import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn


class TripletLogExpLoss(nn.Module):
    """Creates a criterion that measures the triplet loss given an input
    tensors x1, x2, x3.
    This is used for measuring a relative similarity between samples. A triplet
    is composed by `a`, `p` and `n`: anchor, positive examples and negative
    example respectively. The shape of all input variables should be
    :math:`(N, D)`.

    The distance is described in detail in the paper `Improving Pairwise Ranking for Multi-Label
    Image Classification`_ by Y. Li et al.

    .. math::
        L(a, p, n) = log \\left( 1 + exp(d(a_i, p_i) - d(a_i, n_i) \\right)


    Args:
        anchor: anchor input tensor
        positive: positive input tensor
        negative: negative input tensor

    Shape:
        - Input: :math:`(N, D)` where `D = vector dimension`
        - Output: :math:`(N, 1)`

    >>> triplet_loss = nn.TripletLogExpLoss(p=2)
    >>> input1 = autograd.Variable(torch.randn(100, 128))
    >>> input2 = autograd.Variable(torch.randn(100, 128))
    >>> input3 = autograd.Variable(torch.randn(100, 128))
    >>> output = triplet_loss(input1, input2, input3)
    >>> output.backward()

    .. _Learning shallow convolutional feature descriptors with triplet losses:
        http://www.iis.ee.ic.ac.uk/%7Evbalnt/shallow_descr/TFeat_paper.pdf
    """

    def __init__(self, p=2, eps=1e-06, swap=False):
        super(TripletLogExpLoss, self).__init__()
        self.p = p
        self.eps = eps
        self.swap = swap

    def forward(self, anchor, positive, negative):
        assert anchor.size() == positive.size(
            ), 'Input sizes between positive and negative must be equal.'
        assert anchor.size() == negative.size(
            ), 'Input sizes between anchor and negative must be equal.'
        assert positive.size() == negative.size(
            ), 'Input sizes between positive and negative must be equal.'
        assert anchor.dim() == 2, 'Input must be a 2D matrix.'
        d_p = F.pairwise_distance(anchor, positive, self.p, self.eps)
        d_n = F.pairwise_distance(anchor, negative, self.p, self.eps)
        if self.swap:
            d_s = F.pairwise_distance(positive, negative, self.p, self.eps)
            d_n = torch.min(d_n, d_s)
        dist = torch.log(1 + torch.exp(d_p - d_n))
        loss = torch.mean(dist)
        return loss

    def eval_func(self, dp, dn):
        return np.log(1 + np.exp(dp - dn))


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([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.triton_helpers import libdevice, math as tl_math
import numpy as np
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_exp_log_mean_norm_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, 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_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 = 1e-06
    tmp4 = tmp2 + tmp3
    tmp5 = tmp4 * tmp4
    tmp8 = tmp6 - tmp7
    tmp9 = tmp8 + tmp3
    tmp10 = tmp9 * tmp9
    tmp11 = tmp5 + tmp10
    tmp14 = tmp12 - tmp13
    tmp15 = tmp14 + tmp3
    tmp16 = tmp15 * tmp15
    tmp17 = tmp11 + tmp16
    tmp20 = tmp18 - tmp19
    tmp21 = tmp20 + tmp3
    tmp22 = tmp21 * tmp21
    tmp23 = tmp17 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp26 = tmp0 - tmp25
    tmp27 = tmp26 + tmp3
    tmp28 = tmp27 * tmp27
    tmp30 = tmp6 - tmp29
    tmp31 = tmp30 + tmp3
    tmp32 = tmp31 * tmp31
    tmp33 = tmp28 + tmp32
    tmp35 = tmp12 - tmp34
    tmp36 = tmp35 + tmp3
    tmp37 = tmp36 * tmp36
    tmp38 = tmp33 + tmp37
    tmp40 = tmp18 - tmp39
    tmp41 = tmp40 + tmp3
    tmp42 = tmp41 * tmp41
    tmp43 = tmp38 + tmp42
    tmp44 = libdevice.sqrt(tmp43)
    tmp45 = tmp24 - tmp44
    tmp46 = tl_math.exp(tmp45)
    tmp47 = 1.0
    tmp48 = tmp46 + tmp47
    tmp49 = tl_math.log(tmp48)
    tmp50 = tl.broadcast_to(tmp49, [XBLOCK, RBLOCK])
    tmp52 = tl.sum(tmp50, 1)[:, None]
    tmp53 = 4.0
    tmp54 = tmp52 / tmp53
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp54, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_exp_log_mean_norm_sub_0[grid(1)](buf2, arg0_1,
            arg1_1, arg2_1, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf2,


class TripletLogExpLossNew(nn.Module):
    """Creates a criterion that measures the triplet loss given an input
    tensors x1, x2, x3.
    This is used for measuring a relative similarity between samples. A triplet
    is composed by `a`, `p` and `n`: anchor, positive examples and negative
    example respectively. The shape of all input variables should be
    :math:`(N, D)`.

    The distance is described in detail in the paper `Improving Pairwise Ranking for Multi-Label
    Image Classification`_ by Y. Li et al.

    .. math::
        L(a, p, n) = log \\left( 1 + exp(d(a_i, p_i) - d(a_i, n_i) \\right)


    Args:
        anchor: anchor input tensor
        positive: positive input tensor
        negative: negative input tensor

    Shape:
        - Input: :math:`(N, D)` where `D = vector dimension`
        - Output: :math:`(N, 1)`

    >>> triplet_loss = nn.TripletLogExpLoss(p=2)
    >>> input1 = autograd.Variable(torch.randn(100, 128))
    >>> input2 = autograd.Variable(torch.randn(100, 128))
    >>> input3 = autograd.Variable(torch.randn(100, 128))
    >>> output = triplet_loss(input1, input2, input3)
    >>> output.backward()

    .. _Learning shallow convolutional feature descriptors with triplet losses:
        http://www.iis.ee.ic.ac.uk/%7Evbalnt/shallow_descr/TFeat_paper.pdf
    """

    def __init__(self, p=2, eps=1e-06, swap=False):
        super(TripletLogExpLossNew, self).__init__()
        self.p = p
        self.eps = eps
        self.swap = swap

    def eval_func(self, dp, dn):
        return np.log(1 + np.exp(dp - dn))

    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]
