feat: Replaced flake8 with ruff and removed setup.cfg by merging the configurations into pyproject.toml

.pre-commit-config.yaml

@@ -6,6 +6,12 @@ repos:
       - id: trailing-whitespace
       - id: check-toml
       - id: end-of-file-fixer
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.8
+    hooks:
+      # Run the linter.
+      - id: ruff
+        args: [ --fix ]
   - repo: https://github.com/psf/black-pre-commit-mirror
     rev: 23.12.0
     hooks:
@@ -23,21 +29,10 @@ repos:
     rev: v2.2.1
     hooks:
       - id: autoflake
-  - repo: https://github.com/pycqa/flake8
-    rev: 6.1.0
-    hooks:
-      - id: flake8
-        exclude: ^.*__init__.py$
   - repo: https://github.com/PyCQA/docformatter
     rev: v1.7.5
     hooks:
       - id: docformatter
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.8
-    hooks:
-      # Run the linter.
-      - id: ruff
-        args: [ --fix ]
   - repo: https://github.com/unifyai/lint-hook
     rev: a72ffb17562d919311653d7f593cb537d1245c19
     hooks:

ivy/__init__.py

@@ -800,7 +800,7 @@ class Node(str):
     pass  # Added for the finally statement
 try:
     from .compiler.replace_with import replace_with, transform_function
-except:
+except:  # noqa: E722
     pass
 finally:
     # Skip framework imports done by Ivy compiler for now

ivy/data_classes/array/experimental/linear_algebra.py

@@ -323,8 +323,8 @@ def multi_mode_dot(
             If True, the matrices or vectors in in the list are transposed.
             For complex tensors, the conjugate transpose is used.
         out
-            optional output array, for writing the result to. It must have a shape that the
-            result can broadcast to.
+            optional output array, for writing the result to.
+            It must have a shape that the result can broadcast to.
 
         Returns
         -------
@@ -334,8 +334,8 @@ def multi_mode_dot(
         Notes
         -----
         If no modes are specified, just assumes there is one matrix or vector per mode and returns:
-        :math:`\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }` # noqa
-        """
+        :math:`\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }`
+        """  # noqa: E501
         return ivy.multi_mode_dot(
             self._data, mat_or_vec_list, modes, skip, transpose, out=out
         )

ivy/data_classes/container/base.py

@@ -1155,7 +1155,7 @@ def cont_from_disk_as_hdf5(
             ),
         )
         container_dict = {}
-        if type(h5_obj_or_filepath) is str:
+        if isinstance(h5_obj_or_filepath, str):
             h5_obj = h5py.File(h5_obj_or_filepath, "r")
         else:
             h5_obj = h5_obj_or_filepath
@@ -1238,7 +1238,7 @@ def h5_file_size(h5_obj_or_filepath):
                 "the size of hdf5 files."
             ),
         )
-        if type(h5_obj_or_filepath) is str:
+        if isinstance(h5_obj_or_filepath, str):
             h5_obj = h5py.File(h5_obj_or_filepath, "r")
         else:
             h5_obj = h5_obj_or_filepath
@@ -1280,7 +1280,7 @@ def shuffle_h5_file(h5_obj_or_filepath, seed_value=0):
         )
         if seed_value is None:
             seed_value = random.randint(0, 1000)
-        if type(h5_obj_or_filepath) is str:
+        if isinstance(h5_obj_or_filepath, str):
             h5_obj = h5py.File(h5_obj_or_filepath, "a")
         else:
             h5_obj = h5_obj_or_filepath
@@ -1997,7 +1997,7 @@ def cont_to_disk_as_hdf5(
                 "containers to disk as hdf5 files."
             ),
         )
-        if type(h5_obj_or_filepath) is str:
+        if isinstance(h5_obj_or_filepath, str):
             h5_obj = h5py.File(h5_obj_or_filepath, mode)
         else:
             h5_obj = h5_obj_or_filepath

ivy/data_classes/factorized_tensor/cp_tensor.py

@@ -408,11 +408,11 @@ def cp_lstsq_grad(cp_tensor, tensor, return_loss=False, mask=None):
 
         .. math::
 
-            \nabla 0.5 ||\\mathcal{X} - [\\mathbf{w}; \\mathbf{A}, \\mathbf{B}, \\mathbf{C}]||^2 # noqa
+            \nabla 0.5 ||\\mathcal{X} - [\\mathbf{w}; \\mathbf{A}, \\mathbf{B}, \\mathbf{C}]||^2
 
         where :math:`[\\mathbf{w}; \\mathbf{A}, \\mathbf{B}, \\mathbf{C}]`
         is the CP decomposition with weights
-        :math:`\\mathbf{w}` and factor matrices :math:`\\mathbf{A}`, :math:`\\mathbf{B}` and :math:`\\mathbf{C}`. # noqa
+        :math:`\\mathbf{w}` and factor matrices :math:`\\mathbf{A}`, :math:`\\mathbf{B}` and :math:`\\mathbf{C}`.
         Note that this does not return the gradient
         with respect to the weights even if CP is normalized.
 
@@ -444,7 +444,7 @@ def cp_lstsq_grad(cp_tensor, tensor, return_loss=False, mask=None):
         loss : float
             Scalar quantity of the loss function corresponding to cp_gradient. Only returned
             if return_loss = True.
-        """
+        """  # noqa: E501
         ivy.CPTensor.validate_cp_tensor(cp_tensor)
         _, factors = cp_tensor
 

ivy/data_classes/factorized_tensor/parafac2_tensor.py

@@ -419,7 +419,7 @@ def parafac2_to_slices(parafac2_tensor, validate=True):
             weights = None
 
         decomposition = weights, (A, B, C), projections
-        I, _ = A.shape
+        I, _ = A.shape  # noqa: E741
         return [
             ivy.Parafac2Tensor.parafac2_to_slice(decomposition, i, validate=False)
             for i in range(I)

ivy/data_classes/factorized_tensor/tt_tensor.py

@@ -108,7 +108,7 @@ def validate_tt_tensor(tt_tensor):
     def tt_to_tensor(factors):
         """Return the full tensor whose TT decomposition is given by 'factors'.
 
-        Re-assembles 'factors', which represent a tensor in TT/Matrix-Product-State format # noqa: E501
+        Re-assembles 'factors', which represent a tensor in TT/Matrix-Product-State format
         into the corresponding full tensor
 
         Parameters
@@ -120,7 +120,7 @@ def tt_to_tensor(factors):
         -------
         output_tensor
             tensor whose TT/MPS decomposition was given by 'factors'
-        """
+        """  # noqa: E501
         if isinstance(factors, (float, int)):
             return factors
 
@@ -213,8 +213,8 @@ def validate_tt_rank(
             shape of the tensor to decompose
         rank
             way to determine the rank, by default 'same'
-            if 'same': rank is computed to keep the number of parameters (at most) the same # noqa: E501
-            if float, computes a rank so as to keep rank percent of the original number of parameters # noqa: E501
+            if 'same': rank is computed to keep the number of parameters (at most) the same
+            if float, computes a rank so as to keep rank percent of the original number of parameters
             if int or tuple, just returns rank
         constant_rank
             if True, the *same* rank will be chosen for each modes
@@ -233,7 +233,7 @@ def validate_tt_rank(
         -------
         rank
             rank of the decomposition
-        """
+        """  # noqa: E501
         if rounding == "ceil":
             rounding_fn = ivy.ceil
         elif rounding == "floor":

ivy/functional/backends/numpy/manipulation.py

@@ -70,7 +70,7 @@ def flip(
         return x
     if axis is None:
         axis = list(range(num_dims))
-    if type(axis) is int:
+    if isinstance(axis, int):
         axis = [axis]
     axis = [item + num_dims if item < 0 else item for item in axis]
     return np.flip(x, axis)

ivy/functional/backends/tensorflow/manipulation.py

@@ -85,7 +85,7 @@ def flip(
             new_axis = list(range(num_dims))
         else:
             new_axis = axis
-        if type(new_axis) is int:
+        if isinstance(new_axis, int):
             new_axis = [new_axis]
         else:
             new_axis = new_axis

ivy/functional/frontends/jax/numpy/manipulations.py

@@ -331,7 +331,7 @@ def transpose(a, axes=None):
         return a
     if not axes:
         axes = list(range(len(a.shape)))[::-1]
-    if type(axes) is int:
+    if isinstance(axes, int):
         axes = [axes]
     if (len(a.shape) == 0 and not axes) or (len(a.shape) == 1 and axes[0] == 0):
         return a

ivy/functional/frontends/numpy/manipulation_routines/transpose_like_operations.py

@@ -32,7 +32,7 @@ def swapaxes(a, axis1, axis2):
 def transpose(array, /, *, axes=None):
     if not axes:
         axes = list(range(len(array.shape)))[::-1]
-    if type(axes) is int:
+    if isinstance(axes, int):
         axes = [axes]
     if (len(array.shape) == 0 and not axes) or (len(array.shape) == 1 and axes[0] == 0):
         return array

ivy/functional/ivy/elementwise.py

@@ -121,7 +121,7 @@ def abs(
         a: ivy.array([0., 2.6, 3.5]),
         b: ivy.array([4.5, 5.3, 0., 2.3])
     }
-    """
+    """  # noqa: E501
     return ivy.current_backend(x).abs(x, out=out)
 
 
@@ -6109,7 +6109,7 @@ def sqrt(
         b: ivy.array([[7., 1.],
                       [0., 4.47]])
     }
-    """
+    """  # noqa: E501
     return ivy.current_backend(x).sqrt(x, out=out)
 
 

ivy/functional/ivy/experimental/layers.py

@@ -92,15 +92,15 @@ def max_pool1d(
 
        [[16., 17., 18., 19.]]])
     >>> x = ivy.arange(0, 24.).reshape((2, 3, 4))
-    >>> print(ivy.max_pool1d(x, 2, 2, [(1,0)], data_format="NCW", dilation=2, ceil_mode=True)) # noqa
+    >>> print(ivy.max_pool1d(x, 2, 2, [(1,0)], data_format="NCW", dilation=2, ceil_mode=True))
     ivy.array([[[ 1.,  3.],
             [ 5.,  7.],
             [ 9., 11.]],
 
         [[13., 15.],
             [17., 19.],
             [21., 23.]]])
-    """
+    """  # noqa: E501
     return ivy.current_backend(x).max_pool1d(
         x,
         kernel,

ivy/functional/ivy/experimental/linear_algebra.py

@@ -922,8 +922,8 @@ def multi_mode_dot(
     Notes
     -----
     If no modes are specified, just assumes there is one matrix or vector per mode and returns:
-    :math:`\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }` # noqa
-    """
+    :math:`\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }`
+    """  # noqa: E501
     if modes is None:
         modes = range(len(mat_or_vec_list))
 

ivy/functional/ivy/losses.py

@@ -159,7 +159,7 @@ def binary_cross_entropy(
     >>> x = ivy.array([[0, 1, 1, 0]])
     >>> y = ivy.array([[2.6, 6.2, 3.7, 5.3]])
     >>> pos_weight = ivy.array([1, 2, 3, 4])
-    >>> z = ivy.binary_cross_entropy(x, y, pos_weight=pos_weight, from_logits=True, reduction='sum', axis=1) # noqa: E501
+    >>> z = ivy.binary_cross_entropy(x, y, pos_weight=pos_weight, from_logits=True, reduction='sum', axis=1)
     ivy.array([8.05393649])
 
     >>> x = ivy.array([[0, 1, 1, 0]])
@@ -216,7 +216,7 @@ def binary_cross_entropy(
     >>> z = ivy.binary_cross_entropy(x, y)
     >>> print(z)
     ivy.array([0.223, 0.223, 0.223, 0.223])
-    """
+    """  # noqa: E501
     ivy.utils.assertions.check_elem_in_list(reduction, ["none", "sum", "mean"])
 
     if not (0.0 <= epsilon <= 1.0):

ivy/utils/backend/handler.py

@@ -96,7 +96,7 @@ def _determine_backend_from_args(args):
     >>> x = jnp.array([1])
     >>> print(_determine_backend_from_args(x))
     <module 'ivy.functional.backends.jax' from '/ivy/ivy/functional/backends/jax/__init__.py'>    # noqa
-    """
+    """  # noqa: E501
     arg_type = type(args)
     if isinstance(args, ivy.Array):
         args = args.data
@@ -175,7 +175,7 @@ def current_backend(*args, **kwargs):
     >>> x = np.array([2.0])
     >>> print(ivy.current_backend(x))
     <module 'ivy.functional.backends.jax' from '/ivy/ivy/functional/backends/jax/__init__.py'>   # noqa
-    """
+    """  # noqa: E501
     global implicit_backend
     # if a global backend has been set with
     # set_backend then this will be returned

ivy_tests/test_ivy/test_frontends/test_paddle/test_linalg.py

@@ -569,7 +569,7 @@ def test_paddle_eig(
     ret = [ivy.to_numpy(x).astype("float64") for x in ret]
     frontend_ret = [np.asarray(x, dtype=np.float64) for x in frontend_ret]
 
-    l, v = ret
+    l, v = ret  # noqa: E741
     front_l, front_v = frontend_ret
 
     assert_all_close(
@@ -619,7 +619,7 @@ def test_paddle_eigh(
     ret = [ivy.to_numpy(x).astype("float64") for x in ret]
     frontend_ret = [np.asarray(x, dtype=np.float64) for x in frontend_ret]
 
-    l, v = ret
+    l, v = ret  # noqa: E741
     front_l, front_v = frontend_ret
 
     assert_all_close(

ivy_tests/test_ivy/test_frontends/test_torch/test_linalg.py

@@ -458,7 +458,7 @@ def test_torch_eig(
     ret = [ivy.to_numpy(x).astype("float64") for x in ret]
     frontend_ret = [np.asarray(x, dtype=np.float64) for x in frontend_ret]
 
-    l, v = ret
+    l, v = ret  # noqa: E741
     front_l, front_v = frontend_ret
 
     assert_all_close(

ivy_tests/test_ivy/test_functional/test_core/test_device.py

@@ -372,7 +372,7 @@ def test_num_cpu_cores(backend_fw):
         # using multiprocessing module too because ivy uses psutil as basis.
         p_cpu_cores = psutil.cpu_count()
         m_cpu_cores = multiprocessing.cpu_count()
-        assert type(ivy_backend.num_cpu_cores()) == int
+        assert isinstance(ivy_backend.num_cpu_cores(), int)
         assert ivy_backend.num_cpu_cores() == p_cpu_cores
         assert ivy_backend.num_cpu_cores() == m_cpu_cores
 

pyproject.toml

@@ -6,12 +6,41 @@ requires = [
 ]
 build-backend = "setuptools.build_meta"
 
+
+[tool.docformatter]
+wrap-summaries = 88
+pre-summary-newline = true
+
+
+[tool.autoflake]
+in-place = true
+remove-all-unused-imports = true
+ignore-init-module-imports = true
+remove-duplicate-keys = true
+remove-unused-variables = true
+quiet = true
+ignore-pass-after-docstring = true
+exclude = ["__init__.py"]
+
+
 [tool.ruff]
+line-length = 88
 target-version = "py38"
 
-select = ["D"] # Enabling Only pydocstyle checks as of now (https://docs.astral.sh/ruff/rules/#pydocstyle-d)
+select = [
+    # pyflakes
+    "F",
+    # pycodestyle
+    "E", "W",
+    # pydocstyle
+    "D"
+]
+
 
 ignore = [
+    "E203",	# Whitespace-before-punctuation.
+    "E402", # Module-import-not-at-top-of-file.
+    "E731", # Do not assign a lambda expression, use a def.
     "D100", # Missing docstring in public module.
     "D101", # Missing docstring in public class.
     "D102", # Missing docstring in public method.
@@ -32,6 +61,7 @@ ignore = [
     "D417", # Missing argument description in the docstring for argument "X".
 ]
 
-exclude = [
-'ivy/functional/(frontends|backends)/(?!.*/func_wrapper\.py$).*(?!__init__\.py$)',
-]
+[tool.ruff.per-file-ignores]
+'ivy/functional/(frontends|backends)/(?!.*/func_wrapper\.py$).*(?!__init__\.py$)' = ["D"]
+"**/__init__.py" = ["F401","F403","F405","F811","F821", "E501"]
+"ivy/functional/frontends/paddle/**" = ["F401", "F403", "F405"]