


'''simple docstring'''

from typing import Dict, Optional

import numpy as np

import datasets


lowercase__				     =		"\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"

lowercase__				     =		"\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"

lowercase__				     =		"\\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 __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: Dict      ,  __lowerCamelCase		: int = None      ,  __lowerCamelCase		: str = False      ,  )      ->     Tuple:







     '''simple docstring'''






     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(__lowerCAmelCase				)
     _a						=		np.array(__lowerCAmelCase				)

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

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

     _a						=		pred_label[pred_label == label]

     _a						=		np.histogram(__lowerCAmelCase      ,  bins=__lowerCAmelCase      ,  range=(0, num_labels - 1)				)[0]
     _a						=		np.histogram(__lowerCAmelCase      ,  bins=__lowerCAmelCase      ,  range=(0, num_labels - 1)				)[0]
     _a						=		np.histogram(__lowerCAmelCase      ,  bins=__lowerCAmelCase      ,  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 __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: List[Any] = None      ,  __lowerCamelCase		: Optional[int] = False      ,  )      ->     List[str]:







     '''simple docstring'''






     _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(__lowerCAmelCase      ,  __lowerCAmelCase				):
          _a			,			_a			,			_a			,			_a						=		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 __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Tuple      ,  __lowerCamelCase		: List[Any] = None      ,  __lowerCamelCase		: List[Any] = None      ,  __lowerCamelCase		: Dict = False      ,  )      ->     Tuple:







     '''simple docstring'''






     _a			,			_a			,			_a			,			_a						=		total_intersect_and_union(
         __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase				)

     # 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(__lowerCAmelCase				)
     _a						=		np.nanmean(__lowerCAmelCase				)
     _a						=		all_acc
     _a						=		iou
     _a						=		acc

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






     def  a_					(			self		)       ->    str:
          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  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False     ,							)       ->    Tuple:
          _a						=		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





'''simple docstring'''

import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowercase__				     =		object()

# For specifying empty leaf dict `{}`
lowercase__				     =		object()






def __UpperCamelCase   (			__lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Tuple				)      ->     Union[str, Any]:







     '''simple docstring'''






     _a						=		tuple((re.compile(x + "$"				) for x in qs)				)
     for i in range(len(__lowerCamelCase				) - len(__lowerCamelCase				) + 1				):
          _a						=		[x.match(__lowerCamelCase				) for x, y in zip(__lowerCamelCase      ,  ks[i:]				)]
          if matches and all(__lowerCamelCase				):
               return True
     return False






def __UpperCamelCase   (			__lowerCamelCase		: int				)      ->     Union[str, Any]:







     '''simple docstring'''






     def replace(__lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Any				):
          for rule, replacement in rules:
               if _match(__lowerCamelCase      ,  __lowerCamelCase				):
                    return replacement
          return val

     return replace






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P("mp"      ,  __lowerCamelCase				)),
         (("transformer", "wte", "embedding"), P("mp"      ,  __lowerCamelCase				)),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__lowerCamelCase      ,  "mp"				)),
         (("attention", "out_proj", "kernel"), P("mp"      ,  __lowerCamelCase				)),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(__lowerCamelCase      ,  "mp"				)),
         (("mlp", "c_fc", "bias"), P("mp"				)),
         (("mlp", "c_proj", "kernel"), P("mp"      ,  __lowerCamelCase				)),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]






def __UpperCamelCase   (			__lowerCamelCase		: Dict				)      ->     Tuple:







     '''simple docstring'''






     _a						=		_get_partition_rules()
     _a						=		_replacement_rules(__lowerCamelCase				)
     _a						=		{k: _unmatched for k in flatten_dict(__lowerCamelCase				)}
     _a						=		{k: replace(__lowerCamelCase      ,  __lowerCamelCase				) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(__lowerCamelCase				)				)

