
"""simple docstring"""




from typing import Dict, Optional

import numpy as np

import datasets


UpperCAmelCase__      :    int    							= '\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__      :    List[str]    							= '\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__      :    Union[str, Any]    							= '\\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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case = None   ,_snake_case = False   ,):
							if label_map is not None:
														for old_id, new_id in label_map.items():
																					SCREAMING_SNAKE_CASE__						:       Dict           =   new_id

    # turn into Numpy arrays
							SCREAMING_SNAKE_CASE__						:       Dict           =   np.array(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   np.array(_snake_case						)

							if reduce_labels:
														SCREAMING_SNAKE_CASE__						:       Dict           =   255
														SCREAMING_SNAKE_CASE__						:       Dict           =   label - 1
														SCREAMING_SNAKE_CASE__						:       int           =   255

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   label != ignore_index
							SCREAMING_SNAKE_CASE__						:       Dict           =   np.not_equal(_snake_case   ,_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   pred_label[mask]
							SCREAMING_SNAKE_CASE__						:       Any           =   np.array(_snake_case						)[mask]

							SCREAMING_SNAKE_CASE__						:       Tuple           =   pred_label[pred_label == label]

							SCREAMING_SNAKE_CASE__						:       str           =   np.histogram(_snake_case   ,bins=_snake_case   ,range=(0, num_labels - 1)						)[0]
							SCREAMING_SNAKE_CASE__						:       Any           =   np.histogram(_snake_case   ,bins=_snake_case   ,range=(0, num_labels - 1)						)[0]
							SCREAMING_SNAKE_CASE__						:       List[str]           =   np.histogram(_snake_case   ,bins=_snake_case   ,range=(0, num_labels - 1)						)[0]

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case = None   ,_snake_case = False   ,):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   np.zeros((num_labels,)   ,dtype=np.floataa						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   np.zeros((num_labels,)   ,dtype=np.floataa						)
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   np.zeros((num_labels,)   ,dtype=np.floataa						)
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   np.zeros((num_labels,)   ,dtype=np.floataa						)
							for result, gt_seg_map in zip(_snake_case   ,_snake_case						):
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   intersect_and_union(
														    _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case = None   ,_snake_case = None   ,_snake_case = False   ,):
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   total_intersect_and_union(
							    _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case						)

							# compute metrics
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   {}

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   total_area_intersect.sum() / total_area_label.sum()
							SCREAMING_SNAKE_CASE__						:       Dict           =   total_area_intersect / total_area_union
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   total_area_intersect / total_area_label

							SCREAMING_SNAKE_CASE__						:       int           =   np.nanmean(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   np.nanmean(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   all_acc
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   iou
							SCREAMING_SNAKE_CASE__						:       Tuple           =   acc

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

							return metrics



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


				"""simple docstring"""



				def 	__magic_name__    (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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = False   ,       )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   mean_iou(
											    results=SCREAMING_SNAKE_CASE__   ,       gt_seg_maps=SCREAMING_SNAKE_CASE__   ,       num_labels=SCREAMING_SNAKE_CASE__   ,       ignore_index=SCREAMING_SNAKE_CASE__   ,       nan_to_num=SCREAMING_SNAKE_CASE__   ,       label_map=SCREAMING_SNAKE_CASE__   ,       reduce_labels=SCREAMING_SNAKE_CASE__   ,       )
											return iou_result






"""simple docstring"""




UpperCAmelCase__      :    Any    							= '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase__      :    Any    							= [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase__      :    Optional[int]    							= {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




