

'''simple docstring'''






import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
 import bsa
 from bsa import BeautifulSoup


lowerCAmelCase__											=	logging.get_logger(__name__)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,**lowercase__   :     Tuple			):
   requires_backends(self					,['''bs4''']			)
   super().__init__(**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     Tuple			):
   __lowercase							  = []
   __lowercase							  = []
   __lowercase							  = element if element.name else element.parent
   for parent in child.parents:  # type: bs4.element.Tag
    __lowercase							  = parent.find_all(child.name					,recursive=lowercase__			)
    xpath_tags.append(child.name			)
    xpath_subscripts.append(
        0 if 1 == len(lowercase__			) else next(i for i, s in enumerate(lowercase__					,1			) if s is child			)			)
    __lowercase							  = parent
   xpath_tags.reverse()
   xpath_subscripts.reverse()
   return xpath_tags, xpath_subscripts






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     Any			):
   __lowercase							  = BeautifulSoup(lowercase__					,'''html.parser'''			)

   __lowercase							  = []
   __lowercase							  = []
   __lowercase							  = []

   for element in html_code.descendants:
    if type(lowercase__			) == bsa.element.NavigableString:
     if type(element.parent			) != bsa.element.Tag:
      continue

     __lowercase							  = html.unescape(lowercase__			).strip()
     if not text_in_this_tag:
      continue

     all_doc_strings.append(lowercase__			)

     __lowercase  ,	__lowercase							  = self.xpath_soup(lowercase__			)
     stringaxtag_seq.append(lowercase__			)
     stringaxsubs_seq.append(lowercase__			)

   if len(lowercase__			) != len(lowercase__			):
    raise ValueError('''Number of doc strings and xtags does not correspond'''			)
   if len(lowercase__			) != len(lowercase__			):
    raise ValueError('''Number of doc strings and xsubs does not correspond'''			)

   return all_doc_strings, stringaxtag_seq, stringaxsubs_seq






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     str					,lowercase__   :     Union[str, Any]			):
   __lowercase							  = ''''''
   for tagname, subs in zip(lowercase__					,lowercase__			):
    xpath += F"/{tagname}"
    if subs != 0:
     xpath += F"[{subs}]"
   return xpath






  def __call__(      self   :     str					,lowercase__   :     Optional[Any]			):
   __lowercase							  = False

   # Check that strings has a valid type
   if isinstance(lowercase__					,lowercase__			):
    __lowercase							  = True
   elif isinstance(lowercase__					,(list, tuple)			):
    if len(lowercase__			) == 0 or isinstance(html_strings[0]					,lowercase__			):
     __lowercase							  = True

   if not valid_strings:
    raise ValueError(
        '''HTML strings must of type `str`, `List[str]` (batch of examples), '''
        F"but is of type {type(lowercase__			)}."			)

   __lowercase							  = bool(isinstance(lowercase__					,(list, tuple)			) and (isinstance(html_strings[0]					,lowercase__			))			)

   if not is_batched:
    __lowercase							  = [html_strings]

   # Get nodes + xpaths
   __lowercase							  = []
   __lowercase							  = []
   for html_string in html_strings:
    __lowercase  ,	__lowercase  ,	__lowercase							  = self.get_three_from_single(lowercase__			)
    nodes.append(lowercase__			)
    __lowercase							  = []
    for node, tag_list, sub_list in zip(lowercase__					,lowercase__					,lowercase__			):
     __lowercase							  = self.construct_xpath(lowercase__					,lowercase__			)
     xpath_strings.append(lowercase__			)
    xpaths.append(lowercase__			)

   # return as Dict
   __lowercase							  = {'''nodes''': nodes, '''xpaths''': xpaths}
   __lowercase							  = BatchFeature(data=lowercase__					,tensor_type=lowercase__			)

   return encoded_inputs






'''simple docstring'''





from typing import Dict, Optional

import numpy as np

import datasets


lowercase_		       =  "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

lowercase_		       =  "\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

lowercase_		       =  "\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = False  ,	):



     """simple docstring"""



     if label_map is not None:
          for old_id, new_id in label_map.items():
               _a      =  new_id

    # turn into Numpy arrays
     _a      =  np.array(__A)
     _a      =  np.array(__A)

     if reduce_labels:
          _a      =  255
          _a      =  label - 1
          _a      =  255

     _a      =  label != ignore_index
     _a      =  np.not_equal(__A  ,	__A)
     _a      =  pred_label[mask]
     _a      =  np.array(__A)[mask]

     _a      =  pred_label[pred_label == label]

     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]
     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]
     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]

     _a      =  area_pred_label + area_label - area_intersect

     return area_intersect, area_union, area_pred_label, area_label




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = False  ,	):



     """simple docstring"""



     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     for result, gt_seg_map in zip(__A  ,	__A):
          _a  ,    _a  ,    _a  ,    _a      =  intersect_and_union(
              __A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A)
          total_area_intersect += area_intersect
          total_area_union += area_union
          total_area_pred_label += area_pred_label
          total_area_label += area_label
     return total_area_intersect, total_area_union, total_area_pred_label, total_area_label




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = None  ,	__A = False  ,	):



     """simple docstring"""



     _a  ,    _a  ,    _a  ,    _a      =  total_intersect_and_union(
         __A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A)

     # compute metrics
     _a      =  {}

     _a      =  total_area_intersect.sum() / total_area_label.sum()
     _a      =  total_area_intersect / total_area_union
     _a      =  total_area_intersect / total_area_label

     _a      =  np.nanmean(__A)
     _a      =  np.nanmean(__A)
     _a      =  all_acc
     _a      =  iou
     _a      =  acc

     if nan_to_num is not None:
          _a      =  {metric: np.nan_to_num(__A  ,	nan=__A) for metric, metric_value in metrics.items()}

     return metrics






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,   _KWARGS_DESCRIPTION						)
class __A			(  datasets.Metric						):



 '''simple docstring'''







 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      return datasets.MetricInfo(
          description=_DESCRIPTION ,   citation=_CITATION ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(
              # 1st Seq - height dim, 2nd - width dim
              {
                  '''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''       )       )       ),
                  '''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''       )       )       ),
              }       ) ,   reference_urls=[
              '''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
          ] ,   )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A = None ,   A = None ,   A = False ,   )    ->     List[Any]:


      """simple docstring"""



      _a      =  mean_iou(
          results=A ,   gt_seg_maps=A ,   num_labels=A ,   ignore_index=A ,   nan_to_num=A ,   label_map=A ,   reduce_labels=A ,   )
      return iou_result



