
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


a_											=						logging.get_logger(__name__)






class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						None


@experimental
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if ParallelBackendConfig.backend_name is None:
        return _map_with_multiprocessing_pool(
            __snake_case,		__snake_case,		__snake_case,		__snake_case,		__snake_case,		__snake_case,		__snake_case       )

    return _map_with_joblib(__snake_case,		__snake_case,		__snake_case,		__snake_case,		__snake_case,		__snake_case,		__snake_case       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						num_proc if num_proc <= len(__snake_case       ) else len(__snake_case       )
    snake_case_ :    List[Any]     			=						[]  # We organize the splits ourselve (contiguous splits)
    for index in range(__snake_case       ):
        snake_case_ :    Optional[Any]     			=						len(__snake_case       ) // num_proc
        snake_case_ :    Dict     			=						len(__snake_case       ) % num_proc
        snake_case_ :    List[Any]     			=						div * index + min(__snake_case,		__snake_case       )
        snake_case_ :    Union[str, Any]     			=						start + div + (1 if index < mod else 0)
        split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)       )

    if len(__snake_case       ) != sum(len(i[1]       ) for i in split_kwds       ):
        raise ValueError(
            f'Error dividing inputs iterable among processes. '
            f'Total number of objects {len(__snake_case       )}, '
            f'length: {sum(len(i[1]       ) for i in split_kwds       )}'       )

    logger.info(
        f'Spawning {num_proc} processes for {len(__snake_case       )} objects in slices of {[len(i[1]       ) for i in split_kwds]}'       )
    snake_case_			, snake_case_ :    Tuple     			=						None, None
    if not disable_tqdm:
        snake_case_			, snake_case_ :    str     			=						(RLock(),), tqdm.set_lock
    with Pool(__snake_case,		initargs=__snake_case,		initializer=__snake_case       ) as pool:
        snake_case_ :    Optional[int]     			=						pool.map(__snake_case,		__snake_case       )
    logger.info(f'Finished {num_proc} processes'       )
    snake_case_ :    Optional[Any]     			=						[obj for proc_res in mapped for obj in proc_res]
    logger.info(f'Unpacked {len(__snake_case       )} objects'       )

    return mapped

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    import joblib

    with joblib.parallel_backend(ParallelBackendConfig.backend_name,		n_jobs=__snake_case       ):
        return joblib.Parallel()(
            joblib.delayed(__snake_case       )((function, obj, types, None, True, None)       ) for obj in iterable       )


@experimental
@contextlib.contextmanager
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						backend_name

    if backend_name == "spark":
        from joblibspark import register_spark

        register_spark()

        # TODO: call create_cache_and_write_probe if "download" in steps
        # TODO: raise NotImplementedError when Dataset.map etc is called

    try:
        yield
    finally:
        snake_case_ :    int     			=						None






'''simple docstring'''
from typing import Dict, Optional

import numpy as np

import datasets


a_											=						"\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"

a_											=						"\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"

a_											=						"\\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_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		):
    """simple docstring"""






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

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

    if reduce_labels:
        snake_case_ :    str     			=						2_5_5
        snake_case_ :    str     			=						label - 1
        snake_case_ :    List[Any]     			=						2_5_5

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

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

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

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

    return area_intersect, area_union, area_pred_label, area_label

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    snake_case_ :    List[str]     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    snake_case_ :    int     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    snake_case_ :    int     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    for result, gt_seg_map in zip(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
        snake_case_			, snake_case_			, snake_case_			, snake_case_ :    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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		):
    """simple docstring"""






    snake_case_			, snake_case_			, snake_case_			, snake_case_ :    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
    snake_case_ :    Union[str, Any]     			=						{}

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

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

    if nan_to_num is not None:
        snake_case_ :    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    UpperCAmelCase_				(	datasets.Metric					):





 def        snake_case__		(    self):
     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        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = False ,       ):
     snake_case_ :    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




