

from typing import Dict, Optional

import numpy as np

import datasets


A__						=							"\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__						=							"\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__						=							"\\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 					_lowercase     (				a_				:					Optional[int]					,a_				:					Any					,a_				:					Dict					,a_				:					bool					,a_				:					Optional[Dict[int, int]] = None					,a_				:					bool = False					,)				->	Optional[Any]:




					'''simple docstring'''







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

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

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

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

					__magic_name__							=    pred_label[pred_label == label]

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

					__magic_name__							=    area_pred_label + area_label - area_intersect

					return area_intersect, area_union, area_pred_label, area_label




def 					_lowercase     (				a_				:					Tuple					,a_				:					List[Any]					,a_				:					List[str]					,a_				:					bool					,a_				:					Optional[Dict[int, int]] = None					,a_				:					bool = False					,)				->	Optional[Any]:




					'''simple docstring'''







					__magic_name__							=    np.zeros((num_labels,)					,dtype=np.floataa				)
					__magic_name__							=    np.zeros((num_labels,)					,dtype=np.floataa				)
					__magic_name__							=    np.zeros((num_labels,)					,dtype=np.floataa				)
					__magic_name__							=    np.zeros((num_labels,)					,dtype=np.floataa				)
					for result, gt_seg_map in zip(a_					,a_				):
										__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							=    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 					_lowercase     (				a_				:					Optional[int]					,a_				:					List[Any]					,a_				:					Dict					,a_				:					bool					,a_				:					Optional[int] = None					,a_				:					Optional[Dict[int, int]] = None					,a_				:					bool = False					,)				->	Any:




					'''simple docstring'''







					__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							=    total_intersect_and_union(
					    a_					,a_					,a_					,a_					,a_					,a_				)

					# compute metrics
					__magic_name__							=    {}

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

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

					if nan_to_num is not None:
										__magic_name__							=    {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			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, 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 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: List[str]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: int  , __UpperCamelCase: bool  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: Optional[Dict[int, int]] = None  , __UpperCamelCase: bool = False  , ):

						'''simple docstring'''





						__magic_name__							=    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







import os

A__						=							{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}




def 					_lowercase     (				a_				:					str				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					__magic_name__							=    0
					while index < len(a_				) - 1:
										__magic_name__							=    SYMBOLS[numerals[index]]
										__magic_name__							=    SYMBOLS[numerals[index + 1]]
										if current_value < next_value:
															total_value -= current_value
										else:
															total_value += current_value
										index += 1
					total_value += SYMBOLS[numerals[index]]

					return total_value




def 					_lowercase     (				a_				:					int				)				->	str:




					'''simple docstring'''







					__magic_name__							=    ''

					__magic_name__							=    num // 1_0_0_0
					numerals += m_count * "M"
					num %= 1_0_0_0

					__magic_name__							=    num // 1_0_0
					if c_count == 9:
										numerals += "CM"
										c_count -= 9
					elif c_count == 4:
										numerals += "CD"
										c_count -= 4
					if c_count >= 5:
										numerals += "D"
										c_count -= 5
					numerals += c_count * "C"
					num %= 1_0_0

					__magic_name__							=    num // 1_0
					if x_count == 9:
										numerals += "XC"
										x_count -= 9
					elif x_count == 4:
										numerals += "XL"
										x_count -= 4
					if x_count >= 5:
										numerals += "L"
										x_count -= 5
					numerals += x_count * "X"
					num %= 1_0

					if num == 9:
										numerals += "IX"
										num -= 9
					elif num == 4:
										numerals += "IV"
										num -= 4
					if num >= 5:
										numerals += "V"
										num -= 5
					numerals += num * "I"

					return numerals




def 					_lowercase     (				a_				:					str = "/p089_roman.txt"				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					with open(os.path.dirname(a_				) + roman_numerals_filename				) as filea:
										__magic_name__							=    filea.readlines()

					for line in lines:
										__magic_name__							=    line.strip()
										__magic_name__							=    parse_roman_numerals(a_				)
										__magic_name__							=    generate_roman_numerals(a_				)
										savings += len(a_				) - len(a_				)

					return savings


if __name__ == "__main__":
						print(f'''{solution() = }''')




