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]


Measured latency_ms: 0.021866