


from typing import Dict, Optional

import numpy as np

import datasets


a					  =     '\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'

a					  =     '\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'

a					  =     '\\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_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = False						,							):
					if label_map is not None:
										for old_id, new_id in label_map.items():
															lowercase_  							=			new_id

    # turn into Numpy arrays
					lowercase_  							=			np.array(UpperCAmelCase__  )
					lowercase_  							=			np.array(UpperCAmelCase__  )

					if reduce_labels:
										lowercase_  							=			2_5_5
										lowercase_  							=			label - 1
										lowercase_  							=			2_5_5

					lowercase_  							=			label != ignore_index
					lowercase_  							=			np.not_equal(UpperCAmelCase__						,							UpperCAmelCase__  )
					lowercase_  							=			pred_label[mask]
					lowercase_  							=			np.array(UpperCAmelCase__  )[mask]

					lowercase_  							=			pred_label[pred_label == label]

					lowercase_  							=			np.histogram(UpperCAmelCase__						,							bins=UpperCAmelCase__						,							range=(0, num_labels - 1)  )[0]
					lowercase_  							=			np.histogram(UpperCAmelCase__						,							bins=UpperCAmelCase__						,							range=(0, num_labels - 1)  )[0]
					lowercase_  							=			np.histogram(UpperCAmelCase__						,							bins=UpperCAmelCase__						,							range=(0, num_labels - 1)  )[0]

					lowercase_  							=			area_pred_label + area_label - area_intersect

					return area_intersect, area_union, area_pred_label, area_label


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = False						,							):
					lowercase_  							=			np.zeros((num_labels,)						,							dtype=np.floataa  )
					lowercase_  							=			np.zeros((num_labels,)						,							dtype=np.floataa  )
					lowercase_  							=			np.zeros((num_labels,)						,							dtype=np.floataa  )
					lowercase_  							=			np.zeros((num_labels,)						,							dtype=np.floataa  )
					for result, gt_seg_map in zip(UpperCAmelCase__						,							UpperCAmelCase__  ):
										lowercase_							,					lowercase_							,					lowercase_							,					lowercase_  							=			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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = False						,							):
					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_  							=			total_intersect_and_union(
					    UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  )

					# compute metrics
					lowercase_  							=			{}

					lowercase_  							=			total_area_intersect.sum() / total_area_label.sum()
					lowercase_  							=			total_area_intersect / total_area_union
					lowercase_  							=			total_area_intersect / total_area_label

					lowercase_  							=			np.nanmean(UpperCAmelCase__  )
					lowercase_  							=			np.nanmean(UpperCAmelCase__  )
					lowercase_  							=			all_acc
					lowercase_  							=			iou
					lowercase_  							=			acc

					if nan_to_num is not None:
										lowercase_  							=			{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        UpperCamelCase__   (	datasets.Metric   ):

					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''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   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   bool							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[Dict[int, int]] = None							,					UpperCamelCase__       :   bool = False							,					):






										'''simple docstring'''






										lowercase_  							=			mean_iou(
										    results=UpperCamelCase__							,					gt_seg_maps=UpperCamelCase__							,					num_labels=UpperCamelCase__							,					ignore_index=UpperCamelCase__							,					nan_to_num=UpperCamelCase__							,					label_map=UpperCamelCase__							,					reduce_labels=UpperCamelCase__							,					)
										return iou_result






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


a					  =     {
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a					  =     [
							    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TrOCRForCausalLM',
							    'TrOCRPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
							from .processing_trocr import TrOCRProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
							import sys

							a					  =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


