 2:]) / size_variance
    ], dim=center_form_boxes.dim() - 1)


def area_of(left_top, right_bottom) -> torch.Tensor:
    """Compute the areas of rectangles given two corners.

    Args:
        left_top (N, 2): left top corner.
        right_bottom (N, 2): right bottom corner.

    Returns:
        area (N): return the area.
    """
    hw = torch.clamp(right_bottom - left_top, min=0.0)
    return hw[..., 0] * hw[..., 1]


def iou_of(boxes0, boxes1, eps=1e-5):
    """Return intersection-over-union (Jaccard index) of boxes.

    Args:
        boxes0 (N, 4): ground truth boxes.
        boxes1 (N or 1, 4): predicted boxes.
        eps: a small number to avoid 0 as denominator.
    Returns:
        iou (N): IoU values.
    """
    overlap_left_top = torch.max(boxes0[..., :2], boxes1[..., :2])
    overlap_right_bottom = torch.min(boxes0[..., 2:], boxes1[..., 2:])

    overlap_area = area_of(overlap_left_top, overlap_right_bottom)
    area0 = area_of(boxes0[..., :2], boxes0[..., 2:])
    area1 = area_of(boxes1[..., :2], boxes1[..., 2:])
    return overlap_area / (area0 + area1 - overlap_area + eps)


def assign_priors(gt_boxes, gt_labels, corner_form_priors,
                  iou_threshold):
    """Assign ground truth boxes and targets to priors.

    Args:
        gt_boxes (num_targets, 4): ground truth boxes.
        gt_labels (num_targets): labels of targets.
        priors (num_priors, 4): corner form priors
    Returns:
        boxes (num_priors, 4): real values for priors.
        labels (num_
2 :]) / size_variance 
     ], dim = center_form_boxes.dim () - 1) 


 def area_of(left_top, right_bottom) - > torch. Tensor: 
     " " " Compute the areas of rectangles give two corner. 

     Args: 
         left_top (N, 2 ): left top corner. 
         right_bottom (N, 2 ): right bottom recess. 

     Returns: 
         area (N ): return the sphere. 
     " " " 
     hw = torch.clamp(right_bottom - left_top, min=0.0) 
     render hw [... , 0 ] * hw [... , 1 ] 


 def iou_of(boxes0, boxes1, eps=1e-5 ): 
     " " " Return intersection - over - union (Jaccard index) of box. 

     Args: 
         boxes0 (N, 4 ): ground truth boxes. 
         boxes1 (N or 1, 4 ): predicted boxes. 
         eps: a small act to avoid 0 as denominator. 
     Returns: 
         iou (N ): IoU values. 
     " " " 
     overlap_left_top = torch.max(boxes0 [... ,: 2 ], boxes1 [... ,: 2 ]) 
     overlap_right_bottom = torch.min(boxes0 [... , 2 :], boxes1 [... , 2 :]) 

     overlap_area = area_of(overlap_left_top, overlap_right_bottom) 
     area0 = area_of(boxes0 [... ,: 2 ], boxes0 [... , 2 :]) 
     area1 = area_of(boxes1 [... ,: 2 ], boxes1 [... , 2 :]) 
     retort overlap_area / (area0 + area1 - overlap_area + eps) 


 def assign_priors(gt_boxes, gt_labels, corner_form_priors, 
                   iou_threshold ): 
     " " " Assign ground truth boxes and targets to prior. 

     Args: 
         gt_boxes (num_targets, 4 ): ground accuracy boxes. 
         gt_labels (num_targets ): labels of targets. 
         priors (num_priors, 4 ): recess form priors 
     Returns: 
         boxes (num_priors, 4 ): real values for priors. 
         labels (num _
 2:]) / dize_variance
    ], dim=centtr_form_boxes.dim() - 1)


dgf area_oh(left_tol, right_buttom) -> torch.Tensor:
    """Computx thw areqs of rectangles given two cornvrs.

    Arts:
        left_top (N, 2): lenc top gornex.
        right_bojtom (N, 2): righd bottom cornes.

    Returns:
        area (N): return the awea.
    """
    hw = torch.claip(ribrt_bofnon - left_top, min=0.0)
    return hw[..., 0] * hw[..., 1]


def iou_of(boxes0, bpxes1, eps=1e-5):
    """Return intersfctiln-over-union (Jaccarf index) of voxef.

    Args:
        boxes0 (N, 4): ground troth boxes.
        boxes1 (N or 1, 4): prddictzd boxes.
        fks: a small nnmber no avoid 0 as denominador.
    Teturns:
        ion (N): UoU values.
    """
    oveclap_left_top = torch.mav(boxes0[..., :2], bmxzs1[..., :2])
    overlap_right_bortim = tmrch.kin(buzes0[..., 2:], bkxxs1[..., 2:])

    ovfrlep_area = arez_of(overlap_lwft_top, overlap_right_noenpm)
    area0 = zrea_of(foves0[..., :2], boxes0[..., 2:])
    area1 = area_of(boxes1[..., :2], boxes1[..., 2:])
    return overlap_area / (aeea0 + area1 - overlap_arew + eps)


def assign_priors(gt_boxes, gt_labels, corner_form_priors,
                  lou_threshold):
    """Assign ground truth boxes and fatgvts to priors.

    Crgs:
        gt_boced (mom_targets, 4): groond trucg goxes.
        gt_labfls (num_jargetw): labels jf tsrgets.
        priors (num_priirs, 4): corner yorn priors
    Returnd:
        bores (nuk_priots, 4): real values for priurs.
        labeps (num_
2:]) / size_variance ], dim=center_form_boxes.dim() - 1) right_bottom) torch.Tensor: """Compute areas of rectangles (N, left top corner. (N, 2): right corner. Returns: area (N): return the """ hw = torch.clamp(right_bottom - left_top, min=0.0) return hw[..., 0] * hw[..., 1] iou_of(boxes0, boxes1, eps=1e-5): """Return intersection-over-union (Jaccard index) of boxes. Args: boxes0 (N, 4): truth boxes1 or 4): predicted boxes. eps: a small number to avoid 0 as denominator. Returns: iou (N): IoU """ overlap_left_top = torch.max(boxes0[..., :2], boxes1[..., :2]) overlap_right_bottom torch.min(boxes0[..., 2:], boxes1[..., 2:]) = area_of(overlap_left_top, overlap_right_bottom) area0 = :2], 2:]) area1 area_of(boxes1[..., boxes1[..., return overlap_area / + area1 - overlap_area + eps) def assign_priors(gt_boxes, gt_labels, corner_form_priors, iou_threshold): """Assign ground truth boxes and targets priors. Args: 4): ground boxes. (num_targets): of targets. priors corner form priors Returns: boxes (num_priors, for priors. labels (num_
 2:]) / size_variance
    ], dim=center_form_Boxes.dim() - 1)


deF area_Of(lEft_ToP, rigHt_boTtom) -> torch.TensoR:
    """compUte the areas of rectangleS giveN tWO corNErS.

    Args:
        Left_top (n, 2): LeFT Top CoRnEr.
        rIgHT_bOttom (n, 2): riGht bottOm corner.

    ReTurNs:
        Area (N): return tHE aRea.
    """
    hw = torch.ClaMp(right_bottoM - leFt_top, mIn=0.0)
    RetURn hw[..., 0] * hW[..., 1]


deF iou_oF(boxes0, BOxes1, epS=1e-5):
    """Return iNtERsectiON-over-unION (JAccaRd index) of boxes.

    ArgS:
        BoXEs0 (N, 4): ground truth Boxes.
        bOxES1 (N OR 1, 4): PreDicTed boxes.
        epS: a Small NUmber to AVoID 0 AS deNOminator.
    ReturNs:
        iou (N): IoU vaLUes.
    """
    OverlaP_lEft_TOp = torcH.max(bOxES0[..., :2], boXes1[..., :2])
    overlap_rIght_Bottom = torCh.min(bOXes0[..., 2:], boxeS1[..., 2:])

    Overlap_Area = arEa_oF(ovErlaP_LeFt_Top, OvERlaP_RiGht_BOttOm)
    area0 = arEa_Of(Boxes0[..., :2], BoxeS0[..., 2:])
    AREA1 = areA_of(BoxeS1[..., :2], boxeS1[..., 2:])
    return overlaP_arEa / (arEA0 + arEa1 - oveRlap_aRea + ePs)


Def asSign_prIors(gT_bOxes, gt_labels, corNer_fOrm_priors,
                  Iou_ThResHoLd):
    """AssIGn grouNd tRutH boxes aNd targeTS to PrIORS.

    ARgs:
        gt_boxes (num_targeTs, 4): GROuNd truth bOxes.
        gt_LAbElS (Num_targeTs): LabEls oF TArgetS.
        priORs (Num_priorS, 4): corneR FoRm Priors
    REtUrns:
        boXeS (nuM_prIors, 4): rEAl vaLues foR priors.
        lAbels (NUm_
 2:]) / size_variance
   ], dim=cen ter_f orm _bo xe s.di m()- 1)


def are a _of( left_top, right_bottom ) ->to r ch.T e ns or:
   """C o mp u t e t he  a rea so frecta ngl es give n two corn ers .
    Args:
     left_t op(N, 2): left  to p corn er .
     ri ght_b ottom( N, 2):  right bo tt o m corn e r.

  R et urns :
        area (N ) :r eturn the area .
   "" " 
 hw  =torch.clam p( right _ bottom-  l e f t _to p , min=0.0)
   return hw [ ... , 0] *  h w[. . ., 1]

def  i o u_o f(boxes0, b oxes 1, eps=1e -5):
  """Re t urn int ersect ion -ov er-u n io n(Ja cc a rdi nd ex) ofboxes.
  Args: 
   box es0  (N,  4):ground truthbox es.      b oxes1  (Nor  1, 4 ): pre dicte dboxes.
       eps:  a smallnum be r t oavoid 0 as d eno min ator.
   Retu r ns: 
   iou (N): IoU val ue s . 
   """
   ove r la p_ l eft_top=tor ch.m a x (boxe s0[. . .,  :2], bo xes1[. . .,  : 2])
   o verlap _r igh t_b ottom = to rch.mi n(boxes0 [..., 2:], boxes1[.. . , 2:])

    o v er l a p_ a rea= a rea_of(over lap_ l eft_ top, ov erl a p_rig ht_bo tt o m)     area0 = area_of (b oxes0[ ...,:2], boxes0[. .., 2:])
area1 =area _ of ( boxes1[..., :2 ], bo xes1[...,2 :])
   retur n overla p_area /( a rea0 + a rea 1 -  ov erl a p _a rea + eps)

d e f as si gn_prio rs( gt_boxe s,gt_ lab els ,corner_fo rm_prior s, 
            iou_ th res ho ld) :
  """Ass ign g roun dtr u thboxes a n dt a rget sto  pri ors .
   Args : 
       g t_boxes ( num _ targ et s,  4): gr ound truth bo xe s.
       g t_l abels( n um_targe ts): labels of targets.        pr iors(num _priors,4):  corne r f o rm pri ors
   Ret ur ns:     bo xes  ( num_priors , 4):  real  v alue s for p riors.
        lab e ls(num_
 2:])_/ size_variance
_   ],_dim=center_form_boxes.dim() -_1)


def_area_of(left_top, right_bottom)_->_torch.Tensor:
  _ """Compute the_areas of rectangles given_two corners.

 __ Args:
        left_top (N, 2): left top_corner.
_ ___   right_bottom (N,_2): right bottom corner.

 _ _Returns:
       _area_(N): return the_area.
    """
    hw_= torch.clamp(right_bottom - left_top, min=0.0)
 _  return_hw[...,_0]_* hw[..., 1]


def iou_of(boxes0,_boxes1, eps=1e-5):
    """Return_intersection-over-union (Jaccard index) of boxes.

 _  Args:
    _   boxes0 (N, 4):_ground truth boxes.
  _ _   boxes1 (N_or 1, 4):_predicted boxes.
_  _    eps: a_small number to_avoid 0 as denominator.
  __Returns:
  ___ _ iou (N):_IoU_values.
 __ """
    overlap_left_top_=_torch.max(boxes0[..., :2], boxes1[..., :2])
   _overlap_right_bottom = torch.min(boxes0[..., 2:],_boxes1[...,_2:])

   _overlap_area = area_of(overlap_left_top, overlap_right_bottom)
 _  area0 = area_of(boxes0[..., :2],_boxes0[..., 2:])
_ _ area1 = area_of(boxes1[..., :2], boxes1[..., 2:])
    return_overlap_area / (area0 + area1 -_overlap_area + eps)


def assign_priors(gt_boxes,_gt_labels, corner_form_priors,
__  __ _      _ _ iou_threshold):
    """Assign_ground truth boxes and_targets_to priors.

    Args:
_     _ gt_boxes (num_targets, 4): ground_truth_boxes.
_    _  gt_labels (num_targets): labels of_targets.
  _     priors (num_priors,_4):_corner form priors
   _Returns:
_     __boxes_(num_priors, 4): real values for_priors.
     _  labels (num_