





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


__A									=				{
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

__A									=				logging.get_logger(__name__)



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			"maskformer"
 lowercase_		=			{"hidden_size": "mask_feature_size"}
 lowercase_		=			["resnet", "swin"]
 lowercase_		=			["detr"]

 def __init__(self				:   Optional[int]       , UpperCAmelCase_				:   int = 256       , UpperCAmelCase_				:   int = 256       , UpperCAmelCase_				:   float = 0.1       , UpperCAmelCase_				:   bool = False       , UpperCAmelCase_				:   Optional[Dict] = None       , UpperCAmelCase_				:   Optional[Dict] = None       , UpperCAmelCase_				:   float = 0.02       , UpperCAmelCase_				:   float = 1.0       , UpperCAmelCase_				:   float = 1.0       , UpperCAmelCase_				:   float = 1.0       , UpperCAmelCase_				:   float = 20.0       , UpperCAmelCase_				:   Optional[bool] = None       , **UpperCAmelCase_				:   Optional[Any]       , )       ->str:



   '''simple docstring'''



   if backbone_config is None:
     # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
     lowerCamelCase__:      Any			  =SwinConfig(
         image_size=384       , in_channels=3       , patch_size=4       , embed_dim=128       , depths=[2, 2, 18, 2]       , num_heads=[4, 8, 16, 32]       , window_size=12       , drop_path_rate=0.3       , out_features=["stage1", "stage2", "stage3", "stage4"]       , )

   if isinstance(UpperCAmelCase_       , UpperCAmelCase_):
     lowerCamelCase__:      List[str]			  =backbone_config.pop("model_type")
     lowerCamelCase__:      int			  =CONFIG_MAPPING[backbone_model_type]
     lowerCamelCase__:      List[Any]			  =config_class.from_dict(UpperCAmelCase_)

   # verify that the backbone is supported
   if backbone_config.model_type not in self.backbones_supported:
     logger.warning_once(
         F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
         F"""Supported model types: {",".join(self.backbones_supported)}""")

   if decoder_config is None:
     # fall back to https://huggingface.co/facebook/detr-resnet-50
     lowerCamelCase__:      Tuple			  =DetrConfig()
   else:
     # verify that the decoder is supported
     lowerCamelCase__:      Any			  =(
         decoder_config.pop("model_type") if isinstance(UpperCAmelCase_       , UpperCAmelCase_) else decoder_config.model_type
     )
     if decoder_type not in self.decoders_supported:
       raise ValueError(
           F"""Transformer Decoder {decoder_type} not supported, please use one of"""
           F""" {",".join(self.decoders_supported)}""")
     if isinstance(UpperCAmelCase_       , UpperCAmelCase_):
       lowerCamelCase__:      Union[str, Any]			  =CONFIG_MAPPING[decoder_type]
       lowerCamelCase__:      List[Any]			  =config_class.from_dict(UpperCAmelCase_)

   lowerCamelCase__:      List[str]			  =backbone_config
   lowerCamelCase__:      Dict			  =decoder_config
   # main feature dimension for the model
   lowerCamelCase__:      Any			  =fpn_feature_size
   lowerCamelCase__:      Optional[int]			  =mask_feature_size
   # initializer
   lowerCamelCase__:      Union[str, Any]			  =init_std
   lowerCamelCase__:      str			  =init_xavier_std
   # Hungarian matcher && loss
   lowerCamelCase__:      Tuple			  =cross_entropy_weight
   lowerCamelCase__:      Union[str, Any]			  =dice_weight
   lowerCamelCase__:      List[str]			  =mask_weight
   lowerCamelCase__:      List[str]			  =use_auxiliary_loss
   lowerCamelCase__:      List[str]			  =no_object_weight
   lowerCamelCase__:      Dict			  =output_auxiliary_logits

   lowerCamelCase__:      Any			  =self.decoder_config.encoder_attention_heads
   lowerCamelCase__:      List[Any]			  =self.decoder_config.num_hidden_layers
   super().__init__(**UpperCAmelCase_)

 @classmethod
 def 							SCREAMING_SNAKE_CASE_					(cls				:   List[str]       , UpperCAmelCase_				:   PretrainedConfig       , UpperCAmelCase_				:   PretrainedConfig       , **UpperCAmelCase_				:   List[str])       ->int:



   '''simple docstring'''



   return cls(
       backbone_config=UpperCAmelCase_       , decoder_config=UpperCAmelCase_       , **UpperCAmelCase_       , )







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Dict[str, any]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =copy.deepcopy(self.__dict__)
   lowerCamelCase__:      Any			  =self.backbone_config.to_dict()
   lowerCamelCase__:      Any			  =self.decoder_config.to_dict()
   lowerCamelCase__:      List[str]			  =self.__class__.model_type
   return output



from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE							:List[Any]							 =				'\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'

SCREAMING_SNAKE_CASE							:List[str]							 =				'\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'

SCREAMING_SNAKE_CASE							:str							 =				'\\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			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = False		,   )		->	Tuple:







				"""simple docstring"""



				if label_map is not None:
								for old_id, new_id in label_map.items():
												__A   			= new_id

    # turn into Numpy arrays
				__A   			= np.array(a_						)
				__A   			= np.array(a_						)

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

				__A   			= label != ignore_index
				__A   			= np.not_equal(a_		,   a_						)
				__A   			= pred_label[mask]
				__A   			= np.array(a_						)[mask]

				__A   			= pred_label[pred_label == label]

				__A   			= np.histogram(a_		,   bins=a_		,   range=(0, num_labels - 1)						)[0]
				__A   			= np.histogram(a_		,   bins=a_		,   range=(0, num_labels - 1)						)[0]
				__A   			= np.histogram(a_		,   bins=a_		,   range=(0, num_labels - 1)						)[0]

				__A   			= area_pred_label + area_label - area_intersect

				return area_intersect, area_union, area_pred_label, area_label


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = False		,   )		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				for result, gt_seg_map in zip(a_		,   a_						):
								__A			,							__A			,							__A			,							__A   			= 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 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = None		,   a_ = False		,   )		->	str:







				"""simple docstring"""



				__A			,							__A			,							__A			,							__A   			= total_intersect_and_union(
				    a_		,   a_		,   a_		,   a_		,   a_		,   a_						)

				# compute metrics
				__A   			= {}

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

				__A   			= np.nanmean(a_						)
				__A   			= np.nanmean(a_						)
				__A   			= all_acc
				__A   			= iou
				__A   			= acc

				if nan_to_num is not None:
								__A   			= {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 							UpperCAmelCase      (  datasets.Metric  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: List[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					: int							,A					: Optional[Any]							,A					: Optional[Any]							,A					: int							,A					: bool							,A					: Optional[int] = None							,A					: Optional[Dict[int, int]] = None							,A					: bool = False							,):
					__A   			= 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
