

"""simple docstring"""




from typing import Dict, Optional

import numpy as np

import datasets


__UpperCAmelCase =				'\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'

__UpperCAmelCase =				'\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'

__UpperCAmelCase =				'\\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						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase = None							,    __UpperCamelCase = False							,    ):
 '''simple docstring'''





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

    # turn into Numpy arrays
 UpperCAmelCase__					:  int					   =					np.array(__SCREAMING_SNAKE_CASE      )
 UpperCAmelCase__					:  Any					   =					np.array(__SCREAMING_SNAKE_CASE      )

 if reduce_labels:
  UpperCAmelCase__					:  str					   =					255
  UpperCAmelCase__					:  str					   =					label - 1
  UpperCAmelCase__					:  List[Any]					   =					255

 UpperCAmelCase__					:  Union[str, Any]					   =					label != ignore_index
 UpperCAmelCase__					:  Dict					   =					np.not_equal(__SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE      )
 UpperCAmelCase__					:  Tuple					   =					pred_label[mask]
 UpperCAmelCase__					:  Any					   =					np.array(__SCREAMING_SNAKE_CASE      )[mask]

 UpperCAmelCase__					:  List[str]					   =					pred_label[pred_label == label]

 UpperCAmelCase__					:  int					   =					np.histogram(__SCREAMING_SNAKE_CASE							,    bins=__SCREAMING_SNAKE_CASE							,    range=(0, num_labels - 1)      )[0]
 UpperCAmelCase__					:  Tuple					   =					np.histogram(__SCREAMING_SNAKE_CASE							,    bins=__SCREAMING_SNAKE_CASE							,    range=(0, num_labels - 1)      )[0]
 UpperCAmelCase__					:  List[Any]					   =					np.histogram(__SCREAMING_SNAKE_CASE							,    bins=__SCREAMING_SNAKE_CASE							,    range=(0, num_labels - 1)      )[0]

 UpperCAmelCase__					:  List[Any]					   =					area_pred_label + area_label - area_intersect

 return area_intersect, area_union, area_pred_label, area_label
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase = None							,    __UpperCamelCase = False							,    ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[Any]					   =					np.zeros((num_labels,)							,    dtype=np.floataa      )
 UpperCAmelCase__					:  List[str]					   =					np.zeros((num_labels,)							,    dtype=np.floataa      )
 UpperCAmelCase__					:  int					   =					np.zeros((num_labels,)							,    dtype=np.floataa      )
 UpperCAmelCase__					:  int					   =					np.zeros((num_labels,)							,    dtype=np.floataa      )
 for result, gt_seg_map in zip(__SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE      ):
  UpperCAmelCase__					:  int					   =					intersect_and_union(
      __SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE      )
  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						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase = None							,    __UpperCamelCase = None							,    __UpperCamelCase = False							,    ):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					total_intersect_and_union(
     __SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE							,    __SCREAMING_SNAKE_CASE      )

 # compute metrics
 UpperCAmelCase__					:  Union[str, Any]					   =					{}

 UpperCAmelCase__					:  int					   =					total_area_intersect.sum() / total_area_label.sum()
 UpperCAmelCase__					:  str					   =					total_area_intersect / total_area_union
 UpperCAmelCase__					:  Union[str, Any]					   =					total_area_intersect / total_area_label

 UpperCAmelCase__					:  List[str]					   =					np.nanmean(__SCREAMING_SNAKE_CASE      )
 UpperCAmelCase__					:  str					   =					np.nanmean(__SCREAMING_SNAKE_CASE      )
 UpperCAmelCase__					:  Any					   =					all_acc
 UpperCAmelCase__					:  Union[str, Any]					   =					iou
 UpperCAmelCase__					:  Tuple					   =					acc

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

 return metrics







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

  def    __lowercase       (  self		:			Tuple    ):






   '''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    __lowercase       (  self		:			int					,A		:			str					,A		:			str					,A		:			List[str]					,A		:			int					,A		:			Tuple = None					,A		:			Optional[Any] = None					,A		:			List[Any] = False					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					mean_iou(
       results=lowercase_					,gt_seg_maps=lowercase_					,num_labels=lowercase_					,ignore_index=lowercase_					,nan_to_num=lowercase_					,label_map=lowercase_					,reduce_labels=lowercase_					,)
   return iou_result








"""simple docstring"""




# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCAmelCase =				abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 from diffusers.utils.testing_utils import pytest_addoption_shared

 pytest_addoption_shared(__UpperCamelCase      )




def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 from diffusers.utils.testing_utils import pytest_terminal_summary_main

 UpperCAmelCase__					:  Optional[Any]					   =					terminalreporter.config.getoption("""--make-reports"""      )
 if make_reports:
  pytest_terminal_summary_main(__UpperCamelCase							,    id=__UpperCamelCase      )





