diff --git a/.github/actions/pull-caches/action.yml b/.github/actions/pull-caches/action.yml
index a5cf7cafe2d..fec93a58bde 100644
--- a/.github/actions/pull-caches/action.yml
+++ b/.github/actions/pull-caches/action.yml
@@ -90,5 +90,5 @@ runs:
 
     - name: Restored References
       continue-on-error: true
-      run: ls -lh tests/_cache-references/
+      run: py-tree tests/_cache-references/ --show_hidden
       shell: bash
diff --git a/.github/actions/push-caches/action.yml b/.github/actions/push-caches/action.yml
index da757f09b5d..8f5db36b6dd 100644
--- a/.github/actions/push-caches/action.yml
+++ b/.github/actions/push-caches/action.yml
@@ -99,5 +99,5 @@ runs:
         key: cache-references
 
     - name: Post References
-      run: ls -lh tests/_cache-references/
+      run: py-tree tests/_cache-references/ --show_hidden
       shell: bash
diff --git a/.gitignore b/.gitignore
index 6f45b493e3c..cbe31a9b316 100644
--- a/.gitignore
+++ b/.gitignore
@@ -40,7 +40,7 @@ pip-delete-this-directory.txt
 # Unit test / coverage reports
 tests/_data/
 data.zip
-tests/_reference-cache/
+tests/_cache-references/
 htmlcov/
 .coverage
 .coverage.*
diff --git a/src/torchmetrics/utilities/imports.py b/src/torchmetrics/utilities/imports.py
index 085269dd4d9..6e80411f5c1 100644
--- a/src/torchmetrics/utilities/imports.py
+++ b/src/torchmetrics/utilities/imports.py
@@ -17,10 +17,8 @@
 import sys
 
 from lightning_utilities.core.imports import RequirementCache
-from packaging.version import Version, parse
 
 _PYTHON_VERSION = f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
-_PYTHON_LOWER_3_8 = parse(_PYTHON_VERSION) < Version("3.8")
 _TORCH_LOWER_2_0 = RequirementCache("torch<2.0.0")
 _TORCH_GREATER_EQUAL_1_11 = RequirementCache("torch>=1.11.0")
 _TORCH_GREATER_EQUAL_1_12 = RequirementCache("torch>=1.12.0")
@@ -29,7 +27,6 @@
 _TORCH_GREATER_EQUAL_2_1 = RequirementCache("torch>=2.1.0")
 _TORCH_GREATER_EQUAL_2_2 = RequirementCache("torch>=2.2.0")
 
-_JIWER_AVAILABLE = RequirementCache("jiwer")
 _NLTK_AVAILABLE = RequirementCache("nltk")
 _ROUGE_SCORE_AVAILABLE = RequirementCache("rouge_score")
 _BERTSCORE_AVAILABLE = RequirementCache("bert_score")
@@ -49,7 +46,6 @@
 _GAMMATONE_AVAILABLE = RequirementCache("gammatone")
 _TORCHAUDIO_AVAILABLE = RequirementCache("torchaudio")
 _TORCHAUDIO_GREATER_EQUAL_0_10 = RequirementCache("torchaudio>=0.10.0")
-_SACREBLEU_AVAILABLE = RequirementCache("sacrebleu")
 _REGEX_AVAILABLE = RequirementCache("regex")
 _PYSTOI_AVAILABLE = RequirementCache("pystoi")
 _FAST_BSS_EVAL_AVAILABLE = RequirementCache("fast_bss_eval")
diff --git a/tests/unittests/classification/test_group_fairness.py b/tests/unittests/classification/test_group_fairness.py
index 811e2a55ab9..4d76b9301dd 100644
--- a/tests/unittests/classification/test_group_fairness.py
+++ b/tests/unittests/classification/test_group_fairness.py
@@ -26,7 +26,6 @@
 from torchmetrics import Metric
 from torchmetrics.classification.group_fairness import BinaryFairness
 from torchmetrics.functional.classification.group_fairness import binary_fairness
-from torchmetrics.utilities.imports import _PYTHON_LOWER_3_8
 
 from unittests import THRESHOLD
 from unittests.classification._inputs import _group_cases
@@ -222,7 +221,6 @@ def run_precision_test_gpu(
 
 @mock.patch("unittests.helpers.testers._assert_tensor", _assert_tensor)
 @mock.patch("unittests.helpers.testers._assert_allclose", _assert_allclose)
-@pytest.mark.skipif(_PYTHON_LOWER_3_8, reason="`TestBinaryFairness` requires `python>=3.8`.")
 @pytest.mark.parametrize("inputs", _group_cases)
 class TestBinaryFairness(BinaryFairnessTester):
     """Test class for `BinaryFairness` metric."""
diff --git a/tests/unittests/image/test_fid.py b/tests/unittests/image/test_fid.py
index 83b243200da..252f0d0ebba 100644
--- a/tests/unittests/image/test_fid.py
+++ b/tests/unittests/image/test_fid.py
@@ -34,7 +34,7 @@ def test_no_train_network_missing_torch_fidelity():
         NoTrainInceptionV3(name="inception-v3-compat", features_list=["2048"])
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 def test_no_train():
     """Assert that metric never leaves evaluation mode."""
 
@@ -52,7 +52,7 @@ def forward(self, x):
     assert not model.metric.inception.training, "FID metric was changed to training mode which should not happen"
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 def test_fid_pickle():
     """Assert that we can initialize the metric and pickle it."""
     metric = FrechetInceptionDistance()
@@ -80,7 +80,7 @@ def test_fid_raises_errors_and_warnings():
         _ = FrechetInceptionDistance(feature=[1, 2])
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 @pytest.mark.parametrize("feature", [64, 192, 768, 2048])
 def test_fid_same_input(feature):
     """If real and fake are update on the same data the fid score should be 0."""
@@ -111,7 +111,7 @@ def __len__(self) -> int:
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test is too slow without gpu")
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 @pytest.mark.parametrize("equal_size", [False, True])
 def test_compare_fid(tmpdir, equal_size, feature=768):
     """Check that the hole pipeline give the same result as torch-fidelity."""
diff --git a/tests/unittests/image/test_inception.py b/tests/unittests/image/test_inception.py
index 552180cbbcc..627e6a4a57a 100644
--- a/tests/unittests/image/test_inception.py
+++ b/tests/unittests/image/test_inception.py
@@ -24,7 +24,7 @@
 torch.manual_seed(42)
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 def test_no_train():
     """Assert that metric never leaves evaluation mode."""
 
@@ -44,7 +44,7 @@ def forward(self, x):
     ), "InceptionScore metric was changed to training mode which should not happen"
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 def test_is_pickle():
     """Assert that we can initialize the metric and pickle it."""
     metric = InceptionScore()
@@ -79,7 +79,7 @@ def test_is_raises_errors_and_warnings():
         InceptionScore(feature=[1, 2])
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 def test_is_update_compute():
     """Test that inception score works as expected."""
     metric = InceptionScore()
@@ -105,7 +105,7 @@ def __len__(self) -> int:
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test is too slow without gpu")
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 @pytest.mark.parametrize("compute_on_cpu", [True, False])
 def test_compare_is(tmpdir, compute_on_cpu):
     """Check that the hole pipeline give the same result as torch-fidelity."""
diff --git a/tests/unittests/image/test_kid.py b/tests/unittests/image/test_kid.py
index 34d223e24af..a754768003c 100644
--- a/tests/unittests/image/test_kid.py
+++ b/tests/unittests/image/test_kid.py
@@ -24,7 +24,7 @@
 torch.manual_seed(42)
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 def test_no_train():
     """Assert that metric never leaves evaluation mode."""
 
@@ -42,7 +42,7 @@ def forward(self, x):
     assert not model.metric.inception.training, "FID metric was changed to training mode which should not happen"
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 def test_kid_pickle():
     """Assert that we can initialize the metric and pickle it."""
     metric = KernelInceptionDistance()
@@ -83,7 +83,7 @@ def test_kid_raises_errors_and_warnings():
         m.compute()
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 def test_kid_extra_parameters():
     """Test that the different input arguments raises expected errors if wrong."""
     with pytest.raises(ValueError, match="Argument `subsets` expected to be integer larger than 0"):
@@ -102,7 +102,7 @@ def test_kid_extra_parameters():
         KernelInceptionDistance(coef=-1)
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 @pytest.mark.parametrize("feature", [64, 192, 768, 2048])
 def test_kid_same_input(feature):
     """Test that the metric works."""
@@ -132,7 +132,7 @@ def __len__(self) -> int:
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test is too slow without gpu")
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 def test_compare_kid(tmpdir, feature=2048):
     """Check that the hole pipeline give the same result as torch-fidelity."""
     from torch_fidelity import calculate_metrics
diff --git a/tests/unittests/image/test_lpips.py b/tests/unittests/image/test_lpips.py
index 0a7171ab996..026c2b91770 100644
--- a/tests/unittests/image/test_lpips.py
+++ b/tests/unittests/image/test_lpips.py
@@ -16,11 +16,10 @@
 
 import pytest
 import torch
-from lpips import LPIPS as LPIPS_reference  # noqa: N811
 from torch import Tensor
 from torchmetrics.functional.image.lpips import learned_perceptual_image_patch_similarity
 from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-from torchmetrics.utilities.imports import _LPIPS_AVAILABLE, _TORCHVISION_AVAILABLE
+from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE
 
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
@@ -43,7 +42,12 @@ def _reference_lpips(
     img1: Tensor, img2: Tensor, net_type: str, normalize: bool = False, reduction: str = "mean"
 ) -> Tensor:
     """Comparison function for tm implementation."""
-    ref = LPIPS_reference(net=net_type)
+    try:
+        from lpips import LPIPS
+    except ImportError:
+        pytest.skip("test requires lpips package to be installed")
+
+    ref = LPIPS(net=net_type)
     res = ref(img1, img2, normalize=normalize).detach().cpu().numpy()
     if reduction == "mean":
         return res.mean()
@@ -51,7 +55,6 @@ def _reference_lpips(
 
 
 @pytest.mark.skipif(not _TORCHVISION_AVAILABLE, reason="test requires that torchvision is installed")
-@pytest.mark.skipif(not _LPIPS_AVAILABLE, reason="test requires that lpips is installed")
 class TestLPIPS(MetricTester):
     """Test class for `LearnedPerceptualImagePatchSimilarity` metric."""
 
@@ -109,7 +112,6 @@ def test_normalize_arg(normalize):
 
 
 @pytest.mark.skipif(not _TORCHVISION_AVAILABLE, reason="test requires that torchvision is installed")
-@pytest.mark.skipif(not _LPIPS_AVAILABLE, reason="test requires that lpips is installed")
 def test_error_on_wrong_init():
     """Test class raises the expected errors."""
     with pytest.raises(ValueError, match="Argument `net_type` must be one .*"):
@@ -120,7 +122,6 @@ def test_error_on_wrong_init():
 
 
 @pytest.mark.skipif(not _TORCHVISION_AVAILABLE, reason="test requires that torchvision is installed")
-@pytest.mark.skipif(not _LPIPS_AVAILABLE, reason="test requires that lpips is installed")
 @pytest.mark.parametrize(
     ("inp1", "inp2"),
     [
diff --git a/tests/unittests/image/test_mifid.py b/tests/unittests/image/test_mifid.py
index d5bdb95cf68..ae44982b350 100644
--- a/tests/unittests/image/test_mifid.py
+++ b/tests/unittests/image/test_mifid.py
@@ -98,7 +98,7 @@ def calculate_mifid(m1, s1, features1, m2, s2, features2):
     return fid_private / (distance_private_thresholded + 1e-15)
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 def test_no_train():
     """Assert that metric never leaves evaluation mode."""
 
@@ -139,7 +139,7 @@ def test_mifid_raises_errors_and_warnings():
         _ = MemorizationInformedFrechetInceptionDistance(cosine_distance_eps=1.1)
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 @pytest.mark.parametrize("feature", [64, 192, 768, 2048])
 def test_fid_same_input(feature):
     """If real and fake are update on the same data the fid score should be 0."""
@@ -157,7 +157,7 @@ def test_fid_same_input(feature):
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test is too slow without gpu")
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 @pytest.mark.parametrize("equal_size", [False, True])
 def test_compare_mifid(equal_size):
     """Check that our implementation of MIFID is correct by comparing it to the original implementation."""
diff --git a/tests/unittests/image/test_perceptual_path_length.py b/tests/unittests/image/test_perceptual_path_length.py
index dfdd5cfde96..1eb486c6ce3 100644
--- a/tests/unittests/image/test_perceptual_path_length.py
+++ b/tests/unittests/image/test_perceptual_path_length.py
@@ -29,7 +29,7 @@
 seed_all(42)
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch_fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 @pytest.mark.parametrize("interpolation_method", ["lerp", "slerp_any", "slerp_unit"])
 def test_interpolation_methods(interpolation_method):
     """Test that interpolation method works as expected."""
@@ -41,7 +41,7 @@ def test_interpolation_methods(interpolation_method):
     assert torch.allclose(res1, res2)
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch_fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 @skip_on_running_out_of_memory()
 def test_sim_net():
     """Check that the similarity network is the same as the one used in torch_fidelity."""
@@ -100,7 +100,7 @@ def sample(self, num_samples):
         return torch.randn(num_samples, self.z_size)
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch_fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 @pytest.mark.parametrize(
     ("argument", "match"),
     [
@@ -174,7 +174,7 @@ def test_raises_error_on_wrong_generator(generator, errortype, match):
         ppl.update(generator=generator)
 
 
-@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch_fidelity")
+@pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
 @skip_on_running_out_of_memory()
 def test_compare():
diff --git a/tests/unittests/multimodal/test_clip_iqa.py b/tests/unittests/multimodal/test_clip_iqa.py
index c7057226759..314ff0013b8 100644
--- a/tests/unittests/multimodal/test_clip_iqa.py
+++ b/tests/unittests/multimodal/test_clip_iqa.py
@@ -71,7 +71,7 @@ def _reference_clip_iqa(preds, target, reduce=False):
     return res.sum() if reduce else res
 
 
-@pytest.mark.skipif(not _PIQ_GREATER_EQUAL_0_8, reason="test requires piq>=0.8")
+@pytest.mark.skipif(not _PIQ_GREATER_EQUAL_0_8, reason="metric requires piq>=0.8")
 @pytest.mark.skipif(not _TRANSFORMERS_GREATER_EQUAL_4_10, reason="test requires transformers>=4.10")
 class TestCLIPIQA(MetricTester):
     """Test clip iqa metric."""
@@ -104,7 +104,7 @@ def test_clip_iqa_functional(self, shapes):
 
 
 @skip_on_connection_issues()
-@pytest.mark.skipif(not _PIQ_GREATER_EQUAL_0_8, reason="test requires piq>=0.8")
+@pytest.mark.skipif(not _PIQ_GREATER_EQUAL_0_8, reason="metric requires piq>=0.8")
 @pytest.mark.skipif(not _TRANSFORMERS_GREATER_EQUAL_4_10, reason="test requires transformers>=4.10")
 @pytest.mark.skipif(not os.path.isfile(_SAMPLE_IMAGE), reason="test image not found")
 def test_for_correctness_sample_images():
@@ -121,7 +121,7 @@ def test_for_correctness_sample_images():
 
 
 @skip_on_connection_issues()
-@pytest.mark.skipif(not _PIQ_GREATER_EQUAL_0_8, reason="test requires piq>=0.8")
+@pytest.mark.skipif(not _PIQ_GREATER_EQUAL_0_8, reason="metric requires piq>=0.8")
 @pytest.mark.skipif(not _TRANSFORMERS_GREATER_EQUAL_4_10, reason="test requires transformers>=4.10")
 @pytest.mark.parametrize(
     "model",
@@ -148,7 +148,7 @@ def test_other_models(model):
 
 
 @skip_on_connection_issues()
-@pytest.mark.skipif(not _PIQ_GREATER_EQUAL_0_8, reason="test requires piq>=0.8")
+@pytest.mark.skipif(not _PIQ_GREATER_EQUAL_0_8, reason="metric requires piq>=0.8")
 @pytest.mark.skipif(not _TRANSFORMERS_GREATER_EQUAL_4_10, reason="test requires transformers>=4.10")
 @pytest.mark.parametrize(
     "prompts",
@@ -200,7 +200,7 @@ def test_prompt(prompts):
 
 
 @skip_on_connection_issues()
-@pytest.mark.skipif(not _PIQ_GREATER_EQUAL_0_8, reason="test requires piq>=0.8")
+@pytest.mark.skipif(not _PIQ_GREATER_EQUAL_0_8, reason="metric requires piq>=0.8")
 @pytest.mark.skipif(not _TRANSFORMERS_GREATER_EQUAL_4_10, reason="test requires transformers>=4.10")
 def test_plot_method():
     """Test the plot method of CLIPScore separately in this file due to the skipping conditions."""
diff --git a/tests/unittests/nominal/test_cramers.py b/tests/unittests/nominal/test_cramers.py
index 4cebac73e05..42b735ef510 100644
--- a/tests/unittests/nominal/test_cramers.py
+++ b/tests/unittests/nominal/test_cramers.py
@@ -12,13 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-import operator
 from functools import partial
 
 import pytest
 import torch
-from dython.nominal import cramers_v as dython_cramers_v
-from lightning_utilities.core.imports import compare_version
 from torchmetrics.functional.nominal.cramers import cramers_v, cramers_v_matrix
 from torchmetrics.nominal.cramers import CramersV
 
@@ -63,10 +60,15 @@ def cramers_matrix_input():
 
 
 def _reference_dython_cramers_v(preds, target, bias_correction, nan_strategy, nan_replace_value):
+    try:
+        from dython.nominal import cramers_v
+    except ImportError:
+        pytest.skip("This test requires `dython` package to be installed.")
+
     preds = preds.argmax(1) if preds.ndim == 2 else preds
     target = target.argmax(1) if target.ndim == 2 else target
 
-    v = dython_cramers_v(
+    v = cramers_v(
         preds.numpy(),
         target.numpy(),
         bias_correction=bias_correction,
@@ -87,7 +89,6 @@ def _dython_cramers_v_matrix(matrix, bias_correction, nan_strategy, nan_replace_
     return cramers_v_matrix_value
 
 
-@pytest.mark.skipif(compare_version("pandas", operator.lt, "1.3.2"), reason="`dython` package requires `pandas>=1.3.2`")
 @pytest.mark.parametrize(
     "preds, target",
     [
@@ -161,7 +162,6 @@ def test_cramers_v_differentiability(self, preds, target, bias_correction, nan_s
         )
 
 
-@pytest.mark.skipif(compare_version("pandas", operator.lt, "1.3.2"), reason="`dython` package requires `pandas>=1.3.2`")
 @pytest.mark.parametrize("bias_correction", [False, True])
 @pytest.mark.parametrize(("nan_strategy", "nan_replace_value"), [("replace", 1.0), ("drop", None)])
 def test_cramers_v_matrix(cramers_matrix_input, bias_correction, nan_strategy, nan_replace_value):
diff --git a/tests/unittests/nominal/test_pearson.py b/tests/unittests/nominal/test_pearson.py
index 44bf1c0e415..5bec1cd8121 100644
--- a/tests/unittests/nominal/test_pearson.py
+++ b/tests/unittests/nominal/test_pearson.py
@@ -12,13 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-import operator
 
 import pandas as pd
 import pytest
 import torch
-from lightning_utilities.core.imports import compare_version
-from scipy.stats.contingency import association
 from torchmetrics.functional.nominal.pearson import (
     pearsons_contingency_coefficient,
     pearsons_contingency_coefficient_matrix,
@@ -56,6 +53,10 @@ def pearson_matrix_input():
 
 
 def _reference_pd_pearsons_t(preds, target):
+    try:
+        from scipy.stats.contingency import association
+    except ImportError:
+        pytest.skip("test requires scipy package to be installed")
     preds = preds.argmax(1) if preds.ndim == 2 else preds
     target = target.argmax(1) if target.ndim == 2 else target
     preds, target = preds.numpy().astype(int), target.numpy().astype(int)
@@ -74,7 +75,6 @@ def _reference_pd_pearsons_t_matrix(matrix):
     return pearsons_t_matrix_value
 
 
-@pytest.mark.skipif(compare_version("pandas", operator.lt, "1.3.2"), reason="`dython` package requires `pandas>=1.3.2`")
 @pytest.mark.parametrize(
     "preds, target",
     [
@@ -118,7 +118,6 @@ def test_pearsons_t_differentiability(self, preds, target):
         )
 
 
-@pytest.mark.skipif(compare_version("pandas", operator.lt, "1.3.2"), reason="`dython` package requires `pandas>=1.3.2`")
 def test_pearsons_contingency_coefficient_matrix(pearson_matrix_input):
     """Test matrix version of metric works as expected."""
     tm_score = pearsons_contingency_coefficient_matrix(pearson_matrix_input)
diff --git a/tests/unittests/nominal/test_theils_u.py b/tests/unittests/nominal/test_theils_u.py
index b7ae4b29507..c06c6b9bcd2 100644
--- a/tests/unittests/nominal/test_theils_u.py
+++ b/tests/unittests/nominal/test_theils_u.py
@@ -12,13 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-import operator
 from functools import partial
 
 import pytest
 import torch
-from dython.nominal import theils_u as dython_theils_u
-from lightning_utilities.core.imports import compare_version
 from torchmetrics.functional.nominal.theils_u import theils_u, theils_u_matrix
 from torchmetrics.nominal import TheilsU
 
@@ -63,6 +60,11 @@ def theils_u_matrix_input():
 
 
 def _reference_dython_theils_u(preds, target, nan_strategy, nan_replace_value):
+    try:
+        from dython.nominal import theils_u as dython_theils_u
+    except ImportError:
+        pytest.skip("Test requires `dython` package to be installed.")
+
     preds = preds.argmax(1) if preds.ndim == 2 else preds
     target = target.argmax(1) if target.ndim == 2 else target
 
@@ -85,7 +87,6 @@ def _reference_dython_theils_u_matrix(matrix, nan_strategy, nan_replace_value):
     return theils_u_matrix_value
 
 
-@pytest.mark.skipif(compare_version("pandas", operator.lt, "1.3.2"), reason="`dython` package requires `pandas>=1.3.2`")
 @pytest.mark.parametrize(
     "preds, target",
     [
@@ -153,7 +154,6 @@ def test_theils_u_differentiability(self, preds, target, nan_strategy, nan_repla
         )
 
 
-@pytest.mark.skipif(compare_version("pandas", operator.lt, "1.3.2"), reason="`dython` package requires `pandas>=1.3.2`")
 @pytest.mark.parametrize(("nan_strategy", "nan_replace_value"), [("replace", 1.0), ("drop", None)])
 def test_theils_u_matrix(theils_u_matrix_input, nan_strategy, nan_replace_value):
     """Test matrix version of metric works as expected."""
diff --git a/tests/unittests/nominal/test_tschuprows.py b/tests/unittests/nominal/test_tschuprows.py
index 48102ac6f34..91798d88d82 100644
--- a/tests/unittests/nominal/test_tschuprows.py
+++ b/tests/unittests/nominal/test_tschuprows.py
@@ -12,13 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-import operator
 
 import pandas as pd
 import pytest
 import torch
-from lightning_utilities.core.imports import compare_version
-from scipy.stats.contingency import association
 from torchmetrics.functional.nominal.tschuprows import tschuprows_t, tschuprows_t_matrix
 from torchmetrics.nominal.tschuprows import TschuprowsT
 
@@ -53,6 +50,10 @@ def tschuprows_matrix_input():
 
 
 def _reference_pd_tschuprows_t(preds, target):
+    try:
+        from scipy.stats.contingency import association
+    except ImportError:
+        pytest.skip("test requires scipy package to be installed")
     preds = preds.argmax(1) if preds.ndim == 2 else preds
     target = target.argmax(1) if target.ndim == 2 else target
     preds, target = preds.numpy().astype(int), target.numpy().astype(int)
@@ -71,7 +72,6 @@ def _reference_pd_tschuprows_t_matrix(matrix):
     return tschuprows_t_matrix_value
 
 
-@pytest.mark.skipif(compare_version("pandas", operator.lt, "1.3.2"), reason="`dython` package requires `pandas>=1.3.2`")
 @pytest.mark.parametrize(
     "preds, target",
     [
@@ -120,7 +120,6 @@ def test_tschuprows_t_differentiability(self, preds, target):
         )
 
 
-@pytest.mark.skipif(compare_version("pandas", operator.lt, "1.3.2"), reason="`dython` package requires `pandas>=1.3.2`")
 def test_tschuprows_t_matrix(tschuprows_matrix_input):
     """Test matrix version of metric works as expected."""
     tm_score = tschuprows_t_matrix(tschuprows_matrix_input, bias_correction=False)
diff --git a/tests/unittests/text/test_bertscore.py b/tests/unittests/text/test_bertscore.py
index 3b2382a8488..b651ecfddb1 100644
--- a/tests/unittests/text/test_bertscore.py
+++ b/tests/unittests/text/test_bertscore.py
@@ -19,18 +19,13 @@
 from torch import Tensor
 from torchmetrics.functional.text.bert import bert_score
 from torchmetrics.text.bert import BERTScore
-from torchmetrics.utilities.imports import _BERTSCORE_AVAILABLE, _TRANSFORMERS_GREATER_EQUAL_4_4
+from torchmetrics.utilities.imports import _TRANSFORMERS_GREATER_EQUAL_4_4
 from typing_extensions import Literal
 
 from unittests.helpers import skip_on_connection_issues
 from unittests.text._inputs import _inputs_single_reference
 from unittests.text.helpers import TextTester
 
-if _BERTSCORE_AVAILABLE:
-    from bert_score import score as original_bert_score
-else:
-    original_bert_score = None
-
 _METRIC_KEY_TO_IDX = {
     "precision": 0,
     "recall": 1,
@@ -45,7 +40,6 @@
 
 @skip_on_connection_issues()
 @pytest.mark.skipif(not _TRANSFORMERS_GREATER_EQUAL_4_4, reason="test requires transformers>4.4")
-@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
 def _reference_bert_score(
     preds: Sequence[str],
     target: Sequence[str],
@@ -55,6 +49,11 @@ def _reference_bert_score(
     rescale_with_baseline: bool,
     metric_key: Literal["f1", "precision", "recall"],
 ) -> Tensor:
+    try:
+        from bert_score import score as original_bert_score
+    except ImportError:
+        pytest.skip("test requires bert_score package to be installed.")
+
     score_tuple = original_bert_score(
         preds,
         target,
@@ -88,7 +87,6 @@ def _reference_bert_score(
     [(_inputs_single_reference.preds, _inputs_single_reference.target)],
 )
 @pytest.mark.skipif(not _TRANSFORMERS_GREATER_EQUAL_4_4, reason="test requires transformers>4.4")
-@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
 class TestBERTScore(TextTester):
     """Tests for BERTScore."""
 
diff --git a/tests/unittests/text/test_cer.py b/tests/unittests/text/test_cer.py
index 34c7a9735a9..ab09f3e5334 100644
--- a/tests/unittests/text/test_cer.py
+++ b/tests/unittests/text/test_cer.py
@@ -11,28 +11,25 @@
 # 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 Callable, List, Union
+from typing import List, Union
 
 import pytest
 from torchmetrics.functional.text.cer import char_error_rate
 from torchmetrics.text.cer import CharErrorRate
-from torchmetrics.utilities.imports import _JIWER_AVAILABLE
 
 from unittests.text._inputs import _inputs_error_rate_batch_size_1, _inputs_error_rate_batch_size_2
 from unittests.text.helpers import TextTester
 
-if _JIWER_AVAILABLE:
-    from jiwer import cer
-
-else:
-    compute_measures = Callable
-
 
 def _reference_jiwer_cer(preds: Union[str, List[str]], target: Union[str, List[str]]):
+    try:
+        from jiwer import cer
+    except ImportError:
+        pytest.skip("test requires jiwer package to be installed.")
+
     return cer(target, preds)
 
 
-@pytest.mark.skipif(not _JIWER_AVAILABLE, reason="test requires jiwer")
 @pytest.mark.parametrize(
     ["preds", "targets"],
     [
diff --git a/tests/unittests/text/test_chrf.py b/tests/unittests/text/test_chrf.py
index 4df8e5d8c22..6dd328d2c77 100644
--- a/tests/unittests/text/test_chrf.py
+++ b/tests/unittests/text/test_chrf.py
@@ -18,14 +18,10 @@
 from torch import Tensor, tensor
 from torchmetrics.functional.text.chrf import chrf_score
 from torchmetrics.text.chrf import CHRFScore
-from torchmetrics.utilities.imports import _SACREBLEU_AVAILABLE
 
 from unittests.text._inputs import _inputs_multiple_references, _inputs_single_sentence_multiple_references
 from unittests.text.helpers import TextTester
 
-if _SACREBLEU_AVAILABLE:
-    from sacrebleu.metrics import CHRF
-
 
 def _reference_sacrebleu_chrf(
     preds: Sequence[str],
@@ -35,6 +31,11 @@ def _reference_sacrebleu_chrf(
     lowercase: bool,
     whitespace: bool,
 ) -> Tensor:
+    try:
+        from sacrebleu import CHRF
+    except ImportError:
+        pytest.skip("test requires sacrebleu package to be installed")
+
     sacrebleu_chrf = CHRF(
         char_order=char_order, word_order=word_order, lowercase=lowercase, whitespace=whitespace, eps_smoothing=True
     )
@@ -59,7 +60,6 @@ def _reference_sacrebleu_chrf(
     ["preds", "targets"],
     [(_inputs_multiple_references.preds, _inputs_multiple_references.target)],
 )
-@pytest.mark.skipif(not _SACREBLEU_AVAILABLE, reason="test requires sacrebleu")
 class TestCHRFScore(TextTester):
     """Test class for `CHRFScore` metric."""
 
diff --git a/tests/unittests/text/test_infolm.py b/tests/unittests/text/test_infolm.py
index 1ddd6d9bcfe..d8611695ff3 100644
--- a/tests/unittests/text/test_infolm.py
+++ b/tests/unittests/text/test_infolm.py
@@ -36,10 +36,10 @@ def _reference_infolm_score(preds, target, model_name, information_measure, idf,
     https://github.com/stancld/infolm-docker.
 
     """
-    if model_name != "google/bert_uncased_L-2_H-128_A-2":
+    allowed_model = "google/bert_uncased_L-2_H-128_A-2"
+    if model_name != allowed_model:
         raise ValueError(
-            "`model_name` is expected to be 'google/bert_uncased_L-2_H-128_A-2' as this model was used for the result "
-            "generation."
+            f"`model_name` is expected to be '{allowed_model}' as this model was used for the result generation."
         )
     precomputed_result = {
         "kl_divergence": torch.tensor([-3.2250, -0.1784, -0.1784, -2.2182]),
diff --git a/tests/unittests/text/test_mer.py b/tests/unittests/text/test_mer.py
index 9ff0823f173..e6f5222c3b1 100644
--- a/tests/unittests/text/test_mer.py
+++ b/tests/unittests/text/test_mer.py
@@ -11,30 +11,27 @@
 # 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 Callable, List, Union
+from typing import List, Union
 
 import pytest
 from torchmetrics.functional.text.mer import match_error_rate
 from torchmetrics.text.mer import MatchErrorRate
-from torchmetrics.utilities.imports import _JIWER_AVAILABLE
 
 from unittests.helpers import seed_all
 from unittests.text._inputs import _inputs_error_rate_batch_size_1, _inputs_error_rate_batch_size_2
 from unittests.text.helpers import TextTester
 
-if _JIWER_AVAILABLE:
-    from jiwer import compute_measures
-else:
-    compute_measures: Callable
-
 seed_all(42)
 
 
 def _reference_jiwer_mer(preds: Union[str, List[str]], target: Union[str, List[str]]):
+    try:
+        from jiwer import compute_measures
+    except ImportError:
+        pytest.skip("test requires jiwer package to be installed")
     return compute_measures(target, preds)["mer"]
 
 
-@pytest.mark.skipif(not _JIWER_AVAILABLE, reason="test requires jiwer")
 @pytest.mark.parametrize(
     ["preds", "targets"],
     [
diff --git a/tests/unittests/text/test_rouge.py b/tests/unittests/text/test_rouge.py
index 8d59929c488..c9eec8a055a 100644
--- a/tests/unittests/text/test_rouge.py
+++ b/tests/unittests/text/test_rouge.py
@@ -91,7 +91,7 @@ def _reference_rouge_score(
     return torch.tensor(rs_result, dtype=torch.float)
 
 
-@pytest.mark.skipif(not _NLTK_AVAILABLE, reason="test requires nltk")
+@pytest.mark.skipif(not _NLTK_AVAILABLE, reason="metric requires nltk")
 @pytest.mark.parametrize(
     ["pl_rouge_metric_key", "use_stemmer"],
     [
diff --git a/tests/unittests/text/test_sacre_bleu.py b/tests/unittests/text/test_sacre_bleu.py
index 362fcacf59e..d74d032597d 100644
--- a/tests/unittests/text/test_sacre_bleu.py
+++ b/tests/unittests/text/test_sacre_bleu.py
@@ -19,18 +19,19 @@
 from torch import Tensor, tensor
 from torchmetrics.functional.text.sacre_bleu import AVAILABLE_TOKENIZERS, _TokenizersLiteral, sacre_bleu_score
 from torchmetrics.text.sacre_bleu import SacreBLEUScore
-from torchmetrics.utilities.imports import _SACREBLEU_AVAILABLE
 
 from unittests.text._inputs import _inputs_multiple_references
 from unittests.text.helpers import TextTester
 
-if _SACREBLEU_AVAILABLE:
-    from sacrebleu.metrics import BLEU
-
 
 def _reference_sacre_bleu(
     preds: Sequence[str], targets: Sequence[Sequence[str]], tokenize: str, lowercase: bool
 ) -> Tensor:
+    try:
+        from sacrebleu.metrics import BLEU
+    except ImportError:
+        pytest.skip("test requires sacrebleu package to be installed")
+
     sacrebleu_fn = BLEU(tokenize=tokenize, lowercase=lowercase)
     # Sacrebleu expects different format of input
     targets = [[target[i] for target in targets] for i in range(len(targets[0]))]
@@ -44,7 +45,6 @@ def _reference_sacre_bleu(
 )
 @pytest.mark.parametrize(["lowercase"], [(False,), (True,)])
 @pytest.mark.parametrize("tokenize", AVAILABLE_TOKENIZERS)
-@pytest.mark.skipif(not _SACREBLEU_AVAILABLE, reason="test requires sacrebleu")
 class TestSacreBLEUScore(TextTester):
     """Test class for `SacreBLEUScore` metric."""
 
diff --git a/tests/unittests/text/test_ter.py b/tests/unittests/text/test_ter.py
index f6dd90f2c36..eb63451cf36 100644
--- a/tests/unittests/text/test_ter.py
+++ b/tests/unittests/text/test_ter.py
@@ -18,14 +18,10 @@
 from torch import Tensor, tensor
 from torchmetrics.functional.text.ter import translation_edit_rate
 from torchmetrics.text.ter import TranslationEditRate
-from torchmetrics.utilities.imports import _SACREBLEU_AVAILABLE
 
 from unittests.text._inputs import _inputs_multiple_references, _inputs_single_sentence_multiple_references
 from unittests.text.helpers import TextTester
 
-if _SACREBLEU_AVAILABLE:
-    from sacrebleu.metrics import TER as SacreTER  # noqa: N811
-
 
 def _reference_sacrebleu_ter(
     preds: Sequence[str],
@@ -35,7 +31,12 @@ def _reference_sacrebleu_ter(
     asian_support: bool,
     case_sensitive: bool,
 ) -> Tensor:
-    sacrebleu_ter = SacreTER(
+    try:
+        from sacrebleu.metrics import TER
+    except ImportError:
+        pytest.skip("test requires sacrebleu package to be installed")
+
+    sacrebleu_ter = TER(
         normalized=normalized, no_punct=no_punct, asian_support=asian_support, case_sensitive=case_sensitive
     )
     # Sacrebleu CHRF expects different format of input
@@ -59,7 +60,6 @@ def _reference_sacrebleu_ter(
     ["preds", "targets"],
     [(_inputs_multiple_references.preds, _inputs_multiple_references.target)],
 )
-@pytest.mark.skipif(not _SACREBLEU_AVAILABLE, reason="test requires sacrebleu")
 class TestTER(TextTester):
     """Test class for `TranslationEditRate` metric."""
 
diff --git a/tests/unittests/text/test_wer.py b/tests/unittests/text/test_wer.py
index bb781f8f0ac..6aee783d411 100644
--- a/tests/unittests/text/test_wer.py
+++ b/tests/unittests/text/test_wer.py
@@ -11,27 +11,25 @@
 # 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 Callable, List, Union
+from typing import List, Union
 
 import pytest
 from torchmetrics.functional.text.wer import word_error_rate
 from torchmetrics.text.wer import WordErrorRate
-from torchmetrics.utilities.imports import _JIWER_AVAILABLE
 
 from unittests.text._inputs import _inputs_error_rate_batch_size_1, _inputs_error_rate_batch_size_2
 from unittests.text.helpers import TextTester
 
-if _JIWER_AVAILABLE:
-    from jiwer import compute_measures
-else:
-    compute_measures: Callable
-
 
 def _reference_jiwer_wer(preds: Union[str, List[str]], target: Union[str, List[str]]):
+    try:
+        from jiwer import compute_measures
+    except ImportError:
+        pytest.skip("test requires jiwer package to be installed")
+
     return compute_measures(target, preds)["wer"]
 
 
-@pytest.mark.skipif(not _JIWER_AVAILABLE, reason="test requires jiwer")
 @pytest.mark.parametrize(
     ["preds", "targets"],
     [
diff --git a/tests/unittests/text/test_wil.py b/tests/unittests/text/test_wil.py
index 9b88866071a..08ecad16284 100644
--- a/tests/unittests/text/test_wil.py
+++ b/tests/unittests/text/test_wil.py
@@ -14,20 +14,22 @@
 from typing import List, Union
 
 import pytest
-from jiwer import wil
 from torchmetrics.functional.text.wil import word_information_lost
 from torchmetrics.text.wil import WordInfoLost
-from torchmetrics.utilities.imports import _JIWER_AVAILABLE
 
 from unittests.text._inputs import _inputs_error_rate_batch_size_1, _inputs_error_rate_batch_size_2
 from unittests.text.helpers import TextTester
 
 
 def _reference_jiwer_wil(preds: Union[str, List[str]], target: Union[str, List[str]]):
+    try:
+        from jiwer import wil
+    except ImportError:
+        pytest.skip("test requires jiwer package to be installed")
+
     return wil(target, preds)
 
 
-@pytest.mark.skipif(not _JIWER_AVAILABLE, reason="test requires jiwer")
 @pytest.mark.parametrize(
     ["preds", "targets"],
     [
diff --git a/tests/unittests/text/test_wip.py b/tests/unittests/text/test_wip.py
index c6ce8a89a8e..1900f7182b2 100644
--- a/tests/unittests/text/test_wip.py
+++ b/tests/unittests/text/test_wip.py
@@ -14,20 +14,22 @@
 from typing import List, Union
 
 import pytest
-from jiwer import wip
 from torchmetrics.functional.text.wip import word_information_preserved
 from torchmetrics.text.wip import WordInfoPreserved
-from torchmetrics.utilities.imports import _JIWER_AVAILABLE
 
 from unittests.text._inputs import _inputs_error_rate_batch_size_1, _inputs_error_rate_batch_size_2
 from unittests.text.helpers import TextTester
 
 
 def _reference_jiwer_wip(preds: Union[str, List[str]], target: Union[str, List[str]]):
+    try:
+        from jiwer import wip
+    except ImportError:
+        pytest.skip("test requires jiwer package to be installed")
+
     return wip(target, preds)
 
 
-@pytest.mark.skipif(not _JIWER_AVAILABLE, reason="test requires jiwer")
 @pytest.mark.parametrize(
     ["preds", "targets"],
     [