[pre-commit.ci] pre-commit suggestions (#1688)

* [pre-commit.ci] pre-commit suggestions

updates:
- [github.com/psf/black: 23.1.0 → 23.3.0](psf/black@23.1.0...23.3.0)
- [github.com/charliermarsh/ruff-pre-commit: v0.0.255 → v0.0.260](astral-sh/ruff-pre-commit@v0.0.255...v0.0.260)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* S301

* S310

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka <jirka.borovec@seznam.cz>
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>

.pre-commit-config.yaml

@@ -58,7 +58,7 @@ repos:
  require_serial: false
 
  - repo: https://github.com/psf/black
- rev: 23.1.0
+ rev: 23.3.0
  hooks:
  - id: black
  name: Format code
@@ -87,7 +87,7 @@ repos:
  - flake8-bandit
 
  - repo: https://github.com/charliermarsh/ruff-pre-commit
- rev: v0.0.255
+ rev: v0.0.260
  hooks:
  - id: ruff
  args: ["--fix"]

pyproject.toml

@@ -92,6 +92,8 @@ ignore = [
  "D107", # Missing docstring in `__init__`
  "ANN101", # Missing type annotation for `self` in method
  "ANN102", # Missing type annotation for `cls` in classmethod
+ "S301", # `pickle` and modules that wrap it can be unsafe when used to deserialize untrusted data, possible security issue # todo
+ "S310", # Audit URL open for permitted schemes. Allowing use of `file:` or custom schemes is often unexpected. # todo
 ]
 # Exclude a variety of commonly ignored directories.
 exclude = [

src/torchmetrics/collections.py

@@ -384,7 +384,7 @@ def _init_compute_groups(self) -> None:
  simply initialize each metric in the collection as its own group
  """
  if isinstance(self._enable_compute_groups, list):
- self._groups = {i: k for i, k in enumerate(self._enable_compute_groups)}
+ self._groups = dict(enumerate(self._enable_compute_groups))
  for v in self._groups.values():
  for metric in v:
  if metric not in self:

src/torchmetrics/text/chrf.py

@@ -166,12 +166,9 @@ def compute(self) -> Union[Tensor, Tuple[Tensor, Tensor]]:
 
  def _convert_states_to_dicts(self) -> _DICT_STATES_TYPES:
  """Convert global metric states to the n-gram dictionaries to be passed in ``_chrf_score_update``."""
- n_grams_dicts: Dict[str, Dict[int, Tensor]] = {
- name: n_gram_dict
- for name, n_gram_dict in zip(
- _DICT_STATES_NAMES, _prepare_n_grams_dicts(self.n_char_order, self.n_word_order)
- )
- }
+ n_grams_dicts: Dict[str, Dict[int, Tensor]] = dict(
+ zip(_DICT_STATES_NAMES, _prepare_n_grams_dicts(self.n_char_order, self.n_word_order))
+ )
 
  for (n_gram_level, n_gram_order), text in self._get_text_n_gram_iterator():
  for n in range(1, n_gram_order + 1):
@@ -184,7 +181,7 @@ def _convert_states_to_dicts(self) -> _DICT_STATES_TYPES:
 
  def _update_states_from_dicts(self, n_grams_dicts_tuple: _DICT_STATES_TYPES) -> None:
  """Update global metric states based on the n-gram dictionaries calculated on the current batch."""
- n_grams_dicts = {name: n_gram_dict for name, n_gram_dict, in zip(_DICT_STATES_NAMES, n_grams_dicts_tuple)}
+ n_grams_dicts = dict(zip(_DICT_STATES_NAMES, n_grams_dicts_tuple))
  for (n_gram_level, n_gram_order), text in self._get_text_n_gram_iterator():
  for n in range(1, n_gram_order + 1):
  dict_name = self._get_dict_name(text, n_gram_level)

src/torchmetrics/wrappers/tracker.py

@@ -299,7 +299,7 @@ def plot(
  >>> fig_, ax_ = tracker.plot() # plot all epochs
 
  """
- val = val if val is not None else [val for val in self.compute_all()]
+ val = val if val is not None else list(self.compute_all())
  fig, ax = plot_single_or_multi_val(
  val,
  ax=ax,