


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_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a = None     ,       _a = False     ,       ):




		"""simple docstring"""






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

    # turn into Numpy arrays
		lowerCAmelCase__       :      Any   					= np.array(_a	)
		lowerCAmelCase__       :      List[Any]   					= np.array(_a	)

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

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

		lowerCAmelCase__       :      Dict   					= pred_label[pred_label == label]

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

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

		return area_intersect, area_union, area_pred_label, area_label






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a = None     ,       _a = False     ,       ):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= np.zeros((num_labels,)     ,       dtype=np.floataa	)
		lowerCAmelCase__       :      Dict   					= np.zeros((num_labels,)     ,       dtype=np.floataa	)
		lowerCAmelCase__       :      List[str]   					= np.zeros((num_labels,)     ,       dtype=np.floataa	)
		lowerCAmelCase__       :      Dict   					= np.zeros((num_labels,)     ,       dtype=np.floataa	)
		for result, gt_seg_map in zip(_a     ,       _a	):
				lowerCAmelCase__       :      int   					= intersect_and_union(
				    _a     ,       _a     ,       _a     ,       _a     ,       _a     ,       _a	)
				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_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a = None     ,       _a = None     ,       _a = False     ,       ):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= total_intersect_and_union(
		    _a     ,       _a     ,       _a     ,       _a     ,       _a     ,       _a	)

		# compute metrics
		lowerCAmelCase__       :      Optional[Any]   					= {}

		lowerCAmelCase__       :      Optional[Any]   					= total_area_intersect.sum() / total_area_label.sum()
		lowerCAmelCase__       :      Tuple   					= total_area_intersect / total_area_union
		lowerCAmelCase__       :      Optional[Any]   					= total_area_intersect / total_area_label

		lowerCAmelCase__       :      Dict   					= np.nanmean(_a	)
		lowerCAmelCase__       :      int   					= np.nanmean(_a	)
		lowerCAmelCase__       :      Tuple   					= all_acc
		lowerCAmelCase__       :      str   					= iou
		lowerCAmelCase__       :      str   					= acc

		if nan_to_num is not None:
				lowerCAmelCase__       :      Tuple   					= {metric: np.nan_to_num(_a     ,       nan=_a	) for metric, metric_value in metrics.items()}

		return metrics





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,						_KWARGS_DESCRIPTION)
class  _a							(    datasets.Metric):




	def 							UpperCAmelCase__( self						:		List[str]		)->	Optional[Any]:
			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						:		Any		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		bool		, _SCREAMING_SNAKE_CASE						:		Optional[int] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Dict[int, int]] = None		, _SCREAMING_SNAKE_CASE						:		bool = False		, )->	int:
			lowerCAmelCase__       :      Union[str, Any]   					= mean_iou(
			    results=_SCREAMING_SNAKE_CASE		, gt_seg_maps=_SCREAMING_SNAKE_CASE		, num_labels=_SCREAMING_SNAKE_CASE		, ignore_index=_SCREAMING_SNAKE_CASE		, nan_to_num=_SCREAMING_SNAKE_CASE		, label_map=_SCREAMING_SNAKE_CASE		, reduce_labels=_SCREAMING_SNAKE_CASE		, )
			return iou_result








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class  _a							(    _lowercase):
	_a  :  Optional[int]				   =							'''xlm-roberta-xl'''




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		str=25_0880		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2560		, _SCREAMING_SNAKE_CASE						:		int=36		, _SCREAMING_SNAKE_CASE						:		Optional[int]=32		, _SCREAMING_SNAKE_CASE						:		Any=1_0240		, _SCREAMING_SNAKE_CASE						:		List[str]="gelu"		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.1		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=514		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1		, _SCREAMING_SNAKE_CASE						:		Tuple=0.02		, _SCREAMING_SNAKE_CASE						:		Dict=1E-05		, _SCREAMING_SNAKE_CASE						:		Tuple=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Tuple=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="absolute"		, _SCREAMING_SNAKE_CASE						:		Any=True		, _SCREAMING_SNAKE_CASE						:		Any=None		, **_SCREAMING_SNAKE_CASE						:		Tuple		, )->	str:
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Any   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      int   					= num_hidden_layers
			lowerCAmelCase__       :      Dict   					= num_attention_heads
			lowerCAmelCase__       :      str   					= hidden_act
			lowerCAmelCase__       :      List[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      List[str]   					= max_position_embeddings
			lowerCAmelCase__       :      Any   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      Optional[int]   					= layer_norm_eps
			lowerCAmelCase__       :      Optional[int]   					= position_embedding_type
			lowerCAmelCase__       :      Any   					= use_cache
			lowerCAmelCase__       :      List[Any]   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Any		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      int   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[Any]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			    ]		)




