


"""simple docstring"""






from typing import Dict, Optional

import numpy as np

import datasets


_lowerCAmelCase							:    Dict					   =	'''
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.
'''

_lowerCAmelCase							:    Optional[Any]					   =	'''
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.        ])}
'''

_lowerCAmelCase							:    Optional[int]					   =	'''\
@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_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = None			,		_lowerCamelCase = False			,		)    ->     List[str]:
   '''simple docstring'''





   if label_map is not None:
      for old_id, new_id in label_map.items():
         _lowerCamelCase   :				Union[str, Any]							=     new_id

    # turn into Numpy arrays
   _lowerCamelCase   :				Dict							=     np.array(_lowerCamelCase       )
   _lowerCamelCase   :				str							=     np.array(_lowerCamelCase       )

   if reduce_labels:
      _lowerCamelCase   :				Union[str, Any]							=     255
      _lowerCamelCase   :				Optional[Any]							=     label - 1
      _lowerCamelCase   :				Optional[Any]							=     255

   _lowerCamelCase   :				List[Any]							=     label != ignore_index
   _lowerCamelCase   :				int							=     np.not_equal(_lowerCamelCase			,		_lowerCamelCase       )
   _lowerCamelCase   :				Union[str, Any]							=     pred_label[mask]
   _lowerCamelCase   :				List[str]							=     np.array(_lowerCamelCase       )[mask]

   _lowerCamelCase   :				Union[str, Any]							=     pred_label[pred_label == label]

   _lowerCamelCase   :				int							=     np.histogram(_lowerCamelCase			,		bins=_lowerCamelCase			,		range=(0, num_labels - 1)       )[0]
   _lowerCamelCase   :				List[Any]							=     np.histogram(_lowerCamelCase			,		bins=_lowerCamelCase			,		range=(0, num_labels - 1)       )[0]
   _lowerCamelCase   :				Optional[Any]							=     np.histogram(_lowerCamelCase			,		bins=_lowerCamelCase			,		range=(0, num_labels - 1)       )[0]

   _lowerCamelCase   :				List[str]							=     area_pred_label + area_label - area_intersect

   return area_intersect, area_union, area_pred_label, area_label







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = None			,		_lowerCamelCase = False			,		)    ->     Optional[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				List[Any]							=     np.zeros((num_labels,)			,		dtype=np.floataa       )
   _lowerCamelCase   :				List[Any]							=     np.zeros((num_labels,)			,		dtype=np.floataa       )
   _lowerCamelCase   :				int							=     np.zeros((num_labels,)			,		dtype=np.floataa       )
   _lowerCamelCase   :				Union[str, Any]							=     np.zeros((num_labels,)			,		dtype=np.floataa       )
   for result, gt_seg_map in zip(_lowerCamelCase			,		_lowerCamelCase       ):
      _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				Dict							=     intersect_and_union(
          _lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )
      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_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = None			,		_lowerCamelCase = None			,		_lowerCamelCase = False			,		)    ->     Union[str, Any]:
   '''simple docstring'''





   _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				List[Any]							=     total_intersect_and_union(
       _lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )

   # compute metrics
   _lowerCamelCase   :				List[Any]							=     {}

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

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

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

   return metrics




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


  def 							_lowercase					(			self:						Optional[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 							_lowercase					(			self:						Any							,__lowerCAmelCase:						Union[str, Any]							,__lowerCAmelCase:						Optional[int]							,__lowerCAmelCase:						int							,__lowerCAmelCase:						bool							,__lowerCAmelCase:						Optional[int] = None							,__lowerCAmelCase:						Optional[Dict[int, int]] = None							,__lowerCAmelCase:						bool = False							,):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[Any]							=     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





# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


a__		     =     logging.get_logger(__name__)

a__		     =     {}
a__		     =     {}
a__		     =     {}





def 				A__ (snake_case   :		type		,	snake_case   :		Optional[str]		,	snake_case   :		Optional[List[str]] = None		,	)     ->						str:
					__UpperCamelCase					:	int             =      aliases if aliases is not None else []
					if format_type in _FORMAT_TYPES:
										logger.warning(
										    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''				)
					__UpperCamelCase					:	Optional[Any]             =      formatter_cls
					for alias in set(aliases + [format_type]				):
										if alias in _FORMAT_TYPES_ALIASES:
															logger.warning(
															    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''				)
										__UpperCamelCase					:	Optional[int]             =      format_type





def 				A__ (snake_case   :		Exception		,	snake_case   :		Optional[str]		,	snake_case   :		Optional[List[str]] = None				)     ->						Tuple:
					__UpperCamelCase					:	List[Any]             =      aliases if aliases is not None else []
					for alias in set(aliases + [format_type]				):
										__UpperCamelCase					:	Optional[int]             =      unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	a__		     =     ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	a__		     =     ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	a__		     =     ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])





def 				A__ (snake_case   :		Optional[str]				)     ->						Optional[str]:
					if format_type in _FORMAT_TYPES_ALIASES:
										return _FORMAT_TYPES_ALIASES[format_type]
					else:
										return format_type







def 				A__ (snake_case   :		Optional[str]		,	**snake_case   :		List[str]				)     ->						Formatter:
					__UpperCamelCase					:	int             =      get_format_type_from_alias(snake_case				)
					if format_type in _FORMAT_TYPES:
										return _FORMAT_TYPES[format_type](**snake_case				)
					if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
										raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
					else:
										raise ValueError(
										    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None				)}, but got \'{format_type}\''''				)






