🤖 Lint code

docs/overview/contributing/error_handling.rst

@@ -39,7 +39,7 @@ This section, "Error Handling" aims to assist you in navigating through some com
 
    **Solution:**
 
-   As we are explicitly passing in a `dtype` which is not supported in the torch framework itself so torch backend fails here, a possible fix is adding the dtype in the unsupported dtype         decoartor which would look something like this. 
+   As we are explicitly passing in a `dtype` which is not supported in the torch framework itself so torch backend fails here, a possible fix is adding the dtype in the unsupported dtype         decoartor which would look something like this.
 
    .. code-block:: python
 
@@ -77,7 +77,7 @@ This section, "Error Handling" aims to assist you in navigating through some com
 
    **Solution:**
 
-   As both the results are pretty close to each others in this case, adding an `rtol = 10^-3` and `atol = 10^-3` would fix the failing tests here. 
+   As both the results are pretty close to each others in this case, adding an `rtol = 10^-3` and `atol = 10^-3` would fix the failing tests here.
 
          .. code-block:: python
 

ivy/functional/frontends/paddle/math.py

@@ -338,6 +338,12 @@ def log(x, name=None):
     return ivy.log(x)
 
 
+@with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
+@to_ivy_arrays_and_back
+def log10(x, name=None):
+    return ivy.log10(x)
+
+
 @with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
 @to_ivy_arrays_and_back
 def log1p(x, name=None):
@@ -349,11 +355,6 @@ def log1p(x, name=None):
 def log2(x, name=None):
     return ivy.log2(x)
 
-@with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
-@to_ivy_arrays_and_back
-def log10(x, name=None):
-    return ivy.log10(x)
-
 
 @with_unsupported_dtypes({"2.5.1 and below": ("float16", "bfloat16")}, "paddle")
 @to_ivy_arrays_and_back

ivy/functional/frontends/torch/nn/functional/non_linear_activation_functions.py

@@ -4,10 +4,6 @@
 from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
 
 
-# --- Main --- #
-# ------------ #
-
-
 @to_ivy_arrays_and_back
 def celu(input, alpha=1.0, inplace=False):
     prod = ivy.multiply(

ivy_tests/test_ivy/test_frontends/test_paddle/test_math.py

@@ -1413,15 +1413,13 @@ def test_paddle_log(
     )
 
 
-# log1p
 @handle_frontend_test(
-    fn_tree="paddle.log1p",
+    fn_tree="paddle.log10",
     dtype_and_x=helpers.dtype_and_values(
         available_dtypes=helpers.get_dtypes("valid"),
-        max_value=1e5,
     ),
 )
-def test_paddle_log1p(
+def test_paddle_log10(
     *,
     dtype_and_x,
     on_device,
@@ -1442,14 +1440,15 @@ def test_paddle_log1p(
     )
 
 
-# log2
+# log1p
 @handle_frontend_test(
-    fn_tree="paddle.log2",
+    fn_tree="paddle.log1p",
     dtype_and_x=helpers.dtype_and_values(
         available_dtypes=helpers.get_dtypes("valid"),
+        max_value=1e5,
     ),
 )
-def test_paddle_log2(
+def test_paddle_log1p(
     *,
     dtype_and_x,
     on_device,
@@ -1469,13 +1468,15 @@ def test_paddle_log2(
         x=x[0],
     )
 
+
+# log2
 @handle_frontend_test(
-    fn_tree="paddle.log10",
+    fn_tree="paddle.log2",
     dtype_and_x=helpers.dtype_and_values(
         available_dtypes=helpers.get_dtypes("valid"),
     ),
 )
-def test_paddle_log10(
+def test_paddle_log2(
     *,
     dtype_and_x,
     on_device,

ivy_tests/test_ivy/test_frontends/test_torch/test_utilities.py

@@ -6,6 +6,50 @@
 from ivy_tests.test_ivy.helpers import handle_frontend_test
 
 
+# --- Helpers --- #
+# --------------- #
+
+
+@st.composite
+def _elemwise_helper(draw):
+    value_strategy = st.one_of(
+        helpers.dtype_and_values(
+            available_dtypes=helpers.get_dtypes("valid"),
+        ),
+        st.integers(min_value=-10000, max_value=10000),
+        st.floats(min_value=-10000, max_value=10000),
+    )
+
+    dtype_and_x1 = draw(value_strategy)
+    if isinstance(dtype_and_x1, tuple):
+        dtype1 = dtype_and_x1[0]
+        x1 = dtype_and_x1[1][0]
+    else:
+        dtype1 = []
+        x1 = dtype_and_x1
+
+    dtype_and_x2 = draw(value_strategy)
+    if isinstance(dtype_and_x2, tuple):
+        dtype2 = dtype_and_x2[0]
+        x2 = dtype_and_x2[1][0]
+    else:
+        dtype2 = []
+        x2 = dtype_and_x2
+
+    num_pos_args = None
+    if not dtype1 and not dtype2:
+        num_pos_args = 2
+    elif not dtype1:
+        x1, x2 = x2, x1
+    input_dtypes = dtype1 + dtype2
+
+    return x1, x2, input_dtypes, num_pos_args
+
+
+# --- Main --- #
+# ------------ #
+
+
 # ToDo: Fix this test after torch overide of assert is implemented
 # @handle_frontend_test(
 #     fn_tree="torch._assert",
@@ -74,42 +118,6 @@ def test_torch_bincount(
     )
 
 
-@st.composite
-def _elemwise_helper(draw):
-    value_strategy = st.one_of(
-        helpers.dtype_and_values(
-            available_dtypes=helpers.get_dtypes("valid"),
-        ),
-        st.integers(min_value=-10000, max_value=10000),
-        st.floats(min_value=-10000, max_value=10000),
-    )
-
-    dtype_and_x1 = draw(value_strategy)
-    if isinstance(dtype_and_x1, tuple):
-        dtype1 = dtype_and_x1[0]
-        x1 = dtype_and_x1[1][0]
-    else:
-        dtype1 = []
-        x1 = dtype_and_x1
-
-    dtype_and_x2 = draw(value_strategy)
-    if isinstance(dtype_and_x2, tuple):
-        dtype2 = dtype_and_x2[0]
-        x2 = dtype_and_x2[1][0]
-    else:
-        dtype2 = []
-        x2 = dtype_and_x2
-
-    num_pos_args = None
-    if not dtype1 and not dtype2:
-        num_pos_args = 2
-    elif not dtype1:
-        x1, x2 = x2, x1
-    input_dtypes = dtype1 + dtype2
-
-    return x1, x2, input_dtypes, num_pos_args
-
-
 @handle_frontend_test(
     fn_tree="torch.result_type",
     dtypes_and_xs=_elemwise_helper(),