From 2a32b9c814b0fde72dbeed91e4f282bf81aeb9f9 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sun, 9 Jan 2022 01:52:49 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 tests/classification/test_f_beta.py | 17 +++++++++++++----
 torchmetrics/__init__.py            |  2 +-
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/tests/classification/test_f_beta.py b/tests/classification/test_f_beta.py
index 9b7bc913795..0839c81c532 100644
--- a/tests/classification/test_f_beta.py
+++ b/tests/classification/test_f_beta.py
@@ -33,7 +33,8 @@
 from tests.helpers import seed_all
 from tests.helpers.testers import NUM_BATCHES, NUM_CLASSES, THRESHOLD, MetricTester
 from torchmetrics import F1Score, FBeta, Metric
-from torchmetrics.functional import f1_score as f1_score_pl, fbeta
+from torchmetrics.functional import f1_score as f1_score_pl
+from torchmetrics.functional import fbeta
 from torchmetrics.utilities.checks import _input_format_classification
 from torchmetrics.utilities.enums import AverageMethod
 
@@ -178,7 +179,9 @@ def test_no_support(metric_class, metric_fn):
     assert result_cl == result_fn == 0
 
 
-@pytest.mark.parametrize("metric_class, metric_fn", [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)), (F1Score, f1_score_pl)])
+@pytest.mark.parametrize(
+    "metric_class, metric_fn", [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0)), (F1Score, f1_score_pl)]
+)
 @pytest.mark.parametrize(
     "ignore_index, expected", [(None, torch.tensor([1.0, np.nan])), (0, torch.tensor([np.nan, np.nan]))]
 )
@@ -202,7 +205,10 @@ def test_class_not_present(metric_class, metric_fn, ignore_index, expected):
 
 @pytest.mark.parametrize(
     "metric_class, metric_fn, sk_fn",
-    [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)), (F1Score, f1_score_pl, f1_score)],
+    [
+        (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)),
+        (F1Score, f1_score_pl, f1_score),
+    ],
 )
 @pytest.mark.parametrize("average", ["micro", "macro", None, "weighted", "samples"])
 @pytest.mark.parametrize("ignore_index", [None, 0])
@@ -430,7 +436,10 @@ def test_top_k(
 @pytest.mark.parametrize("average", ["micro", "macro", "weighted"])
 @pytest.mark.parametrize(
     "metric_class, metric_functional, sk_fn",
-    [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)), (F1Score, f1_score_pl, f1_score)],
+    [
+        (partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)),
+        (F1Score, f1_score_pl, f1_score),
+    ],
 )
 def test_same_input(metric_class, metric_functional, sk_fn, average, ignore_index):
     preds = _input_miss_class.preds
diff --git a/torchmetrics/__init__.py b/torchmetrics/__init__.py
index 49dd5e76bca..cfd3ba6b62e 100644
--- a/torchmetrics/__init__.py
+++ b/torchmetrics/__init__.py
@@ -17,7 +17,6 @@
 from torchmetrics.classification import (  # noqa: E402, F401
     AUC,
     AUROC,
-    F1Score,
     ROC,
     Accuracy,
     AveragePrecision,
@@ -27,6 +26,7 @@
     CalibrationError,
     CohenKappa,
     ConfusionMatrix,
+    F1Score,
     FBeta,
     HammingDistance,
     Hinge,