4)
    r3 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (r2), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (r1), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (5*r2), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + (5*r0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (r0 + (4*r2)), None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr1 + (5*r1), None, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr1 + (r5), None, eviction_policy='evict_last')
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = r2
    tmp5 = r0
    tmp6 = tmp4 == tmp5
    tmp7 = 1.0
    tmp8 = 0.0
    tmp9 = tl.where(tmp6, tmp7, tmp8)
    tmp10 = tmp3 - tmp9
    tmp12 = tmp11 == tmp1
    tmp13 = tmp12.to(tl.float32)
    tmp14 = r1
    tmp15 = tmp4 == tmp14
    tmp16 = tl.where(tmp15, tmp7, tmp8)
    tmp17 = tmp13 - tmp16
    tmp18 = tmp7 - tmp17
    tmp19 = tmp10 * tmp18
    tmp22 = tmp20 + tmp21
    tmp24 = 2.0
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 - tmp25
    tmp27 = triton_helpers.maximum(tmp26, tmp8)
    tmp28 = 0.0001
    tmp29 = triton_helpers.maximum(tmp27, tmp28)
    tmp30 = libdevice.sqrt(tmp29)
    tmp32 = tmp20 + tmp31
    tmp34 = tmp33 * tmp24
    tmp35 = tmp32 - tmp34
    tmp36 = triton_helpers.maximum(tmp35, tmp8)
    tmp37 = triton_helpers.maximum(tmp36, tmp28)
    tmp38 = libdevice.sqrt(tmp37)
    tmp39 = tmp30 - tmp38
    tmp40 = tmp39 + tmp7
    tmp41 = tl.full([1, 1], 0, tl.int32)
    tmp42 = triton_helpers.maximum(tmp41, tmp40)
    tmp43 = tmp19 * tmp42
    tmp44 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK])
    tmp46 = tl.sum(tmp44, 1)[:, None]
    tmp47 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp49 = tl.sum(tmp47, 1)[:, None]
    tmp50 = tmp46 / tmp49
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp50, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (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: [prod], Original ATen: [aten.mm]
        extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf0)
        del arg0_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [sub_2, M, sub_3, add_1, dist_diff, mul_2, sum_1, sum_2, truediv], Original ATen: [aten.rsub, aten.mul, aten.sub, aten.add, aten.relu, aten.sum, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mul_relu_rsub_sub_sum_0.run(buf4, arg1_1, buf0, 1, 64, grid=grid(1), stream=stream0)
        del arg1_1
        del buf0
    return (buf4, )


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, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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 torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.utils.model_zoo


def pdist(A, squared=False, eps=0.0001):
    prod = torch.mm(A, A.t())
    norm = prod.diag().unsqueeze(1).expand_as(prod)
    res = (norm + norm.t() - 2 * prod).clamp(min=0)
    if squared:
        return res
    else:
        return res.clamp(min=eps).sqrt()


class TripletAllLoss(torch.nn.Module):
    """
    Compute all possible triplets in the batch, where loss > 0
    """

    def __init__(self, margin=1.0, **kwargs):
        None
        self.margin = margin
        torch.nn.Module.__init__(self)

    def forward(self, embeddings, labels):
        d = pdist(embeddings)
        pos = torch.eq(*[labels.unsqueeze(dim).expand_as(d) for dim in [0, 1]]
            ).type_as(d) - torch.eye(len(d)).type_as(d)
        T = d.unsqueeze(1).expand(*((len(d),) * 3))
        M = pos.unsqueeze(1).expand_as(T) * (1 - pos.unsqueeze(2).expand_as(T))
        dist_diff = F.relu(T - T.transpose(1, 2) + self.margin)
        return (M * dist_diff).sum() / M.sum()


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


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

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.utils.model_zoo
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_add_div_mul_relu_rsub_sub_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, 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 % 4
    r2 = rindex // 16
    r1 = rindex // 4 % 4
    r5 = rindex // 4
    tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + r1, None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + 5 * r2, None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + 5 * r0, None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (r0 + 4 * r2), None, eviction_policy='evict_last'
        )
    tmp31 = tl.load(in_ptr1 + 5 * r1, None, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr1 + r5, None, eviction_policy='evict_last')
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = r2
    tmp5 = r0
    tmp6 = tmp4 == tmp5
    tmp7 = 1.0
    tmp8 = 0.0
    tmp9 = tl.where(tmp6, tmp7, tmp8)
    tmp10 = tmp3 - tmp9
    tmp12 = tmp11 == tmp1
    tmp13 = tmp12.to(tl.float32)
    tmp14 = r1
    tmp15 = tmp4 == tmp14
    tmp16 = tl.where(tmp15, tmp7, tmp8)
    tmp17 = tmp13 - tmp16
    tmp18 = tmp7 - tmp17
    tmp19 = tmp10 * tmp18
    tmp22 = tmp20 + tmp21
    tmp24 = 2.0
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 - tmp25
    tmp27 = triton_helpers.maximum(tmp26, tmp8)
    tmp28 = 0.0001
    tmp29 = triton_helpers.maximum(tmp27, tmp28)
    tmp30 = libdevice.sqrt(tmp29)
    tmp32 = tmp20 + tmp31
    tmp34 = tmp33 * tmp24
    tmp35 = tmp32 - tmp34
    tmp36 = triton_helpers.maximum(tmp35, tmp8)
    tmp37 = triton_helpers.maximum(tmp36, tmp28)
    tmp38 = libdevice.sqrt(tmp37)
    tmp39 = tmp30 - tmp38
    tmp40 = tmp39 + tmp7
    tmp41 = tl.full([1, 1], 0, tl.int32)
    tmp42 = triton_helpers.maximum(tmp41, tmp40)
    tmp43 = tmp19 * tmp42
    tmp44 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK])
    tmp46 = tl.sum(tmp44, 1)[:, None]
    tmp47 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp49 = tl.sum(tmp47, 1)[:, None]
    tmp50 = tmp46 / tmp49
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp50, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (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(arg0_1, (4, 4), (1, 4),
            0), out=buf0)
        del arg0_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2
        del buf2
        get_raw_stream(0)
        triton_per_fused_add_div_mul_relu_rsub_sub_sum_0[grid(1)](buf4,
            arg1_1, buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
    return buf4,


def pdist(A, squared=False, eps=0.0001):
    prod = torch.mm(A, A.t())
    norm = prod.diag().unsqueeze(1).expand_as(prod)
    res = (norm + norm.t() - 2 * prod).clamp(min=0)
    if squared:
        return res
    else:
        return res.clamp(min=eps).sqrt()


class TripletAllLossNew(torch.nn.Module):
    """
    Compute all possible triplets in the batch, where loss > 0
    """

    def __init__(self, margin=1.0, **kwargs):
        None
        self.margin = margin
        torch.nn.Module.__init__(self)

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