
"""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 						_snake_case       (	lowercase__		:  int      ,     lowercase__		:  str      ,     lowercase__		:  Dict      ,     lowercase__		:  bool      ,     lowercase__		:  Optional[Dict[int, int]] = None      ,     lowercase__		:  bool = False      ,     )  ->				Tuple:


			'''simple docstring'''





			if label_map is not None:
						for old_id, new_id in label_map.items():
									lowerCAmelCase_    :List[str]			     =  new_id

    # turn into Numpy arrays
			lowerCAmelCase_    :Any			     =  np.array(lowercase__		)
			lowerCAmelCase_    :int			     =  np.array(lowercase__		)

			if reduce_labels:
						lowerCAmelCase_    :Union[str, Any]			     =  2_5_5
						lowerCAmelCase_    :List[Any]			     =  label - 1
						lowerCAmelCase_    :int			     =  2_5_5

			lowerCAmelCase_    :List[str]			     =  label != ignore_index
			lowerCAmelCase_    :Union[str, Any]			     =  np.not_equal(lowercase__      ,     lowercase__		)
			lowerCAmelCase_    :Dict			     =  pred_label[mask]
			lowerCAmelCase_    :Dict			     =  np.array(lowercase__		)[mask]

			lowerCAmelCase_    :Optional[Any]			     =  pred_label[pred_label == label]

			lowerCAmelCase_    :List[str]			     =  np.histogram(lowercase__      ,     bins=lowercase__      ,     range=(0, num_labels - 1)		)[0]
			lowerCAmelCase_    :Dict			     =  np.histogram(lowercase__      ,     bins=lowercase__      ,     range=(0, num_labels - 1)		)[0]
			lowerCAmelCase_    :str			     =  np.histogram(lowercase__      ,     bins=lowercase__      ,     range=(0, num_labels - 1)		)[0]

			lowerCAmelCase_    :Optional[Any]			     =  area_pred_label + area_label - area_intersect

			return area_intersect, area_union, area_pred_label, area_label






def 						_snake_case       (	lowercase__		:  List[str]      ,     lowercase__		:  int      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  bool      ,     lowercase__		:  Optional[Dict[int, int]] = None      ,     lowercase__		:  bool = False      ,     )  ->				int:


			'''simple docstring'''





			lowerCAmelCase_    :int			     =  np.zeros((num_labels,)      ,     dtype=np.floataa		)
			lowerCAmelCase_    :int			     =  np.zeros((num_labels,)      ,     dtype=np.floataa		)
			lowerCAmelCase_    :List[str]			     =  np.zeros((num_labels,)      ,     dtype=np.floataa		)
			lowerCAmelCase_    :str			     =  np.zeros((num_labels,)      ,     dtype=np.floataa		)
			for result, gt_seg_map in zip(lowercase__      ,     lowercase__		):
						lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_    :List[str]			     =  intersect_and_union(
						    lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)
						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 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  bool      ,     lowercase__		:  Optional[int] = None      ,     lowercase__		:  Optional[Dict[int, int]] = None      ,     lowercase__		:  bool = False      ,     )  ->				Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_    :Union[str, Any]			     =  total_intersect_and_union(
			    lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)

			# compute metrics
			lowerCAmelCase_    :List[Any]			     =  {}

			lowerCAmelCase_    :List[str]			     =  total_area_intersect.sum() / total_area_label.sum()
			lowerCAmelCase_    :Optional[int]			     =  total_area_intersect / total_area_union
			lowerCAmelCase_    :str			     =  total_area_intersect / total_area_label

			lowerCAmelCase_    :Union[str, Any]			     =  np.nanmean(lowercase__		)
			lowerCAmelCase_    :Optional[Any]			     =  np.nanmean(lowercase__		)
			lowerCAmelCase_    :Tuple			     =  all_acc
			lowerCAmelCase_    :List[str]			     =  iou
			lowerCAmelCase_    :Optional[Any]			     =  acc

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

			return metrics




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






			def  __lowerCAmelCase (					self       )			->     Optional[int]:
						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  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A = None       ,   __A = None       ,   __A = False       ,   )			->     int:
						lowerCAmelCase_    :Dict			     =  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"""





import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__magic_name__       =			logging.get_logger(__name__)
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				warnings.warn(
				    """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
				    """ use YolosImageProcessor instead."""							,  lowerCAmelCase__							,  )
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)




