



'''simple docstring'''





from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE__          =  '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
'''

SCREAMING_SNAKE_CASE__          =  '''
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric("mean_iou")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'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.        ])}
'''

SCREAMING_SNAKE_CASE__          =  '''\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}'''




def 							lowerCamelCase	(					_snake_case	:    Optional[int]  ,_snake_case	:    Optional[Any]  ,_snake_case	:    str  ,_snake_case	:    bool  ,_snake_case	:    Optional[Dict[int, int]] = None  ,_snake_case	:    bool = False  ,):





    '''simple docstring'''


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

    # turn into Numpy arrays
    lowercase__									=    np.array(__A							)
    lowercase__									=    np.array(__A							)

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

    lowercase__									=    label != ignore_index
    lowercase__									=    np.not_equal(__A  ,__A							)
    lowercase__									=    pred_label[mask]
    lowercase__									=    np.array(__A							)[mask]

    lowercase__									=    pred_label[pred_label == label]

    lowercase__									=    np.histogram(__A  ,bins=__A  ,range=(0, num_labels - 1)							)[0]
    lowercase__									=    np.histogram(__A  ,bins=__A  ,range=(0, num_labels - 1)							)[0]
    lowercase__									=    np.histogram(__A  ,bins=__A  ,range=(0, num_labels - 1)							)[0]

    lowercase__									=    area_pred_label + area_label - area_intersect

    return area_intersect, area_union, area_pred_label, area_label




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





    '''simple docstring'''


    lowercase__									=    np.zeros((num_labels,)  ,dtype=np.floataa							)
    lowercase__									=    np.zeros((num_labels,)  ,dtype=np.floataa							)
    lowercase__									=    np.zeros((num_labels,)  ,dtype=np.floataa							)
    lowercase__									=    np.zeros((num_labels,)  ,dtype=np.floataa							)
    for result, gt_seg_map in zip(__A  ,__A							):
        lowercase__							,							lowercase__							,							lowercase__							,							lowercase__									=    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 							lowerCamelCase	(					_snake_case	:    Any  ,_snake_case	:    int  ,_snake_case	:    Any  ,_snake_case	:    bool  ,_snake_case	:    Optional[int] = None  ,_snake_case	:    Optional[Dict[int, int]] = None  ,_snake_case	:    bool = False  ,):





    '''simple docstring'''


    lowercase__							,							lowercase__							,							lowercase__							,							lowercase__									=    total_intersect_and_union(
        __A  ,__A  ,__A  ,__A  ,__A  ,__A							)

    # compute metrics
    lowercase__									=    {}

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

    lowercase__									=    np.nanmean(__A							)
    lowercase__									=    np.nanmean(__A							)
    lowercase__									=    all_acc
    lowercase__									=    iou
    lowercase__									=    acc

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



       def 				_a   (					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 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = False  ,)	->				List[Any]:
           lowercase__									=    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







import gc
import threading
import time

import psutil
import torch





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             __A    =psutil.Process()
             __A    =False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =-1

             while True:
                   __A    =max(self.process.memory_info().rss ,   self.cpu_memory_peak     )

                   # can't sleep or will not catch the peak right (this comment is here on purpose)
                   if not self.peak_monitoring:
                         break

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =True
             __A    =threading.Thread(target=self.peak_monitor     )
             __A    =True
             self.thread.start()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =False
             self.thread.join()
             return self.cpu_memory_peak

_lowerCamelCase      :					Union[str, Any]					    =      PeakCPUMemory()





def      A__					(			)					->Dict:
      # Time
      __A    ={'''time''': time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =torch.cuda.memory_allocated(__A			)
      torch.cuda.reset_peak_memory_stats()

      return measures





def      A__					(			__A  :						Any			)					->Any:
      # Time
      __A    ={'''time''': time.time() - start_measures['''time''']}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
      __A    =(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =(torch.cuda.memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20
            __A    =(torch.cuda.max_memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20

      return measures





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Any			)					->Union[str, Any]:
      print(F'''{description}:'''			)
      print(F'''- Time: {measures['time']:.2f}s'''			)
      for i in range(torch.cuda.device_count()			):
            print(F'''- GPU {i} allocated: {measures[str(__A			)]:.2f}MiB'''			)
            __A    =measures[F'''{i}-peak''']
            print(F'''- GPU {i} peak: {peak:.2f}MiB'''			)
      print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''			)
      print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''			)


