





from typing import Dict, Optional

import numpy as np

import datasets


a__    :		Union[str, 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"""

a__    :		Optional[Any]      						=     """\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__    :		Optional[Any]      						=     """\\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     snake_case							(UpperCamelCase			:     Optional[int]			,    UpperCamelCase			:     Union[str, Any]			,    UpperCamelCase			:     Tuple			,    UpperCamelCase			:     bool			,    UpperCamelCase			:     Optional[Dict[int, int]] = None			,    UpperCamelCase			:     bool = False			,    ):




	'''simple docstring'''







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

    # turn into Numpy arrays
	lowerCamelCase__										=			np.array(UpperCamelCase__							)
	lowerCamelCase__										=			np.array(UpperCamelCase__							)

	if reduce_labels:
		lowerCamelCase__										=			255
		lowerCamelCase__										=			label - 1
		lowerCamelCase__										=			255

	lowerCamelCase__										=			label != ignore_index
	lowerCamelCase__										=			np.not_equal(UpperCamelCase__			,    UpperCamelCase__							)
	lowerCamelCase__										=			pred_label[mask]
	lowerCamelCase__										=			np.array(UpperCamelCase__							)[mask]

	lowerCamelCase__										=			pred_label[pred_label == label]

	lowerCamelCase__										=			np.histogram(UpperCamelCase__			,    bins=UpperCamelCase__			,    range=(0, num_labels - 1)							)[0]
	lowerCamelCase__										=			np.histogram(UpperCamelCase__			,    bins=UpperCamelCase__			,    range=(0, num_labels - 1)							)[0]
	lowerCamelCase__										=			np.histogram(UpperCamelCase__			,    bins=UpperCamelCase__			,    range=(0, num_labels - 1)							)[0]

	lowerCamelCase__										=			area_pred_label + area_label - area_intersect

	return area_intersect, area_union, area_pred_label, area_label

def     snake_case							(UpperCamelCase			:     int			,    UpperCamelCase			:     Tuple			,    UpperCamelCase			:     List[str]			,    UpperCamelCase			:     bool			,    UpperCamelCase			:     Optional[Dict[int, int]] = None			,    UpperCamelCase			:     bool = False			,    ):




	'''simple docstring'''







	lowerCamelCase__										=			np.zeros((num_labels,)			,    dtype=np.floataa							)
	lowerCamelCase__										=			np.zeros((num_labels,)			,    dtype=np.floataa							)
	lowerCamelCase__										=			np.zeros((num_labels,)			,    dtype=np.floataa							)
	lowerCamelCase__										=			np.zeros((num_labels,)			,    dtype=np.floataa							)
	for result, gt_seg_map in zip(UpperCamelCase__			,    UpperCamelCase__							):
		lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__										=			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     snake_case							(UpperCamelCase			:     int			,    UpperCamelCase			:     Union[str, Any]			,    UpperCamelCase			:     int			,    UpperCamelCase			:     bool			,    UpperCamelCase			:     Optional[int] = None			,    UpperCamelCase			:     Optional[Dict[int, int]] = None			,    UpperCamelCase			:     bool = False			,    ):




	'''simple docstring'''







	lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__										=			total_intersect_and_union(
	    UpperCamelCase__			,    UpperCamelCase__			,    UpperCamelCase__			,    UpperCamelCase__			,    UpperCamelCase__			,    UpperCamelCase__							)

	# compute metrics
	lowerCamelCase__										=			{}

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

	lowerCamelCase__										=			np.nanmean(UpperCamelCase__							)
	lowerCamelCase__										=			np.nanmean(UpperCamelCase__							)
	lowerCamelCase__										=			all_acc
	lowerCamelCase__										=			iou
	lowerCamelCase__										=			acc

	if nan_to_num is not None:
		lowerCamelCase__										=			{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  lowercase    (      datasets.Metric       ):






					"""simple docstring"""



					def       _UpperCamelCase       (    self						:     Any							):

						"""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						:     Dict ,					a_						:     Any ,					a_						:     Dict ,					a_						:     int ,					a_						:     bool ,					a_						:     Optional[int] = None ,					a_						:     Optional[Dict[int, int]] = None ,					a_						:     bool = False ,					):

						"""simple docstring"""



						lowerCamelCase__										=			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







import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible




class 	UpperCamelCase_							(  unittest.TestCase     ):


							def   _snake_case	(				self     :Tuple		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.bin""",
									    """safety_checker/model.safetensors""",
									    """vae/diffusion_pytorch_model.bin""",
									    """vae/diffusion_pytorch_model.safetensors""",
									    """text_encoder/pytorch_model.bin""",
									    """text_encoder/model.safetensors""",
									    """unet/diffusion_pytorch_model.bin""",
									    """unet/diffusion_pytorch_model.safetensors""",
									]
									self.assertTrue(is_safetensors_compatible(__A		)		)


							def   _snake_case	(				self     :List[str]		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """unet/diffusion_pytorch_model.bin""",
									    """unet/diffusion_pytorch_model.safetensors""",
									]
									self.assertTrue(is_safetensors_compatible(__A		)		)


							def   _snake_case	(				self     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.bin""",
									    """safety_checker/model.safetensors""",
									    """vae/diffusion_pytorch_model.bin""",
									    """vae/diffusion_pytorch_model.safetensors""",
									    """text_encoder/pytorch_model.bin""",
									    """text_encoder/model.safetensors""",
									    """unet/diffusion_pytorch_model.bin""",
									    # Removed: 'unet/diffusion_pytorch_model.safetensors',
									]
									self.assertFalse(is_safetensors_compatible(__A		)		)


							def   _snake_case	(				self     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """text_encoder/pytorch_model.bin""",
									    """text_encoder/model.safetensors""",
									]
									self.assertTrue(is_safetensors_compatible(__A		)		)


							def   _snake_case	(				self     :Optional[Any]		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.bin""",
									    """safety_checker/model.safetensors""",
									    """vae/diffusion_pytorch_model.bin""",
									    """vae/diffusion_pytorch_model.safetensors""",
									    """text_encoder/pytorch_model.bin""",
									    # Removed: 'text_encoder/model.safetensors',
									    """unet/diffusion_pytorch_model.bin""",
									    """unet/diffusion_pytorch_model.safetensors""",
									]
									self.assertFalse(is_safetensors_compatible(__A		)		)


							def   _snake_case	(				self     :Tuple		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.fp16.bin""",
									    """safety_checker/model.fp16.safetensors""",
									    """vae/diffusion_pytorch_model.fp16.bin""",
									    """vae/diffusion_pytorch_model.fp16.safetensors""",
									    """text_encoder/pytorch_model.fp16.bin""",
									    """text_encoder/model.fp16.safetensors""",
									    """unet/diffusion_pytorch_model.fp16.bin""",
									    """unet/diffusion_pytorch_model.fp16.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertTrue(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :Any		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """unet/diffusion_pytorch_model.fp16.bin""",
									    """unet/diffusion_pytorch_model.fp16.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertTrue(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :str		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """unet/diffusion_pytorch_model.bin""",
									    """unet/diffusion_pytorch_model.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertTrue(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :List[Any]		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.fp16.bin""",
									    """safety_checker/model.fp16.safetensors""",
									    """vae/diffusion_pytorch_model.fp16.bin""",
									    """vae/diffusion_pytorch_model.fp16.safetensors""",
									    """text_encoder/pytorch_model.fp16.bin""",
									    """text_encoder/model.fp16.safetensors""",
									    """unet/diffusion_pytorch_model.fp16.bin""",
									    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertFalse(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :str		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """text_encoder/pytorch_model.fp16.bin""",
									    """text_encoder/model.fp16.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertTrue(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :Optional[int]		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """text_encoder/pytorch_model.bin""",
									    """text_encoder/model.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertTrue(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :Union[str, Any]		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.fp16.bin""",
									    """safety_checker/model.fp16.safetensors""",
									    """vae/diffusion_pytorch_model.fp16.bin""",
									    """vae/diffusion_pytorch_model.fp16.safetensors""",
									    """text_encoder/pytorch_model.fp16.bin""",
									    # 'text_encoder/model.fp16.safetensors',
									    """unet/diffusion_pytorch_model.fp16.bin""",
									    """unet/diffusion_pytorch_model.fp16.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertFalse(is_safetensors_compatible(__A				,					variant=__A		)		)