

from typing import Dict, Optional

import numpy as np

import datasets


lowerCAmelCase__								=		"""
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__								=		"""
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__								=		"""\
@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_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     bool							,       UpperCAmelCase_							:     Optional[Dict[int, int]] = None							,       UpperCAmelCase_							:     bool = False							,       )						->				Dict:




	'''simple docstring'''




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

    # turn into Numpy arrays
	_UpperCamelCase   :		Dict			=						np.array(UpperCAmelCase_	)
	_UpperCamelCase   :		Tuple			=						np.array(UpperCAmelCase_	)

	if reduce_labels:
		_UpperCamelCase   :		Dict			=						2_5_5
		_UpperCamelCase   :		Tuple			=						label - 1
		_UpperCamelCase   :		Any			=						2_5_5

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

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

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

	_UpperCamelCase   :		Dict			=						area_pred_label + area_label - area_intersect

	return area_intersect, area_union, area_pred_label, area_label






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     bool							,       UpperCAmelCase_							:     Optional[Dict[int, int]] = None							,       UpperCAmelCase_							:     bool = False							,       )						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		List[Any]			=						np.zeros((num_labels,)							,       dtype=np.floataa	)
	_UpperCamelCase   :		Dict			=						np.zeros((num_labels,)							,       dtype=np.floataa	)
	_UpperCamelCase   :		int			=						np.zeros((num_labels,)							,       dtype=np.floataa	)
	_UpperCamelCase   :		List[str]			=						np.zeros((num_labels,)							,       dtype=np.floataa	)
	for result, gt_seg_map in zip(UpperCAmelCase_							,       UpperCAmelCase_	):
		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						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 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     bool							,       UpperCAmelCase_							:     Optional[int] = None							,       UpperCAmelCase_							:     Optional[Dict[int, int]] = None							,       UpperCAmelCase_							:     bool = False							,       )						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		int			=						total_intersect_and_union(
	    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

	# compute metrics
	_UpperCamelCase   :		Dict			=						{}

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

	_UpperCamelCase   :		Union[str, Any]			=						np.nanmean(UpperCAmelCase_	)
	_UpperCamelCase   :		int			=						np.nanmean(UpperCAmelCase_	)
	_UpperCamelCase   :		List[str]			=						all_acc
	_UpperCamelCase   :		List[str]			=						iou
	_UpperCamelCase   :		Tuple			=						acc

	if nan_to_num is not None:
		_UpperCamelCase   :		Dict			=						{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 	lowercase    (				datasets.Metric							):


		"""simple docstring"""




		def 		A__					(				self):
			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	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case = None	,       __snake_case = None	,       __snake_case = False	,       ):
			_UpperCamelCase   :		str			=						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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase__								=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CanineForMultipleChoice""",
							    """CanineForQuestionAnswering""",
							    """CanineForSequenceClassification""",
							    """CanineForTokenClassification""",
							    """CanineLayer""",
							    """CanineModel""",
							    """CaninePreTrainedModel""",
							    """load_tf_weights_in_canine""",
							]


if TYPE_CHECKING:
							from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
							from .tokenization_canine import CanineTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_canine import (
														    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CanineForMultipleChoice,
														    CanineForQuestionAnswering,
														    CanineForSequenceClassification,
														    CanineForTokenClassification,
														    CanineLayer,
														    CanineModel,
														    CaninePreTrainedModel,
														    load_tf_weights_in_canine,
														)


else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




