lintfixbot: Auto-commit fixed lint errors in codebase

ivy/functional/backends/jax/device.py

@@ -47,11 +47,12 @@ def dev(
 
 
 def to_device(
-    x: JaxArray, device: jaxlib.xla_extension.Device,
+    x: JaxArray,
+    device: jaxlib.xla_extension.Device,
     /,
     *,
     stream: Optional[int] = None,
-    out: Optional[JaxArray] = None
+    out: Optional[JaxArray] = None,
 ):
     if device is not None:
         cur_dev = as_native_dev(dev(x))

ivy/functional/backends/numpy/device.py

@@ -61,11 +61,12 @@ def _to_device(x: np.ndarray, device=None) -> np.ndarray:
 
 
 def to_device(
-    x: np.ndarray, device: str,
+    x: np.ndarray,
+    device: str,
     /,
     *,
     stream: Optional[Union[int, Any]] = None,
-    out: Optional[np.ndarray] = None
+    out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     if device is not None:
         device = as_native_dev(device)

ivy/functional/backends/tensorflow/layers.py

@@ -46,7 +46,7 @@ def conv1d_transpose(
     dilations: Optional[int] = 1,
     out: Optional[Union[tf.Tensor, tf.Variable]] = None,
 ):
-    if ivy.dev(x) == 'cpu' and (
+    if ivy.dev(x) == "cpu" and (
         (dilations > 1) if isinstance(dilations, int) else any(d > 1 for d in dilations)
     ):
         raise ivy.exceptions.IvyException(
@@ -109,7 +109,7 @@ def conv2d_transpose(
     dilations: Optional[Union[int, Tuple[int, int]]] = 1,
     out: Optional[Union[tf.Tensor, tf.Variable]] = None,
 ):
-    if ivy.dev(x) == 'cpu' and (
+    if ivy.dev(x) == "cpu" and (
         (dilations > 1) if isinstance(dilations, int) else any(d > 1 for d in dilations)
     ):
         raise ivy.exceptions.IvyException(

ivy/functional/backends/torch/data_type.py

@@ -159,8 +159,9 @@ def as_ivy_dtype(dtype_in: Union[torch.dtype, str, bool, int, float], /) -> ivy.
 
 
 @with_unsupported_dtypes({"1.11.0 and below": ("uint16",)}, backend_version)
-def as_native_dtype(dtype_in: Union[torch.dtype, str, bool, int, float],
-                    /) -> torch.dtype:
+def as_native_dtype(
+    dtype_in: Union[torch.dtype, str, bool, int, float], /
+) -> torch.dtype:
     if dtype_in is int:
         return ivy.default_int_dtype(as_native=True)
     if dtype_in is float:

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

@@ -121,8 +121,8 @@ def swapaxes(a, axis1, axis2):
 @to_ivy_arrays_and_back
 def atleast_3d(*arys):
     return ivy.atleast_3d(*arys)
-    
-   
+
+
 @to_ivy_arrays_and_back
 def atleast_2d(*arys):
     return ivy.atleast_2d(*arys)

ivy_tests/test_ivy/test_frontends/test_jax/test_jax_numpy_manipulation.py

@@ -657,7 +657,7 @@ def test_jax_numpy_atleast_3d(
         on_device=on_device,
         **arys,
     )
-    
+
 
 # atleast_2d
 @handle_frontend_test(
@@ -669,12 +669,12 @@ def test_jax_numpy_atleast_3d(
     test_with_out=st.just(False),
 )
 def test_jax_numpy_atleast_2d(
-        *,
-        dtype_and_x,
-        on_device,
-        fn_tree,
-        frontend,
-        test_flags,
+    *,
+    dtype_and_x,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
 ):
     input_dtype, arrays = dtype_and_x
     arys = {}

ivy_tests/test_ivy/test_frontends/test_numpy/test_random/test_functions.py

@@ -141,8 +141,7 @@ def test_numpy_normal(
     fn_tree="numpy.random.poisson",
     dtype_and_x=helpers.dtype_and_values(
         available_dtypes=helpers.get_dtypes("float"),
-        shape=st.tuples(
-            st.integers(min_value=1, max_value=2)),
+        shape=st.tuples(st.integers(min_value=1, max_value=2)),
         min_value=1,
         max_value=100,
     ),
@@ -167,9 +166,10 @@ def test_numpy_poisson(
         on_device=on_device,
         lam=x[0],
         test_values=False,
-        size=size)
+        size=size,
+    )
+
 
-
 @handle_frontend_test(
     fn_tree="numpy.random.geometric",
     input_dtypes=helpers.get_dtypes("float"),

ivy_tests/test_ivy/test_frontends/test_torch/test_pointwise_ops.py

@@ -2059,5 +2059,5 @@ def test_torch_hypot(
         on_device=on_device,
         atol=1e-2,
         input=x[0],
-        other=x[1]
+        other=x[1],
     )

ivy_tests/test_ivy/test_functional/test_nn/test_layers.py

@@ -322,16 +322,14 @@ def x_and_filters(
     fc = draw(st.sampled_from(group_list)) if general else 1
     strides = draw(
         st.one_of(
-            st.integers(1, 3),
-            st.lists(st.integers(1, 3), min_size=dim, max_size=dim)
+            st.integers(1, 3), st.lists(st.integers(1, 3), min_size=dim, max_size=dim)
         )
         if dim > 1
         else st.integers(1, 3)
     )
     dilations = draw(
         st.one_of(
-            st.integers(1, 3),
-            st.lists(st.integers(1, 3), min_size=dim, max_size=dim)
+            st.integers(1, 3), st.lists(st.integers(1, 3), min_size=dim, max_size=dim)
         )
         if dim > 1
         else st.integers(1, 3)
@@ -359,7 +357,7 @@ def x_and_filters(
                     full_strides[i],
                     filter_shape[i],
                     padding,
-                    full_dilations[i]
+                    full_dilations[i],
                 )
             )
     else:
@@ -415,7 +413,7 @@ def x_and_filters(
             x_dilation = draw(
                 st.one_of(
                     st.integers(1, 3),
-                    st.lists(st.integers(1, 3), min_size=dim, max_size=dim)
+                    st.lists(st.integers(1, 3), min_size=dim, max_size=dim),
                 )
             )
             dilations = (dilations, x_dilation)
@@ -436,12 +434,13 @@ def x_and_filters(
 
 def _assume_tf_dilation_gt_1(backend_fw, on_device, dilations):
     if backend_fw.current_backend_str() == "tensorflow":
-        assume(not (
-            on_device == "cpu"
-            and (dilations > 1)
-            if isinstance(dilations, int)
-            else any(d > 1 for d in dilations)
-        ))
+        assume(
+            not (
+                on_device == "cpu" and (dilations > 1)
+                if isinstance(dilations, int)
+                else any(d > 1 for d in dilations)
+            )
+        )
 
 
 # conv1d

requirement_handler.py

@@ -3,6 +3,7 @@
 import operator
 import requests
 import sys
+
 url = "https://pypi.python.org/pypi/"
 
 
@@ -108,21 +109,20 @@ def get_package_versions(lis: List):
         return dict()
     dic = dict()
     for i in lis:
-        if ';' in i:
-            i=i[:i.index(';')]
+        if ";" in i:
+            i = i[: i.index(";")]
         if " " in i:
             dic[i.split(" ")[0]] = get_version_from_string(i.split(" ")[1])
         else:
-            dic[i]=()
-
+            dic[i] = ()
 
     return dic
 
 
 def resolution(conflict_keys, dic1, dic2):
     resolution = {}
     for i in conflict_keys:
-        if dic1[i]==dic2[i]:
+        if dic1[i] == dic2[i]:
             continue
         for j in dic1[i]:
             for k in dic2[i]:
@@ -159,12 +159,14 @@ def package_conflicts(pkg1, pkg2=None):
 def get_package_requirements(pkg):
     return requests.get(url + str(pkg) + "/json").json()
 
+
 def tuple_to_version(tup):
-    s=''
+    s = ""
     for i in tup:
-        s+=str(i)+'.'
+        s += str(i) + "."
     return s[:-1]
 
+
 # example usage
 # if __name__ == "__main__":
 #     fw1=sys.argv[1]
@@ -192,8 +194,6 @@ def tuple_to_version(tup):
 #         f.write(f"{key}=={val}")
 
 
-
-
 print(package_conflicts("h5py/3.7.0", "h5py/3.7.0"))
 # #
 # # print(get_package_requirements('tensorflow/2.2.0')["info"]["requires_dist"])