






'''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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = False    ,		):


   """simple docstring"""




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

    # turn into Numpy arrays
   lowercase_						:     List[str]							     =  np.array(_UpperCamelCase				)
   lowercase_						:     int							     =  np.array(_UpperCamelCase				)

   if reduce_labels:
      lowercase_						:     int							     =  255
      lowercase_						:     Tuple							     =  label - 1
      lowercase_						:     Optional[int]							     =  255

   lowercase_						:     Optional[Any]							     =  label != ignore_index
   lowercase_						:     List[Any]							     =  np.not_equal(_UpperCamelCase    ,		_UpperCamelCase				)
   lowercase_						:     List[str]							     =  pred_label[mask]
   lowercase_						:     Tuple							     =  np.array(_UpperCamelCase				)[mask]

   lowercase_						:     str							     =  pred_label[pred_label == label]

   lowercase_						:     Tuple							     =  np.histogram(_UpperCamelCase    ,		bins=_UpperCamelCase    ,		range=(0, num_labels - 1)				)[0]
   lowercase_						:     List[Any]							     =  np.histogram(_UpperCamelCase    ,		bins=_UpperCamelCase    ,		range=(0, num_labels - 1)				)[0]
   lowercase_						:     Any							     =  np.histogram(_UpperCamelCase    ,		bins=_UpperCamelCase    ,		range=(0, num_labels - 1)				)[0]

   lowercase_						:     int							     =  area_pred_label + area_label - area_intersect

   return area_intersect, area_union, area_pred_label, area_label




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = False    ,		):


   """simple docstring"""




   lowercase_						:     int							     =  np.zeros((num_labels,)    ,		dtype=np.floataa				)
   lowercase_						:     List[str]							     =  np.zeros((num_labels,)    ,		dtype=np.floataa				)
   lowercase_						:     List[Any]							     =  np.zeros((num_labels,)    ,		dtype=np.floataa				)
   lowercase_						:     List[Any]							     =  np.zeros((num_labels,)    ,		dtype=np.floataa				)
   for result, gt_seg_map in zip(_UpperCamelCase    ,		_UpperCamelCase				):
      lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[Any]							     =  intersect_and_union(
          _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
      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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = False    ,		):


   """simple docstring"""




   lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     List[str]							     =  total_intersect_and_union(
       _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)

   # compute metrics
   lowercase_						:     Union[str, Any]							     =  {}

   lowercase_						:     List[Any]							     =  total_area_intersect.sum() / total_area_label.sum()
   lowercase_						:     str							     =  total_area_intersect / total_area_union
   lowercase_						:     str							     =  total_area_intersect / total_area_label

   lowercase_						:     List[Any]							     =  np.nanmean(_UpperCamelCase				)
   lowercase_						:     Union[str, Any]							     =  np.nanmean(_UpperCamelCase				)
   lowercase_						:     Optional[Any]							     =  all_acc
   lowercase_						:     Dict							     =  iou
   lowercase_						:     List[str]							     =  acc

   if nan_to_num is not None:
      lowercase_						:     Tuple							     =  {metric: np.nan_to_num(_UpperCamelCase    ,		nan=_UpperCamelCase				) for metric, metric_value in metrics.items()}

   return metrics





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




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''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 					lowerCAmelCase__					(      self	:							Dict	,  a	:							List[str]	,  a	:							Union[str, Any]	,  a	:							int	,  a	:							bool	,  a	:							Optional[int] = None	,  a	:							Optional[Dict[int, int]] = None	,  a	:							bool = False	,  ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  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 builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


UpperCamelCase__			=      False
try:
 UpperCamelCase__			=      _is_package_available('google.colab')
except ModuleNotFoundError:
 pass


@input.register
class 							_UpperCAmelCase    :




    def __init__(      self	:							List[Any]	,  a	:							str = None	,  a	:							list = []      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  0
       lowercase_						:     Dict							     =  choices
       lowercase_						:     List[str]							     =  prompt
       if sys.platform == "win32":
          lowercase_						:     List[Any]							     =  "*"
       else:
          lowercase_						:     Union[str, Any]							     =  "➔ "




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							Optional[int]	,  a	:							str = ""      ):






       '''simple docstring'''






       if sys.platform != "win32":
          writeColor(self.choices[index]	,  3_2	,  a      )
       else:
          forceWrite(self.choices[index]	,  a      )




    def 					lowerCAmelCase__					(      self	:							List[Any]	,  a	:							int      ):






       '''simple docstring'''






       if index == self.position:
          forceWrite(f""" {self.arrow_char} """      )
          self.write_choice(a      )
       else:
          forceWrite(f"""    {self.choices[index]}"""      )
       reset_cursor()




    def 					lowerCAmelCase__					(      self	:							str	,  a	:							Direction	,  a	:							int = 1      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.position
       if direction == Direction.DOWN:
          if self.position + 1 >= len(self.choices      ):
             return
          self.position += num_spaces
       else:
          if self.position - 1 < 0:
             return
          self.position -= num_spaces
       clear_line()
       self.print_choice(a      )
       move_cursor(a	,  direction.name      )
       self.print_choice(self.position      )




    @input.mark(KEYMAP["up"]      )
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       self.move_direction(Direction.UP      )




    @input.mark(KEYMAP["down"]      )
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       self.move_direction(Direction.DOWN      )




    @input.mark(KEYMAP["newline"]      )
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       move_cursor(len(self.choices      ) - self.position	,  "DOWN"      )
       return self.position




    @input.mark(KEYMAP["interrupt"]      )
    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       move_cursor(len(self.choices      ) - self.position	,  "DOWN"      )
       raise KeyboardInterrupt




    @input.mark_multiple(*[KEYMAP[str(a      )] for number in range(1_0      )]      )
    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     str							     =  int(chr(self.current_selection      )      )
       lowercase_						:     Tuple							     =  index - self.position
       if index == self.position:
          return
       if index < len(self.choices      ):
          if self.position > index:
             self.move_direction(Direction.UP	,  -movement      )
          elif self.position < index:
             self.move_direction(Direction.DOWN	,  a      )
          else:
             return
       else:
          return





    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							int = 0      ):






       '''simple docstring'''






       if self.prompt:
          linebreak()
          forceWrite(self.prompt	,  "\n"      )
          if in_colab:
             forceWrite("Please input a choice index (starting from 0), and press enter"	,  "\n"      )
          else:
             forceWrite("Please select a choice using the arrow or number keys, and selecting with enter"	,  "\n"      )
       lowercase_						:     Dict							     =  default_choice
       for i in range(len(self.choices      )      ):
          self.print_choice(a      )
          forceWrite("\n"      )
       move_cursor(len(self.choices      ) - self.position	,  "UP"      )
       with cursor.hide():
          while True:
             if in_colab:
                try:
                   lowercase_						:     List[str]							     =  int(builtins.input()      )
                except ValueError:
                   lowercase_						:     List[Any]							     =  default_choice
             else:
                lowercase_						:     Dict							     =  self.handle_input()
             if choice is not None:
                reset_cursor()
                for _ in range(len(self.choices      ) + 1      ):
                   move_cursor(1	,  "UP"      )
                   clear_line()
                self.write_choice(a	,  "\n"      )
                return choice




