

'''simple docstring'''





from typing import Dict, Optional

import numpy as np

import datasets


__snake_case	: Any       				=							"\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"

__snake_case	: Optional[Any]       				=							"\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"

__snake_case	: Dict       				=							"\\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 					_lowercase						(   lowerCamelCase__			:		Optional[Any],		lowerCamelCase__			:		int,		lowerCamelCase__			:		List[str],		lowerCamelCase__			:		Dict,		lowerCamelCase__			:		Union[str, Any] = None,		lowerCamelCase__			:		List[Any] = False,		):







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

    # turn into Numpy arrays
				_a        =   np.array(__UpperCAmelCase							)
				_a        =   np.array(__UpperCAmelCase							)

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

				_a        =   label != ignore_index
				_a        =   np.not_equal(__UpperCAmelCase,		__UpperCAmelCase							)
				_a        =   pred_label[mask]
				_a        =   np.array(__UpperCAmelCase							)[mask]

				_a        =   pred_label[pred_label == label]

				_a        =   np.histogram(__UpperCAmelCase,		bins=__UpperCAmelCase,		range=(0, num_labels - 1)							)[0]
				_a        =   np.histogram(__UpperCAmelCase,		bins=__UpperCAmelCase,		range=(0, num_labels - 1)							)[0]
				_a        =   np.histogram(__UpperCAmelCase,		bins=__UpperCAmelCase,		range=(0, num_labels - 1)							)[0]

				_a        =   area_pred_label + area_label - area_intersect

				return area_intersect, area_union, area_pred_label, area_label



def 					_lowercase						(   lowerCamelCase__			:		Optional[Any],		lowerCamelCase__			:		str,		lowerCamelCase__			:		Optional[Any],		lowerCamelCase__			:		List[str],		lowerCamelCase__			:		List[str] = None,		lowerCamelCase__			:		Optional[int] = False,		):
				_a        =   np.zeros((num_labels,),		dtype=np.floataa							)
				_a        =   np.zeros((num_labels,),		dtype=np.floataa							)
				_a        =   np.zeros((num_labels,),		dtype=np.floataa							)
				_a        =   np.zeros((num_labels,),		dtype=np.floataa							)
				for result, gt_seg_map in zip(__UpperCAmelCase,		__UpperCAmelCase							):
								_a      ,     _a      ,     _a      ,     _a        =   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 					_lowercase						(   lowerCamelCase__			:		int,		lowerCamelCase__			:		Any,		lowerCamelCase__			:		Dict,		lowerCamelCase__			:		List[str],		lowerCamelCase__			:		List[Any] = None,		lowerCamelCase__			:		List[Any] = None,		lowerCamelCase__			:		Optional[int] = False,		):
				_a      ,     _a      ,     _a      ,     _a        =   total_intersect_and_union(
				    __UpperCAmelCase,		__UpperCAmelCase,		__UpperCAmelCase,		__UpperCAmelCase,		__UpperCAmelCase,		__UpperCAmelCase							)

				# compute metrics
				_a        =   {}

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

				_a        =   np.nanmean(__UpperCAmelCase							)
				_a        =   np.nanmean(__UpperCAmelCase							)
				_a        =   all_acc
				_a        =   iou
				_a        =   acc

				if nan_to_num is not None:
								_a        =   {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        A  (	datasets.Metric ):





						def __lowerCAmelCase   (	self			)			->							Union[str, 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 __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_ = None	,    snake_case_ = None	,    snake_case_ = False	,    )			->							List[str]:
										_a        =   mean_iou(
										    results=snake_case_	,    gt_seg_maps=snake_case_	,    num_labels=snake_case_	,    ignore_index=snake_case_	,    nan_to_num=snake_case_	,    label_map=snake_case_	,    reduce_labels=snake_case_	,    )
										return iou_result












'''simple docstring'''

from __future__ import annotations







class __a			:





   def __init__(       self					:      Optional[int]					,lowerCamelCase					:      list[list[int]]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError(
          """Matrices must be formed from a list of zero or more lists containing at """
          """least one and the same number of values, each of which must be of type """
          """int or float."""     )
      if len(lowerCamelCase     ) != 0:
         __SCREAMING_SNAKE_CASE   				=							len(rows[0]     )
         if cols == 0:
            raise error
         for row in rows:
            if len(lowerCamelCase     ) != cols:
               raise error
            for value in row:
               if not isinstance(lowerCamelCase					,(int, float)     ):
                  raise error
         __SCREAMING_SNAKE_CASE   				=							rows
      else:
         __SCREAMING_SNAKE_CASE   				=							[]





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      return [[row[i] for row in self.rows] for i in range(len(self.rows[0]     )     )]





   @property
   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return len(self.rows     )





   @property
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      return len(self.rows[0]     )





   @property
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      return (self.num_rows, self.num_columns)





   @property
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return self.order[0] == self.order[1]





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [0 if column_num != row_num else 1 for column_num in range(self.num_rows     )]
          for row_num in range(self.num_rows     )
      ]
      return Matrix(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      if not self.is_square:
         return 0
      if self.order == (0, 0):
         return 1
      if self.order == (1, 1):
         return int(self.rows[0][0]     )
      if self.order == (2, 2):
         return int(
             (self.rows[0][0] * self.rows[1][1])
             - (self.rows[0][1] * self.rows[1][0])     )
      else:
         return sum(
             self.rows[0][column] * self.cofactors().rows[0][column]
             for column in range(self.num_columns     )     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return bool(self.determinant()     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [
              self.rows[other_row][other_column]
              for other_column in range(self.num_columns     )
              if other_column != column
          ]
          for other_row in range(self.num_rows     )
          if other_row != row
      ]
      return Matrix(lowerCamelCase     ).determinant()





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      int					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      if (row + column) % 2 == 0:
         return self.get_minor(lowerCamelCase					,lowerCamelCase     )
      return -1 * self.get_minor(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return Matrix(
          [
              [self.get_minor(lowerCamelCase					,lowerCamelCase     ) for column in range(self.num_columns     )]
              for row in range(self.num_rows     )
          ]     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return Matrix(
          [
              [
                  self.minors().rows[row][column]
                  if (row + column) % 2 == 0
                  else self.minors().rows[row][column] * -1
                  for column in range(self.minors().num_columns     )
              ]
              for row in range(self.minors().num_rows     )
          ]     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [self.cofactors().rows[column][row] for column in range(self.num_columns     )]
          for row in range(self.num_rows     )
      ]
      return Matrix(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.determinant()
      if not determinant:
         raise TypeError("""Only matrices with a non-zero determinant have an inverse"""     )
      return self.adjugate() * (1 / determinant)





   def __repr__(       self					:      List[Any]     ):

      '''simple docstring'''

      return str(self.rows     )





   def __str__(       self					:      List[str]     ):

      '''simple docstring'''

      if self.num_rows == 0:
         return "[]"
      if self.num_rows == 1:
         return "[[" + ". ".join(str(self.rows[0]     )     ) + "]]"
      return (
          "["
          + "\n ".join(
              [
                  """[""" + """. """.join([str(lowerCamelCase     ) for value in row]     ) + """.]"""
                  for row in self.rows
              ]     )
          + "]"
      )





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      int | None = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError("""Row must be a list containing all ints and/or floats"""     )
      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise type_error
      for value in row:
         if not isinstance(lowerCamelCase					,(int, float)     ):
            raise type_error
      if len(lowerCamelCase     ) != self.num_columns:
         raise ValueError(
             """Row must be equal in length to the other rows in the matrix"""     )
      if position is None:
         self.rows.append(lowerCamelCase     )
      else:
         __SCREAMING_SNAKE_CASE   				=							self.rows[0:position] + [row] + self.rows[position:]





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      int | None = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError(
          """Column must be a list containing all ints and/or floats"""     )
      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise type_error
      for value in column:
         if not isinstance(lowerCamelCase					,(int, float)     ):
            raise type_error
      if len(lowerCamelCase     ) != self.num_rows:
         raise ValueError(
             """Column must be equal in length to the other columns in the matrix"""     )
      if position is None:
         __SCREAMING_SNAKE_CASE   				=							[self.rows[i] + [column[i]] for i in range(self.num_rows     )]
      else:
         __SCREAMING_SNAKE_CASE   				=							[
             self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
             for i in range(self.num_rows     )
         ]





   def __eq__(       self					:      int					,lowerCamelCase					:      object     ):

      '''simple docstring'''

      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         return NotImplemented
      return self.rows == other.rows





   def __ne__(       self					:      Any					,lowerCamelCase					:      object     ):

      '''simple docstring'''

      return not self == other





   def __neg__(       self					:      Any     ):

      '''simple docstring'''

      return self * -1





   def __add__(       self					:      List[Any]					,lowerCamelCase					:      Matrix     ):

      '''simple docstring'''

      if self.order != other.order:
         raise ValueError("""Addition requires matrices of the same order"""     )
      return Matrix(
          [
              [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns     )]
              for i in range(self.num_rows     )
          ]     )





   def __sub__(       self					:      Any					,lowerCamelCase					:      Matrix     ):

      '''simple docstring'''

      if self.order != other.order:
         raise ValueError("""Subtraction requires matrices of the same order"""     )
      return Matrix(
          [
              [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns     )]
              for i in range(self.num_rows     )
          ]     )





   def __mul__(       self					:      Any					,lowerCamelCase					:      Matrix | int | float     ):

      '''simple docstring'''

      if isinstance(lowerCamelCase					,(int, float)     ):
         return Matrix(
             [[int(element * other     ) for element in row] for row in self.rows]     )
      elif isinstance(lowerCamelCase					,lowerCamelCase     ):
         if self.num_columns != other.num_rows:
            raise ValueError(
                """The number of columns in the first matrix must """
                """be equal to the number of rows in the second"""     )
         return Matrix(
             [
                 [Matrix.dot_product(lowerCamelCase					,lowerCamelCase     ) for column in other.columns()]
                 for row in self.rows
             ]     )
      else:
         raise TypeError(
             """A Matrix can only be multiplied by an int, float, or another matrix"""     )





   def __pow__(       self					:      Optional[int]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise TypeError("""A Matrix can only be raised to the power of an int"""     )
      if not self.is_square:
         raise ValueError("""Only square matrices can be raised to a power"""     )
      if other == 0:
         return self.identity()
      if other < 0:
         if self.is_invertable():
            return self.inverse() ** (-other)
         raise ValueError(
             """Only invertable matrices can be raised to a negative power"""     )
      __SCREAMING_SNAKE_CASE   				=							self
      for _ in range(other - 1     ):
         result *= self
      return result





   @classmethod
   def   UpperCAmelCase__  (       cls					:      str					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      list[int]     ):

      '''simple docstring'''

      return sum(row[i] * column[i] for i in range(len(lowerCamelCase     )     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





