






import qiskit







def 	_UpperCamelCase   (    UpperCamelCase_       :					int    ,       UpperCamelCase_       :					int		)  ->   qiskit.result.counts.Counts:

						"""simple docstring"""



						lowerCAmelCase__    						=						qiskit.Aer.get_backend('aer_simulator'		)

						# Create a Quantum Circuit acting on the q register
						lowerCAmelCase__    						=						qiskit.QuantumCircuit(UpperCamelCase_    ,       UpperCamelCase_		)

						# Apply X (NOT) Gate to Qubits 0 & 1
						circuit.x(0		)
						circuit.x(1		)

						# Map the quantum measurement to the classical bits
						circuit.measure([0, 1]    ,       [0, 1]		)

						# Execute the circuit on the qasm simulator
						lowerCAmelCase__    						=						qiskit.execute(UpperCamelCase_    ,       UpperCamelCase_    ,       shots=1000		)

						# Return the histogram data of the results of the experiment.
						return job.result().get_counts(UpperCamelCase_		)


if __name__ == "__main__":
					__snake_case    :    Union[str, Any]         =     single_qubit_measure(2, 2)
					print(f'Total count for various states are: {counts}')













from typing import Dict, Optional

import numpy as np

import datasets


__snake_case    :    str         =     """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
"""

__snake_case    :    Tuple         =     """
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric(\"mean_iou\")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'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.        ])}
"""

__snake_case    :    Any         =     """\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""







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

						"""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 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					bool    ,       UpperCamelCase_       :					Optional[Dict[int, int]] = None    ,       UpperCamelCase_       :					bool = False    ,       )  ->   Union[str, Any]:

						"""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 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					bool    ,       UpperCamelCase_       :					Optional[int] = None    ,       UpperCamelCase_       :					Optional[Dict[int, int]] = None    ,       UpperCamelCase_       :					bool = False    ,       )  ->   List[str]:

						"""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     __SCREAMING_SNAKE_CASE					(   datasets.Metric):






						def       UpperCamelCase__      (	self   ):






												"""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     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						_UpperCamelCase = False     ,						):






												"""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





