
"""simple docstring"""



from typing import Dict, Optional

import numpy as np

import datasets


_snake_case          =						'''\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'''

_snake_case          =						'''\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'''

_snake_case          =						'''\\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  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					bool     ,  SCREAMING_SNAKE_CASE:					Optional[Dict[int, int]] = None     ,  SCREAMING_SNAKE_CASE:					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(lowerCAmelCase__						)
							_lowerCAmelCase  	=			np.array(lowerCAmelCase__						)

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

							_lowerCAmelCase  	=			label != ignore_index
							_lowerCAmelCase  	=			np.not_equal(lowerCAmelCase__     ,  lowerCAmelCase__						)
							_lowerCAmelCase  	=			pred_label[mask]
							_lowerCAmelCase  	=			np.array(lowerCAmelCase__						)[mask]

							_lowerCAmelCase  	=			pred_label[pred_label == label]

							_lowerCAmelCase  	=			np.histogram(lowerCAmelCase__     ,  bins=lowerCAmelCase__     ,  range=(0, num_labels - 1)						)[0]
							_lowerCAmelCase  	=			np.histogram(lowerCAmelCase__     ,  bins=lowerCAmelCase__     ,  range=(0, num_labels - 1)						)[0]
							_lowerCAmelCase  	=			np.histogram(lowerCAmelCase__     ,  bins=lowerCAmelCase__     ,  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  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					bool     ,  SCREAMING_SNAKE_CASE:					Optional[Dict[int, int]] = None     ,  SCREAMING_SNAKE_CASE:					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(lowerCAmelCase__     ,  lowerCAmelCase__						):
														_lowerCAmelCase  	=			intersect_and_union(
														    lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__						)
														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  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					bool     ,  SCREAMING_SNAKE_CASE:					Optional[int] = None     ,  SCREAMING_SNAKE_CASE:					Optional[Dict[int, int]] = None     ,  SCREAMING_SNAKE_CASE:					bool = False     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			total_intersect_and_union(
							    lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__						)

							# 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(lowerCAmelCase__						)
							_lowerCAmelCase  	=			np.nanmean(lowerCAmelCase__						)
							_lowerCAmelCase  	=			all_acc
							_lowerCAmelCase  	=			iou
							_lowerCAmelCase  	=			acc

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

							return metrics

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






				'''simple docstring'''



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







											"""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 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			List[str] = None				,     UpperCAmelCase_	:			Dict = None				,     UpperCAmelCase_	:			Tuple = False				,     )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			mean_iou(
											    results=UpperCamelCase__				,     gt_seg_maps=UpperCamelCase__				,     num_labels=UpperCamelCase__				,     ignore_index=UpperCamelCase__				,     nan_to_num=UpperCamelCase__				,     label_map=UpperCamelCase__				,     reduce_labels=UpperCamelCase__				,     )
											return iou_result



"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					"mobilenet_v1"


				def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			Optional[int]=3				,     UpperCAmelCase_	:			str=224				,     UpperCAmelCase_	:			Union[str, Any]=1.0				,     UpperCAmelCase_	:			List[str]=8				,     UpperCAmelCase_	:			Union[str, Any]="relu6"				,     UpperCAmelCase_	:			Optional[Any]=True				,     UpperCAmelCase_	:			Optional[int]=0.999				,     UpperCAmelCase_	:			Optional[Any]=0.02				,     UpperCAmelCase_	:			Union[str, Any]=0.001				,     **UpperCAmelCase_	:			List[Any]				,     )		->  Dict:







											"""simple docstring"""




											super().__init__(**UpperCAmelCase_  )

											if depth_multiplier <= 0:
																		raise ValueError('depth_multiplier must be greater than zero.'  )

											_lowerCAmelCase  	=			num_channels
											_lowerCAmelCase  	=			image_size
											_lowerCAmelCase  	=			depth_multiplier
											_lowerCAmelCase  	=			min_depth
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			tf_padding
											_lowerCAmelCase  	=			classifier_dropout_prob
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			layer_norm_eps






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					version.parse("1.11"							)


				@property
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											return OrderedDict([('pixel_values', {0: 'batch'})]  )


				@property
				def 				__lowerCamelCase	(					self	:			Dict  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											if self.task == "image-classification":
																		return OrderedDict([('logits', {0: 'batch'})]  )
											else:
																		return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]  )







				@property
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  float:







											"""simple docstring"""




											return 1E-4






