a  d@sHddlmZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZddlmZmZddlmZd+ed eeeeeed ed eed d dZd,eeeed eeeedddZd-eeeeedeed eeeed ddZd.eeeeeeded eeeed ddZd/eeeed eeeedddZd0eeeeedeed eeeed d d!Z d1eeeeeeded eeeed d"d#Z!d2eeed%eeeeeeedeed eeeeeed& d'd(Z"d3eeed%eeeeeeedeed eeeeeed& d)d*Z#dS)4)Optional)Tensor)Literal) "_binary_stat_scores_arg_validation_binary_stat_scores_format%_binary_stat_scores_tensor_validation_binary_stat_scores_update&_multiclass_stat_scores_arg_validation_multiclass_stat_scores_format)_multiclass_stat_scores_tensor_validation_multiclass_stat_scores_update&_multilabel_stat_scores_arg_validation_multilabel_stat_scores_format)_multilabel_stat_scores_tensor_validation_multilabel_stat_scores_update)_adjust_weights_safe_divide _safe_divide)ClassificationTaskglobalF) precisionrecall)binarymicromacroweightednone)rZ samplewise) stattpfptnfnaveragemultidim_average multilabelreturnc Cs|dkr |n|}|dkr&t|||S|dkr|j|dkr>dndd}|j|dkrVdndd}|j|dkrndndd}t|||St|||} t| |||||S)Nrrrrr)Zdim)rsumr) rrrrr r!r"r#Zdifferent_statZscorer'/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/classification/precision_recall.py_precision_recall_reduce%s r)?NT)predstarget thresholdr" ignore_index validate_argsr$c CsZ|rt|||t||||t||||\}}t|||\}}}} td|||| d|dS)a Compute `Precision`_ for binary tasks. .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives respecitively. Accepts the following input tensors: - ``preds`` (int or float tensor): ``(N, ...)``. If preds is a floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (int tensor): ``(N, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels threshold: Threshold for transforming probability to binary {0,1} predictions multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: If ``multidim_average`` is set to ``global``, the metric returns a scalar value. If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value per sample. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.functional.classification import binary_precision >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0, 0, 1, 1, 0, 1]) >>> binary_precision(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.functional.classification import binary_precision >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> binary_precision(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.functional.classification import binary_precision >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) >>> binary_precision(preds, target, multidim_average='samplewise') tensor([0.4000, 0.0000]) rrr!r"rrrrr) r+r,r-r"r.r/rrrr r'r'r(binary_precision<s B r3rr%)rrrr) r+r, num_classesr!top_kr"r.r/r$c Csf|r$t|||||t|||||t|||\}}t|||||||\}} } } td|| | | ||dS)azCompute `Precision`_ for multiclass tasks. .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives respecitively. Accepts the following input tensors: - ``preds``: ``(N, ...)`` (int tensor) or ``(N, C, ..)`` (float tensor). If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into an int tensor. - ``target`` (int tensor): ``(N, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels num_classes: Integer specifing the number of classes average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction top_k: Number of highest probability or logit score predictions considered to find the correct label. Only works when ``preds`` contain probabilities/logits. multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: The returned shape depends on the ``average`` and ``multidim_average`` arguments: - If ``multidim_average`` is set to ``global``: - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor - If ``average=None/'none'``, the shape will be ``(C,)`` - If ``multidim_average`` is set to ``samplewise``: - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)`` - If ``average=None/'none'``, the shape will be ``(N, C)`` Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.functional.classification import multiclass_precision >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> multiclass_precision(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_precision(preds, target, num_classes=3, average=None) tensor([1.0000, 0.5000, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multiclass_precision >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([[0.16, 0.26, 0.58], ... [0.22, 0.61, 0.17], ... [0.71, 0.09, 0.20], ... [0.05, 0.82, 0.13]]) >>> multiclass_precision(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_precision(preds, target, num_classes=3, average=None) tensor([1.0000, 0.5000, 1.0000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multiclass_precision >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]]) >>> multiclass_precision(preds, target, num_classes=3, multidim_average='samplewise') tensor([0.3889, 0.2778]) >>> multiclass_precision(preds, target, num_classes=3, multidim_average='samplewise', average=None) tensor([[0.6667, 0.0000, 0.5000], [0.0000, 0.5000, 0.3333]]) rr0r r r r r) r+r,r4r!r5r"r.r/rrrr r'r'r(multiclass_precisionsa r8) r+r, num_labelsr-r!r"r.r/r$c Csd|r$t|||||t|||||t|||||\}}t|||\}} } } td|| | | ||ddS)a2Compute `Precision`_ for multilabel tasks. .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives respecitively. Accepts the following input tensors: - ``preds`` (int or float tensor): ``(N, C, ...)``. If preds is a floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (int tensor): ``(N, C, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifing the number of labels threshold: Threshold for transforming probability to binary (0,1) predictions average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: The returned shape depends on the ``average`` and ``multidim_average`` arguments: - If ``multidim_average`` is set to ``global``: - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor - If ``average=None/'none'``, the shape will be ``(C,)`` - If ``multidim_average`` is set to ``samplewise``: - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)`` - If ``average=None/'none'``, the shape will be ``(N, C)`` Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.functional.classification import multilabel_precision >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_precision(preds, target, num_labels=3) tensor(0.5000) >>> multilabel_precision(preds, target, num_labels=3, average=None) tensor([1.0000, 0.0000, 0.5000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_precision >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> multilabel_precision(preds, target, num_labels=3) tensor(0.5000) >>> multilabel_precision(preds, target, num_labels=3, average=None) tensor([1.0000, 0.0000, 0.5000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multilabel_precision >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) >>> multilabel_precision(preds, target, num_labels=3, multidim_average='samplewise') tensor([0.3333, 0.0000]) >>> multilabel_precision(preds, target, num_labels=3, multidim_average='samplewise', average=None) tensor([[0.5000, 0.5000, 0.0000], [0.0000, 0.0000, 0.0000]]) rTr!r"r#r rrrr) r+r,r9r-r!r"r.r/rrrr r'r'r(multilabel_precisions]r=c CsZ|rt|||t||||t||||\}}t|||\}}}} td|||| d|dS)a Compute `Recall`_ for binary tasks. .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. Accepts the following input tensors: - ``preds`` (int or float tensor): ``(N, ...)``. If preds is a floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (int tensor): ``(N, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels threshold: Threshold for transforming probability to binary {0,1} predictions multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: If ``multidim_average`` is set to ``global``, the metric returns a scalar value. If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value per sample. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.functional.classification import binary_recall >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0, 0, 1, 1, 0, 1]) >>> binary_recall(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.functional.classification import binary_recall >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> binary_recall(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.functional.classification import binary_recall >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) >>> binary_recall(preds, target, multidim_average='samplewise') tensor([0.6667, 0.0000]) rrr0r1r2r'r'r( binary_recallXs B r>c Csf|r$t|||||t|||||t|||\}}t|||||||\}} } } td|| | | ||dS)aYCompute `Recall`_ for multiclass tasks. .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. Accepts the following input tensors: - ``preds``: ``(N, ...)`` (int tensor) or ``(N, C, ..)`` (float tensor). If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into an int tensor. - ``target`` (int tensor): ``(N, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels num_classes: Integer specifing the number of classes average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction top_k: Number of highest probability or logit score predictions considered to find the correct label. Only works when ``preds`` contain probabilities/logits. multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: The returned shape depends on the ``average`` and ``multidim_average`` arguments: - If ``multidim_average`` is set to ``global``: - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor - If ``average=None/'none'``, the shape will be ``(C,)`` - If ``multidim_average`` is set to ``samplewise``: - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)`` - If ``average=None/'none'``, the shape will be ``(N, C)`` Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.functional.classification import multiclass_recall >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> multiclass_recall(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_recall(preds, target, num_classes=3, average=None) tensor([0.5000, 1.0000, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multiclass_recall >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([[0.16, 0.26, 0.58], ... [0.22, 0.61, 0.17], ... [0.71, 0.09, 0.20], ... [0.05, 0.82, 0.13]]) >>> multiclass_recall(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_recall(preds, target, num_classes=3, average=None) tensor([0.5000, 1.0000, 1.0000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multiclass_recall >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]]) >>> multiclass_recall(preds, target, num_classes=3, multidim_average='samplewise') tensor([0.5000, 0.2778]) >>> multiclass_recall(preds, target, num_classes=3, multidim_average='samplewise', average=None) tensor([[1.0000, 0.0000, 0.5000], [0.0000, 0.3333, 0.5000]]) rr0r6r7r'r'r(multiclass_recallsa r?c Csd|r$t|||||t|||||t|||||\}}t|||\}} } } td|| | | ||ddS)aCompute `Recall`_ for multilabel tasks. .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. Accepts the following input tensors: - ``preds`` (int or float tensor): ``(N, C, ...)``. If preds is a floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (int tensor): ``(N, C, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifing the number of labels threshold: Threshold for transforming probability to binary (0,1) predictions average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: The returned shape depends on the ``average`` and ``multidim_average`` arguments: - If ``multidim_average`` is set to ``global``: - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor - If ``average=None/'none'``, the shape will be ``(C,)`` - If ``multidim_average`` is set to ``samplewise``: - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)`` - If ``average=None/'none'``, the shape will be ``(N, C)`` Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.functional.classification import multilabel_recall >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_recall(preds, target, num_labels=3) tensor(0.6667) >>> multilabel_recall(preds, target, num_labels=3, average=None) tensor([1., 0., 1.]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_recall >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> multilabel_recall(preds, target, num_labels=3) tensor(0.6667) >>> multilabel_recall(preds, target, num_labels=3, average=None) tensor([1., 0., 1.]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multilabel_recall >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) >>> multilabel_recall(preds, target, num_labels=3, multidim_average='samplewise') tensor([0.6667, 0.0000]) >>> multilabel_recall(preds, target, num_labels=3, multidim_average='samplewise', average=None) tensor([[1., 1., 0.], [0., 0., 0.]]) rTr:r;r<r'r'r(multilabel_recall s]r@r)rZ multiclassr#) r+r,taskr-r4r9r!r"r5r.r/r$c Cs|dus J|tjkr(t||||| | S|tjkrt|tsPtdt|dt|tsntdt|dt||||||| | S|tj krt|tstdt|dt ||||||| | Std|dS)aUCompute `Precision`_. .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives respecitively. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of :func:`~torchmetrics.functional.classification.binary_precision`, :func:`~torchmetrics.functional.classification.multiclass_precision` and :func:`~torchmetrics.functional.classification.multilabel_precision` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> preds = tensor([2, 0, 2, 1]) >>> target = tensor([1, 1, 2, 0]) >>> precision(preds, target, task="multiclass", average='macro', num_classes=3) tensor(0.1667) >>> precision(preds, target, task="multiclass", average='micro', num_classes=3) tensor(0.2500) N+`num_classes` is expected to be `int` but ` was passed.`%`top_k` is expected to be `int` but `*`num_labels` is expected to be `int` but `z[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got ) rBINARYr3 MULTICLASS isinstanceint ValueErrortyper8 MULTILABELr= r+r,rAr-r4r9r!r"r5r.r/r'r'r(rts(%       rc Cst|}|dusJ|tjkr2t||||| | S|tjkrt|tsZtdt|dt|tsxtdt|dt ||||||| | S|tj krt|tstdt|dt ||||||| | Std|dS)a@Compute `Recall`_. .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of :func:`~torchmetrics.functional.classification.binary_recall`, :func:`~torchmetrics.functional.classification.multiclass_recall` and :func:`~torchmetrics.functional.classification.multilabel_recall` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> preds = tensor([2, 0, 2, 1]) >>> target = tensor([1, 1, 2, 0]) >>> recall(preds, target, task="multiclass", average='macro', num_classes=3) tensor(0.3333) >>> recall(preds, target, task="multiclass", average='micro', num_classes=3) tensor(0.2500) NrBrCrDrEzNot handled value: ) rZfrom_strrFr>rGrHrIrJrKr?rLr@rMr'r'r(rs&%        r)rF)r*rNT)rr%rNT)r*rrNT)r*rNT)rr%rNT)r*rrNT)r*NNrrr%NT)r*NNrrr%NT)$typingrZtorchrZtyping_extensionsrZ2torchmetrics.functional.classification.stat_scoresrrrrr r r r r rrrZtorchmetrics.utilities.computerrZtorchmetrics.utilities.enumsrboolr)floatrIr3r8r=r>r?r@rrr'r'r'r(s0   8    N  o  j N  o  k   ?