


'''simple docstring'''
from typing import Dict, Optional

import numpy as np

import datasets


UpperCAmelCase_			:  str										=   '\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_			:  Optional[int]										=   '\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_			:  Optional[Any]										=   '\\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							(					UpperCamelCase__							:       Tuple,					UpperCamelCase__							:       str,					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       bool,					UpperCamelCase__							:       Optional[Dict[int, int]] = None,					UpperCamelCase__							:       bool = False,					):
 if label_map is not None:
  for old_id, new_id in label_map.items():
   __A	:		Dict	  =      new_id

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

 if reduce_labels:
  __A	:		Any	  =      255
  __A	:		List[str]	  =      label - 1
  __A	:		int	  =      255

 __A	:		Union[str, Any]	  =      label != ignore_index
 __A	:		Tuple	  =      np.not_equal(UpperCamelCase__,					UpperCamelCase__      )
 __A	:		Optional[Any]	  =      pred_label[mask]
 __A	:		int	  =      np.array(UpperCamelCase__      )[mask]

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

 __A	:		Tuple	  =      np.histogram(UpperCamelCase__,					bins=UpperCamelCase__,					range=(0, num_labels - 1)      )[0]
 __A	:		List[str]	  =      np.histogram(UpperCamelCase__,					bins=UpperCamelCase__,					range=(0, num_labels - 1)      )[0]
 __A	:		int	  =      np.histogram(UpperCamelCase__,					bins=UpperCamelCase__,					range=(0, num_labels - 1)      )[0]

 __A	:		Union[str, Any]	  =      area_pred_label + area_label - area_intersect

 return area_intersect, area_union, area_pred_label, area_label


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

 # compute metrics
 __A	:		Dict	  =      {}

 __A	:		str	  =      total_area_intersect.sum() / total_area_label.sum()
 __A	:		str	  =      total_area_intersect / total_area_union
 __A	:		Union[str, Any]	  =      total_area_intersect / total_area_label

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

 if nan_to_num is not None:
  __A	:		int	  =      {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     _lowerCamelCase       (     datasets.Metric					):




 '''simple docstring'''


 def 			snake_case__ (   self    ):

  """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 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase = None   ,   __lowercase = None   ,   __lowercase = False   ,   ):

  """simple docstring"""







  __A	:		List[Any]	  =      mean_iou(
      results=__lowercase   ,   gt_seg_maps=__lowercase   ,   num_labels=__lowercase   ,   ignore_index=__lowercase   ,   nan_to_num=__lowercase   ,   label_map=__lowercase   ,   reduce_labels=__lowercase   ,   )
  return iou_result










'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def       _lowercase							(					):
 __A	:		List[Any]	  =      ArgumentParser(
     description=(
         'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
     )      )

 # Optional arguments for the launch helper
 parser.add_argument('--num_cores',					type=UpperCamelCase__,					default=1,					help='Number of TPU cores to use (1 or 8).'      )

 # positional
 parser.add_argument(
     'training_script',					type=UpperCamelCase__,					help=(
         'The full path to the single TPU training '
         'program/script to be launched in parallel, '
         'followed by all the arguments for the '
         'training script'
     ),					)

 # rest from the training program
 parser.add_argument('training_script_args',					nargs=UpperCamelCase__      )

 return parser.parse_args()


def       _lowercase							(					):
 __A	:		str	  =      parse_args()

 # Import training_script as a module.
 __A	:		Tuple	  =      Path(args.training_script      )
 sys.path.append(str(script_fpath.parent.resolve()      )      )
 __A	:		str	  =      script_fpath.stem
 __A	:		Dict	  =      importlib.import_module(UpperCamelCase__      )

 # Patch sys.argv
 __A	:		Optional[Any]	  =      [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores      )]

 xmp.spawn(mod._mp_fn,					args=(),					nprocs=args.num_cores      )


if __name__ == "__main__":
 main()






