
from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE							:Optional[Any]							 =				logging.get_logger(__name__)





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     ["pixel_values"]






	def __init__(       self					: Dict							,A					: bool = True							,A					: Dict[str, int] = None							,A					: PILImageResampling = PILImageResampling.BICUBIC							,A					: bool = True							,A					: Dict[str, int] = None							,A					: bool = True							,A					: Union[int, float] = 1 / 2_55							,A					: bool = True							,A					: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN							,A					: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD							,**A					: Dict							,):
					super().__init__(**A )
					__A   			= size if size is not None else {"shortest_edge": 2_24}
					__A   			= get_size_dict(A							,default_to_square=A )
					__A   			= crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
					__A   			= get_size_dict(A							,param_name="crop_size" )

					__A   			= do_resize
					__A   			= size
					__A   			= resample
					__A   			= do_center_crop
					__A   			= crop_size
					__A   			= do_rescale
					__A   			= rescale_factor
					__A   			= do_normalize
					__A   			= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
					__A   			= image_std if image_std is not None else IMAGENET_DEFAULT_STD






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: np.ndarray							,A					: Dict[str, int]							,A					: PILImageResampling = PILImageResampling.BICUBIC							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: Optional[Any]							,):
					__A   			= get_size_dict(A							,default_to_square=A )
					# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
					if "shortest_edge" in size:
									__A   			= int((2_56 / 2_24) * size["shortest_edge"] )
									__A   			= get_resize_output_image_size(A							,size=A							,default_to_square=A )
									__A   			= {"height": output_size[0], "width": output_size[1]}
					if "height" not in size_dict or "width" not in size_dict:
									raise ValueError(
									    f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}''' )
					return resize(
					    A							,size=(size_dict["height"], size_dict["width"])							,resample=A							,data_format=A							,**A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: np.ndarray							,A					: Dict[str, int]							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: Tuple							,):
					__A   			= get_size_dict(A )
					if "height" not in size or "width" not in size:
									raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}''' )
					return center_crop(A							,size=(size["height"], size["width"])							,data_format=A							,**A )






	def 				UpperCamelCase_  (       self					: Any							,A					: np.ndarray							,A					: Union[int, float]							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: str							,):
					return rescale(A							,scale=A							,data_format=A							,**A )






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: np.ndarray							,A					: Union[float, List[float]]							,A					: Union[float, List[float]]							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: str							,):
					return normalize(A							,mean=A							,std=A							,data_format=A							,**A )







	def 				UpperCamelCase_  (       self					: int							,A					: ImageInput							,A					: Optional[bool] = None							,A					: Optional[Dict[str, int]] = None							,A					: PILImageResampling = None							,A					: Optional[bool] = None							,A					: Optional[Dict[str, int]] = None							,A					: Optional[bool] = None							,A					: Optional[float] = None							,A					: Optional[bool] = None							,A					: Optional[Union[float, Iterable[float]]] = None							,A					: Optional[Union[float, Iterable[float]]] = None							,A					: Optional[TensorType] = None							,A					: ChannelDimension = ChannelDimension.FIRST							,**A					: str							,):
					__A   			= do_resize if do_resize is not None else self.do_resize
					__A   			= resample if resample is not None else self.resample
					__A   			= do_center_crop if do_center_crop is not None else self.do_center_crop
					__A   			= do_rescale if do_rescale is not None else self.do_rescale
					__A   			= rescale_factor if rescale_factor is not None else self.rescale_factor
					__A   			= do_normalize if do_normalize is not None else self.do_normalize
					__A   			= image_mean if image_mean is not None else self.image_mean
					__A   			= image_std if image_std is not None else self.image_std

					__A   			= size if size is not None else self.size
					__A   			= get_size_dict(A							,default_to_square=A )
					__A   			= crop_size if crop_size is not None else self.crop_size
					__A   			= get_size_dict(A							,param_name="crop_size" )

					__A   			= make_list_of_images(A )

					if not valid_images(A ):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray." )

					if do_resize and size is None:
									raise ValueError("Size must be specified if do_resize is True." )

					if do_center_crop and crop_size is None:
									raise ValueError("Crop size must be specified if do_center_crop is True." )

					if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True." )

					if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("Image mean and std must be specified if do_normalize is True." )

					# All transformations expect numpy arrays.
					__A   			= [to_numpy_array(A ) for image in images]

					if do_resize:
									__A   			= [self.resize(A							,A							,A ) for image in images]

					if do_center_crop:
									__A   			= [self.center_crop(A							,A ) for image in images]

					if do_rescale:
									__A   			= [self.rescale(A							,A ) for image in images]

					if do_normalize:
									__A   			= [self.normalize(A							,A							,A ) for image in images]

					__A   			= [to_channel_dimension_format(A							,A ) for image in images]

					__A   			= {"pixel_values": images}
					return BatchFeature(data=A							,tensor_type=A )


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
