





'''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    __UpperCamelCase	(	a    : Optional[Any]					,   a    : List[str]					,   a    : Optional[Any]					,   a    : bool					,   a    : Optional[Dict[int, int]] = None					,   a    : bool = False					,   ) ->Optional[Any]:
 if label_map is not None:
  for old_id, new_id in label_map.items():
   snake_case				=			new_id

    # turn into Numpy arrays
 snake_case				=			np.array(a     )
 snake_case				=			np.array(a     )

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

 snake_case				=			label != ignore_index
 snake_case				=			np.not_equal(a					,   a     )
 snake_case				=			pred_label[mask]
 snake_case				=			np.array(a     )[mask]

 snake_case				=			pred_label[pred_label == label]

 snake_case				=			np.histogram(a					,   bins=a					,   range=(0, num_labels - 1)     )[0]
 snake_case				=			np.histogram(a					,   bins=a					,   range=(0, num_labels - 1)     )[0]
 snake_case				=			np.histogram(a					,   bins=a					,   range=(0, num_labels - 1)     )[0]

 snake_case				=			area_pred_label + area_label - area_intersect

 return area_intersect, area_union, area_pred_label, area_label

def    __UpperCamelCase	(	a    : List[str]					,   a    : Optional[Any]					,   a    : List[Any]					,   a    : bool					,   a    : Optional[Dict[int, int]] = None					,   a    : bool = False					,   ) ->Any:
 snake_case				=			np.zeros((num_labels,)					,   dtype=np.floataa     )
 snake_case				=			np.zeros((num_labels,)					,   dtype=np.floataa     )
 snake_case				=			np.zeros((num_labels,)					,   dtype=np.floataa     )
 snake_case				=			np.zeros((num_labels,)					,   dtype=np.floataa     )
 for result, gt_seg_map in zip(a					,   a     ):
  snake_case				,		snake_case				,		snake_case				,		snake_case				=			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    __UpperCamelCase	(	a    : int					,   a    : List[str]					,   a    : Dict					,   a    : bool					,   a    : Optional[int] = None					,   a    : Optional[Dict[int, int]] = None					,   a    : bool = False					,   ) ->List[str]:
 snake_case				,		snake_case				,		snake_case				,		snake_case				=			total_intersect_and_union(
     a					,   a					,   a					,   a					,   a					,   a     )

 # compute metrics
 snake_case				=			{}

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

 snake_case				=			np.nanmean(a     )
 snake_case				=			np.nanmean(a     )
 snake_case				=			all_acc
 snake_case				=			iou
 snake_case				=			acc

 if nan_to_num is not None:
  snake_case				=			{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 			_lowercase						(       datasets.Metric					):







     def     UpperCamelCase			( self    ) ->	Any:
      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     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__ = None    ,   A__ = None    ,   A__ = False    ,   ) ->	Optional[Any]:
      snake_case				=			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








'''simple docstring'''







_lowercase       						=			{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

