





from typing import Dict, Optional

import numpy as np

import datasets


lowerCAmelCase_    =    '''\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'''

lowerCAmelCase_    =    '''\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'''

lowerCAmelCase_    =    '''\\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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase = None			,    _UpperCamelCase = False			,    )		->						Union[str, Any]:






				"""simple docstring"""


				if label_map is not None:
								for old_id, new_id in label_map.items():
												snake_case_						:   Optional[int]           =  new_id

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

				if reduce_labels:
								snake_case_						:   Optional[Any]           =  255
								snake_case_						:   str           =  label - 1
								snake_case_						:   Optional[int]           =  255

				snake_case_						:   Dict           =  label != ignore_index
				snake_case_						:   Any           =  np.not_equal(lowerCAmelCase__			,    lowerCAmelCase__			)
				snake_case_						:   Optional[Any]           =  pred_label[mask]
				snake_case_						:   List[str]           =  np.array(lowerCAmelCase__			)[mask]

				snake_case_						:   Optional[int]           =  pred_label[pred_label == label]

				snake_case_						:   Optional[Any]           =  np.histogram(lowerCAmelCase__			,    bins=lowerCAmelCase__			,    range=(0, num_labels - 1)			)[0]
				snake_case_						:   List[str]           =  np.histogram(lowerCAmelCase__			,    bins=lowerCAmelCase__			,    range=(0, num_labels - 1)			)[0]
				snake_case_						:   List[Any]           =  np.histogram(lowerCAmelCase__			,    bins=lowerCAmelCase__			,    range=(0, num_labels - 1)			)[0]

				snake_case_						:   Optional[Any]           =  area_pred_label + area_label - area_intersect

				return area_intersect, area_union, area_pred_label, area_label






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase = None			,    _UpperCamelCase = False			,    )		->						Dict:






				"""simple docstring"""


				snake_case_						:   Tuple           =  np.zeros((num_labels,)			,    dtype=np.floataa			)
				snake_case_						:   int           =  np.zeros((num_labels,)			,    dtype=np.floataa			)
				snake_case_						:   int           =  np.zeros((num_labels,)			,    dtype=np.floataa			)
				snake_case_						:   Optional[int]           =  np.zeros((num_labels,)			,    dtype=np.floataa			)
				for result, gt_seg_map in zip(lowerCAmelCase__			,    lowerCAmelCase__			):
								snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   int           =  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_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase = None			,    _UpperCamelCase = None			,    _UpperCamelCase = False			,    )		->						List[str]:






				"""simple docstring"""


				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Optional[int]           =  total_intersect_and_union(
				    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			)

				# compute metrics
				snake_case_						:   List[str]           =  {}

				snake_case_						:   List[str]           =  total_area_intersect.sum() / total_area_label.sum()
				snake_case_						:   Optional[int]           =  total_area_intersect / total_area_union
				snake_case_						:   int           =  total_area_intersect / total_area_label

				snake_case_						:   Any           =  np.nanmean(lowerCAmelCase__			)
				snake_case_						:   List[str]           =  np.nanmean(lowerCAmelCase__			)
				snake_case_						:   List[Any]           =  all_acc
				snake_case_						:   int           =  iou
				snake_case_						:   Dict           =  acc

				if nan_to_num is not None:
								snake_case_						:   Dict           =  {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  __lowerCAmelCase	(      datasets.Metric					):






			def       lowerCamelCase  (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       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = False ,							)					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  mean_iou(
							    results=_a ,							gt_seg_maps=_a ,							num_labels=_a ,							ignore_index=_a ,							nan_to_num=_a ,							label_map=_a ,							reduce_labels=_a ,							)
							return iou_result







import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class  __lowerCAmelCase	(      unittest.TestCase,					_a					):






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Dict           =  load_tool('''text-to-speech'''   )
							self.tool.setup()






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   Optional[int]           =  self.tool('''hey'''   )
							snake_case_						:   Union[str, Any]           =  result.to_raw()
							self.assertTrue(
							    torch.allclose(
							        resulting_tensor[:3] ,							torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]   ) ,							)   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   Any           =  self.tool('''hey'''   )
							snake_case_						:   str           =  result.to_raw()
							self.assertTrue(
							    torch.allclose(
							        resulting_tensor[:3] ,							torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]   ) ,							)   )
