import torch
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed


class ContrastiveEmbeddingLoss(nn.Module):
    """
    Contrastive embedding loss

    paper: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
    """

    def __init__(self, margin=1.0, reduction='mean'):
        """
        Constructor method for the ContrastiveEmbeddingLoss class.
        Args:
            margin: margin parameter.
            reduction: criterion reduction type.
        """
        super().__init__()
        self.margin = margin
        self.reduction = reduction or 'none'

    def forward(self, embeddings_left, embeddings_right, distance_true):
        """
        Forward propagation method for the contrastive loss.
        Args:
            embeddings_left: left objects embeddings
            embeddings_right: right objects embeddings
            distance_true: true distances

        Returns:
            loss
        """
        diff = embeddings_left - embeddings_right
        distance_pred = torch.sqrt(torch.sum(torch.pow(diff, 2), 1))
        bs = len(distance_true)
        margin_distance = self.margin - distance_pred
        margin_distance_ = torch.clamp(margin_distance, min=0.0)
        loss = (1 - distance_true) * torch.pow(distance_pred, 2
            ) + distance_true * torch.pow(margin_distance_, 2)
        if self.reduction == 'mean':
            loss = torch.sum(loss) / 2.0 / bs
        elif self.reduction == 'sum':
            loss = torch.sum(loss)
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
        [4, 4, 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
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_pow_sqrt_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = libdevice.sqrt(tmp18)
    tl.store(out_ptr0 + x2, tmp19, xmask)


@triton.jit
def triton_per_fused_add_clamp_div_mul_pow_rsub_sum_1(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    r0 = rindex % 64
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp3 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 * tmp4
    tmp6 = tmp1 - tmp3
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp0 * tmp9
    tmp11 = tmp5 + tmp10
    tmp12 = tl.broadcast_to(tmp11, [RBLOCK])
    tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp12, 0))
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = 0.25
    tmp18 = tmp16 * tmp17
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp18, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pow_sqrt_sub_sum_0[grid(64)](arg0_1, arg1_1, buf0,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_add_clamp_div_mul_pow_rsub_sum_1[grid(1)](buf2,
            arg2_1, buf0, 1, 256, num_warps=2, num_stages=1)
        del arg2_1
        del buf0
    return buf2,


class ContrastiveEmbeddingLossNew(nn.Module):
    """
    Contrastive embedding loss

    paper: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
    """

    def __init__(self, margin=1.0, reduction='mean'):
        """
        Constructor method for the ContrastiveEmbeddingLoss class.
        Args:
            margin: margin parameter.
            reduction: criterion reduction type.
        """
        super().__init__()
        self.margin = margin
        self.reduction = reduction or 'none'

    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]
