resolve all complains from current ruff

docs/utils/__init__.py

@@ -2,6 +2,7 @@
 
 from PIL.Image import fromarray
 from IPython import get_ipython
+from IPython.display import display_html
 
 
 def display_np_arrays_as_images():
@@ -23,7 +24,7 @@ def np_to_text(obj, p, cycle):
  get_ipython().display_formatter.formatters['text/plain'].for_type(np.ndarray, np_to_text)
 
 
-from IPython.display import display_html
+
 
 _style_inline = """<style>
 .einops-answer {

einops/__init__.py

@@ -1,3 +1,6 @@
+# imports can use EinopsError class
+# ruff: noqa: E402
+
 __author__ = 'Alex Rogozhnikov'
 __version__ = '0.7.0'
 
@@ -11,5 +14,5 @@ class EinopsError(RuntimeError):
  'pack', 'unpack',
  'parse_shape', 'asnumpy', 'EinopsError']
 
-from .einops import rearrange, reduce, repeat, einsum, parse_shape, asnumpy
+from .einops import rearrange, reduce, repeat, einsum, parse_shape, asnumpy 
 from .packing import pack, unpack

einops/_backends.py

@@ -429,7 +429,7 @@ def shape(self, x):
  try:
  hash(shape)
  return shape
- except:
+ except BaseException:
  # unhashable symbols in shape. Wrap tuple to be hashable.
  return HashableTuple(shape)
 

einops/einops.py

@@ -7,7 +7,7 @@
 
 if typing.TYPE_CHECKING:
  # for docstrings in pycharm
- import numpy as np
+ import numpy as np # noqa E401
 
 from . import EinopsError
 from ._backends import get_backend

pyproject.toml

@@ -94,10 +94,7 @@ filterwarnings = [
  "ignore:Call to deprecated create function OneofDescriptor",
 ]
 
-[tool.black]
-line-length = 120
-target-version = ['py311']
-
 
 [tool.ruff]
 line-length = 120
+target-version = 'py311'

tests/test_einsum.py

@@ -1,5 +1,4 @@
 from typing import Any, Callable
-from venv import create
 from . import collect_test_backends
 from einops.einops import _compactify_pattern_for_einsum, einsum, EinopsError
 import numpy as np
@@ -267,7 +266,8 @@ def test_functional_errors():
  # during the pattern creation.
 
  rstate = np.random.RandomState(0)
- create_tensor = lambda *shape: rstate.uniform(size=shape).astype('float32')
+ def create_tensor(*shape):
+ return rstate.uniform(size=shape).astype('float32')
 
  # raise NotImplementedError("Singleton () axes are not yet supported in einsum.")
  with pytest.raises(NotImplementedError, match="^Singleton"):

tests/test_examples.py

@@ -111,7 +111,7 @@ def unet_like_1d(x, usual_convolution):
  return x
 
  # mock for convolution (works for all backends)
- convolve_mock = lambda x: x
+ def convolve_mock(x): return x
 
  tests = [test1, test2, test3, test4, test5, test6, test7, test8, test9, test10, test11,
  lambda x: shufflenet(x, convolve=convolve_mock, c1=4, c2=5),

tests/test_layers.py

@@ -45,7 +45,7 @@ def test_rearrange_imperative():
  for shape in wrong_shapes:
  try:
  layer(backend.from_numpy(numpy.zeros(shape, dtype="float32")))
- except:
+ except BaseException:
  pass
  else:
  raise AssertionError("Failure expected")
@@ -119,7 +119,7 @@ def test_reduce_imperative():
  for shape in wrong_shapes:
  try:
  layer(backend.from_numpy(numpy.zeros(shape, dtype="float32")))
- except:
+ except BaseException:
  pass
  else:
  raise AssertionError("Failure expected")
@@ -359,7 +359,7 @@ def __call__(self, x):
  model = NN()
  fixed_input = jnp.ones([10, 2 * 2, 3 * 3, 4])
  params = model.init(jax.random.PRNGKey(0), fixed_input)
- eval_at_point = lambda params: jnp.linalg.norm(model.apply(params, fixed_input))
+ def eval_at_point(params): return jnp.linalg.norm(model.apply(params, fixed_input))
 
  vandg = jax.value_and_grad(eval_at_point)
  value0 = eval_at_point(params)

tests/test_notebooks.py

@@ -2,7 +2,7 @@
 
 from io import StringIO
 
-from tests import parse_backends_to_test, is_backend_tested
+from tests import is_backend_tested
 
 __author__ = "Alex Rogozhnikov"
 

tests/test_packing.py

@@ -37,7 +37,7 @@ def unpack_and_pack_against_numpy(x, ps, pattern: str):
  unpacked_np = unpack(x_np, ps, pattern)
  packed_np, ps3 = pack(unpacked_np, pattern=pattern)
 
- assert type(capturer_numpy.exception) == type(capturer_backend.exception)
+ assert type(capturer_numpy.exception) == type(capturer_backend.exception) # noqa E721
  if capturer_numpy.exception is not None:
  # both failed
  return