Bump mypy from 0.982 to 1.0.0 in /requirements (#1516)

* Bump mypy from 0.982 to 1.0.0 in /requirements

Bumps [mypy](https://github.com/python/mypy) from 0.982 to 1.0.0.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](python/mypy@v0.982...v1.0.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <[email protected]>

* mypy: Fix PEP 484 issues
* mypy: Drop unused ignore comments
* mypy: Function "Callable[[], bool]" could always be true in boolean context  [truthy-function]
* Apply suggestions from code review
* ignore
* optional

---------

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: stancld <[email protected]>
Co-authored-by: Jirka Borovec <[email protected]>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
Co-authored-by: Jirka <[email protected]>

requirements/typing.txt

@@ -1,4 +1,5 @@
-mypy==0.982
+mypy==1.0.0
+
 types-PyYAML
 types-emoji
 types-protobuf

src/torchmetrics/functional/regression/mae.py

@@ -29,8 +29,8 @@ def _mean_absolute_error_update(preds: Tensor, target: Tensor) -> Tuple[Tensor,
         target: Ground truth tensor
     """
     _check_same_shape(preds, target)
-    preds = preds if preds.is_floating_point else preds.float()
-    target = target if target.is_floating_point else target.float()
+    preds = preds if preds.is_floating_point else preds.float()  # type: ignore[truthy-function] # todo
+    target = target if target.is_floating_point else target.float()  # type: ignore[truthy-function] # todo
     sum_abs_error = torch.sum(torch.abs(preds - target))
     n_obs = target.numel()
     return sum_abs_error, n_obs

src/torchmetrics/functional/text/bert.py

@@ -60,7 +60,7 @@ def _get_embeddings_and_idf_scale(
     all_layers: bool = False,
     idf: bool = False,
     verbose: bool = False,
-    user_forward_fn: Callable[[Module, Dict[str, Tensor]], Tensor] = None,
+    user_forward_fn: Optional[Callable[[Module, Dict[str, Tensor]], Tensor]] = None,
 ) -> Tuple[Tensor, Tensor]:
     """Calculate sentence embeddings and the inverse-document-frequency scaling factor.
 
@@ -249,7 +249,7 @@ def bert_score(
     all_layers: bool = False,
     model: Optional[Module] = None,
     user_tokenizer: Any = None,
-    user_forward_fn: Callable[[Module, Dict[str, Tensor]], Tensor] = None,
+    user_forward_fn: Optional[Callable[[Module, Dict[str, Tensor]], Tensor]] = None,
     verbose: bool = False,
     idf: bool = False,
     device: Optional[Union[str, torch.device]] = None,

src/torchmetrics/functional/text/rouge.py

@@ -163,8 +163,8 @@ def find_union(lcs_tables: Sequence[Sequence[int]]) -> Sequence[int]:
 def _normalize_and_tokenize_text(
     text: str,
     stemmer: Optional[Any] = None,
-    normalizer: Callable[[str], str] = None,
-    tokenizer: Callable[[str], Sequence[str]] = None,
+    normalizer: Optional[Callable[[str], str]] = None,
+    tokenizer: Optional[Callable[[str], Sequence[str]]] = None,
 ) -> Sequence[str]:
     """Rouge score should be calculated only over lowercased words and digits. Optionally, Porter stemmer can be
     used to strip word suffixes to improve matching. The text normalization follows the implemantion from `Rouge
@@ -282,8 +282,8 @@ def _rouge_score_update(
     rouge_keys_values: List[Union[int, str]],
     accumulate: str,
     stemmer: Optional[Any] = None,
-    normalizer: Callable[[str], str] = None,
-    tokenizer: Callable[[str], Sequence[str]] = None,
+    normalizer: Optional[Callable[[str], str]] = None,
+    tokenizer: Optional[Callable[[str], Sequence[str]]] = None,
 ) -> Dict[Union[int, str], List[Dict[str, Tensor]]]:
     """Update the rouge score with the current set of predicted and target sentences.
 
@@ -412,8 +412,8 @@ def rouge_score(
     target: Union[str, Sequence[str], Sequence[Sequence[str]]],
     accumulate: Literal["avg", "best"] = "best",
     use_stemmer: bool = False,
-    normalizer: Callable[[str], str] = None,
-    tokenizer: Callable[[str], Sequence[str]] = None,
+    normalizer: Optional[Callable[[str], str]] = None,
+    tokenizer: Optional[Callable[[str], Sequence[str]]] = None,
     rouge_keys: Union[str, Tuple[str, ...]] = ("rouge1", "rouge2", "rougeL", "rougeLsum"),
 ) -> Dict[str, Tensor]:
     """Calculate `Calculate Rouge Score`_ , used for automatic summarization.

src/torchmetrics/metric.py

@@ -688,7 +688,7 @@ def persistent(self, mode: bool = False) -> None:
 
     def state_dict(  # type: ignore[override]  # todo
         self,
-        destination: Dict[str, Any] = None,
+        destination: Optional[Dict[str, Any]] = None,
         prefix: str = "",
         keep_vars: bool = False,
     ) -> Dict[str, Any]:

src/torchmetrics/text/bert.py

@@ -133,7 +133,7 @@ def __init__(
         all_layers: bool = False,
         model: Optional[Module] = None,
         user_tokenizer: Optional[Any] = None,
-        user_forward_fn: Callable[[Module, Dict[str, Tensor]], Tensor] = None,
+        user_forward_fn: Optional[Callable[[Module, Dict[str, Tensor]], Tensor]] = None,
         verbose: bool = False,
         idf: bool = False,
         device: Optional[Union[str, torch.device]] = None,

src/torchmetrics/text/rouge.py

@@ -11,7 +11,7 @@
 # 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 Any, Callable, Dict, List, Sequence, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 from torch import Tensor
 from typing_extensions import Literal
@@ -97,8 +97,8 @@ class ROUGEScore(Metric):
     def __init__(
         self,
         use_stemmer: bool = False,
-        normalizer: Callable[[str], str] = None,
-        tokenizer: Callable[[str], Sequence[str]] = None,
+        normalizer: Optional[Callable[[str], str]] = None,
+        tokenizer: Optional[Callable[[str], Sequence[str]]] = None,
         accumulate: Literal["avg", "best"] = "best",
         rouge_keys: Union[str, Tuple[str, ...]] = ("rouge1", "rouge2", "rougeL", "rougeLsum"),
         **kwargs: Any,