[pre-commit.ci] pre-commit suggestions (#2136)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka <[email protected]>

.github/assistant.py

@@ -79,7 +79,7 @@ def set_min_torch_by_python(fpath: str = "requirements/base.txt") -> None:
             return
         with open(fpath) as fp:
             reqs = parse_requirements(fp.readlines())
-        pkg_ver = [p for p in reqs if p.name == "torch"][0]
+        pkg_ver = next(p for p in reqs if p.name == "torch")
         pt_ver = min([LooseVersion(v[1]) for v in pkg_ver.specs])
         pt_ver = max(LooseVersion(LUT_PYTHON_TORCH[py_ver]), pt_ver)
         with open(fpath) as fp:

.pre-commit-config.yaml

@@ -38,22 +38,22 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.9.0
+    rev: v3.14.0
     hooks:
       - id: pyupgrade
-        args: [--py38-plus]
+        args: ["--py38-plus"]
         name: Upgrade code
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.5
+    rev: v2.2.6
     hooks:
       - id: codespell
         additional_dependencies: [tomli]
-        #args: ["--write-changes"]
+        args: ["--write-changes"]
         exclude: pyproject.toml
 
   - repo: https://github.com/crate-ci/typos
-    rev: v1.16.12
+    rev: v1.16.17
     hooks:
       - id: typos
         # empty to do not write fixes
@@ -68,13 +68,13 @@ repos:
         args: ["--in-place"]
 
   - repo: https://github.com/psf/black
-    rev: 23.7.0
+    rev: 23.9.1
     hooks:
       - id: black
         name: Format code
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.16
+    rev: 0.7.17
     hooks:
       - id: mdformat
         additional_dependencies:
@@ -130,7 +130,7 @@ repos:
       - id: text-unicode-replacement-char
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.277
+    rev: v0.0.292
     hooks:
       - id: ruff
         args: ["--fix"]

pyproject.toml

@@ -22,10 +22,8 @@ addopts = [
 #filterwarnings = ["error::FutureWarning"] # ToDo
 xfail_strict = true
 junit_duration_report = "call"
-
 [tool.coverage.report]
 exclude_lines = ["pragma: no cover", "pass"]
-
 [tool.coverage.run]
 parallel = true
 concurrency = "thread"
@@ -81,6 +79,7 @@ wil = "wil"
 
 
 [tool.ruff]
+target-version = "py38"
 line-length = 120
 # Enable Pyflakes `E` and `F` codes by default.
 select = [
@@ -122,6 +121,8 @@ ignore = [
     "S301",   # todo: `pickle` and modules that wrap it can be unsafe when used to deserialize untrusted data, possible security issue  # todo
     "S310",   # todo: Audit URL open for permitted schemes. Allowing use of `file:` or custom schemes is often unexpected.  # todo
     "B905",   # todo: `zip()` without an explicit `strict=` parameter
+    "PYI024",  # todo: Use `typing.NamedTuple` instead of `collections.namedtuple`
+    "PYI041",  # todo: Use `float` instead of `int | float``
 ]
 # Exclude a variety of commonly ignored directories.
 exclude = [

src/torchmetrics/audio/__init__.py

@@ -41,16 +41,16 @@
 ]
 
 if _PESQ_AVAILABLE:
-    from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality  # noqa: F401
+    from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality
 
-    __all__.append("PerceptualEvaluationSpeechQuality")
+    __all__ += ["PerceptualEvaluationSpeechQuality"]
 
 if _PYSTOI_AVAILABLE:
-    from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility  # noqa: F401
+    from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility
 
-    __all__.append("ShortTimeObjectiveIntelligibility")
+    __all__ += ["ShortTimeObjectiveIntelligibility"]
 
 if _GAMMATONE_AVAILABLE and _TORCHAUDIO_AVAILABLE and _TORCHAUDIO_GREATER_EQUAL_0_10:
-    from torchmetrics.audio.srmr import SpeechReverberationModulationEnergyRatio  # noqa: F401
+    from torchmetrics.audio.srmr import SpeechReverberationModulationEnergyRatio
 
-    __all__.append("SpeechReverberationModulationEnergyRatio")
+    __all__ += ["SpeechReverberationModulationEnergyRatio"]

src/torchmetrics/audio/stoi.py

@@ -34,7 +34,7 @@ class ShortTimeObjectiveIntelligibility(Metric):
     The STOI-measure is intrusive, i.e., a function of the clean and degraded speech signals. STOI may be a good
     alternative to the speech intelligibility index (SII) or the speech transmission index (STI), when you are
     interested in the effect of nonlinear processing to noisy speech, e.g., noise reduction, binary masking algorithms,
-    on speech intelligibility. Description taken from  `Cees Taal's website`_ and for further defails see `STOI ref1`_
+    on speech intelligibility. Description taken from  `Cees Taal's website`_ and for further details see `STOI ref1`_
     and `STOI ref2`_.
 
     This metric is a wrapper for the `pystoi package`_. As the implementation backend implementation only supports

src/torchmetrics/detection/mean_ap.py

@@ -879,7 +879,7 @@ def _get_coco_format(
                         f"Invalid input box of sample {image_id}, element {k} (expected 4 values, got {len(image_box)})"
                     )
 
-                if type(image_label) != int:
+                if not isinstance(image_label, int):
                     raise ValueError(
                         f"Invalid input class of sample {image_id}, element {k}"
                         f" (expected value of type integer, got type {type(image_label)})"
@@ -915,7 +915,7 @@ def _get_coco_format(
 
                 if scores is not None:
                     score = scores[image_id][k].cpu().tolist()
-                    if type(score) != float:
+                    if not isinstance(score, float):
                         raise ValueError(
                             f"Invalid input score of sample {image_id}, element {k}"
                             f" (expected value of type float, got type {type(score)})"

src/torchmetrics/functional/audio/__init__.py

@@ -42,16 +42,16 @@
 ]
 
 if _PESQ_AVAILABLE:
-    from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality  # noqa: F401
+    from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality
 
-    __all__.append("perceptual_evaluation_speech_quality")
+    __all__ += ["perceptual_evaluation_speech_quality"]
 
 if _PYSTOI_AVAILABLE:
-    from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility  # noqa: F401
+    from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility
 
-    __all__.append("short_time_objective_intelligibility")
+    __all__ += ["short_time_objective_intelligibility"]
 
 if _GAMMATONE_AVAILABLE and _TORCHAUDIO_AVAILABLE and _TORCHAUDIO_GREATER_EQUAL_0_10:
-    from torchmetrics.functional.audio.srmr import speech_reverberation_modulation_energy_ratio  # noqa: F401
+    from torchmetrics.functional.audio.srmr import speech_reverberation_modulation_energy_ratio
 
-    __all__.append("speech_reverberation_modulation_energy_ratio")
+    __all__ += ["speech_reverberation_modulation_energy_ratio"]

src/torchmetrics/functional/audio/sdr.py

@@ -63,7 +63,7 @@ def _symmetric_toeplitz(vector: Tensor) -> Tensor:
 def _compute_autocorr_crosscorr(target: Tensor, preds: Tensor, corr_len: int) -> Tuple[Tensor, Tensor]:
     r"""Compute the auto correlation of `target` and the cross correlation of `target` and `preds`.
 
-    This calculation is done using the fast Fourier transform (FFT). Let's denotes the symmetric Toeplitz matric of the
+    This calculation is done using the fast Fourier transform (FFT). Let's denotes the symmetric Toeplitz metric of the
     auto correlation of `target` as `R`, the cross correlation as 'b', then solving the equation `Rh=b` could have `h`
     as the coordinate of `preds` in the column space of the `corr_len` shifts of `target`.
 
@@ -81,7 +81,7 @@ def _compute_autocorr_crosscorr(target: Tensor, preds: Tensor, corr_len: int) ->
     n_fft = 2 ** math.ceil(math.log2(preds.shape[-1] + target.shape[-1] - 1))
 
     # computes the auto correlation of `target`
-    # r_0 is the first row of the symmetric Toeplitz matric
+    # r_0 is the first row of the symmetric Toeplitz metric
     t_fft = torch.fft.rfft(target, n=n_fft, dim=-1)
     r_0 = torch.fft.irfft(t_fft.real**2 + t_fft.imag**2, n=n_fft)[..., :corr_len]
 

src/torchmetrics/functional/audio/stoi.py

@@ -35,7 +35,7 @@ def short_time_objective_intelligibility(
     STOI-measure is intrusive, i.e., a function of the clean and degraded speech signals. STOI may be a good alternative
     to the speech intelligibility index (SII) or the speech transmission index (STI), when you are interested in
     the effect of nonlinear processing to noisy speech, e.g., noise reduction, binary masking algorithms, on speech
-    intelligibility. Description taken from  `Cees Taal's website`_ and for further defails see `STOI ref1`_ and
+    intelligibility. Description taken from  `Cees Taal's website`_ and for further details see `STOI ref1`_ and
     `STOI ref2`_.
 
     This metric is a wrapper for the `pystoi package`_. As the implementation backend implementation only supports

src/torchmetrics/functional/detection/__init__.py

@@ -22,15 +22,13 @@
 __all__ = ["modified_panoptic_quality", "panoptic_quality"]
 
 if _TORCHVISION_AVAILABLE and _TORCHVISION_GREATER_EQUAL_0_8:
-    from torchmetrics.functional.detection.giou import generalized_intersection_over_union  # noqa: F401
-    from torchmetrics.functional.detection.iou import intersection_over_union  # noqa: F401
+    from torchmetrics.functional.detection.giou import generalized_intersection_over_union
+    from torchmetrics.functional.detection.iou import intersection_over_union
 
-    __all__.append("generalized_intersection_over_union")
-    __all__.append("intersection_over_union")
+    __all__ += ["generalized_intersection_over_union", "intersection_over_union"]
 
 if _TORCHVISION_AVAILABLE and _TORCHVISION_GREATER_EQUAL_0_13:
-    from torchmetrics.functional.detection.ciou import complete_intersection_over_union  # noqa: F401
-    from torchmetrics.functional.detection.diou import distance_intersection_over_union  # noqa: F401
+    from torchmetrics.functional.detection.ciou import complete_intersection_over_union
+    from torchmetrics.functional.detection.diou import distance_intersection_over_union
 
-    __all__.append("complete_intersection_over_union")
-    __all__.append("distance_intersection_over_union")
+    __all__ += ["complete_intersection_over_union", "distance_intersection_over_union"]

src/torchmetrics/functional/image/ssim.py

@@ -479,7 +479,7 @@ def multiscale_structural_similarity_index_measure(
             the range is calculated as the difference and input is clamped between the values.
         k1: Parameter of structural similarity index measure.
         k2: Parameter of structural similarity index measure.
-        betas: Exponent parameters for individual similarities and contrastive sensitivies returned by different image
+        betas: Exponent parameters for individual similarities and contrastive sensitivities returned by different image
             resolutions.
         normalize: When MultiScaleSSIM loss is used for training, it is desirable to use normalizes to improve the
             training stability. This `normalize` argument is out of scope of the original implementation [1], and it is

src/torchmetrics/functional/text/__init__.py

@@ -47,8 +47,7 @@
 
 
 if _TRANSFORMERS_GREATER_EQUAL_4_4:
-    from torchmetrics.functional.text.bert import bert_score  # noqa: F401
-    from torchmetrics.functional.text.infolm import infolm  # noqa: F401
+    from torchmetrics.functional.text.bert import bert_score
+    from torchmetrics.functional.text.infolm import infolm
 
-    __all__.append("bert_score")
-    __all__.append("infolm")
+    __all__ += ["bert_score", "infolm"]

src/torchmetrics/functional/text/chrf.py

@@ -268,7 +268,7 @@ def _calculate_fscore(
         beta: A parameter determining an importance of recall w.r.t. precision. If `beta=1`, their importance is equal.
 
     Return:
-        A chrF/chrF++ score. This function is universal both for sentence-level and corpus-level calucation.
+        A chrF/chrF++ score. This function is universal both for sentence-level and corpus-level calculation.
 
     """
 

src/torchmetrics/functional/text/eed.py

@@ -145,7 +145,7 @@ def _eed_function(
     next_row = [inf] * (len(hyp) + 1)
 
     for w in range(1, len(ref) + 1):
-        for i in range(0, len(hyp) + 1):
+        for i in range(len(hyp) + 1):
             if i > 0:
                 next_row[i] = min(
                     next_row[i - 1] + deletion,
@@ -252,7 +252,7 @@ def _eed_compute(sentence_level_scores: List[Tensor]) -> Tensor:
 def _preprocess_sentences(
     preds: Union[str, Sequence[str]],
     target: Sequence[Union[str, Sequence[str]]],
-    language: Union[Literal["en"], Literal["ja"]],
+    language: Literal["en", "ja"],
 ) -> Tuple[Union[str, Sequence[str]], Sequence[Union[str, Sequence[str]]]]:
     """Preprocess strings according to language requirements.
 

src/torchmetrics/functional/text/helper.py

@@ -242,7 +242,7 @@ def _add_cache(self, prediction_tokens: List[str], edit_distance: List[List[Tupl
             node = value[0]  # type: ignore
 
     def _find_cache(self, prediction_tokens: List[str]) -> Tuple[int, List[List[Tuple[int, _EditOperations]]]]:
-        """Find the already calculated rows of the Levenshtein edit distance matric.
+        """Find the already calculated rows of the Levenshtein edit distance metric.
 
         Args:
             prediction_tokens: A tokenized predicted sentence.

src/torchmetrics/functional/text/rouge.py

@@ -490,7 +490,7 @@ def rouge_score(
     if not isinstance(rouge_keys, tuple):
         rouge_keys = (rouge_keys,)
     for key in rouge_keys:
-        if key not in ALLOWED_ROUGE_KEYS.keys():
+        if key not in ALLOWED_ROUGE_KEYS:
             raise ValueError(f"Got unknown rouge key {key}. Expected to be one of {list(ALLOWED_ROUGE_KEYS.keys())}")
     rouge_keys_values = [ALLOWED_ROUGE_KEYS[key] for key in rouge_keys]
 

src/torchmetrics/functional/text/sacre_bleu.py

@@ -333,7 +333,7 @@ def sacre_bleu_score(
     if tokenize not in AVAILABLE_TOKENIZERS:
         raise ValueError(f"Argument `tokenize` expected to be one of {AVAILABLE_TOKENIZERS} but got {tokenize}.")
 
-    if tokenize not in _SacreBLEUTokenizer._TOKENIZE_FN.keys():
+    if tokenize not in _SacreBLEUTokenizer._TOKENIZE_FN:
         raise ValueError(
             f"Unsupported tokenizer selected. Please, choose one of {list(_SacreBLEUTokenizer._TOKENIZE_FN.keys())}"
         )

src/torchmetrics/functional/text/squad.py

@@ -119,7 +119,7 @@ def _squad_input_check(
             )
 
         answers: Dict[str, Union[List[str], List[int]]] = target["answers"]  # type: ignore[assignment]
-        if "text" not in answers.keys():
+        if "text" not in answers:
             raise KeyError(
                 "Expected keys in a 'answers' are 'text'."
                 "Please make sure that 'answer' maps to a `SQuAD` format dictionary.\n"

src/torchmetrics/image/ssim.py

@@ -249,7 +249,7 @@ class MultiScaleStructuralSimilarityIndexMeasure(Metric):
             The ``data_range`` must be given when ``dim`` is not None.
         k1: Parameter of structural similarity index measure.
         k2: Parameter of structural similarity index measure.
-        betas: Exponent parameters for individual similarities and contrastive sensitivies returned by different image
+        betas: Exponent parameters for individual similarities and contrastive sensitivities returned by different image
             resolutions.
         normalize: When MultiScaleStructuralSimilarityIndexMeasure loss is used for training, it is desirable to use
             normalizes to improve the training stability. This `normalize` argument is out of scope of the original

src/torchmetrics/retrieval/fall_out.py

@@ -40,7 +40,7 @@ class RetrievalFallOut(RetrievalMetric):
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``fo@k`` (:class:`~torch.Tensor`): A tensor with the computed metric
+    - ``fallout@k`` (:class:`~torch.Tensor`): A tensor with the computed metric
 
     All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
     so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by

src/torchmetrics/text/__init__.py

@@ -45,8 +45,7 @@
 ]
 
 if _TRANSFORMERS_GREATER_EQUAL_4_4:
-    from torchmetrics.text.bert import BERTScore  # noqa: F401
-    from torchmetrics.text.infolm import InfoLM  # noqa: F401
+    from torchmetrics.text.bert import BERTScore
+    from torchmetrics.text.infolm import InfoLM
 
-    __all__.append("BERTScore")
-    __all__.append("InfoLM")
+    __all__ += ["BERTScore", "InfoLM"]

src/torchmetrics/wrappers/tracker.py

@@ -152,7 +152,7 @@ def compute_all(self) -> Any:
         """Compute the metric value for all tracked metrics.
 
         Return:
-            By default will try stacking the results from all increaments into a single tensor if the tracked base
+            By default will try stacking the results from all increments into a single tensor if the tracked base
             object is a single metric. If a metric collection is provided a dict of stacked tensors will be returned.
             If the stacking process fails a list of the computed results will be returned.
 

tests/unittests/bases/test_composition.py

@@ -67,7 +67,7 @@ def test_metrics_add(second_operand, expected_result):
 
 @pytest.mark.parametrize(
     ("second_operand", "expected_result"),
-    [(DummyMetric(3), tensor(2)), (3, tensor(2)), (3, tensor(2)), (tensor(3), tensor(2))],
+    [(DummyMetric(3), tensor(2)), (3, tensor(2)), (tensor(3), tensor(2))],
 )
 def test_metrics_and(second_operand, expected_result):
     """Test that `and` operator works and returns a compositional metric."""

tests/unittests/text/test_rouge.py

@@ -73,7 +73,7 @@ def _compute_rouge_score(
         aggregator_avg = BootstrapAggregator()
 
         if accumulate == "best":
-            key_curr = list(list_results[0].keys())[0]
+            key_curr = next(iter(list_results[0].keys()))
             all_fmeasure = torch.tensor([v[key_curr].fmeasure for v in list_results])
             highest_idx = torch.argmax(all_fmeasure).item()
             aggregator.add_scores(list_results[highest_idx])