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
from torch.nn import Module
from torch.nn.functional import cosine_similarity
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + x2, xmask)
    tmp17 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp32 = tl.load(in_ptr2 + x2, xmask)
    tmp33 = tl.load(in_ptr2 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr2 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp38 = tl.load(in_ptr2 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp41 = tl.load(in_ptr2 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-08
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = triton_helpers.maximum(tmp28, tmp13)
    tmp30 = tmp16 / tmp29
    tmp31 = tmp15 * tmp30
    tmp34 = tmp33 * tmp33
    tmp36 = tmp35 * tmp35
    tmp37 = tmp34 + tmp36
    tmp39 = tmp38 * tmp38
    tmp40 = tmp37 + tmp39
    tmp42 = tmp41 * tmp41
    tmp43 = tmp40 + tmp42
    tmp44 = libdevice.sqrt(tmp43)
    tmp45 = triton_helpers.maximum(tmp44, tmp13)
    tmp46 = tmp32 / tmp45
    tmp47 = tmp15 * tmp46
    tl.store(out_ptr0 + x2, tmp31, xmask)
    tl.store(out_ptr1 + x2, tmp47, xmask)


@triton.jit
def triton_per_fused_add_clamp_mean_rsub_sum_1(in_out_ptr0, in_ptr0,
    in_ptr1, 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 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 1.0
    tmp8 = tmp7 - tmp6
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp8 + tmp15
    tmp17 = 0.0
    tmp18 = triton_helpers.maximum(tmp16, tmp17)
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
    tmp21 = tl.sum(tmp19, 1)[:, None]
    tmp22 = 4.0
    tmp23 = tmp21 / tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp23, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0[grid(16)](
            arg0_1, arg1_1, arg2_1, buf0, buf1, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_add_clamp_mean_rsub_sum_1[grid(1)](buf3, buf0,
            buf1, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
    return buf3,


def triplet_margin_cosine_loss(anchor, positive, negative, margin=1.0, eps=
    1e-08, sum_loss=False):
    'Creates a criterion that measures the triplet cosine loss given input\n    tensors x1, x2, x3 and a margin with a value greater than 0.\n    This is used for measuring a relative similarity between samples. A triplet\n    is composed by `a`, `p` and `n`: anchor, positive example and negative\n    example(s) respectively. The shape of the anchor and positive variables should \n    be\n    math:`(N, D)`.\n    The shape of the negative variable should be\n    math:`(N, D)`, for 1 negative sample, or\n    math:`(N, m, D)`, for m negative samples.\n\n\n    .. math::\n        L(a, p, n) = \x0crac{1}{N} \\left( \\sum_{i=1}^N \\max \\{0, margin - cos(a_i, p_i) + cos(a_i, n_i)\\} \right)\n\n    Args:\n        anchor: anchor input tensor\n        positive: positive input tensor\n        negative: negative input tensor\n        margin: the margin value. Default: 1\n        eps: small epsilon value to avoid numerical issues. Default: 1e-6\n        sum_loss: if True the hinge loss will be summed across batch instances\n\n    Shape:\n        - Input: :math:`(N, D)` where `D = vector dimension`\n        - Output: :math:`(N, 1)`\n\n    Example::\n\n        >>> input1 = autograd.Variable(torch.randn(100, 128))\n        >>> input2 = autograd.Variable(torch.randn(100, 128))\n        >>> input3 = autograd.Variable(torch.randn(100, 10, 128))\n        >>> output = triplet_margin_cosine_loss(input1, input2, input3)\n        >>> output.backward()\n    '
    assert anchor.size() == positive.size(
        ), 'Input sizes between anchor and positive must be equal.'
    assert anchor.dim() == 2, 'Anchor and positive must be 2D matrices.'
    assert negative.dim(
        ) <= 3, 'Negative must be 2D (1 negative sample) or 3D matrix (multiple negatives).'
    assert margin > 0.0, 'Margin should be positive value.'
    if negative.dim() == 2:
        assert anchor.size() == negative.size(
            ), 'Input sizes between anchor and negative must be equal (if 1 negative sample).'
        d_p = cosine_similarity(anchor, positive, eps=eps)
        d_n = cosine_similarity(anchor, negative, eps=eps)
        dist_hinge = torch.clamp(margin - d_p + d_n, min=0.0)
    else:
        assert anchor.size()[0] == negative.size()[0] and anchor.size()[1
            ] == negative.size()[2
            ], 'If using multiple negatives samples, their size: [B, #neg, emb_size].'
        d_p = cosine_similarity(anchor, positive, eps=eps)
        d_n = cosine_similarity(anchor.unsqueeze(1), negative, dim=2, eps=eps)
        dist_hinge = torch.clamp(margin - d_p.unsqueeze(1) + d_n, min=0.0).sum(
            dim=1)
    if not sum_loss:
        loss = torch.mean(dist_hinge)
    else:
        loss = torch.sum(dist_hinge)
    return loss


class TripletMarginCosineLossNew(Module):
    'Creates a criterion that measures the triplet cosine loss given an input\n    tensors x1, x2, x3 and a margin with a value greater than 0.\n    This is used for measuring a relative similarity between samples. A triplet\n    is composed by `a`, `p` and `n`: anchor, positive examples and negative\n    example(s) respectively. The shape of the anchor and positive variables should \n    be\n    math:`(N, D)`.\n    The shape of the negative variable should be\n    math:`(N, D)`, for 1 negative sample, or\n    math:`(N, m, D)`, for m negative samples.\n\n\n    .. math::\n        L(a, p, n) = \x0crac{1}{N} \\left( \\sum_{i=1}^N \\max \\{0, margin - cos(a_i, p_i) + cos(a_i, n_i)\\} \right)\n\n    Args:\n        anchor: anchor input tensor\n        positive: positive input tensor\n        negative: negative input tensor\n\n    Shape:\n        - Input: :math:`(N, D)` where `D = vector dimension`\n        - Output: :math:`(N, 1)`\n\n    >>> triplet_loss = nn.TripletMarginCosineLoss(margin=1.0, p=2)\n    >>> input1 = autograd.Variable(torch.randn(100, 128))\n    >>> input2 = autograd.Variable(torch.randn(100, 128))\n    >>> input3 = autograd.Variable(torch.randn(100, 10, 128))\n    >>> output = triplet_loss(input1, input2, input3)\n    >>> output.backward()\n    '

    def __init__(self, margin=1.0, eps=1e-08, sum_loss=False):
        super(TripletMarginCosineLossNew, self).__init__()
        self.margin = margin
        self.eps = eps
        self.sum_loss = sum_loss

    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]


Measured latency_ms: 0.030904