From b7a8dc77475a5a29fc9a5a8a9a5acc50d99f2165 Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Fri, 3 Feb 2023 14:22:45 +0100
Subject: [PATCH 01/20] add enum

---
 src/torchmetrics/utilities/enums.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index c9672626727..fea1fe2aaac 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -81,3 +81,15 @@ class MDMCAverageMethod(EnumStr):
 
     GLOBAL = "global"
     SAMPLEWISE = "samplewise"
+
+
+class ClassificationTask(EnumStr):
+    """Enum to represent the different tasks in classification metrics.
+
+    >>> "binary" in list(ClassificationTask)
+    True
+    """
+
+    BINARY = "binary"
+    MULTICLASS = "multiclass"
+    MULTILABEL = "multilabel"

From aee0f126bd9d91fdf51d97e9e0b1e34b7ef2f0ff Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Fri, 3 Feb 2023 14:23:18 +0100
Subject: [PATCH 02/20] add enum

---
 src/torchmetrics/classification/accuracy.py         |  7 ++++---
 src/torchmetrics/classification/auroc.py            |  7 ++++---
 .../classification/average_precision.py             |  7 ++++---
 .../classification/calibration_error.py             |  5 +++--
 src/torchmetrics/classification/cohen_kappa.py      |  5 +++--
 src/torchmetrics/classification/confusion_matrix.py |  7 ++++---
 src/torchmetrics/classification/exact_match.py      |  5 +++--
 src/torchmetrics/classification/f_beta.py           | 13 +++++++------
 src/torchmetrics/classification/hamming.py          |  7 ++++---
 src/torchmetrics/classification/hinge.py            |  5 +++--
 src/torchmetrics/classification/jaccard.py          |  7 ++++---
 .../classification/matthews_corrcoef.py             |  7 ++++---
 src/torchmetrics/classification/precision_recall.py | 13 +++++++------
 .../classification/precision_recall_curve.py        |  7 ++++---
 .../classification/recall_at_fixed_precision.py     |  7 ++++---
 src/torchmetrics/classification/roc.py              |  7 ++++---
 src/torchmetrics/classification/specificity.py      |  7 ++++---
 .../classification/specificity_at_sensitivity.py    |  7 ++++---
 src/torchmetrics/classification/stat_scores.py      |  7 ++++---
 .../functional/classification/accuracy.py           |  7 ++++---
 src/torchmetrics/functional/classification/auroc.py |  7 ++++---
 .../functional/classification/average_precision.py  |  7 ++++---
 .../functional/classification/calibration_error.py  |  5 +++--
 .../functional/classification/cohen_kappa.py        |  5 +++--
 .../functional/classification/confusion_matrix.py   |  7 ++++---
 .../functional/classification/exact_match.py        |  3 ++-
 .../functional/classification/f_beta.py             | 13 +++++++------
 .../functional/classification/hamming.py            |  7 ++++---
 src/torchmetrics/functional/classification/hinge.py |  5 +++--
 .../functional/classification/jaccard.py            |  7 ++++---
 .../functional/classification/matthews_corrcoef.py  |  7 ++++---
 .../functional/classification/precision_recall.py   | 13 +++++++------
 .../classification/precision_recall_curve.py        |  7 ++++---
 .../classification/recall_at_fixed_precision.py     |  7 ++++---
 src/torchmetrics/functional/classification/roc.py   |  7 ++++---
 .../functional/classification/specificity.py        |  7 ++++---
 .../classification/specificity_at_sensitivity.py    |  7 ++++---
 .../functional/classification/stat_scores.py        |  8 ++++----
 38 files changed, 155 insertions(+), 118 deletions(-)

diff --git a/src/torchmetrics/classification/accuracy.py b/src/torchmetrics/classification/accuracy.py
index 6cb1704ab04..9547a979322 100644
--- a/src/torchmetrics/classification/accuracy.py
+++ b/src/torchmetrics/classification/accuracy.py
@@ -18,6 +18,7 @@
 
 from torchmetrics.functional.classification.accuracy import _accuracy_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
 
@@ -453,13 +454,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAccuracy(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassAccuracy(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAccuracy(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/auroc.py b/src/torchmetrics/classification/auroc.py
index 95be484f01b..6b531213f0e 100644
--- a/src/torchmetrics/classification/auroc.py
+++ b/src/torchmetrics/classification/auroc.py
@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryAUROC(BinaryPrecisionRecallCurve):
@@ -353,12 +354,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAUROC(max_fpr, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassAUROC(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAUROC(num_labels, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/average_precision.py b/src/torchmetrics/classification/average_precision.py
index 5a78ce0401c..6cbb1492bb2 100644
--- a/src/torchmetrics/classification/average_precision.py
+++ b/src/torchmetrics/classification/average_precision.py
@@ -30,6 +30,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
@@ -357,12 +358,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAveragePrecision(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassAveragePrecision(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAveragePrecision(num_labels, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/calibration_error.py b/src/torchmetrics/classification/calibration_error.py
index 335ee4ae916..c8849dc479d 100644
--- a/src/torchmetrics/classification/calibration_error.py
+++ b/src/torchmetrics/classification/calibration_error.py
@@ -29,6 +29,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryCalibrationError(Metric):
@@ -268,9 +269,9 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"n_bins": n_bins, "norm": norm, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryCalibrationError(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassCalibrationError(num_classes, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/cohen_kappa.py b/src/torchmetrics/classification/cohen_kappa.py
index 87045f6c04d..73950596a4a 100644
--- a/src/torchmetrics/classification/cohen_kappa.py
+++ b/src/torchmetrics/classification/cohen_kappa.py
@@ -23,6 +23,7 @@
     _multiclass_cohen_kappa_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryCohenKappa(BinaryConfusionMatrix):
@@ -222,9 +223,9 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"weights": weights, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryCohenKappa(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassCohenKappa(num_classes, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/confusion_matrix.py b/src/torchmetrics/classification/confusion_matrix.py
index 57c0e931f89..f88e23206ea 100644
--- a/src/torchmetrics/classification/confusion_matrix.py
+++ b/src/torchmetrics/classification/confusion_matrix.py
@@ -35,6 +35,7 @@
     _multilabel_confusion_matrix_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _PLOT_OUT_TYPE, plot_confusion_matrix
 
@@ -398,12 +399,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"normalize": normalize, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryConfusionMatrix(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassConfusionMatrix(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelConfusionMatrix(num_labels, threshold, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/exact_match.py b/src/torchmetrics/classification/exact_match.py
index 4c0bd64768d..c86ff796bd4 100644
--- a/src/torchmetrics/classification/exact_match.py
+++ b/src/torchmetrics/classification/exact_match.py
@@ -32,6 +32,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class MulticlassExactMatch(Metric):
@@ -291,10 +292,10 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassExactMatch(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelExactMatch(num_labels, threshold, **kwargs)
         raise ValueError(f"Expected argument `task` to either be `'multiclass'` or `'multilabel'` but got {task}")
diff --git a/src/torchmetrics/classification/f_beta.py b/src/torchmetrics/classification/f_beta.py
index 01c4b2f05e7..027c800ed3a 100644
--- a/src/torchmetrics/classification/f_beta.py
+++ b/src/torchmetrics/classification/f_beta.py
@@ -24,6 +24,7 @@
     _multilabel_fbeta_score_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryFBetaScore(BinaryStatScores):
@@ -729,13 +730,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryFBetaScore(beta, threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassFBetaScore(beta, num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelFBetaScore(beta, num_labels, threshold, average, **kwargs)
         raise ValueError(
@@ -780,13 +781,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryF1Score(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassF1Score(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelF1Score(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/hamming.py b/src/torchmetrics/classification/hamming.py
index c4da43ffbf8..c08798c3ec1 100644
--- a/src/torchmetrics/classification/hamming.py
+++ b/src/torchmetrics/classification/hamming.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.hamming import _hamming_distance_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryHammingDistance(BinaryStatScores):
@@ -349,13 +350,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryHammingDistance(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassHammingDistance(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelHammingDistance(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/hinge.py b/src/torchmetrics/classification/hinge.py
index c27b84090da..0f3798c892e 100644
--- a/src/torchmetrics/classification/hinge.py
+++ b/src/torchmetrics/classification/hinge.py
@@ -29,6 +29,7 @@
     _multiclass_hinge_loss_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryHingeLoss(Metric):
@@ -253,9 +254,9 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryHingeLoss(squared, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassHingeLoss(num_classes, squared, multiclass_mode, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/jaccard.py b/src/torchmetrics/classification/jaccard.py
index 637a862466c..589f75b840e 100644
--- a/src/torchmetrics/classification/jaccard.py
+++ b/src/torchmetrics/classification/jaccard.py
@@ -23,6 +23,7 @@
     _multilabel_jaccard_index_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryJaccardIndex(BinaryConfusionMatrix):
@@ -296,12 +297,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryJaccardIndex(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassJaccardIndex(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelJaccardIndex(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/matthews_corrcoef.py b/src/torchmetrics/classification/matthews_corrcoef.py
index d049c061878..8886c02887f 100644
--- a/src/torchmetrics/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/classification/matthews_corrcoef.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification import BinaryConfusionMatrix, MulticlassConfusionMatrix, MultilabelConfusionMatrix
 from torchmetrics.functional.classification.matthews_corrcoef import _matthews_corrcoef_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryMatthewsCorrCoef(BinaryConfusionMatrix):
@@ -238,12 +239,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryMatthewsCorrCoef(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassMatthewsCorrCoef(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelMatthewsCorrCoef(num_labels, threshold, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/precision_recall.py b/src/torchmetrics/classification/precision_recall.py
index daff2af6d3a..fc0f5767669 100644
--- a/src/torchmetrics/classification/precision_recall.py
+++ b/src/torchmetrics/classification/precision_recall.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.precision_recall import _precision_recall_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryPrecision(BinaryStatScores):
@@ -620,13 +621,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryPrecision(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassPrecision(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelPrecision(num_labels, threshold, average, **kwargs)
         raise ValueError(
@@ -676,13 +677,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryRecall(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassRecall(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelRecall(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/precision_recall_curve.py b/src/torchmetrics/classification/precision_recall_curve.py
index 386c994bff9..8e7070a16e1 100644
--- a/src/torchmetrics/classification/precision_recall_curve.py
+++ b/src/torchmetrics/classification/precision_recall_curve.py
@@ -37,6 +37,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryPrecisionRecallCurve(Metric):
@@ -467,12 +468,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryPrecisionRecallCurve(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassPrecisionRecallCurve(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelPrecisionRecallCurve(num_labels, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/recall_at_fixed_precision.py b/src/torchmetrics/classification/recall_at_fixed_precision.py
index 2f57eb05e8f..516761d62e3 100644
--- a/src/torchmetrics/classification/recall_at_fixed_precision.py
+++ b/src/torchmetrics/classification/recall_at_fixed_precision.py
@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryRecallAtFixedPrecision(BinaryPrecisionRecallCurve):
@@ -323,14 +324,14 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryRecallAtFixedPrecision(min_precision, thresholds, ignore_index, validate_args, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassRecallAtFixedPrecision(
                 num_classes, min_precision, thresholds, ignore_index, validate_args, **kwargs
             )
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelRecallAtFixedPrecision(
                 num_labels, min_precision, thresholds, ignore_index, validate_args, **kwargs
diff --git a/src/torchmetrics/classification/roc.py b/src/torchmetrics/classification/roc.py
index 32c2aa5cbaf..5984fad6dd8 100644
--- a/src/torchmetrics/classification/roc.py
+++ b/src/torchmetrics/classification/roc.py
@@ -28,6 +28,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryROC(BinaryPrecisionRecallCurve):
@@ -382,12 +383,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryROC(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassROC(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelROC(num_labels, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/specificity.py b/src/torchmetrics/classification/specificity.py
index f8a65ca2a21..4c249d43c7c 100644
--- a/src/torchmetrics/classification/specificity.py
+++ b/src/torchmetrics/classification/specificity.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.specificity import _specificity_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinarySpecificity(BinaryStatScores):
@@ -324,13 +325,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinarySpecificity(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassSpecificity(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelSpecificity(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/specificity_at_sensitivity.py b/src/torchmetrics/classification/specificity_at_sensitivity.py
index f31935f9d0c..a9effa59d84 100644
--- a/src/torchmetrics/classification/specificity_at_sensitivity.py
+++ b/src/torchmetrics/classification/specificity_at_sensitivity.py
@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinarySpecificityAtSensitivity(BinaryPrecisionRecallCurve):
@@ -327,14 +328,14 @@ def __new__(  # type: ignore
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinarySpecificityAtSensitivity(min_sensitivity, thresholds, ignore_index, validate_args, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassSpecificityAtSensitivity(
                 num_classes, min_sensitivity, thresholds, ignore_index, validate_args, **kwargs
             )
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelSpecificityAtSensitivity(
                 num_labels, min_sensitivity, thresholds, ignore_index, validate_args, **kwargs
diff --git a/src/torchmetrics/classification/stat_scores.py b/src/torchmetrics/classification/stat_scores.py
index 0bd18ae41d8..f2604cbd80c 100644
--- a/src/torchmetrics/classification/stat_scores.py
+++ b/src/torchmetrics/classification/stat_scores.py
@@ -36,6 +36,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class _AbstractStatScores(Metric):
@@ -499,13 +500,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryStatScores(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassStatScores(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelStatScores(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/functional/classification/accuracy.py b/src/torchmetrics/functional/classification/accuracy.py
index a6e08093a5b..fa36ccc92d1 100644
--- a/src/torchmetrics/functional/classification/accuracy.py
+++ b/src/torchmetrics/functional/classification/accuracy.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _accuracy_reduce(
@@ -397,15 +398,15 @@ def accuracy(
         tensor(0.6667)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_accuracy(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_accuracy(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_accuracy(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/auroc.py b/src/torchmetrics/functional/classification/auroc.py
index ddfccb392c4..875cd0846b4 100644
--- a/src/torchmetrics/functional/classification/auroc.py
+++ b/src/torchmetrics/functional/classification/auroc.py
@@ -38,6 +38,7 @@
 )
 from torchmetrics.utilities.compute import _auc_compute_without_check, _safe_divide
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
@@ -450,12 +451,12 @@ def auroc(
         >>> auroc(preds, target, task='multiclass', num_classes=3)
         tensor(0.7778)
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_auroc(preds, target, max_fpr, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_auroc(preds, target, num_classes, average, thresholds, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_auroc(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/average_precision.py b/src/torchmetrics/functional/classification/average_precision.py
index 95507e6e2ee..4c8d84ab9b8 100644
--- a/src/torchmetrics/functional/classification/average_precision.py
+++ b/src/torchmetrics/functional/classification/average_precision.py
@@ -36,6 +36,7 @@
 )
 from torchmetrics.utilities.compute import _safe_divide
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
@@ -438,14 +439,14 @@ def average_precision(
         >>> average_precision(pred, target, task="multiclass", num_classes=5, average=None)
         tensor([1.0000, 1.0000, 0.2500, 0.2500,    nan])
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_average_precision(preds, target, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_average_precision(
             preds, target, num_classes, average, thresholds, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_average_precision(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/calibration_error.py b/src/torchmetrics/functional/classification/calibration_error.py
index d87f9bb5861..8c06e373772 100644
--- a/src/torchmetrics/functional/classification/calibration_error.py
+++ b/src/torchmetrics/functional/classification/calibration_error.py
@@ -23,6 +23,7 @@
     _multiclass_confusion_matrix_format,
     _multiclass_confusion_matrix_tensor_validation,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _binning_bucketize(
@@ -348,9 +349,9 @@ def calibration_error(
     each argument influence and examples.
     """
     assert norm is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_calibration_error(preds, target, n_bins, norm, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_calibration_error(preds, target, num_classes, n_bins, norm, ignore_index, validate_args)
     raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multiclass'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/cohen_kappa.py b/src/torchmetrics/functional/classification/cohen_kappa.py
index e01243bbd63..faa008bea16 100644
--- a/src/torchmetrics/functional/classification/cohen_kappa.py
+++ b/src/torchmetrics/functional/classification/cohen_kappa.py
@@ -27,6 +27,7 @@
     _multiclass_confusion_matrix_tensor_validation,
     _multiclass_confusion_matrix_update,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _cohen_kappa_reduce(confmat: Tensor, weights: Optional[Literal["linear", "quadratic", "none"]] = None) -> Tensor:
@@ -256,9 +257,9 @@ class labels.
         >>> cohen_kappa(preds, target, task="multiclass", num_classes=2)
         tensor(0.5000)
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_cohen_kappa(preds, target, threshold, weights, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_cohen_kappa(preds, target, num_classes, weights, ignore_index, validate_args)
     raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multiclass'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/confusion_matrix.py b/src/torchmetrics/functional/classification/confusion_matrix.py
index 047cff8351e..26970967866 100644
--- a/src/torchmetrics/functional/classification/confusion_matrix.py
+++ b/src/torchmetrics/functional/classification/confusion_matrix.py
@@ -19,6 +19,7 @@
 
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
@@ -630,12 +631,12 @@ def confusion_matrix(
                 [[1, 0], [1, 0]],
                 [[0, 1], [0, 1]]])
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_confusion_matrix(preds, target, threshold, normalize, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_confusion_matrix(preds, target, num_classes, normalize, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_confusion_matrix(preds, target, num_labels, threshold, normalize, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/exact_match.py b/src/torchmetrics/functional/classification/exact_match.py
index b4ca3ce7dd1..df1dc380487 100644
--- a/src/torchmetrics/functional/classification/exact_match.py
+++ b/src/torchmetrics/functional/classification/exact_match.py
@@ -26,6 +26,7 @@
     _multilabel_stat_scores_tensor_validation,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _exact_match_reduce(
@@ -229,7 +230,7 @@ def exact_match(
         >>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='samplewise')
         tensor([1., 0.])
     """
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert num_classes is not None
         return multiclass_exact_match(preds, target, num_classes, multidim_average, ignore_index, validate_args)
     if task == "multilalbe":
diff --git a/src/torchmetrics/functional/classification/f_beta.py b/src/torchmetrics/functional/classification/f_beta.py
index c1a68e13d5b..11a7d191f63 100644
--- a/src/torchmetrics/functional/classification/f_beta.py
+++ b/src/torchmetrics/functional/classification/f_beta.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _fbeta_reduce(
@@ -693,15 +694,15 @@ def fbeta_score(
         tensor(0.3333)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_fbeta_score(preds, target, beta, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_fbeta_score(
             preds, target, beta, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_fbeta_score(
             preds, target, beta, num_labels, threshold, average, multidim_average, ignore_index, validate_args
@@ -742,15 +743,15 @@ def f1_score(
         tensor(0.3333)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_f1_score(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_f1_score(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_f1_score(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/hamming.py b/src/torchmetrics/functional/classification/hamming.py
index c7725931fb8..9114e3f7ee7 100644
--- a/src/torchmetrics/functional/classification/hamming.py
+++ b/src/torchmetrics/functional/classification/hamming.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _hamming_distance_reduce(
@@ -399,15 +400,15 @@ def hamming_distance(
         tensor(0.2500)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_hamming_distance(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_hamming_distance(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_hamming_distance(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/hinge.py b/src/torchmetrics/functional/classification/hinge.py
index e2ff98141e2..fa270abd5f9 100644
--- a/src/torchmetrics/functional/classification/hinge.py
+++ b/src/torchmetrics/functional/classification/hinge.py
@@ -24,6 +24,7 @@
     _multiclass_confusion_matrix_tensor_validation,
 )
 from torchmetrics.utilities.data import to_onehot
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _hinge_loss_compute(measure: Tensor, total: Tensor) -> Tensor:
@@ -276,9 +277,9 @@ def hinge_loss(
         >>> hinge_loss(preds, target, task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
         tensor([1.3743, 1.1945, 1.2359])
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_hinge_loss(preds, target, squared, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_hinge_loss(preds, target, num_classes, squared, multiclass_mode, ignore_index, validate_args)
     raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multilabel'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/jaccard.py b/src/torchmetrics/functional/classification/jaccard.py
index f593a424fd2..2cbf03fb317 100644
--- a/src/torchmetrics/functional/classification/jaccard.py
+++ b/src/torchmetrics/functional/classification/jaccard.py
@@ -32,6 +32,7 @@
     _multilabel_confusion_matrix_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _jaccard_index_reduce(
@@ -321,12 +322,12 @@ def jaccard_index(
         >>> jaccard_index(pred, target, task="multiclass", num_classes=2)
         tensor(0.9660)
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_jaccard_index(preds, target, threshold, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_jaccard_index(preds, target, num_classes, average, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_jaccard_index(preds, target, num_labels, threshold, average, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/matthews_corrcoef.py b/src/torchmetrics/functional/classification/matthews_corrcoef.py
index cc5f94b70e2..6e1a6c4b5d1 100644
--- a/src/torchmetrics/functional/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/functional/classification/matthews_corrcoef.py
@@ -31,6 +31,7 @@
     _multilabel_confusion_matrix_tensor_validation,
     _multilabel_confusion_matrix_update,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _matthews_corrcoef_reduce(confmat: Tensor) -> Tensor:
@@ -235,12 +236,12 @@ def matthews_corrcoef(
         >>> matthews_corrcoef(preds, target, task="multiclass", num_classes=2)
         tensor(0.5774)
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_matthews_corrcoef(preds, target, threshold, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_matthews_corrcoef(preds, target, num_classes, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_matthews_corrcoef(preds, target, num_labels, threshold, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/precision_recall.py b/src/torchmetrics/functional/classification/precision_recall.py
index e00746341aa..ac6725c704a 100644
--- a/src/torchmetrics/functional/classification/precision_recall.py
+++ b/src/torchmetrics/functional/classification/precision_recall.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _precision_recall_reduce(
@@ -652,15 +653,15 @@ def precision(
         tensor(0.2500)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_precision(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_precision(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_precision(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
@@ -705,15 +706,15 @@ def recall(
         tensor(0.2500)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_recall(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_recall(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_recall(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/precision_recall_curve.py b/src/torchmetrics/functional/classification/precision_recall_curve.py
index 9291f31d26b..8c58d185371 100644
--- a/src/torchmetrics/functional/classification/precision_recall_curve.py
+++ b/src/torchmetrics/functional/classification/precision_recall_curve.py
@@ -22,6 +22,7 @@
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.compute import _safe_divide
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _binary_clf_curve(
@@ -815,12 +816,12 @@ def precision_recall_curve(
         >>> thresholds
         [tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])]
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_precision_recall_curve(preds, target, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_precision_recall_curve(preds, target, num_classes, thresholds, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_precision_recall_curve(preds, target, num_labels, thresholds, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/recall_at_fixed_precision.py b/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
index 8c0e9f38578..89f60a54481 100644
--- a/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
+++ b/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
@@ -34,6 +34,7 @@
     _multilabel_precision_recall_curve_tensor_validation,
     _multilabel_precision_recall_curve_update,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _recall_at_precision(
@@ -384,14 +385,14 @@ def recall_at_fixed_precision(
     :func:`binary_recall_at_fixed_precision`, :func:`multiclass_recall_at_fixed_precision` and
     :func:`multilabel_recall_at_fixed_precision` for the specific details of each argument influence and examples.
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_recall_at_fixed_precision(preds, target, min_precision, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_recall_at_fixed_precision(
             preds, target, num_classes, min_precision, thresholds, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_recall_at_fixed_precision(
             preds, target, num_labels, min_precision, thresholds, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/roc.py b/src/torchmetrics/functional/classification/roc.py
index 38e46a1755d..575b0376280 100644
--- a/src/torchmetrics/functional/classification/roc.py
+++ b/src/torchmetrics/functional/classification/roc.py
@@ -34,6 +34,7 @@
 )
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _binary_roc_compute(
@@ -483,12 +484,12 @@ def roc(
          tensor([1.0000, 0.7576, 0.3680, 0.3468, 0.0745]),
          tensor([1.0000, 0.1837, 0.1338, 0.1183, 0.1138])]
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_roc(preds, target, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_roc(preds, target, num_classes, thresholds, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_roc(preds, target, num_labels, thresholds, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/specificity.py b/src/torchmetrics/functional/classification/specificity.py
index 1a1c351a2d3..cd167768314 100644
--- a/src/torchmetrics/functional/classification/specificity.py
+++ b/src/torchmetrics/functional/classification/specificity.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _specificity_reduce(
@@ -370,15 +371,15 @@ def specificity(
         tensor(0.6250)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_specificity(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_specificity(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_specificity(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/specificity_at_sensitivity.py b/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
index c654ea26c61..034ef4e161d 100644
--- a/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
+++ b/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
@@ -36,6 +36,7 @@
     _multiclass_roc_compute,
     _multilabel_roc_compute,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _convert_fpr_to_specificity(fpr: Tensor) -> Tensor:
@@ -413,16 +414,16 @@ def specicity_at_sensitivity(
     :func:`binary_specificity_at_sensitivity`, :func:`multiclass_specicity_at_sensitivity` and
     :func:`multilabel_specifity_at_sensitvity` for the specific details of each argument influence and examples.
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_specificity_at_sensitivity(  # type: ignore
             preds, target, min_sensitivity, thresholds, ignore_index, validate_args
         )
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_specificity_at_sensitivity(  # type: ignore
             preds, target, num_classes, min_sensitivity, thresholds, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_specificity_at_sensitivity(  # type: ignore
             preds, target, num_labels, min_sensitivity, thresholds, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/stat_scores.py b/src/torchmetrics/functional/classification/stat_scores.py
index e9ad0d5d5e1..fceb25bce27 100644
--- a/src/torchmetrics/functional/classification/stat_scores.py
+++ b/src/torchmetrics/functional/classification/stat_scores.py
@@ -19,7 +19,7 @@
 
 from torchmetrics.utilities.checks import _check_same_shape, _input_format_classification
 from torchmetrics.utilities.data import _bincount, select_topk
-from torchmetrics.utilities.enums import AverageMethod, DataType, MDMCAverageMethod
+from torchmetrics.utilities.enums import AverageMethod, ClassificationTask, DataType, MDMCAverageMethod
 
 
 def _binary_stat_scores_arg_validation(
@@ -1083,15 +1083,15 @@ def stat_scores(
                 [1, 0, 3, 0, 1]])
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_stat_scores(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_stat_scores(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_stat_scores(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args

From 3bec0238c02aa75cfde9c159829af3f22c7e04b0 Mon Sep 17 00:00:00 2001
From: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Date: Fri, 3 Feb 2023 22:24:34 +0900
Subject: [PATCH 03/20] gh: update templates (#1477)Co-authored-by:
 pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
 Co-authored-by: Nicki Skafte Detlefsen <skaftenicki@gmail.com>

* gh: update templates

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 .github/ISSUE_TEMPLATE/bug_report.md    |  5 ++++-
 .github/ISSUE_TEMPLATE/documentation.md |  5 ++---
 .github/PULL_REQUEST_TEMPLATE.md        | 12 +++++++++---
 3 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
index 487b6e8bdc2..150f7849963 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -16,11 +16,14 @@ Steps to reproduce the behavior...
 
 <!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
 
-#### Code sample
+<details>
+  <summary>Code sample</summary>
 
 <!-- Ideally attach a minimal code sample to reproduce the decried issue.
 Minimal means having the shortest code but still preserving the bug. -->
 
+</details>
+
 ### Expected behavior
 
 <!-- A clear and concise description of what you expected to happen. -->
diff --git a/.github/ISSUE_TEMPLATE/documentation.md b/.github/ISSUE_TEMPLATE/documentation.md
index c74b3408000..456d49be454 100644
--- a/.github/ISSUE_TEMPLATE/documentation.md
+++ b/.github/ISSUE_TEMPLATE/documentation.md
@@ -10,8 +10,7 @@ assignees: ''
 
 For typos and doc fixes, please go ahead and:
 
-1. Create an issue.
-1. Fix the typo.
-1. Submit a PR.
+- For a simple typo or fix, please send directly a PR (no need to create an issue)
+- If you are not sure about the proper solution, please describe here your finding...
 
 Thanks!
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 0c6881d0228..318ee5483eb 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -2,18 +2,24 @@
 
 Fixes #\<issue_number>
 
-## Before submitting
+<details>
+  <summary>Before submitting</summary>
 
-- [ ] Was this **discussed/approved** via a Github issue? (no need for typos and docs improvements)
+- [ ] Was this **discussed/agreed** via a Github issue? (no need for typos and docs improvements)
 - [ ] Did you read the [contributor guideline](https://github.com/Lightning-AI/metrics/blob/master/.github/CONTRIBUTING.md), Pull Request section?
 - [ ] Did you make sure to **update the docs**?
 - [ ] Did you write any new **necessary tests**?
 
-## PR review
+</details>
+
+<details>
+  <summary>PR review</summary>
 
 Anyone in the community is free to review the PR once the tests have passed.
 If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
 
+</details>
+
 ## Did you have fun?
 
 Make sure you had fun coding 🙃

From 0140d12a38737286dc969baa2d3db502be281c2a Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Fri, 3 Feb 2023 14:22:45 +0100
Subject: [PATCH 04/20] add enum

---
 src/torchmetrics/utilities/enums.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index c9672626727..fea1fe2aaac 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -81,3 +81,15 @@ class MDMCAverageMethod(EnumStr):
 
     GLOBAL = "global"
     SAMPLEWISE = "samplewise"
+
+
+class ClassificationTask(EnumStr):
+    """Enum to represent the different tasks in classification metrics.
+
+    >>> "binary" in list(ClassificationTask)
+    True
+    """
+
+    BINARY = "binary"
+    MULTICLASS = "multiclass"
+    MULTILABEL = "multilabel"

From 9bd0063d435d35d288bc14b23ce5950ccbcc883a Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Fri, 3 Feb 2023 14:23:18 +0100
Subject: [PATCH 05/20] add enum

---
 src/torchmetrics/classification/accuracy.py         |  7 ++++---
 src/torchmetrics/classification/auroc.py            |  7 ++++---
 .../classification/average_precision.py             |  7 ++++---
 .../classification/calibration_error.py             |  5 +++--
 src/torchmetrics/classification/cohen_kappa.py      |  5 +++--
 src/torchmetrics/classification/confusion_matrix.py |  7 ++++---
 src/torchmetrics/classification/exact_match.py      |  5 +++--
 src/torchmetrics/classification/f_beta.py           | 13 +++++++------
 src/torchmetrics/classification/hamming.py          |  7 ++++---
 src/torchmetrics/classification/hinge.py            |  5 +++--
 src/torchmetrics/classification/jaccard.py          |  7 ++++---
 .../classification/matthews_corrcoef.py             |  7 ++++---
 src/torchmetrics/classification/precision_recall.py | 13 +++++++------
 .../classification/precision_recall_curve.py        |  7 ++++---
 .../classification/recall_at_fixed_precision.py     |  7 ++++---
 src/torchmetrics/classification/roc.py              |  7 ++++---
 src/torchmetrics/classification/specificity.py      |  7 ++++---
 .../classification/specificity_at_sensitivity.py    |  7 ++++---
 src/torchmetrics/classification/stat_scores.py      |  7 ++++---
 .../functional/classification/accuracy.py           |  7 ++++---
 src/torchmetrics/functional/classification/auroc.py |  7 ++++---
 .../functional/classification/average_precision.py  |  7 ++++---
 .../functional/classification/calibration_error.py  |  5 +++--
 .../functional/classification/cohen_kappa.py        |  5 +++--
 .../functional/classification/confusion_matrix.py   |  7 ++++---
 .../functional/classification/exact_match.py        |  3 ++-
 .../functional/classification/f_beta.py             | 13 +++++++------
 .../functional/classification/hamming.py            |  7 ++++---
 src/torchmetrics/functional/classification/hinge.py |  5 +++--
 .../functional/classification/jaccard.py            |  7 ++++---
 .../functional/classification/matthews_corrcoef.py  |  7 ++++---
 .../functional/classification/precision_recall.py   | 13 +++++++------
 .../classification/precision_recall_curve.py        |  7 ++++---
 .../classification/recall_at_fixed_precision.py     |  7 ++++---
 src/torchmetrics/functional/classification/roc.py   |  7 ++++---
 .../functional/classification/specificity.py        |  7 ++++---
 .../classification/specificity_at_sensitivity.py    |  7 ++++---
 .../functional/classification/stat_scores.py        |  8 ++++----
 38 files changed, 155 insertions(+), 118 deletions(-)

diff --git a/src/torchmetrics/classification/accuracy.py b/src/torchmetrics/classification/accuracy.py
index 6cb1704ab04..9547a979322 100644
--- a/src/torchmetrics/classification/accuracy.py
+++ b/src/torchmetrics/classification/accuracy.py
@@ -18,6 +18,7 @@
 
 from torchmetrics.functional.classification.accuracy import _accuracy_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
 
@@ -453,13 +454,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAccuracy(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassAccuracy(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAccuracy(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/auroc.py b/src/torchmetrics/classification/auroc.py
index 95be484f01b..6b531213f0e 100644
--- a/src/torchmetrics/classification/auroc.py
+++ b/src/torchmetrics/classification/auroc.py
@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryAUROC(BinaryPrecisionRecallCurve):
@@ -353,12 +354,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAUROC(max_fpr, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassAUROC(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAUROC(num_labels, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/average_precision.py b/src/torchmetrics/classification/average_precision.py
index 5a78ce0401c..6cbb1492bb2 100644
--- a/src/torchmetrics/classification/average_precision.py
+++ b/src/torchmetrics/classification/average_precision.py
@@ -30,6 +30,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
@@ -357,12 +358,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAveragePrecision(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassAveragePrecision(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAveragePrecision(num_labels, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/calibration_error.py b/src/torchmetrics/classification/calibration_error.py
index 335ee4ae916..c8849dc479d 100644
--- a/src/torchmetrics/classification/calibration_error.py
+++ b/src/torchmetrics/classification/calibration_error.py
@@ -29,6 +29,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryCalibrationError(Metric):
@@ -268,9 +269,9 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"n_bins": n_bins, "norm": norm, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryCalibrationError(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassCalibrationError(num_classes, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/cohen_kappa.py b/src/torchmetrics/classification/cohen_kappa.py
index 87045f6c04d..73950596a4a 100644
--- a/src/torchmetrics/classification/cohen_kappa.py
+++ b/src/torchmetrics/classification/cohen_kappa.py
@@ -23,6 +23,7 @@
     _multiclass_cohen_kappa_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryCohenKappa(BinaryConfusionMatrix):
@@ -222,9 +223,9 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"weights": weights, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryCohenKappa(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassCohenKappa(num_classes, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/confusion_matrix.py b/src/torchmetrics/classification/confusion_matrix.py
index 57c0e931f89..f88e23206ea 100644
--- a/src/torchmetrics/classification/confusion_matrix.py
+++ b/src/torchmetrics/classification/confusion_matrix.py
@@ -35,6 +35,7 @@
     _multilabel_confusion_matrix_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _PLOT_OUT_TYPE, plot_confusion_matrix
 
@@ -398,12 +399,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"normalize": normalize, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryConfusionMatrix(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassConfusionMatrix(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelConfusionMatrix(num_labels, threshold, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/exact_match.py b/src/torchmetrics/classification/exact_match.py
index 4c0bd64768d..c86ff796bd4 100644
--- a/src/torchmetrics/classification/exact_match.py
+++ b/src/torchmetrics/classification/exact_match.py
@@ -32,6 +32,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class MulticlassExactMatch(Metric):
@@ -291,10 +292,10 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassExactMatch(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelExactMatch(num_labels, threshold, **kwargs)
         raise ValueError(f"Expected argument `task` to either be `'multiclass'` or `'multilabel'` but got {task}")
diff --git a/src/torchmetrics/classification/f_beta.py b/src/torchmetrics/classification/f_beta.py
index 01c4b2f05e7..027c800ed3a 100644
--- a/src/torchmetrics/classification/f_beta.py
+++ b/src/torchmetrics/classification/f_beta.py
@@ -24,6 +24,7 @@
     _multilabel_fbeta_score_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryFBetaScore(BinaryStatScores):
@@ -729,13 +730,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryFBetaScore(beta, threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassFBetaScore(beta, num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelFBetaScore(beta, num_labels, threshold, average, **kwargs)
         raise ValueError(
@@ -780,13 +781,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryF1Score(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassF1Score(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelF1Score(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/hamming.py b/src/torchmetrics/classification/hamming.py
index c4da43ffbf8..c08798c3ec1 100644
--- a/src/torchmetrics/classification/hamming.py
+++ b/src/torchmetrics/classification/hamming.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.hamming import _hamming_distance_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryHammingDistance(BinaryStatScores):
@@ -349,13 +350,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryHammingDistance(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassHammingDistance(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelHammingDistance(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/hinge.py b/src/torchmetrics/classification/hinge.py
index c27b84090da..0f3798c892e 100644
--- a/src/torchmetrics/classification/hinge.py
+++ b/src/torchmetrics/classification/hinge.py
@@ -29,6 +29,7 @@
     _multiclass_hinge_loss_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryHingeLoss(Metric):
@@ -253,9 +254,9 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryHingeLoss(squared, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassHingeLoss(num_classes, squared, multiclass_mode, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/jaccard.py b/src/torchmetrics/classification/jaccard.py
index 637a862466c..589f75b840e 100644
--- a/src/torchmetrics/classification/jaccard.py
+++ b/src/torchmetrics/classification/jaccard.py
@@ -23,6 +23,7 @@
     _multilabel_jaccard_index_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryJaccardIndex(BinaryConfusionMatrix):
@@ -296,12 +297,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryJaccardIndex(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassJaccardIndex(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelJaccardIndex(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/matthews_corrcoef.py b/src/torchmetrics/classification/matthews_corrcoef.py
index d049c061878..8886c02887f 100644
--- a/src/torchmetrics/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/classification/matthews_corrcoef.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification import BinaryConfusionMatrix, MulticlassConfusionMatrix, MultilabelConfusionMatrix
 from torchmetrics.functional.classification.matthews_corrcoef import _matthews_corrcoef_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryMatthewsCorrCoef(BinaryConfusionMatrix):
@@ -238,12 +239,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryMatthewsCorrCoef(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassMatthewsCorrCoef(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelMatthewsCorrCoef(num_labels, threshold, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/precision_recall.py b/src/torchmetrics/classification/precision_recall.py
index daff2af6d3a..fc0f5767669 100644
--- a/src/torchmetrics/classification/precision_recall.py
+++ b/src/torchmetrics/classification/precision_recall.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.precision_recall import _precision_recall_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryPrecision(BinaryStatScores):
@@ -620,13 +621,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryPrecision(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassPrecision(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelPrecision(num_labels, threshold, average, **kwargs)
         raise ValueError(
@@ -676,13 +677,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryRecall(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassRecall(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelRecall(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/precision_recall_curve.py b/src/torchmetrics/classification/precision_recall_curve.py
index 386c994bff9..8e7070a16e1 100644
--- a/src/torchmetrics/classification/precision_recall_curve.py
+++ b/src/torchmetrics/classification/precision_recall_curve.py
@@ -37,6 +37,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryPrecisionRecallCurve(Metric):
@@ -467,12 +468,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryPrecisionRecallCurve(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassPrecisionRecallCurve(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelPrecisionRecallCurve(num_labels, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/recall_at_fixed_precision.py b/src/torchmetrics/classification/recall_at_fixed_precision.py
index 2f57eb05e8f..516761d62e3 100644
--- a/src/torchmetrics/classification/recall_at_fixed_precision.py
+++ b/src/torchmetrics/classification/recall_at_fixed_precision.py
@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryRecallAtFixedPrecision(BinaryPrecisionRecallCurve):
@@ -323,14 +324,14 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryRecallAtFixedPrecision(min_precision, thresholds, ignore_index, validate_args, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassRecallAtFixedPrecision(
                 num_classes, min_precision, thresholds, ignore_index, validate_args, **kwargs
             )
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelRecallAtFixedPrecision(
                 num_labels, min_precision, thresholds, ignore_index, validate_args, **kwargs
diff --git a/src/torchmetrics/classification/roc.py b/src/torchmetrics/classification/roc.py
index 32c2aa5cbaf..5984fad6dd8 100644
--- a/src/torchmetrics/classification/roc.py
+++ b/src/torchmetrics/classification/roc.py
@@ -28,6 +28,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryROC(BinaryPrecisionRecallCurve):
@@ -382,12 +383,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryROC(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassROC(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelROC(num_labels, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/specificity.py b/src/torchmetrics/classification/specificity.py
index f8a65ca2a21..4c249d43c7c 100644
--- a/src/torchmetrics/classification/specificity.py
+++ b/src/torchmetrics/classification/specificity.py
@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.specificity import _specificity_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinarySpecificity(BinaryStatScores):
@@ -324,13 +325,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinarySpecificity(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassSpecificity(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelSpecificity(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/classification/specificity_at_sensitivity.py b/src/torchmetrics/classification/specificity_at_sensitivity.py
index f31935f9d0c..a9effa59d84 100644
--- a/src/torchmetrics/classification/specificity_at_sensitivity.py
+++ b/src/torchmetrics/classification/specificity_at_sensitivity.py
@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinarySpecificityAtSensitivity(BinaryPrecisionRecallCurve):
@@ -327,14 +328,14 @@ def __new__(  # type: ignore
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinarySpecificityAtSensitivity(min_sensitivity, thresholds, ignore_index, validate_args, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassSpecificityAtSensitivity(
                 num_classes, min_sensitivity, thresholds, ignore_index, validate_args, **kwargs
             )
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelSpecificityAtSensitivity(
                 num_labels, min_sensitivity, thresholds, ignore_index, validate_args, **kwargs
diff --git a/src/torchmetrics/classification/stat_scores.py b/src/torchmetrics/classification/stat_scores.py
index 0bd18ae41d8..f2604cbd80c 100644
--- a/src/torchmetrics/classification/stat_scores.py
+++ b/src/torchmetrics/classification/stat_scores.py
@@ -36,6 +36,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class _AbstractStatScores(Metric):
@@ -499,13 +500,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryStatScores(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassStatScores(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelStatScores(num_labels, threshold, average, **kwargs)
         raise ValueError(
diff --git a/src/torchmetrics/functional/classification/accuracy.py b/src/torchmetrics/functional/classification/accuracy.py
index a6e08093a5b..fa36ccc92d1 100644
--- a/src/torchmetrics/functional/classification/accuracy.py
+++ b/src/torchmetrics/functional/classification/accuracy.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _accuracy_reduce(
@@ -397,15 +398,15 @@ def accuracy(
         tensor(0.6667)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_accuracy(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_accuracy(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_accuracy(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/auroc.py b/src/torchmetrics/functional/classification/auroc.py
index ddfccb392c4..875cd0846b4 100644
--- a/src/torchmetrics/functional/classification/auroc.py
+++ b/src/torchmetrics/functional/classification/auroc.py
@@ -38,6 +38,7 @@
 )
 from torchmetrics.utilities.compute import _auc_compute_without_check, _safe_divide
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
@@ -450,12 +451,12 @@ def auroc(
         >>> auroc(preds, target, task='multiclass', num_classes=3)
         tensor(0.7778)
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_auroc(preds, target, max_fpr, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_auroc(preds, target, num_classes, average, thresholds, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_auroc(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/average_precision.py b/src/torchmetrics/functional/classification/average_precision.py
index 95507e6e2ee..4c8d84ab9b8 100644
--- a/src/torchmetrics/functional/classification/average_precision.py
+++ b/src/torchmetrics/functional/classification/average_precision.py
@@ -36,6 +36,7 @@
 )
 from torchmetrics.utilities.compute import _safe_divide
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
@@ -438,14 +439,14 @@ def average_precision(
         >>> average_precision(pred, target, task="multiclass", num_classes=5, average=None)
         tensor([1.0000, 1.0000, 0.2500, 0.2500,    nan])
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_average_precision(preds, target, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_average_precision(
             preds, target, num_classes, average, thresholds, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_average_precision(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/calibration_error.py b/src/torchmetrics/functional/classification/calibration_error.py
index d87f9bb5861..8c06e373772 100644
--- a/src/torchmetrics/functional/classification/calibration_error.py
+++ b/src/torchmetrics/functional/classification/calibration_error.py
@@ -23,6 +23,7 @@
     _multiclass_confusion_matrix_format,
     _multiclass_confusion_matrix_tensor_validation,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _binning_bucketize(
@@ -348,9 +349,9 @@ def calibration_error(
     each argument influence and examples.
     """
     assert norm is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_calibration_error(preds, target, n_bins, norm, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_calibration_error(preds, target, num_classes, n_bins, norm, ignore_index, validate_args)
     raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multiclass'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/cohen_kappa.py b/src/torchmetrics/functional/classification/cohen_kappa.py
index e01243bbd63..faa008bea16 100644
--- a/src/torchmetrics/functional/classification/cohen_kappa.py
+++ b/src/torchmetrics/functional/classification/cohen_kappa.py
@@ -27,6 +27,7 @@
     _multiclass_confusion_matrix_tensor_validation,
     _multiclass_confusion_matrix_update,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _cohen_kappa_reduce(confmat: Tensor, weights: Optional[Literal["linear", "quadratic", "none"]] = None) -> Tensor:
@@ -256,9 +257,9 @@ class labels.
         >>> cohen_kappa(preds, target, task="multiclass", num_classes=2)
         tensor(0.5000)
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_cohen_kappa(preds, target, threshold, weights, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_cohen_kappa(preds, target, num_classes, weights, ignore_index, validate_args)
     raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multiclass'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/confusion_matrix.py b/src/torchmetrics/functional/classification/confusion_matrix.py
index 047cff8351e..26970967866 100644
--- a/src/torchmetrics/functional/classification/confusion_matrix.py
+++ b/src/torchmetrics/functional/classification/confusion_matrix.py
@@ -19,6 +19,7 @@
 
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
@@ -630,12 +631,12 @@ def confusion_matrix(
                 [[1, 0], [1, 0]],
                 [[0, 1], [0, 1]]])
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_confusion_matrix(preds, target, threshold, normalize, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_confusion_matrix(preds, target, num_classes, normalize, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_confusion_matrix(preds, target, num_labels, threshold, normalize, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/exact_match.py b/src/torchmetrics/functional/classification/exact_match.py
index b4ca3ce7dd1..df1dc380487 100644
--- a/src/torchmetrics/functional/classification/exact_match.py
+++ b/src/torchmetrics/functional/classification/exact_match.py
@@ -26,6 +26,7 @@
     _multilabel_stat_scores_tensor_validation,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _exact_match_reduce(
@@ -229,7 +230,7 @@ def exact_match(
         >>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='samplewise')
         tensor([1., 0.])
     """
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert num_classes is not None
         return multiclass_exact_match(preds, target, num_classes, multidim_average, ignore_index, validate_args)
     if task == "multilalbe":
diff --git a/src/torchmetrics/functional/classification/f_beta.py b/src/torchmetrics/functional/classification/f_beta.py
index c1a68e13d5b..11a7d191f63 100644
--- a/src/torchmetrics/functional/classification/f_beta.py
+++ b/src/torchmetrics/functional/classification/f_beta.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _fbeta_reduce(
@@ -693,15 +694,15 @@ def fbeta_score(
         tensor(0.3333)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_fbeta_score(preds, target, beta, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_fbeta_score(
             preds, target, beta, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_fbeta_score(
             preds, target, beta, num_labels, threshold, average, multidim_average, ignore_index, validate_args
@@ -742,15 +743,15 @@ def f1_score(
         tensor(0.3333)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_f1_score(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_f1_score(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_f1_score(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/hamming.py b/src/torchmetrics/functional/classification/hamming.py
index c7725931fb8..9114e3f7ee7 100644
--- a/src/torchmetrics/functional/classification/hamming.py
+++ b/src/torchmetrics/functional/classification/hamming.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _hamming_distance_reduce(
@@ -399,15 +400,15 @@ def hamming_distance(
         tensor(0.2500)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_hamming_distance(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_hamming_distance(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_hamming_distance(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/hinge.py b/src/torchmetrics/functional/classification/hinge.py
index e2ff98141e2..fa270abd5f9 100644
--- a/src/torchmetrics/functional/classification/hinge.py
+++ b/src/torchmetrics/functional/classification/hinge.py
@@ -24,6 +24,7 @@
     _multiclass_confusion_matrix_tensor_validation,
 )
 from torchmetrics.utilities.data import to_onehot
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _hinge_loss_compute(measure: Tensor, total: Tensor) -> Tensor:
@@ -276,9 +277,9 @@ def hinge_loss(
         >>> hinge_loss(preds, target, task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
         tensor([1.3743, 1.1945, 1.2359])
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_hinge_loss(preds, target, squared, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_hinge_loss(preds, target, num_classes, squared, multiclass_mode, ignore_index, validate_args)
     raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multilabel'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/jaccard.py b/src/torchmetrics/functional/classification/jaccard.py
index f593a424fd2..2cbf03fb317 100644
--- a/src/torchmetrics/functional/classification/jaccard.py
+++ b/src/torchmetrics/functional/classification/jaccard.py
@@ -32,6 +32,7 @@
     _multilabel_confusion_matrix_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _jaccard_index_reduce(
@@ -321,12 +322,12 @@ def jaccard_index(
         >>> jaccard_index(pred, target, task="multiclass", num_classes=2)
         tensor(0.9660)
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_jaccard_index(preds, target, threshold, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_jaccard_index(preds, target, num_classes, average, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_jaccard_index(preds, target, num_labels, threshold, average, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/matthews_corrcoef.py b/src/torchmetrics/functional/classification/matthews_corrcoef.py
index cc5f94b70e2..6e1a6c4b5d1 100644
--- a/src/torchmetrics/functional/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/functional/classification/matthews_corrcoef.py
@@ -31,6 +31,7 @@
     _multilabel_confusion_matrix_tensor_validation,
     _multilabel_confusion_matrix_update,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _matthews_corrcoef_reduce(confmat: Tensor) -> Tensor:
@@ -235,12 +236,12 @@ def matthews_corrcoef(
         >>> matthews_corrcoef(preds, target, task="multiclass", num_classes=2)
         tensor(0.5774)
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_matthews_corrcoef(preds, target, threshold, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_matthews_corrcoef(preds, target, num_classes, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_matthews_corrcoef(preds, target, num_labels, threshold, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/precision_recall.py b/src/torchmetrics/functional/classification/precision_recall.py
index e00746341aa..ac6725c704a 100644
--- a/src/torchmetrics/functional/classification/precision_recall.py
+++ b/src/torchmetrics/functional/classification/precision_recall.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _precision_recall_reduce(
@@ -652,15 +653,15 @@ def precision(
         tensor(0.2500)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_precision(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_precision(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_precision(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
@@ -705,15 +706,15 @@ def recall(
         tensor(0.2500)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_recall(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_recall(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_recall(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/precision_recall_curve.py b/src/torchmetrics/functional/classification/precision_recall_curve.py
index 9291f31d26b..8c58d185371 100644
--- a/src/torchmetrics/functional/classification/precision_recall_curve.py
+++ b/src/torchmetrics/functional/classification/precision_recall_curve.py
@@ -22,6 +22,7 @@
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.compute import _safe_divide
 from torchmetrics.utilities.data import _bincount
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _binary_clf_curve(
@@ -815,12 +816,12 @@ def precision_recall_curve(
         >>> thresholds
         [tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])]
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_precision_recall_curve(preds, target, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_precision_recall_curve(preds, target, num_classes, thresholds, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_precision_recall_curve(preds, target, num_labels, thresholds, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/recall_at_fixed_precision.py b/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
index 8c0e9f38578..89f60a54481 100644
--- a/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
+++ b/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
@@ -34,6 +34,7 @@
     _multilabel_precision_recall_curve_tensor_validation,
     _multilabel_precision_recall_curve_update,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _recall_at_precision(
@@ -384,14 +385,14 @@ def recall_at_fixed_precision(
     :func:`binary_recall_at_fixed_precision`, :func:`multiclass_recall_at_fixed_precision` and
     :func:`multilabel_recall_at_fixed_precision` for the specific details of each argument influence and examples.
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_recall_at_fixed_precision(preds, target, min_precision, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_recall_at_fixed_precision(
             preds, target, num_classes, min_precision, thresholds, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_recall_at_fixed_precision(
             preds, target, num_labels, min_precision, thresholds, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/roc.py b/src/torchmetrics/functional/classification/roc.py
index 38e46a1755d..575b0376280 100644
--- a/src/torchmetrics/functional/classification/roc.py
+++ b/src/torchmetrics/functional/classification/roc.py
@@ -34,6 +34,7 @@
 )
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _binary_roc_compute(
@@ -483,12 +484,12 @@ def roc(
          tensor([1.0000, 0.7576, 0.3680, 0.3468, 0.0745]),
          tensor([1.0000, 0.1837, 0.1338, 0.1183, 0.1138])]
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_roc(preds, target, thresholds, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_roc(preds, target, num_classes, thresholds, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_roc(preds, target, num_labels, thresholds, ignore_index, validate_args)
     raise ValueError(
diff --git a/src/torchmetrics/functional/classification/specificity.py b/src/torchmetrics/functional/classification/specificity.py
index 1a1c351a2d3..cd167768314 100644
--- a/src/torchmetrics/functional/classification/specificity.py
+++ b/src/torchmetrics/functional/classification/specificity.py
@@ -32,6 +32,7 @@
     _multilabel_stat_scores_update,
 )
 from torchmetrics.utilities.compute import _safe_divide
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _specificity_reduce(
@@ -370,15 +371,15 @@ def specificity(
         tensor(0.6250)
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_specificity(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_specificity(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_specificity(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/specificity_at_sensitivity.py b/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
index c654ea26c61..034ef4e161d 100644
--- a/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
+++ b/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
@@ -36,6 +36,7 @@
     _multiclass_roc_compute,
     _multilabel_roc_compute,
 )
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 def _convert_fpr_to_specificity(fpr: Tensor) -> Tensor:
@@ -413,16 +414,16 @@ def specicity_at_sensitivity(
     :func:`binary_specificity_at_sensitivity`, :func:`multiclass_specicity_at_sensitivity` and
     :func:`multilabel_specifity_at_sensitvity` for the specific details of each argument influence and examples.
     """
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_specificity_at_sensitivity(  # type: ignore
             preds, target, min_sensitivity, thresholds, ignore_index, validate_args
         )
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_specificity_at_sensitivity(  # type: ignore
             preds, target, num_classes, min_sensitivity, thresholds, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_specificity_at_sensitivity(  # type: ignore
             preds, target, num_labels, min_sensitivity, thresholds, ignore_index, validate_args
diff --git a/src/torchmetrics/functional/classification/stat_scores.py b/src/torchmetrics/functional/classification/stat_scores.py
index e9ad0d5d5e1..fceb25bce27 100644
--- a/src/torchmetrics/functional/classification/stat_scores.py
+++ b/src/torchmetrics/functional/classification/stat_scores.py
@@ -19,7 +19,7 @@
 
 from torchmetrics.utilities.checks import _check_same_shape, _input_format_classification
 from torchmetrics.utilities.data import _bincount, select_topk
-from torchmetrics.utilities.enums import AverageMethod, DataType, MDMCAverageMethod
+from torchmetrics.utilities.enums import AverageMethod, ClassificationTask, DataType, MDMCAverageMethod
 
 
 def _binary_stat_scores_arg_validation(
@@ -1083,15 +1083,15 @@ def stat_scores(
                 [1, 0, 3, 0, 1]])
     """
     assert multidim_average is not None
-    if task == "binary":
+    if task == ClassificationTask.BINARY:
         return binary_stat_scores(preds, target, threshold, multidim_average, ignore_index, validate_args)
-    if task == "multiclass":
+    if task == ClassificationTask.MULTICLASS:
         assert isinstance(num_classes, int)
         assert isinstance(top_k, int)
         return multiclass_stat_scores(
             preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
         )
-    if task == "multilabel":
+    if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_stat_scores(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args

From 5a4caef9d59f788278de482c45e7109a27b19b51 Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Fri, 3 Feb 2023 14:35:12 +0100
Subject: [PATCH 06/20] StrEnum

---
 src/torchmetrics/utilities/enums.py | 40 ++++-------------------------
 1 file changed, 5 insertions(+), 35 deletions(-)

diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index fea1fe2aaac..b56e2a8e25d 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -11,41 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from enum import Enum
-from typing import Optional, Union
 
+from lightning_utilities.core.enums import StrEnum
 
-class EnumStr(str, Enum):
-    """Type of any enumerator with allowed comparison to string invariant to cases.
 
-    Example:
-        >>> class MyEnum(EnumStr):
-        ...     ABC = 'abc'
-        >>> MyEnum.from_str('Abc')
-        <MyEnum.ABC: 'abc'>
-        >>> {MyEnum.ABC: 123}
-        {<MyEnum.ABC: 'abc'>: 123}
-    """
-
-    @classmethod
-    def from_str(cls, value: str) -> Optional["EnumStr"]:
-        statuses = [status for status in dir(cls) if not status.startswith("_")]
-        for st in statuses:
-            if st.lower() == value.lower():
-                return getattr(cls, st)
-        return None
-
-    def __eq__(self, other: Union[str, "EnumStr", None]) -> bool:  # type: ignore
-        other = other.value if isinstance(other, Enum) else str(other)
-        return self.value.lower() == other.lower()
-
-    def __hash__(self) -> int:
-        # re-enable hashtable so it can be used as a dict key or in a set
-        # example: set(EnumStr)
-        return hash(self.name)
-
-
-class DataType(EnumStr):
+class DataType(StrEnum):
     """Enum to represent data type.
 
     >>> "Binary" in list(DataType)
@@ -58,7 +28,7 @@ class DataType(EnumStr):
     MULTIDIM_MULTICLASS = "multi-dim multi-class"
 
 
-class AverageMethod(EnumStr):
+class AverageMethod(StrEnum):
     """Enum to represent average method.
 
     >>> None in list(AverageMethod)
@@ -76,14 +46,14 @@ class AverageMethod(EnumStr):
     SAMPLES = "samples"
 
 
-class MDMCAverageMethod(EnumStr):
+class MDMCAverageMethod(StrEnum):
     """Enum to represent multi-dim multi-class average method."""
 
     GLOBAL = "global"
     SAMPLEWISE = "samplewise"
 
 
-class ClassificationTask(EnumStr):
+class ClassificationTask(StrEnum):
     """Enum to represent the different tasks in classification metrics.
 
     >>> "binary" in list(ClassificationTask)

From 169586fd817e9fe94bd6fd3f4d45195ff45f04bf Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Sun, 5 Feb 2023 02:14:28 +0100
Subject: [PATCH 07/20] utils 0.5.0

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index a121c783161..4b5ec226720 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy>=1.17.2
 torch>=1.8.1
 typing-extensions; python_version < '3.9'
 packaging  # hotfix for utils, can be dropped with lit-utils >=0.5
-lightning-utilities>=0.4.1
+lightning-utilities>=0.5.0

From 2720af1f43da9324b80f93fee94da71f93fef921 Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Sun, 5 Feb 2023 02:30:30 +0100
Subject: [PATCH 08/20] with error

---
 src/torchmetrics/functional/text/infolm.py | 15 +---------
 src/torchmetrics/utilities/enums.py        | 33 ++++++++++++++++++----
 2 files changed, 29 insertions(+), 19 deletions(-)

diff --git a/src/torchmetrics/functional/text/infolm.py b/src/torchmetrics/functional/text/infolm.py
index 1afc40c6023..38c190be0fd 100644
--- a/src/torchmetrics/functional/text/infolm.py
+++ b/src/torchmetrics/functional/text/infolm.py
@@ -54,6 +54,7 @@
 class _IMEnum(EnumStr):
     """A helper Enum class for storing the information measure."""
 
+    task = "Information measure"
     KL_DIVERGENCE = "kl_divergence"
     ALPHA_DIVERGENCE = "alpha_divergence"
     BETA_DIVERGENCE = "beta_divergence"
@@ -64,20 +65,6 @@ class _IMEnum(EnumStr):
     L_INFINITY_DISTANCE = "l_infinity_distance"
     FISHER_RAO_DISTANCE = "fisher_rao_distance"
 
-    @classmethod
-    def from_str(cls, value: str) -> Optional["EnumStr"]:
-        """
-        Raises:
-            ValueError:
-                If required information measure is not among the supported options.
-        """
-        _allowed_im = [im.lower() for im in _IMEnum._member_names_]
-
-        enum_key = super().from_str(value)
-        if enum_key is not None and enum_key in _allowed_im:
-            return enum_key
-        raise ValueError(f"Invalid information measure. Expected one of {_allowed_im}, but got {enum_key}.")
-
 
 class _InformationMeasure:
     """A wrapper class used for the calculation the result of information measure between the discrete reference
diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index b56e2a8e25d..83f977cc734 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -11,24 +11,44 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from typing import Optional
 
-from lightning_utilities.core.enums import StrEnum
+from lightning_utilities.core.enums import StrEnum as _StrEnum
 
 
-class DataType(StrEnum):
+class EnumStr(_StrEnum):
+    task: str = "Task"
+
+    @classmethod
+    def from_str(cls, value: str) -> Optional["EnumStr"]:
+        """
+        Raises:
+            ValueError:
+                If required information measure is not among the supported options.
+        """
+        _allowed_im = [im.lower() for im in cls._member_names_]
+
+        enum_key = super().from_str(value)
+        if enum_key is not None and enum_key in _allowed_im:
+            return enum_key
+        raise ValueError(f"Invalid {cls.task}: expected one of {_allowed_im}, but got {enum_key}.")
+
+
+class DataType(EnumStr):
     """Enum to represent data type.
 
     >>> "Binary" in list(DataType)
     True
     """
 
+    task = "Data type"
     BINARY = "binary"
     MULTILABEL = "multi-label"
     MULTICLASS = "multi-class"
     MULTIDIM_MULTICLASS = "multi-dim multi-class"
 
 
-class AverageMethod(StrEnum):
+class AverageMethod(EnumStr):
     """Enum to represent average method.
 
     >>> None in list(AverageMethod)
@@ -39,6 +59,7 @@ class AverageMethod(StrEnum):
     True
     """
 
+    task = "Average method"
     MICRO = "micro"
     MACRO = "macro"
     WEIGHTED = "weighted"
@@ -46,20 +67,22 @@ class AverageMethod(StrEnum):
     SAMPLES = "samples"
 
 
-class MDMCAverageMethod(StrEnum):
+class MDMCAverageMethod(EnumStr):
     """Enum to represent multi-dim multi-class average method."""
 
+    task = "MDMC Average method"
     GLOBAL = "global"
     SAMPLEWISE = "samplewise"
 
 
-class ClassificationTask(StrEnum):
+class ClassificationTask(EnumStr):
     """Enum to represent the different tasks in classification metrics.
 
     >>> "binary" in list(ClassificationTask)
     True
     """
 
+    task = "Classification"
     BINARY = "binary"
     MULTICLASS = "multiclass"
     MULTILABEL = "multilabel"

From a6cd44f60a65ab131dac402da03fbdbf1e09bf0b Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Mon, 6 Feb 2023 06:17:03 +0100
Subject: [PATCH 09/20] links

---
 docs/source/links.rst                         |  4 +-
 src/torchmetrics/classification/accuracy.py   | 40 +++++++++----------
 src/torchmetrics/functional/image/psnr.py     |  8 ++--
 src/torchmetrics/functional/regression/mae.py |  4 +-
 src/torchmetrics/image/psnr.py                |  2 +-
 src/torchmetrics/regression/mae.py            |  4 +-
 6 files changed, 31 insertions(+), 31 deletions(-)

diff --git a/docs/source/links.rst b/docs/source/links.rst
index f5b847fe601..ed3f7732cff 100644
--- a/docs/source/links.rst
+++ b/docs/source/links.rst
@@ -64,10 +64,10 @@
 .. _Improved Techniques for Training GANs: https://arxiv.org/abs/1606.03498
 .. _KID Score: https://github.com/toshas/torch-fidelity/blob/v0.3.0/torch_fidelity/metric_kid.py
 .. _Demystifying MMD GANs: https://arxiv.org/abs/1801.01401
-.. _Computes Peak Signal-to-Noise Ratio: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
+.. _Compute Peak Signal-to-Noise Ratio: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
 .. _Turn a Metric into a Bootstrapped: https://en.wikipedia.org/wiki/Bootstrapping_(statistics)
 .. _Metric Test for Reset: https://github.com/Lightning-AI/pytorch-lightning/pull/7055
-.. _Computes Mean Absolute Error: https://en.wikipedia.org/wiki/Mean_absolute_error
+.. _Compute Mean Absolute Error: https://en.wikipedia.org/wiki/Mean_absolute_error
 .. _Mean Absolute Percentage Error: https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
 .. _mean squared error: https://en.wikipedia.org/wiki/Mean_squared_error
 .. _Aggregate the statistics from multiple devices: https://stackoverflow.com/questions/68395368/estimate-running-correlation-on-multiple-nodes
diff --git a/src/torchmetrics/classification/accuracy.py b/src/torchmetrics/classification/accuracy.py
index 6cb1704ab04..bc52756ba74 100644
--- a/src/torchmetrics/classification/accuracy.py
+++ b/src/torchmetrics/classification/accuracy.py
@@ -32,7 +32,7 @@
 
 
 class BinaryAccuracy(BinaryStatScores):
-    r"""Computes `Accuracy`_ for binary tasks:
+    r"""Compute `Accuracy`_ for binary tasks.
 
     .. math::
         \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)
@@ -41,16 +41,16 @@ class BinaryAccuracy(BinaryStatScores):
 
     As input to ``forward`` and ``update`` the metric accepts the following input:
 
-    - ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(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`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
+        - ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(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`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``ba`` (:class:`~torch.Tensor`): 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.
+        - ``ba`` (:class:`~torch.Tensor`): If ``multidim_average`` is set to ``global``, metric returns a scalar value.
+          If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar
+          value per sample.
 
     Args:
         threshold: Threshold for transforming probability to binary {0,1} predictions
@@ -161,25 +161,25 @@ class MulticlassAccuracy(MulticlassStatScores):
 
     As input to ``forward`` and ``update`` the metric accepts the following input:
 
-    - ``preds`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` or float tensor of shape ``(N, C, ..)``.
-      If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert
-      probabilities/logits into an int tensor.
-    - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
+        - ``preds`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` or float tensor
+          of shape ``(N, C, ..)``. If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension
+          to automatically convert probabilities/logits into an int tensor.
+        - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``mca`` (:class:`~torch.Tensor`): A tensor with the accuracy score whose returned shape depends on the
-      ``average`` and ``multidim_average`` arguments:
+        - ``mca`` (:class:`~torch.Tensor`): A tensor with the accuracy score whose returned shape depends on the
+          ``average`` and ``multidim_average`` arguments:
 
-        - If ``multidim_average`` is set to ``global``:
+            - 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 ``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 ``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)``
+              - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
+              - If ``average=None/'none'``, the shape will be ``(N, C)``
 
     Args:
         num_classes: Integer specifing the number of classes
diff --git a/src/torchmetrics/functional/image/psnr.py b/src/torchmetrics/functional/image/psnr.py
index 77db51915d0..1fb7024f1e1 100644
--- a/src/torchmetrics/functional/image/psnr.py
+++ b/src/torchmetrics/functional/image/psnr.py
@@ -27,7 +27,7 @@ def _psnr_compute(
     base: float = 10.0,
     reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
 ) -> Tensor:
-    """Computes peak signal-to-noise ratio.
+    """Compute peak signal-to-noise ratio.
 
     Args:
         sum_squared_error: Sum of square of errors over all observations
@@ -60,13 +60,13 @@ def _psnr_update(
     target: Tensor,
     dim: Optional[Union[int, Tuple[int, ...]]] = None,
 ) -> Tuple[Tensor, Tensor]:
-    """Updates and returns variables required to compute peak signal-to-noise ratio.
+    """Update and return variables required to compute peak signal-to-noise ratio.
 
     Args:
         preds: Predicted tensor
         target: Ground truth tensor
-        dim: Dimensions to reduce PSNR scores over provided as either an integer or a list of integers. Default is
-            None meaning scores will be reduced across all dimensions.
+        dim: Dimensions to reduce PSNR scores over provided as either an integer or a list of integers.
+            Default is None meaning scores will be reduced across all dimensions.
     """
 
     if dim is None:
diff --git a/src/torchmetrics/functional/regression/mae.py b/src/torchmetrics/functional/regression/mae.py
index f3508c02cea..4f38f8f4397 100644
--- a/src/torchmetrics/functional/regression/mae.py
+++ b/src/torchmetrics/functional/regression/mae.py
@@ -37,7 +37,7 @@ def _mean_absolute_error_update(preds: Tensor, target: Tensor) -> Tuple[Tensor,
 
 
 def _mean_absolute_error_compute(sum_abs_error: Tensor, n_obs: int) -> Tensor:
-    """Computes Mean Absolute Error.
+    """Compute Mean Absolute Error.
 
     Args:
         sum_abs_error: Sum of absolute value of errors over all observations
@@ -55,7 +55,7 @@ def _mean_absolute_error_compute(sum_abs_error: Tensor, n_obs: int) -> Tensor:
 
 
 def mean_absolute_error(preds: Tensor, target: Tensor) -> Tensor:
-    """Computes mean absolute error.
+    """Compute mean absolute error.
 
     Args:
         preds: estimated labels
diff --git a/src/torchmetrics/image/psnr.py b/src/torchmetrics/image/psnr.py
index ca2e2ad6f5b..8b4eb0c169d 100644
--- a/src/torchmetrics/image/psnr.py
+++ b/src/torchmetrics/image/psnr.py
@@ -23,7 +23,7 @@
 
 
 class PeakSignalNoiseRatio(Metric):
-    r"""Computes `Computes Peak Signal-to-Noise Ratio`_ (PSNR):
+    r"""`Compute Peak Signal-to-Noise Ratio`_ (PSNR):
 
     .. math:: \text{PSNR}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)}\right)
 
diff --git a/src/torchmetrics/regression/mae.py b/src/torchmetrics/regression/mae.py
index 33f1b5a1e67..79c5b243f54 100644
--- a/src/torchmetrics/regression/mae.py
+++ b/src/torchmetrics/regression/mae.py
@@ -20,7 +20,7 @@
 
 
 class MeanAbsoluteError(Metric):
-    r"""`Computes Mean Absolute Error`_ (MAE):
+    r"""`Compute Mean Absolute Error`_ (MAE):
 
     .. math:: \text{MAE} = \frac{1}{N}\sum_i^N | y_i - \hat{y_i} |
 
@@ -70,5 +70,5 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
         self.total += n_obs
 
     def compute(self) -> Tensor:
-        """Computes mean absolute error over state."""
+        """Compute mean absolute error over state."""
         return _mean_absolute_error_compute(self.sum_abs_error, self.total)

From fa0fa1653d6d34da25dac54c0aeb05cf74df5ced Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Mon, 6 Feb 2023 13:03:56 +0100
Subject: [PATCH 10/20] property

---
 .../functional/regression/kendall.py          | 36 +++++--------------
 src/torchmetrics/functional/text/infolm.py    |  5 ++-
 src/torchmetrics/utilities/enums.py           | 28 +++++++++++----
 3 files changed, 33 insertions(+), 36 deletions(-)

diff --git a/src/torchmetrics/functional/regression/kendall.py b/src/torchmetrics/functional/regression/kendall.py
index 3db0cf2b565..28a8d145f10 100644
--- a/src/torchmetrics/functional/regression/kendall.py
+++ b/src/torchmetrics/functional/regression/kendall.py
@@ -26,46 +26,26 @@
 class _MetricVariant(EnumStr):
     """Enumerate for metric variants."""
 
+    @property
+    def task(self) -> str:
+        return "variant"
+
     A = "a"
     B = "b"
     C = "c"
 
-    @classmethod
-    def from_str(cls, value: Literal["a", "b", "c"]) -> "_MetricVariant":  # type: ignore[override]
-        """
-        Raises:
-            ValueError:
-                If required metric variant is not among the supported options.
-        """
-        _allowed_variants = [im.lower() for im in _MetricVariant._member_names_]
-
-        enum_key = super().from_str(value)
-        if enum_key is not None and enum_key in _allowed_variants:
-            return enum_key  # type: ignore[return-value]  # use override
-        raise ValueError(f"Invalid metric variant. Expected one of {_allowed_variants}, but got {enum_key}.")
-
 
 class _TestAlternative(EnumStr):
     """Enumerate for test altenative options."""
 
+    @property
+    def task(self) -> str:
+        return "alternative"
+
     TWO_SIDED = "two-sided"
     LESS = "less"
     GREATER = "greater"
 
-    @classmethod
-    def from_str(cls, value: Literal["two-sided", "less", "greater"]) -> "_TestAlternative":  # type: ignore[override]
-        """
-        Raises:
-            ValueError:
-                If required test alternative is not among the supported options.
-        """
-        _allowed_alternatives = [im.lower().replace("_", "-") for im in _TestAlternative._member_names_]
-
-        enum_key = super().from_str(value.replace("-", "_"))
-        if enum_key is not None and enum_key in _allowed_alternatives:
-            return enum_key  # type: ignore[return-value]  # use override
-        raise ValueError(f"Invalid test alternative. Expected one of {_allowed_alternatives}, but got {enum_key}.")
-
 
 def _sort_on_first_sequence(x: Tensor, y: Tensor) -> Tuple[Tensor, Tensor]:
     """Sort sequences in an ascent order according to the sequence ``x``."""
diff --git a/src/torchmetrics/functional/text/infolm.py b/src/torchmetrics/functional/text/infolm.py
index 38c190be0fd..f27ff97b27e 100644
--- a/src/torchmetrics/functional/text/infolm.py
+++ b/src/torchmetrics/functional/text/infolm.py
@@ -54,7 +54,10 @@
 class _IMEnum(EnumStr):
     """A helper Enum class for storing the information measure."""
 
-    task = "Information measure"
+    @property
+    def task(self) -> str:
+        return "Information measure"
+
     KL_DIVERGENCE = "kl_divergence"
     ALPHA_DIVERGENCE = "alpha_divergence"
     BETA_DIVERGENCE = "beta_divergence"
diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index 83f977cc734..3375ebdce92 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -13,11 +13,13 @@
 # limitations under the License.
 from typing import Optional
 
-from lightning_utilities.core.enums import StrEnum as _StrEnum
+from lightning_utilities.core.enums import StrEnum as StrEnum
 
 
-class EnumStr(_StrEnum):
-    task: str = "Task"
+class EnumStr(StrEnum):
+    @property
+    def task(self) -> str:
+        return "Task"
 
     @classmethod
     def from_str(cls, value: str) -> Optional["EnumStr"]:
@@ -41,7 +43,10 @@ class DataType(EnumStr):
     True
     """
 
-    task = "Data type"
+    @property
+    def task(self) -> str:
+        return "Data type"
+
     BINARY = "binary"
     MULTILABEL = "multi-label"
     MULTICLASS = "multi-class"
@@ -59,7 +64,10 @@ class AverageMethod(EnumStr):
     True
     """
 
-    task = "Average method"
+    @property
+    def task(self) -> str:
+        return "Average method"
+
     MICRO = "micro"
     MACRO = "macro"
     WEIGHTED = "weighted"
@@ -70,7 +78,10 @@ class AverageMethod(EnumStr):
 class MDMCAverageMethod(EnumStr):
     """Enum to represent multi-dim multi-class average method."""
 
-    task = "MDMC Average method"
+    @property
+    def task(self) -> str:
+        return "MDMC Average method"
+
     GLOBAL = "global"
     SAMPLEWISE = "samplewise"
 
@@ -82,7 +93,10 @@ class ClassificationTask(EnumStr):
     True
     """
 
-    task = "Classification"
+    @property
+    def task(self) -> str:
+        return "Classification"
+
     BINARY = "binary"
     MULTICLASS = "multiclass"
     MULTILABEL = "multilabel"

From 64da325595d344263d31b05cae9b61d5a48dd09d Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Mon, 6 Feb 2023 13:04:47 +0100
Subject: [PATCH 11/20] _name

---
 src/torchmetrics/functional/regression/kendall.py |  4 ++--
 src/torchmetrics/functional/text/infolm.py        |  2 +-
 src/torchmetrics/utilities/enums.py               | 12 ++++++------
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/torchmetrics/functional/regression/kendall.py b/src/torchmetrics/functional/regression/kendall.py
index 28a8d145f10..6a4cb3c369b 100644
--- a/src/torchmetrics/functional/regression/kendall.py
+++ b/src/torchmetrics/functional/regression/kendall.py
@@ -27,7 +27,7 @@ class _MetricVariant(EnumStr):
     """Enumerate for metric variants."""
 
     @property
-    def task(self) -> str:
+    def _name(self) -> str:
         return "variant"
 
     A = "a"
@@ -39,7 +39,7 @@ class _TestAlternative(EnumStr):
     """Enumerate for test altenative options."""
 
     @property
-    def task(self) -> str:
+    def _name(self) -> str:
         return "alternative"
 
     TWO_SIDED = "two-sided"
diff --git a/src/torchmetrics/functional/text/infolm.py b/src/torchmetrics/functional/text/infolm.py
index f27ff97b27e..3acc092b92f 100644
--- a/src/torchmetrics/functional/text/infolm.py
+++ b/src/torchmetrics/functional/text/infolm.py
@@ -55,7 +55,7 @@ class _IMEnum(EnumStr):
     """A helper Enum class for storing the information measure."""
 
     @property
-    def task(self) -> str:
+    def _name(self) -> str:
         return "Information measure"
 
     KL_DIVERGENCE = "kl_divergence"
diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index 3375ebdce92..78a4b7ac006 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -18,7 +18,7 @@
 
 class EnumStr(StrEnum):
     @property
-    def task(self) -> str:
+    def _name(self) -> str:
         return "Task"
 
     @classmethod
@@ -33,7 +33,7 @@ def from_str(cls, value: str) -> Optional["EnumStr"]:
         enum_key = super().from_str(value)
         if enum_key is not None and enum_key in _allowed_im:
             return enum_key
-        raise ValueError(f"Invalid {cls.task}: expected one of {_allowed_im}, but got {enum_key}.")
+        raise ValueError(f"Invalid {cls._name}: expected one of {_allowed_im}, but got {enum_key}.")
 
 
 class DataType(EnumStr):
@@ -44,7 +44,7 @@ class DataType(EnumStr):
     """
 
     @property
-    def task(self) -> str:
+    def _name(self) -> str:
         return "Data type"
 
     BINARY = "binary"
@@ -65,7 +65,7 @@ class AverageMethod(EnumStr):
     """
 
     @property
-    def task(self) -> str:
+    def _name(self) -> str:
         return "Average method"
 
     MICRO = "micro"
@@ -79,7 +79,7 @@ class MDMCAverageMethod(EnumStr):
     """Enum to represent multi-dim multi-class average method."""
 
     @property
-    def task(self) -> str:
+    def _name(self) -> str:
         return "MDMC Average method"
 
     GLOBAL = "global"
@@ -94,7 +94,7 @@ class ClassificationTask(EnumStr):
     """
 
     @property
-    def task(self) -> str:
+    def _name(self) -> str:
         return "Classification"
 
     BINARY = "binary"

From 0ca463d2d10b8f1206a4735572662fb26dceab7f Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 6 Feb 2023 12:08:08 +0000
Subject: [PATCH 12/20] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 src/torchmetrics/utilities/enums.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index 78a4b7ac006..c8294cf6703 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -23,10 +23,9 @@ def _name(self) -> str:
 
     @classmethod
     def from_str(cls, value: str) -> Optional["EnumStr"]:
-        """
-        Raises:
-            ValueError:
-                If required information measure is not among the supported options.
+        """Raises:
+        ValueError:
+        If required information measure is not among the supported options.
         """
         _allowed_im = [im.lower() for im in cls._member_names_]
 

From 53554830061f2155fe16271640da58a3263a9921 Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Mon, 6 Feb 2023 13:23:08 +0100
Subject: [PATCH 13/20] chlog

---
 CHANGELOG.md                        | 6 ++++++
 src/torchmetrics/utilities/enums.py | 8 +++++---
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7cf7115e8b8..6815c621bd8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -20,6 +20,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `classes` to output from `MAP` metric ([#1419](https://github.com/Lightning-AI/metrics/pull/1419))
 
 
+- Add `ClassificationTask` Enum and use in metrics ([#1479](https://github.com/Lightning-AI/metrics/pull/1479))
+
+
 ### Changed
 
 - Changed `update_count` and `update_called` from private to public methods ([#1370](https://github.com/Lightning-AI/metrics/pull/1370))
@@ -28,6 +31,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Raise exception for invalid kwargs in Metric base class ([#1427](https://github.com/Lightning-AI/metrics/pull/1427))
 
 
+- Extend `EnumStr` raising `ValueError` for invalid value ([#1479](https://github.com/Lightning-AI/metrics/pull/1479))
+
+
 ### Deprecated
 
 -
diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index c8294cf6703..9acaaecd35d 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -23,9 +23,11 @@ def _name(self) -> str:
 
     @classmethod
     def from_str(cls, value: str) -> Optional["EnumStr"]:
-        """Raises:
-        ValueError:
-        If required information measure is not among the supported options.
+        """Load from string.
+
+        Raises:
+            ValueError:
+                If required information measure is not among the supported options.
         """
         _allowed_im = [im.lower() for im in cls._member_names_]
 

From fbcc0e52a1736fde069a085ad187ea616cc65dd3 Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Mon, 6 Feb 2023 13:48:54 +0100
Subject: [PATCH 14/20] docstring

---
 .../functional/regression/kendall.py          |  8 ++--
 src/torchmetrics/functional/text/infolm.py    |  4 +-
 src/torchmetrics/utilities/enums.py           | 39 ++++++++++++-------
 3 files changed, 30 insertions(+), 21 deletions(-)

diff --git a/src/torchmetrics/functional/regression/kendall.py b/src/torchmetrics/functional/regression/kendall.py
index f05d9e695f2..f96fecca4dd 100644
--- a/src/torchmetrics/functional/regression/kendall.py
+++ b/src/torchmetrics/functional/regression/kendall.py
@@ -26,8 +26,8 @@
 class _MetricVariant(EnumStr):
     """Enumerate for metric variants."""
 
-    @property
-    def _name(self) -> str:
+    @staticmethod
+    def _name() -> str:
         return "variant"
 
     A = "a"
@@ -38,8 +38,8 @@ def _name(self) -> str:
 class _TestAlternative(EnumStr):
     """Enumerate for test altenative options."""
 
-    @property
-    def _name(self) -> str:
+    @staticmethod
+    def _name() -> str:
         return "alternative"
 
     TWO_SIDED = "two-sided"
diff --git a/src/torchmetrics/functional/text/infolm.py b/src/torchmetrics/functional/text/infolm.py
index 7c31a167508..5b9088dd4f3 100644
--- a/src/torchmetrics/functional/text/infolm.py
+++ b/src/torchmetrics/functional/text/infolm.py
@@ -54,8 +54,8 @@
 class _IMEnum(EnumStr):
     """A helper Enum class for storing the information measure."""
 
-    @property
-    def _name(self) -> str:
+    @staticmethod
+    def _name() -> str:
         return "Information measure"
 
     KL_DIVERGENCE = "kl_divergence"
diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index 9acaaecd35d..9067239f9bd 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -17,24 +17,33 @@
 
 
 class EnumStr(StrEnum):
-    @property
-    def _name(self) -> str:
+    @staticmethod
+    def _name() -> str:
         return "Task"
 
     @classmethod
-    def from_str(cls, value: str) -> Optional["EnumStr"]:
+    def from_str(cls, value: str) -> "EnumStr":
         """Load from string.
 
         Raises:
             ValueError:
                 If required information measure is not among the supported options.
-        """
-        _allowed_im = [im.lower() for im in cls._member_names_]
 
+        >>> class MyEnum(EnumStr):
+        ...     a = "aaa"
+        ...     b = "bbb"
+        >>> MyEnum.from_str("a")
+        <MyEnum.a: 'aaa'>
+        >>> MyEnum.from_str("c")
+        Traceback (most recent call last):
+          ...
+        ValueError: Invalid Task: expected one of ['a', 'b'], but got c.
+        """
         enum_key = super().from_str(value)
-        if enum_key is not None and enum_key in _allowed_im:
+        if enum_key is not None:
             return enum_key
-        raise ValueError(f"Invalid {cls._name}: expected one of {_allowed_im}, but got {enum_key}.")
+        _allowed_im = [m.lower() for m in cls._member_names_]
+        raise ValueError(f"Invalid {cls._name()}: expected one of {_allowed_im}, but got {value}.")
 
 
 class DataType(EnumStr):
@@ -44,8 +53,8 @@ class DataType(EnumStr):
     True
     """
 
-    @property
-    def _name(self) -> str:
+    @staticmethod
+    def _name() -> str:
         return "Data type"
 
     BINARY = "binary"
@@ -65,8 +74,8 @@ class AverageMethod(EnumStr):
     True
     """
 
-    @property
-    def _name(self) -> str:
+    @staticmethod
+    def _name() -> str:
         return "Average method"
 
     MICRO = "micro"
@@ -79,8 +88,8 @@ def _name(self) -> str:
 class MDMCAverageMethod(EnumStr):
     """Enum to represent multi-dim multi-class average method."""
 
-    @property
-    def _name(self) -> str:
+    @staticmethod
+    def _name() -> str:
         return "MDMC Average method"
 
     GLOBAL = "global"
@@ -94,8 +103,8 @@ class ClassificationTask(EnumStr):
     True
     """
 
-    @property
-    def _name(self) -> str:
+    @staticmethod
+    def _name() -> str:
         return "Classification"
 
     BINARY = "binary"

From 01b693c6ff12a471b7ce2a7fb6e17d90e7658224 Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Mon, 6 Feb 2023 14:27:51 +0100
Subject: [PATCH 15/20] remove valueerror + add from_str eval

---
 src/torchmetrics/classification/accuracy.py   |  4 +--
 src/torchmetrics/classification/auroc.py      |  4 +--
 .../classification/average_precision.py       |  4 +--
 .../classification/calibration_error.py       | 10 +++----
 .../classification/cohen_kappa.py             | 10 +++----
 .../classification/confusion_matrix.py        |  4 +--
 .../classification/exact_match.py             |  8 ++---
 src/torchmetrics/classification/f_beta.py     |  4 +--
 src/torchmetrics/classification/hamming.py    |  5 +---
 src/torchmetrics/classification/hinge.py      | 10 +++----
 src/torchmetrics/classification/jaccard.py    |  4 +--
 .../classification/matthews_corrcoef.py       |  4 +--
 .../classification/precision_recall.py        |  8 ++---
 .../classification/precision_recall_curve.py  |  4 +--
 .../recall_at_fixed_precision.py              |  4 +--
 src/torchmetrics/classification/roc.py        |  4 +--
 .../classification/specificity.py             |  4 +--
 .../specificity_at_sensitivity.py             |  4 +--
 .../classification/stat_scores.py             |  4 +--
 .../functional/classification/accuracy.py     |  4 +--
 .../functional/classification/auroc.py        |  4 +--
 .../classification/average_precision.py       |  4 +--
 .../classification/calibration_error.py       |  7 +++--
 .../functional/classification/cohen_kappa.py  |  8 ++---
 .../classification/confusion_matrix.py        |  4 +--
 .../functional/classification/exact_match.py  |  8 ++---
 .../functional/classification/f_beta.py       |  8 ++---
 .../functional/classification/hamming.py      |  4 +--
 .../functional/classification/hinge.py        |  8 ++---
 .../functional/classification/jaccard.py      |  4 +--
 .../classification/matthews_corrcoef.py       |  4 +--
 .../classification/precision_recall.py        |  4 +--
 .../classification/precision_recall_curve.py  |  4 +--
 .../recall_at_fixed_precision.py              |  4 +--
 .../functional/classification/roc.py          |  4 +--
 .../functional/classification/specificity.py  |  4 +--
 .../specificity_at_sensitivity.py             |  4 +--
 .../functional/classification/stat_scores.py  |  4 +--
 src/torchmetrics/utilities/enums.py           | 30 +++++++++++++++++++
 39 files changed, 94 insertions(+), 134 deletions(-)

diff --git a/src/torchmetrics/classification/accuracy.py b/src/torchmetrics/classification/accuracy.py
index a121bf071ca..53900438bb5 100644
--- a/src/torchmetrics/classification/accuracy.py
+++ b/src/torchmetrics/classification/accuracy.py
@@ -445,6 +445,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
@@ -457,6 +458,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAccuracy(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/auroc.py b/src/torchmetrics/classification/auroc.py
index f203714db22..0405f21c97a 100644
--- a/src/torchmetrics/classification/auroc.py
+++ b/src/torchmetrics/classification/auroc.py
@@ -353,6 +353,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
             return BinaryAUROC(max_fpr, **kwargs)
@@ -362,6 +363,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAUROC(num_labels, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/average_precision.py b/src/torchmetrics/classification/average_precision.py
index ec8c6159d18..1f11703b8a7 100644
--- a/src/torchmetrics/classification/average_precision.py
+++ b/src/torchmetrics/classification/average_precision.py
@@ -357,6 +357,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
             return BinaryAveragePrecision(**kwargs)
@@ -366,6 +367,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAveragePrecision(num_labels, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/calibration_error.py b/src/torchmetrics/classification/calibration_error.py
index c8849dc479d..d3385ea7b3b 100644
--- a/src/torchmetrics/classification/calibration_error.py
+++ b/src/torchmetrics/classification/calibration_error.py
@@ -29,7 +29,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
-from torchmetrics.utilities.enums import ClassificationTask
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 class BinaryCalibrationError(Metric):
@@ -268,12 +268,10 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTaskNoMultilabel.from_str(task)
         kwargs.update({"n_bins": n_bins, "norm": norm, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == ClassificationTask.BINARY:
+        if task == ClassificationTaskNoMultilabel.BINARY:
             return BinaryCalibrationError(**kwargs)
-        if task == ClassificationTask.MULTICLASS:
+        if task == ClassificationTaskNoMultilabel.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassCalibrationError(num_classes, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/cohen_kappa.py b/src/torchmetrics/classification/cohen_kappa.py
index b8cd2e33ec2..1d184522d86 100644
--- a/src/torchmetrics/classification/cohen_kappa.py
+++ b/src/torchmetrics/classification/cohen_kappa.py
@@ -23,7 +23,7 @@
     _multiclass_cohen_kappa_arg_validation,
 )
 from torchmetrics.metric import Metric
-from torchmetrics.utilities.enums import ClassificationTask
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 class BinaryCohenKappa(BinaryConfusionMatrix):
@@ -222,12 +222,10 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTaskNoMultilabel.from_str(task)
         kwargs.update({"weights": weights, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == ClassificationTask.BINARY:
+        if task == ClassificationTaskNoMultilabel.BINARY:
             return BinaryCohenKappa(threshold, **kwargs)
-        if task == ClassificationTask.MULTICLASS:
+        if task == ClassificationTaskNoMultilabel.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassCohenKappa(num_classes, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/confusion_matrix.py b/src/torchmetrics/classification/confusion_matrix.py
index d6155442675..b4277e2298a 100644
--- a/src/torchmetrics/classification/confusion_matrix.py
+++ b/src/torchmetrics/classification/confusion_matrix.py
@@ -398,6 +398,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"normalize": normalize, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
             return BinaryConfusionMatrix(threshold, **kwargs)
@@ -407,6 +408,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelConfusionMatrix(num_labels, threshold, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/exact_match.py b/src/torchmetrics/classification/exact_match.py
index dd03009da35..bcf75dbeafc 100644
--- a/src/torchmetrics/classification/exact_match.py
+++ b/src/torchmetrics/classification/exact_match.py
@@ -32,7 +32,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
-from torchmetrics.utilities.enums import ClassificationTask
+from torchmetrics.utilities.enums import ClassificationTaskNoBinary
 
 
 class MulticlassExactMatch(Metric):
@@ -289,13 +289,13 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTaskNoBinary.from_str(task)
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == ClassificationTask.MULTICLASS:
+        if task == ClassificationTaskNoBinary.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassExactMatch(num_classes, **kwargs)
-        if task == ClassificationTask.MULTILABEL:
+        if task == ClassificationTaskNoBinary.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelExactMatch(num_labels, threshold, **kwargs)
-        raise ValueError(f"Expected argument `task` to either be `'multiclass'` or `'multilabel'` but got {task}")
diff --git a/src/torchmetrics/classification/f_beta.py b/src/torchmetrics/classification/f_beta.py
index b381cfc077f..572ad691505 100644
--- a/src/torchmetrics/classification/f_beta.py
+++ b/src/torchmetrics/classification/f_beta.py
@@ -777,6 +777,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
@@ -790,6 +791,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelF1Score(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/hamming.py b/src/torchmetrics/classification/hamming.py
index 16f65eee3af..251f16e5dd4 100644
--- a/src/torchmetrics/classification/hamming.py
+++ b/src/torchmetrics/classification/hamming.py
@@ -345,7 +345,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
-
+        task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
@@ -359,6 +359,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelHammingDistance(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/hinge.py b/src/torchmetrics/classification/hinge.py
index 084048cab9a..e1896a64f1c 100644
--- a/src/torchmetrics/classification/hinge.py
+++ b/src/torchmetrics/classification/hinge.py
@@ -29,7 +29,7 @@
     _multiclass_hinge_loss_update,
 )
 from torchmetrics.metric import Metric
-from torchmetrics.utilities.enums import ClassificationTask
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 class BinaryHingeLoss(Metric):
@@ -253,12 +253,10 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTaskNoMultilabel.from_str(task)
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == ClassificationTask.BINARY:
+        if task == ClassificationTaskNoMultilabel.BINARY:
             return BinaryHingeLoss(squared, **kwargs)
-        if task == ClassificationTask.MULTICLASS:
+        if task == ClassificationTaskNoMultilabel.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassHingeLoss(num_classes, squared, multiclass_mode, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/jaccard.py b/src/torchmetrics/classification/jaccard.py
index 6d0271cd9b3..64a44519475 100644
--- a/src/torchmetrics/classification/jaccard.py
+++ b/src/torchmetrics/classification/jaccard.py
@@ -296,6 +296,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
             return BinaryJaccardIndex(threshold, **kwargs)
@@ -305,6 +306,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelJaccardIndex(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/matthews_corrcoef.py b/src/torchmetrics/classification/matthews_corrcoef.py
index 85bf524a8ee..a776f17fb8a 100644
--- a/src/torchmetrics/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/classification/matthews_corrcoef.py
@@ -238,6 +238,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
             return BinaryMatthewsCorrCoef(threshold, **kwargs)
@@ -247,6 +248,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelMatthewsCorrCoef(num_labels, threshold, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/precision_recall.py b/src/torchmetrics/classification/precision_recall.py
index 2f961481d41..a3341b8dd90 100644
--- a/src/torchmetrics/classification/precision_recall.py
+++ b/src/torchmetrics/classification/precision_recall.py
@@ -621,6 +621,7 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
+        task = ClassificationTask.from_str(task)
         if task == ClassificationTask.BINARY:
             return BinaryPrecision(threshold, **kwargs)
         if task == ClassificationTask.MULTICLASS:
@@ -630,9 +631,6 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelPrecision(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
 
 
 class Recall:
@@ -673,6 +671,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
@@ -686,6 +685,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelRecall(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/precision_recall_curve.py b/src/torchmetrics/classification/precision_recall_curve.py
index e2b3832d5c0..0bdcd73af47 100644
--- a/src/torchmetrics/classification/precision_recall_curve.py
+++ b/src/torchmetrics/classification/precision_recall_curve.py
@@ -467,6 +467,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
             return BinaryPrecisionRecallCurve(**kwargs)
@@ -476,6 +477,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelPrecisionRecallCurve(num_labels, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/recall_at_fixed_precision.py b/src/torchmetrics/classification/recall_at_fixed_precision.py
index eab8d2d747d..3acc2ec6ba6 100644
--- a/src/torchmetrics/classification/recall_at_fixed_precision.py
+++ b/src/torchmetrics/classification/recall_at_fixed_precision.py
@@ -324,6 +324,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         if task == ClassificationTask.BINARY:
             return BinaryRecallAtFixedPrecision(min_precision, thresholds, ignore_index, validate_args, **kwargs)
         if task == ClassificationTask.MULTICLASS:
@@ -336,6 +337,3 @@ def __new__(
             return MultilabelRecallAtFixedPrecision(
                 num_labels, min_precision, thresholds, ignore_index, validate_args, **kwargs
             )
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/roc.py b/src/torchmetrics/classification/roc.py
index ca15425bff0..fa92dd8de15 100644
--- a/src/torchmetrics/classification/roc.py
+++ b/src/torchmetrics/classification/roc.py
@@ -382,6 +382,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
             return BinaryROC(**kwargs)
@@ -391,6 +392,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelROC(num_labels, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/specificity.py b/src/torchmetrics/classification/specificity.py
index c3c1a10cd2d..9fbfa7df8a1 100644
--- a/src/torchmetrics/classification/specificity.py
+++ b/src/torchmetrics/classification/specificity.py
@@ -321,6 +321,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
@@ -334,6 +335,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelSpecificity(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/specificity_at_sensitivity.py b/src/torchmetrics/classification/specificity_at_sensitivity.py
index d1c4c9effa7..a595e5ddd21 100644
--- a/src/torchmetrics/classification/specificity_at_sensitivity.py
+++ b/src/torchmetrics/classification/specificity_at_sensitivity.py
@@ -328,6 +328,7 @@ def __new__(  # type: ignore
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         if task == ClassificationTask.BINARY:
             return BinarySpecificityAtSensitivity(min_sensitivity, thresholds, ignore_index, validate_args, **kwargs)
         if task == ClassificationTask.MULTICLASS:
@@ -340,6 +341,3 @@ def __new__(  # type: ignore
             return MultilabelSpecificityAtSensitivity(
                 num_labels, min_sensitivity, thresholds, ignore_index, validate_args, **kwargs
             )
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/classification/stat_scores.py b/src/torchmetrics/classification/stat_scores.py
index eb035b93fd6..a38321e667f 100644
--- a/src/torchmetrics/classification/stat_scores.py
+++ b/src/torchmetrics/classification/stat_scores.py
@@ -496,6 +496,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
@@ -509,6 +510,3 @@ def __new__(
         if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelStatScores(num_labels, threshold, average, **kwargs)
-        raise ValueError(
-            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-        )
diff --git a/src/torchmetrics/functional/classification/accuracy.py b/src/torchmetrics/functional/classification/accuracy.py
index de562eb6196..ea783378fa2 100644
--- a/src/torchmetrics/functional/classification/accuracy.py
+++ b/src/torchmetrics/functional/classification/accuracy.py
@@ -397,6 +397,7 @@ def accuracy(
         >>> accuracy(preds, target, task="multiclass", num_classes=3, top_k=2)
         tensor(0.6667)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
     if task == ClassificationTask.BINARY:
         return binary_accuracy(preds, target, threshold, multidim_average, ignore_index, validate_args)
@@ -411,6 +412,3 @@ def accuracy(
         return multilabel_accuracy(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/auroc.py b/src/torchmetrics/functional/classification/auroc.py
index b504f15db17..35e7a4cfebb 100644
--- a/src/torchmetrics/functional/classification/auroc.py
+++ b/src/torchmetrics/functional/classification/auroc.py
@@ -451,6 +451,7 @@ def auroc(
         >>> auroc(preds, target, task='multiclass', num_classes=3)
         tensor(0.7778)
     """
+    task = ClassificationTask.from_str(task)
     if task == ClassificationTask.BINARY:
         return binary_auroc(preds, target, max_fpr, thresholds, ignore_index, validate_args)
     if task == ClassificationTask.MULTICLASS:
@@ -459,6 +460,3 @@ def auroc(
     if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_auroc(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/average_precision.py b/src/torchmetrics/functional/classification/average_precision.py
index a2af70e1b1d..c976e43fa93 100644
--- a/src/torchmetrics/functional/classification/average_precision.py
+++ b/src/torchmetrics/functional/classification/average_precision.py
@@ -438,6 +438,7 @@ def average_precision(
         >>> average_precision(pred, target, task="multiclass", num_classes=5, average=None)
         tensor([1.0000, 1.0000, 0.2500, 0.2500,    nan])
     """
+    task = ClassificationTask.from_str(task)
     if task == ClassificationTask.BINARY:
         return binary_average_precision(preds, target, thresholds, ignore_index, validate_args)
     if task == ClassificationTask.MULTICLASS:
@@ -448,6 +449,3 @@ def average_precision(
     if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_average_precision(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/calibration_error.py b/src/torchmetrics/functional/classification/calibration_error.py
index 1e0e233d0f6..6ee2f33f4a1 100644
--- a/src/torchmetrics/functional/classification/calibration_error.py
+++ b/src/torchmetrics/functional/classification/calibration_error.py
@@ -23,7 +23,7 @@
     _multiclass_confusion_matrix_format,
     _multiclass_confusion_matrix_tensor_validation,
 )
-from torchmetrics.utilities.enums import ClassificationTask
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 def _binning_bucketize(
@@ -348,10 +348,11 @@ def calibration_error(
     :func:`binary_calibration_error` and :func:`multiclass_calibration_error` for the specific details of
     each argument influence and examples.
     """
+    task = ClassificationTaskNoMultilabel.from_str(task)
     assert norm is not None
-    if task == ClassificationTask.BINARY:
+    if task == ClassificationTaskNoMultilabel.BINARY:
         return binary_calibration_error(preds, target, n_bins, norm, ignore_index, validate_args)
-    if task == ClassificationTask.MULTICLASS:
+    if task == ClassificationTaskNoMultilabel.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_calibration_error(preds, target, num_classes, n_bins, norm, ignore_index, validate_args)
     raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multiclass'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/cohen_kappa.py b/src/torchmetrics/functional/classification/cohen_kappa.py
index 39b01480727..017f487498d 100644
--- a/src/torchmetrics/functional/classification/cohen_kappa.py
+++ b/src/torchmetrics/functional/classification/cohen_kappa.py
@@ -27,7 +27,7 @@
     _multiclass_confusion_matrix_tensor_validation,
     _multiclass_confusion_matrix_update,
 )
-from torchmetrics.utilities.enums import ClassificationTask
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 def _cohen_kappa_reduce(confmat: Tensor, weights: Optional[Literal["linear", "quadratic", "none"]] = None) -> Tensor:
@@ -257,9 +257,9 @@ class labels.
         >>> cohen_kappa(preds, target, task="multiclass", num_classes=2)
         tensor(0.5000)
     """
-    if task == ClassificationTask.BINARY:
+    task = ClassificationTaskNoMultilabel.from_str(task)
+    if task == ClassificationTaskNoMultilabel.BINARY:
         return binary_cohen_kappa(preds, target, threshold, weights, ignore_index, validate_args)
-    if task == ClassificationTask.MULTICLASS:
+    if task == ClassificationTaskNoMultilabel.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_cohen_kappa(preds, target, num_classes, weights, ignore_index, validate_args)
-    raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multiclass'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/confusion_matrix.py b/src/torchmetrics/functional/classification/confusion_matrix.py
index 07acd82285b..3774b289d19 100644
--- a/src/torchmetrics/functional/classification/confusion_matrix.py
+++ b/src/torchmetrics/functional/classification/confusion_matrix.py
@@ -631,6 +631,7 @@ def confusion_matrix(
                 [[1, 0], [1, 0]],
                 [[0, 1], [0, 1]]])
     """
+    task = ClassificationTask.from_str(task)
     if task == ClassificationTask.BINARY:
         return binary_confusion_matrix(preds, target, threshold, normalize, ignore_index, validate_args)
     if task == ClassificationTask.MULTICLASS:
@@ -639,6 +640,3 @@ def confusion_matrix(
     if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_confusion_matrix(preds, target, num_labels, threshold, normalize, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/exact_match.py b/src/torchmetrics/functional/classification/exact_match.py
index 76d8d17ea4c..c66c180cd93 100644
--- a/src/torchmetrics/functional/classification/exact_match.py
+++ b/src/torchmetrics/functional/classification/exact_match.py
@@ -26,7 +26,7 @@
     _multilabel_stat_scores_tensor_validation,
 )
 from torchmetrics.utilities.compute import _safe_divide
-from torchmetrics.utilities.enums import ClassificationTask
+from torchmetrics.utilities.enums import ClassificationTaskNoBinary
 
 
 def _exact_match_reduce(
@@ -230,12 +230,12 @@ def exact_match(
         >>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='samplewise')
         tensor([1., 0.])
     """
-    if task == ClassificationTask.MULTICLASS:
+    task = ClassificationTaskNoBinary.from_str(task)
+    if task == ClassificationTaskNoBinary.MULTICLASS:
         assert num_classes is not None
         return multiclass_exact_match(preds, target, num_classes, multidim_average, ignore_index, validate_args)
-    if task == "multilabel":
+    if task == ClassificationTaskNoBinary.MULTILABEL:
         assert num_labels is not None
         return multilabel_exact_match(
             preds, target, num_labels, threshold, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(f"Expected argument `task` to either be `'multiclass'` or `'multilabel'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/f_beta.py b/src/torchmetrics/functional/classification/f_beta.py
index 808662e739c..e66822e4055 100644
--- a/src/torchmetrics/functional/classification/f_beta.py
+++ b/src/torchmetrics/functional/classification/f_beta.py
@@ -693,6 +693,7 @@ def fbeta_score(
         >>> fbeta_score(preds, target, task="multiclass", num_classes=3, beta=0.5)
         tensor(0.3333)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
     if task == ClassificationTask.BINARY:
         return binary_fbeta_score(preds, target, beta, threshold, multidim_average, ignore_index, validate_args)
@@ -707,9 +708,6 @@ def fbeta_score(
         return multilabel_fbeta_score(
             preds, target, beta, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
 
 
 def f1_score(
@@ -742,6 +740,7 @@ def f1_score(
         >>> f1_score(preds, target, task="multiclass", num_classes=3)
         tensor(0.3333)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
     if task == ClassificationTask.BINARY:
         return binary_f1_score(preds, target, threshold, multidim_average, ignore_index, validate_args)
@@ -756,6 +755,3 @@ def f1_score(
         return multilabel_f1_score(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/hamming.py b/src/torchmetrics/functional/classification/hamming.py
index 35fa99f0dbb..2d2f36c728c 100644
--- a/src/torchmetrics/functional/classification/hamming.py
+++ b/src/torchmetrics/functional/classification/hamming.py
@@ -399,6 +399,7 @@ def hamming_distance(
         >>> hamming_distance(preds, target, task="binary")
         tensor(0.2500)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
     if task == ClassificationTask.BINARY:
         return binary_hamming_distance(preds, target, threshold, multidim_average, ignore_index, validate_args)
@@ -413,6 +414,3 @@ def hamming_distance(
         return multilabel_hamming_distance(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/hinge.py b/src/torchmetrics/functional/classification/hinge.py
index 5c63ef0e0de..2dd69ead792 100644
--- a/src/torchmetrics/functional/classification/hinge.py
+++ b/src/torchmetrics/functional/classification/hinge.py
@@ -24,7 +24,7 @@
     _multiclass_confusion_matrix_tensor_validation,
 )
 from torchmetrics.utilities.data import to_onehot
-from torchmetrics.utilities.enums import ClassificationTask
+from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
 
 
 def _hinge_loss_compute(measure: Tensor, total: Tensor) -> Tensor:
@@ -277,9 +277,9 @@ def hinge_loss(
         >>> hinge_loss(preds, target, task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
         tensor([1.3743, 1.1945, 1.2359])
     """
-    if task == ClassificationTask.BINARY:
+    task = ClassificationTaskNoMultilabel.from_str(task)
+    if task == ClassificationTaskNoMultilabel.BINARY:
         return binary_hinge_loss(preds, target, squared, ignore_index, validate_args)
-    if task == ClassificationTask.MULTICLASS:
+    if task == ClassificationTaskNoMultilabel.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_hinge_loss(preds, target, num_classes, squared, multiclass_mode, ignore_index, validate_args)
-    raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multilabel'` but got {task}")
diff --git a/src/torchmetrics/functional/classification/jaccard.py b/src/torchmetrics/functional/classification/jaccard.py
index 39fbb10b81f..b339a49b7f9 100644
--- a/src/torchmetrics/functional/classification/jaccard.py
+++ b/src/torchmetrics/functional/classification/jaccard.py
@@ -322,6 +322,7 @@ def jaccard_index(
         >>> jaccard_index(pred, target, task="multiclass", num_classes=2)
         tensor(0.9660)
     """
+    task = ClassificationTask.from_str(task)
     if task == ClassificationTask.BINARY:
         return binary_jaccard_index(preds, target, threshold, ignore_index, validate_args)
     if task == ClassificationTask.MULTICLASS:
@@ -330,6 +331,3 @@ def jaccard_index(
     if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_jaccard_index(preds, target, num_labels, threshold, average, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/matthews_corrcoef.py b/src/torchmetrics/functional/classification/matthews_corrcoef.py
index 6d86d938ae3..8094a3c2ed1 100644
--- a/src/torchmetrics/functional/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/functional/classification/matthews_corrcoef.py
@@ -236,6 +236,7 @@ def matthews_corrcoef(
         >>> matthews_corrcoef(preds, target, task="multiclass", num_classes=2)
         tensor(0.5774)
     """
+    task = ClassificationTask.from_str(task)
     if task == ClassificationTask.BINARY:
         return binary_matthews_corrcoef(preds, target, threshold, ignore_index, validate_args)
     if task == ClassificationTask.MULTICLASS:
@@ -244,6 +245,3 @@ def matthews_corrcoef(
     if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_matthews_corrcoef(preds, target, num_labels, threshold, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/precision_recall.py b/src/torchmetrics/functional/classification/precision_recall.py
index a9e719b2874..7f52fdfd67c 100644
--- a/src/torchmetrics/functional/classification/precision_recall.py
+++ b/src/torchmetrics/functional/classification/precision_recall.py
@@ -705,6 +705,7 @@ def recall(
         >>> recall(preds, target, task="multiclass", average='micro', num_classes=3)
         tensor(0.2500)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
     if task == ClassificationTask.BINARY:
         return binary_recall(preds, target, threshold, multidim_average, ignore_index, validate_args)
@@ -719,6 +720,3 @@ def recall(
         return multilabel_recall(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/precision_recall_curve.py b/src/torchmetrics/functional/classification/precision_recall_curve.py
index ee452bb16fe..c0bb03c45c4 100644
--- a/src/torchmetrics/functional/classification/precision_recall_curve.py
+++ b/src/torchmetrics/functional/classification/precision_recall_curve.py
@@ -816,6 +816,7 @@ def precision_recall_curve(
         >>> thresholds
         [tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])]
     """
+    task = ClassificationTask.from_str(task)
     if task == ClassificationTask.BINARY:
         return binary_precision_recall_curve(preds, target, thresholds, ignore_index, validate_args)
     if task == ClassificationTask.MULTICLASS:
@@ -824,6 +825,3 @@ def precision_recall_curve(
     if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_precision_recall_curve(preds, target, num_labels, thresholds, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/recall_at_fixed_precision.py b/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
index 1324d99a826..83092ebd759 100644
--- a/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
+++ b/src/torchmetrics/functional/classification/recall_at_fixed_precision.py
@@ -385,6 +385,7 @@ def recall_at_fixed_precision(
     :func:`binary_recall_at_fixed_precision`, :func:`multiclass_recall_at_fixed_precision` and
     :func:`multilabel_recall_at_fixed_precision` for the specific details of each argument influence and examples.
     """
+    task = ClassificationTask.from_str(task)
     if task == ClassificationTask.BINARY:
         return binary_recall_at_fixed_precision(preds, target, min_precision, thresholds, ignore_index, validate_args)
     if task == ClassificationTask.MULTICLASS:
@@ -397,6 +398,3 @@ def recall_at_fixed_precision(
         return multilabel_recall_at_fixed_precision(
             preds, target, num_labels, min_precision, thresholds, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/roc.py b/src/torchmetrics/functional/classification/roc.py
index cd3535bf04f..da9751d684f 100644
--- a/src/torchmetrics/functional/classification/roc.py
+++ b/src/torchmetrics/functional/classification/roc.py
@@ -484,6 +484,7 @@ def roc(
          tensor([1.0000, 0.7576, 0.3680, 0.3468, 0.0745]),
          tensor([1.0000, 0.1837, 0.1338, 0.1183, 0.1138])]
     """
+    task = ClassificationTask.from_str(task)
     if task == ClassificationTask.BINARY:
         return binary_roc(preds, target, thresholds, ignore_index, validate_args)
     if task == ClassificationTask.MULTICLASS:
@@ -492,6 +493,3 @@ def roc(
     if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_roc(preds, target, num_labels, thresholds, ignore_index, validate_args)
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/specificity.py b/src/torchmetrics/functional/classification/specificity.py
index 12a02e26a67..8243e308ca9 100644
--- a/src/torchmetrics/functional/classification/specificity.py
+++ b/src/torchmetrics/functional/classification/specificity.py
@@ -370,6 +370,7 @@ def specificity(
         >>> specificity(preds, target, task="multiclass", average='micro', num_classes=3)
         tensor(0.6250)
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
     if task == ClassificationTask.BINARY:
         return binary_specificity(preds, target, threshold, multidim_average, ignore_index, validate_args)
@@ -384,6 +385,3 @@ def specificity(
         return multilabel_specificity(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/specificity_at_sensitivity.py b/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
index fa59907aaec..79aabe49c80 100644
--- a/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
+++ b/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
@@ -414,6 +414,7 @@ def specicity_at_sensitivity(
     :func:`binary_specificity_at_sensitivity`, :func:`multiclass_specicity_at_sensitivity` and
     :func:`multilabel_specifity_at_sensitvity` for the specific details of each argument influence and examples.
     """
+    task = ClassificationTask.from_str(task)
     if task == ClassificationTask.BINARY:
         return binary_specificity_at_sensitivity(  # type: ignore
             preds, target, min_sensitivity, thresholds, ignore_index, validate_args
@@ -428,6 +429,3 @@ def specicity_at_sensitivity(
         return multilabel_specificity_at_sensitivity(  # type: ignore
             preds, target, num_labels, min_sensitivity, thresholds, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/functional/classification/stat_scores.py b/src/torchmetrics/functional/classification/stat_scores.py
index 328c4cf9a28..e071eef531c 100644
--- a/src/torchmetrics/functional/classification/stat_scores.py
+++ b/src/torchmetrics/functional/classification/stat_scores.py
@@ -1081,6 +1081,7 @@ def stat_scores(
                 [1, 1, 1, 1, 2],
                 [1, 0, 3, 0, 1]])
     """
+    task = ClassificationTask.from_str(task)
     assert multidim_average is not None
     if task == ClassificationTask.BINARY:
         return binary_stat_scores(preds, target, threshold, multidim_average, ignore_index, validate_args)
@@ -1095,6 +1096,3 @@ def stat_scores(
         return multilabel_stat_scores(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
-    raise ValueError(
-        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
-    )
diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index 9067239f9bd..815fcff5aba 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -110,3 +110,33 @@ def _name() -> str:
     BINARY = "binary"
     MULTICLASS = "multiclass"
     MULTILABEL = "multilabel"
+
+
+class ClassificationTaskNoBinary(EnumStr):
+    """Enum to represent the different tasks in classification metrics.
+
+    >>> "binary" in list(ClassificationTask)
+    False
+    """
+
+    @staticmethod
+    def _name() -> str:
+        return "Classification"
+
+    MULTILABEL = "multilabel"
+    MULTICLASS = "multiclass"
+
+
+class ClassificationTaskNoMultilabel(EnumStr):
+    """Enum to represent the different tasks in classification metrics.
+
+    >>> "multilabel" in list(ClassificationTask)
+    False
+    """
+
+    @staticmethod
+    def _name() -> str:
+        return "Classification"
+
+    BINARY = "binary"
+    MULTICLASS = "multiclass"

From c62dc6418feab4b03f7a74ae9a326034f049ef89 Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Mon, 6 Feb 2023 17:17:56 +0100
Subject: [PATCH 16/20] doctests

---
 src/torchmetrics/utilities/enums.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index 57651edcdf7..8d9bdd2ad8d 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -115,7 +115,7 @@ def _name() -> str:
 class ClassificationTaskNoBinary(EnumStr):
     """Enum to represent the different tasks in classification metrics.
 
-    >>> "binary" in list(ClassificationTask)
+    >>> "binary" in list(ClassificationTaskNoBinary)
     False
     """
 
@@ -130,7 +130,7 @@ def _name() -> str:
 class ClassificationTaskNoMultilabel(EnumStr):
     """Enum to represent the different tasks in classification metrics.
 
-    >>> "multilabel" in list(ClassificationTask)
+    >>> "multilabel" in list(ClassificationTaskNoMultilabel)
     False
     """
 

From 940dd6edfe77d77256d8236ec0dfc011393e0d2b Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Tue, 7 Feb 2023 03:08:08 +0100
Subject: [PATCH 17/20] docs

---
 src/torchmetrics/utilities/enums.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index 8d9bdd2ad8d..016c71f9c3c 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -27,7 +27,7 @@ def from_str(cls, value: str) -> "EnumStr":
 
         Raises:
             ValueError:
-                If required information measure is not among the supported options.
+                If required value is not among the supported options.
 
         >>> class MyEnum(EnumStr):
         ...     a = "aaa"

From 76dc87261326c9b43227b295e27233bcac4eb5c4 Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Tue, 7 Feb 2023 03:52:01 +0100
Subject: [PATCH 18/20] -

---
 src/torchmetrics/utilities/enums.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/torchmetrics/utilities/enums.py b/src/torchmetrics/utilities/enums.py
index 016c71f9c3c..f2327854165 100644
--- a/src/torchmetrics/utilities/enums.py
+++ b/src/torchmetrics/utilities/enums.py
@@ -39,7 +39,7 @@ def from_str(cls, value: str) -> "EnumStr":
           ...
         ValueError: Invalid Task: expected one of ['a', 'b'], but got c.
         """
-        enum_key = super().from_str(value)
+        enum_key = super().from_str(value.replace("-", "_"))
         if enum_key is not None:
             return enum_key
         _allowed_im = [m.lower() for m in cls._member_names_]

From aca9f389feabe66c5a89950d7d5d91760d098e50 Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Tue, 7 Feb 2023 08:47:50 +0100
Subject: [PATCH 19/20] mypy

---
 src/torchmetrics/functional/classification/accuracy.py        | 1 +
 src/torchmetrics/functional/classification/cohen_kappa.py     | 1 +
 src/torchmetrics/functional/classification/exact_match.py     | 1 +
 src/torchmetrics/functional/classification/hamming.py         | 1 +
 src/torchmetrics/functional/classification/hinge.py           | 1 +
 src/torchmetrics/functional/classification/jaccard.py         | 1 +
 .../functional/classification/matthews_corrcoef.py            | 3 ++-
 .../functional/classification/precision_recall.py             | 1 +
 src/torchmetrics/functional/classification/specificity.py     | 1 +
 .../functional/classification/specificity_at_sensitivity.py   | 1 +
 src/torchmetrics/functional/multimodal/clip_score.py          | 4 +---
 src/torchmetrics/functional/text/infolm.py                    | 2 +-
 12 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/src/torchmetrics/functional/classification/accuracy.py b/src/torchmetrics/functional/classification/accuracy.py
index 1e3ee1fb07f..109ebf35c41 100644
--- a/src/torchmetrics/functional/classification/accuracy.py
+++ b/src/torchmetrics/functional/classification/accuracy.py
@@ -412,3 +412,4 @@ def accuracy(
         return multilabel_accuracy(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/cohen_kappa.py b/src/torchmetrics/functional/classification/cohen_kappa.py
index 86b4ac8f600..3a771858ae1 100644
--- a/src/torchmetrics/functional/classification/cohen_kappa.py
+++ b/src/torchmetrics/functional/classification/cohen_kappa.py
@@ -263,3 +263,4 @@ class labels.
     if task == ClassificationTaskNoMultilabel.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_cohen_kappa(preds, target, num_classes, weights, ignore_index, validate_args)
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/exact_match.py b/src/torchmetrics/functional/classification/exact_match.py
index afc7cef8f09..da41cb8c016 100644
--- a/src/torchmetrics/functional/classification/exact_match.py
+++ b/src/torchmetrics/functional/classification/exact_match.py
@@ -239,3 +239,4 @@ def exact_match(
         return multilabel_exact_match(
             preds, target, num_labels, threshold, multidim_average, ignore_index, validate_args
         )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/hamming.py b/src/torchmetrics/functional/classification/hamming.py
index 3ea3380c6e2..e6c75379b5e 100644
--- a/src/torchmetrics/functional/classification/hamming.py
+++ b/src/torchmetrics/functional/classification/hamming.py
@@ -414,3 +414,4 @@ def hamming_distance(
         return multilabel_hamming_distance(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/hinge.py b/src/torchmetrics/functional/classification/hinge.py
index faf470cb619..72cbdd85fb2 100644
--- a/src/torchmetrics/functional/classification/hinge.py
+++ b/src/torchmetrics/functional/classification/hinge.py
@@ -283,3 +283,4 @@ def hinge_loss(
     if task == ClassificationTaskNoMultilabel.MULTICLASS:
         assert isinstance(num_classes, int)
         return multiclass_hinge_loss(preds, target, num_classes, squared, multiclass_mode, ignore_index, validate_args)
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/jaccard.py b/src/torchmetrics/functional/classification/jaccard.py
index c0caeb2758d..203dccb0b09 100644
--- a/src/torchmetrics/functional/classification/jaccard.py
+++ b/src/torchmetrics/functional/classification/jaccard.py
@@ -331,3 +331,4 @@ def jaccard_index(
     if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_jaccard_index(preds, target, num_labels, threshold, average, ignore_index, validate_args)
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/matthews_corrcoef.py b/src/torchmetrics/functional/classification/matthews_corrcoef.py
index f71dbe62c2c..999746f11a4 100644
--- a/src/torchmetrics/functional/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/functional/classification/matthews_corrcoef.py
@@ -214,7 +214,7 @@ def multilabel_matthews_corrcoef(
 def matthews_corrcoef(
     preds: Tensor,
     target: Tensor,
-    task: Literal["binary", "multiclass", "multilabel"] = None,
+    task: Literal["binary", "multiclass", "multilabel"],
     threshold: float = 0.5,
     num_classes: Optional[int] = None,
     num_labels: Optional[int] = None,
@@ -245,3 +245,4 @@ def matthews_corrcoef(
     if task == ClassificationTask.MULTILABEL:
         assert isinstance(num_labels, int)
         return multilabel_matthews_corrcoef(preds, target, num_labels, threshold, ignore_index, validate_args)
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/precision_recall.py b/src/torchmetrics/functional/classification/precision_recall.py
index ab85189ba10..c131b2169b1 100644
--- a/src/torchmetrics/functional/classification/precision_recall.py
+++ b/src/torchmetrics/functional/classification/precision_recall.py
@@ -720,3 +720,4 @@ def recall(
         return multilabel_recall(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/specificity.py b/src/torchmetrics/functional/classification/specificity.py
index f37d25a3613..fa5f2b11567 100644
--- a/src/torchmetrics/functional/classification/specificity.py
+++ b/src/torchmetrics/functional/classification/specificity.py
@@ -385,3 +385,4 @@ def specificity(
         return multilabel_specificity(
             preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
         )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/classification/specificity_at_sensitivity.py b/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
index 96962216f05..a97fcfa0a56 100644
--- a/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
+++ b/src/torchmetrics/functional/classification/specificity_at_sensitivity.py
@@ -429,3 +429,4 @@ def specicity_at_sensitivity(
         return multilabel_specificity_at_sensitivity(  # type: ignore
             preds, target, num_labels, min_sensitivity, thresholds, ignore_index, validate_args
         )
+    raise ValueError(f"Not handled value: {task}")  # this is for compliant of mypy
diff --git a/src/torchmetrics/functional/multimodal/clip_score.py b/src/torchmetrics/functional/multimodal/clip_score.py
index e5757c86619..4ee5c12c89f 100644
--- a/src/torchmetrics/functional/multimodal/clip_score.py
+++ b/src/torchmetrics/functional/multimodal/clip_score.py
@@ -51,9 +51,7 @@ def _clip_score_update(
             f"Expected the number of images and text examples to be the same but got {len(images)} and {len(text)}"
         )
     device = images[0].device
-    processed_input = processor(
-        text=text, images=[i.cpu() for i in images], return_tensors="pt", padding=True
-    )  # type: ignore
+    processed_input = processor(text=text, images=[i.cpu() for i in images], return_tensors="pt", padding=True)
 
     img_features = model.get_image_features(processed_input["pixel_values"].to(device))
     img_features = img_features / img_features.norm(p=2, dim=-1, keepdim=True)
diff --git a/src/torchmetrics/functional/text/infolm.py b/src/torchmetrics/functional/text/infolm.py
index 25033d00100..f9c9c65b045 100644
--- a/src/torchmetrics/functional/text/infolm.py
+++ b/src/torchmetrics/functional/text/infolm.py
@@ -384,7 +384,7 @@ def _get_batch_distribution(
     for mask_idx in range(seq_len):
         input_ids = batch["input_ids"].clone()
         input_ids[:, mask_idx] = special_tokens_map["mask_token_id"]
-        logits_distribution = model(input_ids, batch["attention_mask"]).logits  # type: ignore
+        logits_distribution = model(input_ids, batch["attention_mask"]).logits
         # [batch_size, seq_len, vocab_size] -> [batch_size, vocab_size]
         logits_distribution = logits_distribution[:, mask_idx, :]
         prob_distribution = F.softmax(logits_distribution / temperature, dim=-1)

From 70314f074694b9f75ec7267c3a8c343563f5da84 Mon Sep 17 00:00:00 2001
From: Jirka <jirka.borovec@seznam.cz>
Date: Tue, 7 Feb 2023 08:56:41 +0100
Subject: [PATCH 20/20] mypy

---
 src/torchmetrics/functional/multimodal/clip_score.py | 4 +++-
 src/torchmetrics/functional/text/infolm.py           | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/torchmetrics/functional/multimodal/clip_score.py b/src/torchmetrics/functional/multimodal/clip_score.py
index 4ee5c12c89f..e5757c86619 100644
--- a/src/torchmetrics/functional/multimodal/clip_score.py
+++ b/src/torchmetrics/functional/multimodal/clip_score.py
@@ -51,7 +51,9 @@ def _clip_score_update(
             f"Expected the number of images and text examples to be the same but got {len(images)} and {len(text)}"
         )
     device = images[0].device
-    processed_input = processor(text=text, images=[i.cpu() for i in images], return_tensors="pt", padding=True)
+    processed_input = processor(
+        text=text, images=[i.cpu() for i in images], return_tensors="pt", padding=True
+    )  # type: ignore
 
     img_features = model.get_image_features(processed_input["pixel_values"].to(device))
     img_features = img_features / img_features.norm(p=2, dim=-1, keepdim=True)
diff --git a/src/torchmetrics/functional/text/infolm.py b/src/torchmetrics/functional/text/infolm.py
index f9c9c65b045..25033d00100 100644
--- a/src/torchmetrics/functional/text/infolm.py
+++ b/src/torchmetrics/functional/text/infolm.py
@@ -384,7 +384,7 @@ def _get_batch_distribution(
     for mask_idx in range(seq_len):
         input_ids = batch["input_ids"].clone()
         input_ids[:, mask_idx] = special_tokens_map["mask_token_id"]
-        logits_distribution = model(input_ids, batch["attention_mask"]).logits
+        logits_distribution = model(input_ids, batch["attention_mask"]).logits  # type: ignore
         # [batch_size, seq_len, vocab_size] -> [batch_size, vocab_size]
         logits_distribution = logits_distribution[:, mask_idx, :]
         prob_distribution = F.softmax(logits_distribution / temperature, dim=-1)