






from typing import Dict, Optional

import numpy as np

import datasets


UpperCamelCase			    =     '''
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.
'''

UpperCamelCase			    =     '''
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.        ])}
'''

UpperCamelCase			    =     '''\
@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__	,snake_case__	,snake_case__	,snake_case__	,snake_case__ = None	,snake_case__ = False	,)							->  Dict:






						"""simple docstring"""





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

    # turn into Numpy arrays
						_SCREAMING_SNAKE_CASE     				=  np.array(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  np.array(snake_case__		)

						if reduce_labels:
												_SCREAMING_SNAKE_CASE     				=  2_55
												_SCREAMING_SNAKE_CASE     				=  label - 1
												_SCREAMING_SNAKE_CASE     				=  2_55

						_SCREAMING_SNAKE_CASE     				=  label != ignore_index
						_SCREAMING_SNAKE_CASE     				=  np.not_equal(snake_case__	,snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  pred_label[mask]
						_SCREAMING_SNAKE_CASE     				=  np.array(snake_case__		)[mask]

						_SCREAMING_SNAKE_CASE     				=  pred_label[pred_label == label]

						_SCREAMING_SNAKE_CASE     				=  np.histogram(snake_case__	,bins=snake_case__	,range=(0, num_labels - 1)		)[0]
						_SCREAMING_SNAKE_CASE     				=  np.histogram(snake_case__	,bins=snake_case__	,range=(0, num_labels - 1)		)[0]
						_SCREAMING_SNAKE_CASE     				=  np.histogram(snake_case__	,bins=snake_case__	,range=(0, num_labels - 1)		)[0]

						_SCREAMING_SNAKE_CASE     				=  area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__ = None	,snake_case__ = False	,)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  np.zeros((num_labels,)	,dtype=np.floataa		)
						_SCREAMING_SNAKE_CASE     				=  np.zeros((num_labels,)	,dtype=np.floataa		)
						_SCREAMING_SNAKE_CASE     				=  np.zeros((num_labels,)	,dtype=np.floataa		)
						_SCREAMING_SNAKE_CASE     				=  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     				=  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 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__ = None	,snake_case__ = None	,snake_case__ = False	,)							->  Any:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  total_intersect_and_union(
						    snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)

						# compute metrics
						_SCREAMING_SNAKE_CASE     				=  {}

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

						_SCREAMING_SNAKE_CASE     				=  np.nanmean(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  np.nanmean(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  all_acc
						_SCREAMING_SNAKE_CASE     				=  iou
						_SCREAMING_SNAKE_CASE     				=  acc

						if nan_to_num is not None:
												_SCREAMING_SNAKE_CASE     				=  {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 				__UpperCAmelCase		(datasets.Metric   ):







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''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 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  bool				,     UpperCAmelCase_:  Optional[int] = None				,     UpperCAmelCase_:  Optional[Dict[int, int]] = None				,     UpperCAmelCase_:  bool = False				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  mean_iou(
									    results=UpperCAmelCase_				,     gt_seg_maps=UpperCAmelCase_				,     num_labels=UpperCAmelCase_				,     ignore_index=UpperCAmelCase_				,     nan_to_num=UpperCAmelCase_				,     label_map=UpperCAmelCase_				,     reduce_labels=UpperCAmelCase_				,     )
									return iou_result









import copy
import re



class 				__UpperCAmelCase		:
			__snake_case    :				Any									=							"hp"
			__snake_case    :				str									=							{}
			__snake_case    :				List[Any]									=							None







			@classmethod
			def 							UpperCamelCase				(     cls:  Optional[Any]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  prefix
									_SCREAMING_SNAKE_CASE     				=  defaults
									cls.build_naming_info()







			@staticmethod
			def 							UpperCamelCase				(     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									if len(UpperCAmelCase_					) == 0:
															return ""
									_SCREAMING_SNAKE_CASE     				=  None
									if any(char.isdigit() for char in word					):
															raise Exception(F'Parameters should not contain numbers: \'{word}\' contains a number'					)
									if word in info["short_word"]:
															return info["short_word"][word]
									for prefix_len in range(1				,     len(UpperCAmelCase_					) + 1					):
															_SCREAMING_SNAKE_CASE     				=  word[:prefix_len]
															if prefix in info["reverse_short_word"]:
																					continue
															else:
																					_SCREAMING_SNAKE_CASE     				=  prefix
																					break

									if short_word is None:
															# Paranoid fallback
															def int_to_alphabetic(UpperCAmelCase_:  List[Any]					):
																					_SCREAMING_SNAKE_CASE     				=  """"""
																					while integer != 0:
																											_SCREAMING_SNAKE_CASE     				=  chr(ord("""A"""					) + integer % 10					) + s
																											integer //= 10
																					return s

															_SCREAMING_SNAKE_CASE     				=  0
															while True:
																					_SCREAMING_SNAKE_CASE     				=  word + """#""" + int_to_alphabetic(UpperCAmelCase_					)
																					if sword in info["reverse_short_word"]:
																											continue
																					else:
																											_SCREAMING_SNAKE_CASE     				=  sword
																											break

									_SCREAMING_SNAKE_CASE     				=  short_word
									_SCREAMING_SNAKE_CASE     				=  word
									return short_word







			@staticmethod
			def 							UpperCamelCase				(     UpperCAmelCase_:  List[str]				,     UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  param_name.split("""_"""					)

									_SCREAMING_SNAKE_CASE     				=  [TrialShortNamer.shortname_for_word(UpperCAmelCase_				,     UpperCAmelCase_					) for word in words]

									# We try to create a separatorless short name, but if there is a collision we have to fallback
									# to a separated short name
									_SCREAMING_SNAKE_CASE     				=  ["""""", """_"""]

									for separator in separators:
															_SCREAMING_SNAKE_CASE     				=  separator.join(UpperCAmelCase_					)
															if shortname not in info["reverse_short_param"]:
																					_SCREAMING_SNAKE_CASE     				=  shortname
																					_SCREAMING_SNAKE_CASE     				=  param_name
																					return shortname

									return param_name







			@staticmethod
			def 							UpperCamelCase				(     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TrialShortNamer.shortname_for_key(UpperCAmelCase_				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  short_name
									_SCREAMING_SNAKE_CASE     				=  param_name







			@classmethod
			def 							UpperCamelCase				(     cls:  str					):







									'''simple docstring'''




									if cls.NAMING_INFO is not None:
															return

									_SCREAMING_SNAKE_CASE     				=  {
									    """short_word""": {},
									    """reverse_short_word""": {},
									    """short_param""": {},
									    """reverse_short_param""": {},
									}

									_SCREAMING_SNAKE_CASE     				=  list(cls.DEFAULTS.keys()					)

									for k in field_keys:
															cls.add_new_param_name(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  info







			@classmethod
			def 							UpperCamelCase				(     cls:  Any				,     UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									cls.build_naming_info()
									assert cls.PREFIX is not None
									_SCREAMING_SNAKE_CASE     				=  [copy.copy(cls.PREFIX					)]

									for k, v in params.items():
															if k not in cls.DEFAULTS:
																					raise Exception(F'You should provide a default value for the param name {k} with value {v}'					)
															if v == cls.DEFAULTS[k]:
																					# The default value is not added to the name
																					continue

															_SCREAMING_SNAKE_CASE     				=  cls.NAMING_INFO["""short_param"""][k]

															if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  1 if v else 0

															_SCREAMING_SNAKE_CASE     				=  """""" if isinstance(UpperCAmelCase_				,     (int, float)					) else """-"""
															_SCREAMING_SNAKE_CASE     				=  F'{key}{sep}{v}'
															name.append(UpperCAmelCase_					)

									return "_".join(UpperCAmelCase_					)







			@classmethod
			def 							UpperCamelCase				(     cls:  int				,     UpperCAmelCase_:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  repr[len(cls.PREFIX					) + 1 :]
									if repr == "":
															_SCREAMING_SNAKE_CASE     				=  []
									else:
															_SCREAMING_SNAKE_CASE     				=  repr.split("""_"""					)

									_SCREAMING_SNAKE_CASE     				=  {}

									for value in values:
															if "-" in value:
																					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  value.split("""-"""					)
															else:
																					_SCREAMING_SNAKE_CASE     				=  re.sub("""[0-9.]"""				,     """"""				,     UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  float(re.sub("""[^0-9.]"""				,     """"""				,     UpperCAmelCase_					)					)

															_SCREAMING_SNAKE_CASE     				=  cls.NAMING_INFO["""reverse_short_param"""][p_k]

															_SCREAMING_SNAKE_CASE     				=  p_v

									for k in cls.DEFAULTS:
															if k not in parameters:
																					_SCREAMING_SNAKE_CASE     				=  cls.DEFAULTS[k]

									return parameters

