






"""simple docstring"""



from typing import Dict, Optional

import numpy as np

import datasets


__A		:       Tuple  				=		'''
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.
'''

__A		:       List[Any]  				=		'''
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.        ])}
'''

__A		:       Union[str, Any]  				=		'''\
@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 						lowercase    (				__snake_case				: Tuple		,					__snake_case				: int		,					__snake_case				: Tuple		,					__snake_case				: bool		,					__snake_case				: Optional[Dict[int, int]] = None		,					__snake_case				: bool = False		,					):
		if label_map is not None:
				for old_id, new_id in label_map.items():
						lowercase_ : Any							       =   new_id

    # turn into Numpy arrays
		lowercase_ : str							       =   np.array(__snake_case	)
		lowercase_ : List[str]							       =   np.array(__snake_case	)

		if reduce_labels:
				lowercase_ : Optional[Any]							       =   2_5_5
				lowercase_ : List[Any]							       =   label - 1
				lowercase_ : int							       =   2_5_5

		lowercase_ : Optional[Any]							       =   label != ignore_index
		lowercase_ : Union[str, Any]							       =   np.not_equal(__snake_case		,					__snake_case	)
		lowercase_ : str							       =   pred_label[mask]
		lowercase_ : Optional[Any]							       =   np.array(__snake_case	)[mask]

		lowercase_ : List[str]							       =   pred_label[pred_label == label]

		lowercase_ : Dict							       =   np.histogram(__snake_case		,					bins=__snake_case		,					range=(0, num_labels - 1)	)[0]
		lowercase_ : str							       =   np.histogram(__snake_case		,					bins=__snake_case		,					range=(0, num_labels - 1)	)[0]
		lowercase_ : Union[str, Any]							       =   np.histogram(__snake_case		,					bins=__snake_case		,					range=(0, num_labels - 1)	)[0]

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

		return area_intersect, area_union, area_pred_label, area_label



def 						lowercase    (				__snake_case				: Optional[int]		,					__snake_case				: List[str]		,					__snake_case				: str		,					__snake_case				: bool		,					__snake_case				: Optional[Dict[int, int]] = None		,					__snake_case				: bool = False		,					):
		lowercase_ : Union[str, Any]							       =   np.zeros((num_labels,)		,					dtype=np.floataa	)
		lowercase_ : List[Any]							       =   np.zeros((num_labels,)		,					dtype=np.floataa	)
		lowercase_ : Optional[int]							       =   np.zeros((num_labels,)		,					dtype=np.floataa	)
		lowercase_ : Dict							       =   np.zeros((num_labels,)		,					dtype=np.floataa	)
		for result, gt_seg_map in zip(__snake_case		,					__snake_case	):
				lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_ : List[Any]							       =   intersect_and_union(
				    __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case	)
				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 						lowercase    (				__snake_case				: str		,					__snake_case				: Optional[Any]		,					__snake_case				: Tuple		,					__snake_case				: bool		,					__snake_case				: Optional[int] = None		,					__snake_case				: Optional[Dict[int, int]] = None		,					__snake_case				: bool = False		,					):
		lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_ : Any							       =   total_intersect_and_union(
		    __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case	)

		# compute metrics
		lowercase_ : Dict							       =   {}

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

		lowercase_ : Any							       =   np.nanmean(__snake_case	)
		lowercase_ : Optional[int]							       =   np.nanmean(__snake_case	)
		lowercase_ : Optional[int]							       =   all_acc
		lowercase_ : Any							       =   iou
		lowercase_ : Tuple							       =   acc

		if nan_to_num is not None:
				lowercase_ : int							       =   {metric: np.nan_to_num(__snake_case		,					nan=__snake_case	) for metric, metric_value in metrics.items()}

		return metrics






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




					def   A  (				self     :						Union[str, Any]   )  ->  Tuple:
							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     :						Dict    ,						A     :						Optional[Any]    ,						A     :						Tuple    ,						A     :						int    ,						A     :						bool    ,						A     :						Optional[int] = None    ,						A     :						Optional[Dict[int, int]] = None    ,						A     :						bool = False    ,						)  ->  Dict:
							lowercase_ : Dict							       =   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




from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase  							=						logging.get_logger(__name__)

UpperCamelCase  							=						{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}



class   snake_case_       (		__A       ):
   __A	:		List[str]         =				"convbert"






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     str=3_05_22	,						lowercase_				:     Any=7_68	,						lowercase_				:     Tuple=12	,						lowercase_				:     List[str]=12	,						lowercase_				:     Optional[int]=30_72	,						lowercase_				:     Union[str, Any]="gelu"	,						lowercase_				:     str=0.1	,						lowercase_				:     List[str]=0.1	,						lowercase_				:     Optional[Any]=5_12	,						lowercase_				:     Dict=2	,						lowercase_				:     Union[str, Any]=0.02	,						lowercase_				:     Optional[Any]=1E-12	,						lowercase_				:     Optional[int]=1	,						lowercase_				:     List[Any]=0	,						lowercase_				:     Optional[int]=2	,						lowercase_				:     str=7_68	,						lowercase_				:     Dict=2	,						lowercase_				:     Optional[Any]=9	,						lowercase_				:     Union[str, Any]=1	,						lowercase_				:     Any=None	,						**lowercase_				:     Optional[Any]	,						)					->						Dict:
       super().__init__(
           pad_token_id=lowercase_	,						bos_token_id=lowercase_	,						eos_token_id=lowercase_	,						**lowercase_	,						)

       lowercase__ :   List[str]    					=				vocab_size
       lowercase__ :   Union[str, Any]    					=				hidden_size
       lowercase__ :   Any    					=				num_hidden_layers
       lowercase__ :   List[str]    					=				num_attention_heads
       lowercase__ :   Union[str, Any]    					=				intermediate_size
       lowercase__ :   Optional[Any]    					=				hidden_act
       lowercase__ :   int    					=				hidden_dropout_prob
       lowercase__ :   str    					=				attention_probs_dropout_prob
       lowercase__ :   Union[str, Any]    					=				max_position_embeddings
       lowercase__ :   Optional[int]    					=				type_vocab_size
       lowercase__ :   Tuple    					=				initializer_range
       lowercase__ :   List[str]    					=				layer_norm_eps
       lowercase__ :   List[Any]    					=				embedding_size
       lowercase__ :   Optional[Any]    					=				head_ratio
       lowercase__ :   Dict    					=				conv_kernel_size
       lowercase__ :   Tuple    					=				num_groups
       lowercase__ :   Optional[int]    					=				classifier_dropout



class   snake_case_       (		__A       ):






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           lowercase__ :   Tuple    					=				{0: "batch", 1: "choice", 2: "sequence"}
       else:
           lowercase__ :   str    					=				{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ]  )



