






'''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					(     a_							: int    ,   a_							: Tuple    ,   a_							: List[Any]    ,   a_							: bool    ,   a_							: Optional[Dict[int, int]] = None    ,   a_							: bool = False    ,   )       ->				List[Any]:





      """simple docstring"""

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

    # turn into Numpy arrays
      UpperCamelCase_				:   Union[str, Any]					    =							np.array(a_	)
      UpperCamelCase_				:   int					    =							np.array(a_	)

      if reduce_labels:
            UpperCamelCase_				:   Optional[Any]					    =							255
            UpperCamelCase_				:   List[Any]					    =							label - 1
            UpperCamelCase_				:   Optional[int]					    =							255

      UpperCamelCase_				:   Optional[Any]					    =							label != ignore_index
      UpperCamelCase_				:   Optional[Any]					    =							np.not_equal(a_    ,   a_	)
      UpperCamelCase_				:   int					    =							pred_label[mask]
      UpperCamelCase_				:   Optional[Any]					    =							np.array(a_	)[mask]

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

      UpperCamelCase_				:   str					    =							np.histogram(a_    ,   bins=a_    ,   range=(0, num_labels - 1)	)[0]
      UpperCamelCase_				:   Optional[Any]					    =							np.histogram(a_    ,   bins=a_    ,   range=(0, num_labels - 1)	)[0]
      UpperCamelCase_				:   Any					    =							np.histogram(a_    ,   bins=a_    ,   range=(0, num_labels - 1)	)[0]

      UpperCamelCase_				:   str					    =							area_pred_label + area_label - area_intersect

      return area_intersect, area_union, area_pred_label, area_label



def 							snake_case					(     a_							: Dict    ,   a_							: List[str]    ,   a_							: Any    ,   a_							: bool    ,   a_							: Optional[Dict[int, int]] = None    ,   a_							: bool = False    ,   )       ->				Union[str, Any]:





      """simple docstring"""

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





      """simple docstring"""

      UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Union[str, Any]					    =							total_intersect_and_union(
          a_    ,   a_    ,   a_    ,   a_    ,   a_    ,   a_	)

      # compute metrics
      UpperCamelCase_				:   Any					    =							{}

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

      UpperCamelCase_				:   int					    =							np.nanmean(a_	)
      UpperCamelCase_				:   Tuple					    =							np.nanmean(a_	)
      UpperCamelCase_				:   Dict					    =							all_acc
      UpperCamelCase_				:   Optional[Any]					    =							iou
      UpperCamelCase_				:   List[Any]					    =							acc

      if nan_to_num is not None:
            UpperCamelCase_				:   Optional[int]					    =							{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     A    (      datasets.Metric						):






 """simple docstring"""





 def   _UpperCAmelCase       (							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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = False			,     ):
       UpperCamelCase_				:   str					    =							mean_iou(
           results=__lowerCAmelCase			,     gt_seg_maps=__lowerCAmelCase			,     num_labels=__lowerCAmelCase			,     ignore_index=__lowerCAmelCase			,     nan_to_num=__lowerCAmelCase			,     label_map=__lowerCAmelCase			,     reduce_labels=__lowerCAmelCase			,     )
       return iou_result














'''simple docstring'''

from datetime import datetime

import matplotlib.pyplot as plt
import torch



def 							snake_case					(     a_							: List[Any]	)       ->				Any:





      """simple docstring"""

      for param in module.parameters():
            UpperCamelCase_				:   Dict					    =							False



def 							snake_case					(     )       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_				:   List[str]					    =							"""cuda""" if torch.cuda.is_available() else """cpu"""
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            UpperCamelCase_				:   int					    =							"""mps"""
      if device == "mps":
            print(
                """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
                """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
                """ with generations."""	)
      return device



def 							snake_case					(     a_							: str	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							plt.imshow(a_	)
      fig.axes.get_xaxis().set_visible(a_	)
      fig.axes.get_yaxis().set_visible(a_	)
      plt.show()



def 							snake_case					(     )       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							datetime.now()
      UpperCamelCase_				:   int					    =							current_time.strftime("""%H:%M:%S"""	)
      return timestamp






