internlm/SWE-Fixer-Train-110K huggingface dataset:
Repository: ivy-llc/ivy

Issue:
 Fix Ivy Failing Test: numpy - statistical.sum



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/statistical.py b/ivy/functional/backends/paddle/statistical.py
--- a/ivy/functional/backends/paddle/statistical.py
+++ b/ivy/functional/backends/paddle/statistical.py
@@ -8,7 +8,6 @@
 import ivy
 from ivy.func_wrapper import (
     with_supported_dtypes,
-    with_unsupported_dtypes,
     with_supported_device_and_dtypes,
 )
 import ivy.functional.backends.paddle as paddle_backend
@@ -184,8 +183,8 @@ def std(
     return _std(x, axis, correction, keepdims).cast(x.dtype)
 
 
-@with_unsupported_dtypes(
-    {"2.6.0 and below": ("int8", "int16", "uint8")},
+@with_supported_dtypes(
+    {"2.6.0 and below": ("bool", "float16", "float32", "float64", "int32", "int64")},
     backend_version,
 )
 def sum(
diff --git a/ivy/functional/backends/torch/statistical.py b/ivy/functional/backends/torch/statistical.py
--- a/ivy/functional/backends/torch/statistical.py
+++ b/ivy/functional/backends/torch/statistical.py
@@ -181,7 +181,9 @@ def std(
 
 # Function does support uint8, but allowing support for unsigned will cause
 # the function to break the upcasting rule defined in the Array API Standard
-@with_unsupported_dtypes({"2.1.2 and below": ("uint8",)}, backend_version)
+@with_unsupported_dtypes(
+    {"2.1.2 and below": ("uint8", "float16", "bfloat16")}, backend_version
+)
 def sum(
     x: torch.Tensor,
     /,


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: paddle - statistical.cumsum



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/jax/statistical.py b/ivy/functional/backends/jax/statistical.py
--- a/ivy/functional/backends/jax/statistical.py
+++ b/ivy/functional/backends/jax/statistical.py
@@ -183,6 +183,7 @@ def cumprod(
         return jnp.flip(x, axis=axis)
 
 
+@with_unsupported_dtypes({"0.4.23 and below": "bool"}, backend_version)
 def cumsum(
     x: JaxArray,
     axis: int = 0,
diff --git a/ivy/functional/backends/tensorflow/statistical.py b/ivy/functional/backends/tensorflow/statistical.py
--- a/ivy/functional/backends/tensorflow/statistical.py
+++ b/ivy/functional/backends/tensorflow/statistical.py
@@ -200,6 +200,7 @@ def cumprod(
     return tf.math.cumprod(x, axis, exclusive, reverse)
 
 
+@with_unsupported_dtypes({"2.15.0 and below": "bool"}, backend_version)
 def cumsum(
     x: Union[tf.Tensor, tf.Variable],
     axis: int = 0,
diff --git a/ivy/functional/backends/torch/statistical.py b/ivy/functional/backends/torch/statistical.py
--- a/ivy/functional/backends/torch/statistical.py
+++ b/ivy/functional/backends/torch/statistical.py
@@ -290,8 +290,8 @@ def cumprod(
 # TODO: bfloat16 support is added in PyTorch 1.12.1
 @with_unsupported_dtypes(
     {
-        "1.12.1 and below": ("uint8", "float16", "bfloat16"),
-        "1.12.1 and above": ("uint8", "float16"),
+        "1.12.1 and below": ("uint8", "bool", "float16", "bfloat16"),
+        "1.12.1 and above": ("uint8", "bool", "float16"),
     },
     backend_version,
 )


Test patch:
diff --git a/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py b/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py
--- a/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py
+++ b/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py
@@ -153,6 +153,7 @@ def test_cumprod(
     dtype_x_axis_castable=_get_castable_dtype(),
     exclusive=st.booleans(),
     reverse=st.booleans(),
+    test_gradients=st.just(False),
 )
 def test_cumsum(
     *,
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: paddle - elementwise.fix
To-do List: https://github.com/unifyai/ivy/issues/27501


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/experimental/elementwise.py b/ivy/functional/backends/paddle/experimental/elementwise.py
--- a/ivy/functional/backends/paddle/experimental/elementwise.py
+++ b/ivy/functional/backends/paddle/experimental/elementwise.py
@@ -266,6 +266,7 @@ def allclose(
     return paddle.allclose(x1, x2, rtol=rtol, atol=atol, equal_nan=equal_nan).squeeze(0)
 
 
+@with_unsupported_dtypes({"2.6.0 and below": ("float16",)}, backend_version)
 def fix(
     x: paddle.Tensor,
     /,


Test patch:
Repository: ivy-llc/ivy

Issue:
 Fix Frontend Failing Test: paddle - tensor.paddle.Tensor.any
To-do List: https://github.com/unifyai/ivy/issues/27500


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/tensor.py b/ivy/functional/frontends/paddle/tensor/tensor.py
--- a/ivy/functional/frontends/paddle/tensor/tensor.py
+++ b/ivy/functional/frontends/paddle/tensor/tensor.py
@@ -619,7 +619,7 @@ def logical_or(self, y, out=None, name=None):
     def bitwise_xor(self, y, out=None, name=None):
         return paddle_frontend.bitwise_xor(self, y)
 
-    @with_supported_dtypes({"2.6.0 and below": ("float16", "bfloat16")}, "paddle")
+    @with_unsupported_dtypes({"2.6.0 and below": ("float16", "bfloat16")}, "paddle")
     def any(self, axis=None, keepdim=False, name=None):
         return paddle_frontend.any(self, axis=axis, keepdim=keepdim)
 


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: paddle - manipulation.zero_pad
Todo list: #27501


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/manipulation.py b/ivy/functional/backends/paddle/manipulation.py
--- a/ivy/functional/backends/paddle/manipulation.py
+++ b/ivy/functional/backends/paddle/manipulation.py
@@ -407,6 +407,7 @@ def constant_pad(
     return paddle.nn.functional.pad(x=x, pad=paddings, value=value)
 
 
+@with_unsupported_dtypes({"2.6.0 and below": ("float16",)}, backend_version)
 def zero_pad(
     x: paddle.Tensor,
     /,


Test patch:
Repository: ivy-llc/ivy

Issue:
 Fix Ivy Failing Test: paddle - elementwise.not_equal



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/elementwise.py b/ivy/functional/backends/paddle/elementwise.py
--- a/ivy/functional/backends/paddle/elementwise.py
+++ b/ivy/functional/backends/paddle/elementwise.py
@@ -612,6 +612,10 @@ def negative(
     return paddle.neg(x)
 
 
+@with_supported_dtypes(
+    {"2.6.0 and below": ("float32", "float64", "int32", "int64")},
+    backend_version,
+)
 def not_equal(
     x1: Union[float, paddle.Tensor],
     x2: Union[float, paddle.Tensor],


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: paddle - manipulation.concat



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/manipulation.py b/ivy/functional/backends/paddle/manipulation.py
--- a/ivy/functional/backends/paddle/manipulation.py
+++ b/ivy/functional/backends/paddle/manipulation.py
@@ -22,6 +22,10 @@
 # -------------------#
 
 
+@with_unsupported_dtypes(
+    {"2.6.0 and below": ("bfloat16", "float16")},
+    backend_version,
+)
 def concat(
     xs: Union[Tuple[paddle.Tensor, ...], List[paddle.Tensor]],
     /,


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: numpy - manipulation.reshape
ToDo List: #27501 
Sub-Task in the "Priority Open" section


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/numpy/manipulation.py b/ivy/functional/backends/numpy/manipulation.py
--- a/ivy/functional/backends/numpy/manipulation.py
+++ b/ivy/functional/backends/numpy/manipulation.py
@@ -108,6 +108,8 @@ def reshape(
             new_s if con else old_s
             for new_s, con, old_s in zip(shape, np.array(shape) != 0, x.shape)
         ]
+    if copy:
+        x = x.copy()
     return np.reshape(x, shape, order=order)
 
 


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: torch - manipulation.reshape
ToDo List: #27501 
Sub-Task in the "Priority Open" section


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/torch/manipulation.py b/ivy/functional/backends/torch/manipulation.py
--- a/ivy/functional/backends/torch/manipulation.py
+++ b/ivy/functional/backends/torch/manipulation.py
@@ -1,7 +1,7 @@
 # global
 import math
 from numbers import Number
-from typing import Union, Optional, Tuple, List, Sequence, Iterable
+from typing import Iterable, List, Optional, Sequence, Tuple, Union
 
 import torch
 
@@ -11,6 +11,7 @@
 
 # noinspection PyProtectedMember
 from ivy.functional.ivy.manipulation import _calculate_out_shape
+
 from . import backend_version
 
 
@@ -61,7 +62,7 @@ def flip(
     out: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
     if copy:
-        x = x.clone().detach()
+        x = x.clone()
     num_dims = len(x.shape)
     if not num_dims:
         return x
@@ -88,6 +89,10 @@ def permute_dims(
     return torch.permute(x, axes)
 
 
+@with_unsupported_dtypes(
+    {"2.1.2 and below": ("bfloat16",)},
+    backend_version,
+)
 def reshape(
     x: torch.Tensor,
     /,
@@ -98,6 +103,8 @@ def reshape(
     allowzero: bool = True,
     out: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
+    if copy:
+        x = x.clone()
     ivy.utils.assertions.check_elem_in_list(order, ["C", "F"])
     if not allowzero:
         shape = [


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: torch - creation.zeros_like
Todo list issue: #27501 


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/creation.py b/ivy/functional/backends/paddle/creation.py
--- a/ivy/functional/backends/paddle/creation.py
+++ b/ivy/functional/backends/paddle/creation.py
@@ -530,6 +530,10 @@ def zeros(
     return paddle.zeros(shape=shape).cast(dtype)
 
 
+@with_unsupported_device_and_dtypes(
+    {"2.6.0 and below": {"cpu": ("uint8", "int8", "int16", "float16", "bfloat16")}},
+    backend_version,
+)
 def zeros_like(
     x: paddle.Tensor,
     /,


Test patch:
diff --git a/ivy_tests/test_ivy/test_functional/test_core/test_creation.py b/ivy_tests/test_ivy/test_functional/test_core/test_creation.py
--- a/ivy_tests/test_ivy/test_functional/test_core/test_creation.py
+++ b/ivy_tests/test_ivy/test_functional/test_core/test_creation.py
@@ -848,6 +848,7 @@ def test_zeros(*, shape, dtype, test_flags, backend_fw, fn_name, on_device):
         min_dim_size=1,
         max_dim_size=5,
     ),
+    test_gradients=st.just(False),
 )
 def test_zeros_like(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device):
     dtype, x = dtype_and_x
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: torch - manipulation.squeeze
ToDo List: #27501 
Sub-Task in the "Priority Open" section


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/jax/manipulation.py b/ivy/functional/backends/jax/manipulation.py
--- a/ivy/functional/backends/jax/manipulation.py
+++ b/ivy/functional/backends/jax/manipulation.py
@@ -1,7 +1,8 @@
 # global
 import math
 from numbers import Number
-from typing import Union, Tuple, Optional, List, Sequence, Iterable
+from typing import Iterable, List, Optional, Sequence, Tuple, Union
+
 import jax.numpy as jnp
 import numpy as np
 
@@ -9,6 +10,7 @@
 import ivy
 from ivy.func_wrapper import with_unsupported_dtypes
 from ivy.functional.backends.jax import JaxArray
+
 from . import backend_version
 
 
@@ -125,6 +127,8 @@ def squeeze(
     copy: Optional[bool] = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
+    if copy:
+        x = jnp.copy(x)
     if x.shape == ():
         if axis is None or axis == 0 or axis == -1:
             return x
diff --git a/ivy/functional/backends/numpy/manipulation.py b/ivy/functional/backends/numpy/manipulation.py
--- a/ivy/functional/backends/numpy/manipulation.py
+++ b/ivy/functional/backends/numpy/manipulation.py
@@ -134,6 +134,8 @@ def squeeze(
 ) -> np.ndarray:
     if isinstance(axis, list):
         axis = tuple(axis)
+    if copy:
+        x = x.copy()
     if x.shape == ():
         if axis is None or axis == 0 or axis == -1:
             return x
diff --git a/ivy/functional/backends/torch/manipulation.py b/ivy/functional/backends/torch/manipulation.py
--- a/ivy/functional/backends/torch/manipulation.py
+++ b/ivy/functional/backends/torch/manipulation.py
@@ -157,12 +157,10 @@ def squeeze(
                 f"Expected size of axis to be 1 but was {x.shape[axis]}"
             )
         return torch.squeeze(x, axis)
+    if copy:
+        x = x.clone()
     if axis is None:
-        if copy:
-            newarr = torch.clone(x)
-            return torch.squeeze(newarr)
         return torch.squeeze(x)
-    newarr = torch.clone(x)
     if isinstance(axis, tuple):
         axis = list(axis)
     normalise_axis = [
@@ -179,12 +177,7 @@ def squeeze(
                 f" {shape}"
             )
         else:
-            if copy:
-                newarr = torch.squeeze(newarr, i)
-            else:
-                x = torch.squeeze(x, i)
-    if copy:
-        return newarr
+            x = torch.squeeze(x, i)
     return x
 
 


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: numpy - manipulation.swapaxes
ToDo list: #27501
Section: Priority Open


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/numpy/manipulation.py b/ivy/functional/backends/numpy/manipulation.py
--- a/ivy/functional/backends/numpy/manipulation.py
+++ b/ivy/functional/backends/numpy/manipulation.py
@@ -1,12 +1,14 @@
 # global
 import math
 from numbers import Number
-from typing import Union, Tuple, Optional, List, Sequence
+from typing import List, Optional, Sequence, Tuple, Union
+
 import numpy as np
 
 # local
 import ivy
 from ivy.func_wrapper import with_unsupported_dtypes
+
 from . import backend_version
 
 
@@ -233,6 +235,8 @@ def swapaxes(
     copy: Optional[bool] = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
+    if copy:
+        x = x.copy()
     return np.swapaxes(x, axis0, axis1)
 
 


Test patch:
Repository: ivy-llc/ivy

Issue:
Wrong key-word argument `name` in `ivy.remainder()` function call
In the following line, the name argument is passed,
https://github.com/unifyai/ivy/blob/bec4752711c314f01298abc3845f02c24a99acab/ivy/functional/frontends/tensorflow/variable.py#L191
From the actual function definition, there is no such argument
https://github.com/unifyai/ivy/blob/8ff497a8c592b75f010160b313dc431218c2b475/ivy/functional/ivy/elementwise.py#L5415-L5422


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/tensorflow/variable.py b/ivy/functional/frontends/tensorflow/variable.py
--- a/ivy/functional/frontends/tensorflow/variable.py
+++ b/ivy/functional/frontends/tensorflow/variable.py
@@ -188,7 +188,7 @@ def __mul__(self, x, name="mul"):
         return tf_frontend.math.multiply(x, self._ivy_array, name=name)
 
     def __mod__(self, x, name="mod"):
-        return ivy.remainder(x, self._ivy_array, name=name)
+        return tf_frontend.math.mod(x, self._ivy_array, name=name)
 
     def __ne__(self, other):
         return tf_frontend.raw_ops.NotEqual(


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: paddle - elementwise.multiply
ToDo: #27501
Type: Priority


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/elementwise.py b/ivy/functional/backends/paddle/elementwise.py
--- a/ivy/functional/backends/paddle/elementwise.py
+++ b/ivy/functional/backends/paddle/elementwise.py
@@ -1,15 +1,16 @@
 # global
-from typing import Union, Optional
-
 import math
+from typing import Optional, Union
+
 import paddle
-import ivy.functional.backends.paddle as paddle_backend
+
 import ivy
+import ivy.functional.backends.paddle as paddle_backend
 from ivy import promote_types_of_inputs
 from ivy.func_wrapper import (
-    with_unsupported_device_and_dtypes,
     with_supported_device_and_dtypes,
     with_supported_dtypes,
+    with_unsupported_device_and_dtypes,
     with_unsupported_dtypes,
 )
 
@@ -340,15 +341,8 @@ def less(
     return paddle.less_than(x1, x2)
 
 
-@with_unsupported_dtypes(
-    {
-        "2.6.0 and below": (
-            "int8",
-            "int16",
-            "uint8",
-            "float16",
-        )
-    },
+@with_supported_dtypes(
+    {"2.6.0 and below": ("bool", "int32", "int64", "float32", "float64", "complex")},
     backend_version,
 )
 def multiply(
@@ -359,6 +353,14 @@ def multiply(
     out: Optional[paddle.Tensor] = None,
 ) -> paddle.Tensor:
     x1, x2, ret_dtype = _elementwise_helper(x1, x2)
+    if isinstance(x1, paddle.Tensor) and isinstance(x2, paddle.Tensor):
+        if paddle.is_complex(x1) or paddle.is_complex(x2):
+            a, b = x1.real(), x1.imag()
+            c, d = x2.real(), x2.imag()
+            real = a * c - b * d
+            imag = a * d + b * c
+            return paddle.complex(real, imag)
+
     return paddle.multiply(x1, x2).astype(ret_dtype)
 
 


Test patch:
diff --git a/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py b/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
--- a/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
+++ b/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
@@ -2,15 +2,17 @@
 
 # global
 import math
+
 import numpy as np
-from hypothesis import assume, strategies as st
+from hypothesis import assume
+from hypothesis import strategies as st
 
 # local
 import ivy
 import ivy_tests.test_ivy.helpers as helpers
+import ivy_tests.test_ivy.helpers.globals as test_globals
 from ivy_tests.test_ivy.helpers import handle_test
 from ivy_tests.test_ivy.helpers.pipeline_helper import BackendHandler
-import ivy_tests.test_ivy.helpers.globals as test_globals
 
 _one = np.asarray(1, dtype="uint8")
 _zero = np.asarray(0, dtype="uint8")
@@ -1483,8 +1485,9 @@ def test_minimum(
 @handle_test(
     fn_tree="functional.ivy.multiply",
     dtype_and_x=helpers.dtype_and_values(
-        available_dtypes=helpers.get_dtypes("numeric"), num_arrays=2
+        available_dtypes=helpers.get_dtypes("valid"), num_arrays=2
     ),
+    ground_truth_backend="torch",
 )
 def test_multiply(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device):
     input_dtype, x = dtype_and_x
Repository: ivy-llc/ivy

Issue:
Fix Frontend Failing Test: paddle - tensor.paddle.Tensor.rsqrt_
To-do List: https://github.com/unifyai/ivy/issues/27500


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/elementwise.py b/ivy/functional/backends/paddle/elementwise.py
--- a/ivy/functional/backends/paddle/elementwise.py
+++ b/ivy/functional/backends/paddle/elementwise.py
@@ -399,12 +399,13 @@ def divide(
     *,
     out: Optional[paddle.Tensor] = None,
 ) -> paddle.Tensor:
-    if paddle.is_complex(x1) or paddle.is_complex(x2):
-        angle_value = paddle.angle(x1) - paddle.angle(x2)
-        abs_value = paddle.abs(x1) / paddle.abs(x2)
-        return paddle.complex(
-            abs_value * paddle.cos(angle_value), abs_value * paddle.sin(angle_value)
-        )
+    if isinstance(x1, paddle.Tensor) and isinstance(x2, paddle.Tensor):
+        if paddle.is_complex(x1) or paddle.is_complex(x2):
+            angle_value = paddle.angle(x1) - paddle.angle(x2)
+            abs_value = paddle.abs(x1) / paddle.abs(x2)
+            return paddle.complex(
+                abs_value * paddle.cos(angle_value), abs_value * paddle.sin(angle_value)
+            )
     x1, x2, ret_dtype = _elementwise_helper(x1, x2)
     return (x1 / x2).astype(ret_dtype)
 


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Frontend Failing Test: torch - creation.paddle.eye
To-do List: https://github.com/unifyai/ivy/issues/27498


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/creation.py b/ivy/functional/frontends/paddle/creation.py
--- a/ivy/functional/frontends/paddle/creation.py
+++ b/ivy/functional/frontends/paddle/creation.py
@@ -87,6 +87,9 @@ def empty_like(x, dtype=None, name=None):
     return ivy.empty_like(x, dtype=dtype)
 
 
+@with_supported_dtypes(
+    {"2.6.0 and below": ("float16", "float32", "float64", "int32", "int64")}, "paddle"
+)
 @to_ivy_arrays_and_back
 def eye(num_rows, num_columns=None, dtype=None, name=None):
     return ivy.eye(num_rows, num_columns, dtype=dtype)


Test patch:
Repository: ivy-llc/ivy

Issue:
 Fix Ivy Failing Test: paddle - elementwise.divide



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/elementwise.py b/ivy/functional/backends/paddle/elementwise.py
--- a/ivy/functional/backends/paddle/elementwise.py
+++ b/ivy/functional/backends/paddle/elementwise.py
@@ -399,15 +399,14 @@ def divide(
     *,
     out: Optional[paddle.Tensor] = None,
 ) -> paddle.Tensor:
-    if isinstance(x1, paddle.Tensor) and isinstance(x2, paddle.Tensor):
-        if paddle.is_complex(x1) or paddle.is_complex(x2):
-            angle_value = paddle.angle(x1) - paddle.angle(x2)
-            abs_value = paddle.abs(x1) / paddle.abs(x2)
-            return paddle.complex(
-                abs_value * paddle.cos(angle_value), abs_value * paddle.sin(angle_value)
-            )
-    x1, x2, ret_dtype = _elementwise_helper(x1, x2)
-    return (x1 / x2).astype(ret_dtype)
+    if paddle.is_complex(x1) or paddle.is_complex(x2):
+        angle_value = paddle.angle(x1) - paddle.angle(x2)
+        abs_value = paddle.abs(x1) / paddle.abs(x2)
+        return paddle.complex(
+            abs_value * paddle.cos(angle_value), abs_value * paddle.sin(angle_value)
+        )
+    x1, x2, _ = _elementwise_helper(x1, x2)
+    return x1 / x2
 
 
 @with_supported_dtypes(


Test patch:
diff --git a/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py b/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
--- a/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
+++ b/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
@@ -659,8 +659,9 @@ def test_deg2rad(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device):
 # divide
 @handle_test(
     fn_tree="functional.ivy.divide",
+    test_gradients=st.just(False),
     dtype_and_x=helpers.dtype_and_values(
-        available_dtypes=helpers.get_dtypes("numeric"),
+        available_dtypes=helpers.get_dtypes("numeric", full=False),
         num_arrays=2,
         large_abs_safety_factor=2,
         small_abs_safety_factor=2,
Repository: ivy-llc/ivy

Issue:
Fix Frontend Failing Test: paddle - stat.paddle.mean
To-do List: https://github.com/unifyai/ivy/issues/27500


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/stat.py b/ivy/functional/frontends/paddle/stat.py
--- a/ivy/functional/frontends/paddle/stat.py
+++ b/ivy/functional/frontends/paddle/stat.py
@@ -1,12 +1,12 @@
 # global
 import ivy
-from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
+from ivy.func_wrapper import with_supported_dtypes
 from ivy.functional.frontends.paddle.func_wrapper import (
     to_ivy_arrays_and_back,
 )
 
 
-@with_unsupported_dtypes({"2.6.0 and below": ("float16", "bfloat16")}, "paddle")
+@with_supported_dtypes({"2.6.0 and below": ("float32", "float64")}, "paddle")
 @to_ivy_arrays_and_back
 def mean(input, axis=None, keepdim=False, out=None):
     ret = ivy.mean(input, axis=axis, keepdims=keepdim, out=out)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_stat.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_stat.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_stat.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_stat.py
@@ -26,7 +26,7 @@ def test_paddle_mean(
     backend_fw,
     test_flags,
 ):
-    input_dtype, x, axis = dtype_and_x
+    input_dtype, x, axis = dtype_and_x[:3]
     test_flags.num_positional_args = len(dtype_and_x) - 2
     helpers.test_frontend_function(
         input_dtypes=input_dtype,
@@ -35,6 +35,8 @@ def test_paddle_mean(
         test_flags=test_flags,
         fn_tree=fn_tree,
         on_device=on_device,
+        rtol=1e-2,
+        atol=1e-2,
         input=x[0],
         axis=axis,
         keepdim=keepdim,