[pre-commit.ci] pre-commit autoupdate (#1332)

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

updates:
- [github.com/psf/black-pre-commit-mirror: 23.12.1 → 24.1.1](psf/black-pre-commit-mirror@23.12.1...24.1.1)

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

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

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Fabian Hoppe <112093564+mrfh92@users.noreply.github.com>
Co-authored-by: Michael Tarnawa <m.tarnawa@fz-juelich.de>

.pre-commit-config.yaml

@@ -10,7 +10,7 @@ repos:
     -   id: check-added-large-files
     -   id: check-toml
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.12.1
+    rev: 24.1.1
     hooks:
     -   id: black
 -   repo: https://github.com/PyCQA/flake8

heat/classification/kneighborsclassifier.py

@@ -1,4 +1,5 @@
 """Implements the k-nearest neighbors (kNN) classifier"""
+
 from typing import Callable
 
 import heat as ht

heat/cluster/kmeans.py

@@ -1,6 +1,7 @@
 """
 Module Implementing the Kmeans Algorithm
 """
+
 from typing import Optional, Union, TypeVar
 
 import heat as ht

heat/cluster/kmedians.py

@@ -1,6 +1,7 @@
 """
 Module Implementing the Kmedians Algorithm
 """
+
 import heat as ht
 from heat.cluster._kcluster import _KCluster
 from heat.core.dndarray import DNDarray

heat/cluster/kmedoids.py

@@ -1,6 +1,7 @@
 """
 Module Implementing the Kmedoids Algorithm
 """
+
 import heat as ht
 from heat.cluster._kcluster import _KCluster
 from heat.core.dndarray import DNDarray

heat/core/communication.py

@@ -1,6 +1,7 @@
 """
 Module implementing the communication layer of HeAT
 """
+
 from __future__ import annotations
 
 import numpy as np

heat/core/dndarray.py

@@ -1,4 +1,5 @@
 """Provides HeAT's core data structure, the DNDarray, a distributed n-dimensional array"""
+
 from __future__ import annotations
 
 import math

heat/core/factories.py

@@ -318,9 +318,11 @@ def array(
             try:
                 obj = torch.tensor(
                     obj,
-                    device=device.torch_device
-                    if device is not None
-                    else devices.get_device().torch_device,
+                    device=(
+                        device.torch_device
+                        if device is not None
+                        else devices.get_device().torch_device
+                    ),
                 )
             except RuntimeError:
                 raise TypeError(f"invalid data of type {type(obj)}")
@@ -341,9 +343,9 @@ def array(
         try:
             obj = torch.as_tensor(
                 obj,
-                device=device.torch_device
-                if device is not None
-                else devices.get_device().torch_device,
+                device=(
+                    device.torch_device if device is not None else devices.get_device().torch_device
+                ),
             )
         except RuntimeError:
             raise TypeError(f"invalid data of type {type(obj)}")

heat/core/io.py

@@ -1,4 +1,5 @@
 """Enables parallel I/O with data on disk."""
+
 from __future__ import annotations
 
 import os.path

heat/core/linalg/basics.py

@@ -1,6 +1,7 @@
 """
 Basic linear algebra operations on distributed ``DNDarray``
 """
+
 import itertools
 import numpy as np
 import torch

heat/core/linalg/qr.py

@@ -1,6 +1,7 @@
 """
 QR decomposition of (distributed) 2-D ``DNDarray``s.
 """
+
 import collections
 import torch
 from typing import Type, Callable, Dict, Any, TypeVar, Union, Tuple

heat/core/linalg/solver.py

@@ -1,6 +1,7 @@
 """
 Collection of solvers for systems of linear equations.
 """
+
 import heat as ht
 from ..dndarray import DNDarray
 from ..sanitation import sanitize_out

heat/core/linalg/svd.py

@@ -1,6 +1,7 @@
 """
 file for future "full" SVD implementation
 """
+
 from typing import Tuple
 from ..dndarray import DNDarray
 

heat/core/linalg/svdtools.py

@@ -1,6 +1,7 @@
 """
 distributed hierarchical SVD
 """
+
 import numpy as np
 import collections
 import torch

heat/core/logical.py

@@ -161,10 +161,10 @@ def allclose(
     return bool(_local_allclose.item())
 
 
-DNDarray.allclose: Callable[
-    [DNDarray, DNDarray, float, float, bool], bool
-] = lambda self, other, rtol=1e-05, atol=1e-08, equal_nan=False: allclose(
-    self, other, rtol, atol, equal_nan
+DNDarray.allclose: Callable[[DNDarray, DNDarray, float, float, bool], bool] = (
+    lambda self, other, rtol=1e-05, atol=1e-08, equal_nan=False: allclose(
+        self, other, rtol, atol, equal_nan
+    )
 )
 DNDarray.allclose.__doc__ = all.__doc__
 
@@ -219,9 +219,9 @@ def local_any(t, *args, **kwargs):
     )
 
 
-DNDarray.any: Callable[
-    [DNDarray, Optional[int], Optional[DNDarray], bool], DNDarray
-] = lambda self, axis=None, out=None, keepdims=False: any(self, axis, out, keepdims)
+DNDarray.any: Callable[[DNDarray, Optional[int], Optional[DNDarray], bool], DNDarray] = (
+    lambda self, axis=None, out=None, keepdims=False: any(self, axis, out, keepdims)
+)
 DNDarray.any.__doc__ = any.__doc__
 
 
@@ -376,10 +376,10 @@ def isposinf(x: DNDarray, out: Optional[DNDarray] = None):
     return _operations.__local_op(torch.isposinf, x, out, no_cast=True)
 
 
-DNDarray.isclose: Callable[
-    [DNDarray, DNDarray, float, float, bool], DNDarray
-] = lambda self, other, rtol=1e-05, atol=1e-08, equal_nan=False: isclose(
-    self, other, rtol, atol, equal_nan
+DNDarray.isclose: Callable[[DNDarray, DNDarray, float, float, bool], DNDarray] = (
+    lambda self, other, rtol=1e-05, atol=1e-08, equal_nan=False: isclose(
+        self, other, rtol, atol, equal_nan
+    )
 )
 DNDarray.isclose.__doc__ = isclose.__doc__
 

heat/core/manipulations.py

@@ -1,6 +1,7 @@
 """
 Manipulation operations for (potentially distributed) `DNDarray`s.
 """
+
 from __future__ import annotations
 
 import numpy as np
@@ -3010,9 +3011,9 @@ def squeeze(x: DNDarray, axis: Union[int, Tuple[int, ...]] = None) -> DNDarray:
     )
 
 
-DNDarray.squeeze: Callable[
-    [DNDarray, Union[int, Tuple[int, ...]]], DNDarray
-] = lambda self, axis=None: squeeze(self, axis)
+DNDarray.squeeze: Callable[[DNDarray, Union[int, Tuple[int, ...]]], DNDarray] = (
+    lambda self, axis=None: squeeze(self, axis)
+)
 DNDarray.squeeze.__doc__ = squeeze.__doc__
 
 
@@ -3404,10 +3405,10 @@ def unique(
     return return_value
 
 
-DNDarray.unique: Callable[
-    [DNDarray, bool, bool, int], Tuple[DNDarray, torch.tensor]
-] = lambda self, sorted=False, return_inverse=False, axis=None: unique(
-    self, sorted, return_inverse, axis
+DNDarray.unique: Callable[[DNDarray, bool, bool, int], Tuple[DNDarray, torch.tensor]] = (
+    lambda self, sorted=False, return_inverse=False, axis=None: unique(
+        self, sorted, return_inverse, axis
+    )
 )
 DNDarray.unique.__doc__ = unique.__doc__
 

heat/core/random.py

@@ -1,4 +1,5 @@
 """Provides parallel random number generators (pRNG)"""
+
 from __future__ import annotations
 
 import time

heat/core/relational.py

@@ -1,6 +1,7 @@
 """
 Functions for relational oprations, i.e. equal/no equal...
 """
+
 from __future__ import annotations
 
 import torch

heat/core/rounding.py

@@ -60,9 +60,9 @@ def abs(
     return absolute_values
 
 
-DNDarray.abs: Callable[
-    [DNDarray, Optional[DNDarray], Optional[datatype]], DNDarray
-] = lambda self, out=None, dtype=None: abs(self, out, dtype)
+DNDarray.abs: Callable[[DNDarray, Optional[DNDarray], Optional[datatype]], DNDarray] = (
+    lambda self, out=None, dtype=None: abs(self, out, dtype)
+)
 DNDarray.abs.__doc__ = abs.__doc__
 
 
@@ -87,9 +87,9 @@ def absolute(
     return abs(x, out, dtype)
 
 
-DNDarray.absolute: Callable[
-    [DNDarray, Optional[DNDarray], Optional[datatype]], DNDarray
-] = lambda self, out=None, dtype=None: absolute(self, out, dtype)
+DNDarray.absolute: Callable[[DNDarray, Optional[DNDarray], Optional[datatype]], DNDarray] = (
+    lambda self, out=None, dtype=None: absolute(self, out, dtype)
+)
 DNDarray.absolute.__doc__ = absolute.__doc__
 
 
@@ -330,9 +330,9 @@ def round(
     return rounded_values
 
 
-DNDarray.round: Callable[
-    [DNDarray, int, Optional[DNDarray], Optional[datatype]], DNDarray
-] = lambda self, decimals=0, out=None, dtype=None: round(self, decimals, out, dtype)
+DNDarray.round: Callable[[DNDarray, int, Optional[DNDarray], Optional[datatype]], DNDarray] = (
+    lambda self, decimals=0, out=None, dtype=None: round(self, decimals, out, dtype)
+)
 DNDarray.round.__doc__ = round.__doc__
 
 

heat/core/sanitation.py

@@ -1,6 +1,7 @@
 """
 Collection of validation/sanitation routines.
 """
+
 from __future__ import annotations
 
 import numpy as np

heat/core/statistics.py

@@ -1,6 +1,7 @@
 """
 Distributed statistical operations.
 """
+
 import numpy as np
 import torch
 from typing import Any, Callable, Union, Tuple, List, Optional
@@ -108,9 +109,9 @@ def local_argmax(*args, **kwargs):
     )
 
 
-DNDarray.argmax: Callable[
-    [DNDarray, int, DNDarray, object], DNDarray
-] = lambda self, axis=None, out=None, **kwargs: argmax(self, axis, out, **kwargs)
+DNDarray.argmax: Callable[[DNDarray, int, DNDarray, object], DNDarray] = (
+    lambda self, axis=None, out=None, **kwargs: argmax(self, axis, out, **kwargs)
+)
 DNDarray.argmax.__doc__ = argmax.__doc__
 
 
@@ -180,9 +181,9 @@ def local_argmin(*args, **kwargs):
     )
 
 
-DNDarray.argmin: Callable[
-    [DNDarray, int, DNDarray, object], DNDarray
-] = lambda self, axis=None, out=None, **kwargs: argmin(self, axis, out, **kwargs)
+DNDarray.argmin: Callable[[DNDarray, int, DNDarray, object], DNDarray] = (
+    lambda self, axis=None, out=None, **kwargs: argmin(self, axis, out, **kwargs)
+)
 DNDarray.argmin.__doc__ = argmin.__doc__
 
 
@@ -773,9 +774,9 @@ def kurtosis(
         return __moment_w_axis(__torch_kurtosis, x, axis, None, unbiased, Fischer)
 
 
-DNDarray.kurtosis: Callable[
-    [DNDarray, int, bool, bool], DNDarray
-] = lambda x, axis=None, unbiased=True, Fischer=True: kurtosis(x, axis, unbiased, Fischer)
+DNDarray.kurtosis: Callable[[DNDarray, int, bool, bool], DNDarray] = (
+    lambda x, axis=None, unbiased=True, Fischer=True: kurtosis(x, axis, unbiased, Fischer)
+)
 DNDarray.kurtosis.__doc__ = average.__doc__
 
 
@@ -832,9 +833,9 @@ def local_max(*args, **kwargs):
     )
 
 
-DNDarray.max: Callable[
-    [DNDarray, Union[int, Tuple[int, ...]], DNDarray, bool], DNDarray
-] = lambda x, axis=None, out=None, keepdims=None: max(x, axis, out, keepdims)
+DNDarray.max: Callable[[DNDarray, Union[int, Tuple[int, ...]], DNDarray, bool], DNDarray] = (
+    lambda x, axis=None, out=None, keepdims=None: max(x, axis, out, keepdims)
+)
 DNDarray.max.__doc__ = max.__doc__
 
 
@@ -1035,9 +1036,9 @@ def median(x: DNDarray, axis: Optional[int] = None, keepdims: bool = False) -> D
     return percentile(x, q=50, axis=axis, keepdims=keepdims)
 
 
-DNDarray.median: Callable[
-    [DNDarray, int, bool], DNDarray
-] = lambda x, axis=None, keepdims=False: median(x, axis, keepdims)
+DNDarray.median: Callable[[DNDarray, int, bool], DNDarray] = (
+    lambda x, axis=None, keepdims=False: median(x, axis, keepdims)
+)
 DNDarray.mean.__doc__ = mean.__doc__
 
 
@@ -1164,9 +1165,9 @@ def local_min(*args, **kwargs):
     )
 
 
-DNDarray.min: Callable[
-    [DNDarray, Union[int, Tuple[int, ...]], DNDarray, bool], DNDarray
-] = lambda self, axis=None, out=None, keepdims=None: min(self, axis, out, keepdims)
+DNDarray.min: Callable[[DNDarray, Union[int, Tuple[int, ...]], DNDarray, bool], DNDarray] = (
+    lambda self, axis=None, out=None, keepdims=None: min(self, axis, out, keepdims)
+)
 DNDarray.min.__doc__ = min.__doc__
 
 
@@ -1709,9 +1710,9 @@ def skew(x: DNDarray, axis: int = None, unbiased: bool = True) -> DNDarray:
         return __moment_w_axis(__torch_skew, x, axis, None, unbiased)
 
 
-DNDarray.skew: Callable[
-    [DNDarray, int, bool], DNDarray
-] = lambda self, axis=None, unbiased=True: skew(self, axis, unbiased)
+DNDarray.skew: Callable[[DNDarray, int, bool], DNDarray] = (
+    lambda self, axis=None, unbiased=True: skew(self, axis, unbiased)
+)
 DNDarray.skew.__doc__ = skew.__doc__
 
 
@@ -1772,9 +1773,9 @@ def std(
     return exponential.sqrt(var(x, axis, ddof, **kwargs), out=None)
 
 
-DNDarray.std: Callable[
-    [DNDarray, Union[int, Tuple[int], List[int]], int, object], DNDarray
-] = lambda self, axis=None, ddof=0, **kwargs: std(self, axis, ddof, **kwargs)
+DNDarray.std: Callable[[DNDarray, Union[int, Tuple[int], List[int]], int, object], DNDarray] = (
+    lambda self, axis=None, ddof=0, **kwargs: std(self, axis, ddof, **kwargs)
+)
 DNDarray.std.__doc__ = std.__doc__
 
 
@@ -1988,7 +1989,7 @@ def reduce_vars_elementwise(output_shape_i: torch.Tensor) -> DNDarray:
         return __moment_w_axis(torch.var, x, axis, reduce_vars_elementwise, unbiased)
 
 
-DNDarray.var: Callable[
-    [DNDarray, Union[int, Tuple[int], List[int]], int, object], DNDarray
-] = lambda self, axis=None, ddof=0, **kwargs: var(self, axis, ddof, **kwargs)
+DNDarray.var: Callable[[DNDarray, Union[int, Tuple[int], List[int]], int, object], DNDarray] = (
+    lambda self, axis=None, ddof=0, **kwargs: var(self, axis, ddof, **kwargs)
+)
 DNDarray.var.__doc__ = var.__doc__

heat/core/tiling.py

@@ -2,7 +2,6 @@
 Tiling functions/classes. With these classes, you can classes you can address blocks of data in a DNDarray
 """
 
-
 from __future__ import annotations
 import itertools
 import torch