Change logic for testing for complex input

Suggested from code review. Uses paddle's `is_complex()` function

Co-authored-by: Mahmoud Ashraf <[email protected]>

ivy/functional/backends/paddle/activations.py

@@ -68,7 +68,7 @@ def gelu(
     approximate: bool = False,
     out: Optional[paddle.Tensor] = None,
 ) -> paddle.Tensor:
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         sqrt_2_over_pi = 0.7978845608
         # the other magic number comes directly from the formula in
         # https://doi.org/10.48550/arXiv.1606.08415

ivy/functional/backends/paddle/elementwise.py

@@ -210,7 +210,7 @@ def asin(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.
     ]:
         ret_dtype = x.dtype
         return paddle.asin(x.astype("float32")).astype(ret_dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         asinh_iz = paddle_backend.asinh(paddle.complex(-x.imag(), x.real()))
         return paddle.complex(asinh_iz.imag(), -asinh_iz.real())
     return paddle.asin(x)
@@ -231,7 +231,7 @@ def asinh(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle
     ]:
         ret_dtype = x.dtype
         return paddle.asinh(x.astype("float32")).astype(ret_dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         # From https://github.com/python/cpython/blob/39ef93edb9802dccdb6555d4209ac2e60875a011/Modules/cmathmodule.c#L276 # noqa
         s1 = paddle_backend.sqrt(paddle.complex(1 + x.imag(), -x.real()))
         s2 = paddle_backend.sqrt(paddle.complex(1 - x.imag(), x.real()))
@@ -304,7 +304,7 @@ def cosh(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.
     ]:
         ret_dtype = x.dtype
         return paddle.cosh(x.astype("float32")).astype(ret_dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         re = x.real()
         im = x.imag()
         return paddle.complex(
@@ -434,7 +434,7 @@ def cos(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.T
     ]:
         ret_dtype = x.dtype
         return paddle.cos(x.astype("float32")).astype(ret_dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         re = x.real()
         im = x.imag()
         return paddle.complex(
@@ -537,7 +537,7 @@ def acos(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.
         paddle.float16,
     ]:
         return paddle.acos(x.astype("float32")).astype(x.dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         # From https://github.com/python/cpython/blob/39ef93edb9802dccdb6555d4209ac2e60875a011/Modules/cmathmodule.c#L178 # noqa
         s1 = paddle_backend.sqrt(1 - x)
         s2 = paddle_backend.sqrt(1 + x)
@@ -617,7 +617,7 @@ def acosh(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle
         paddle.float16,
     ]:
         return paddle.acosh(x.astype("float32")).astype(x.dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         # From https://github.com/python/cpython/blob/39ef93edb9802dccdb6555d4209ac2e60875a011/Modules/cmathmodule.c#L221 # noqa
         s1 = paddle_backend.sqrt(paddle.complex(x.real() - 1, x.imag()))
         s2 = paddle_backend.sqrt(paddle.complex(x.real() + 1, x.imag()))
@@ -642,7 +642,7 @@ def sin(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.T
         paddle.float16,
     ]:
         return paddle.sin(x.astype("float32")).astype(x.dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         re = x.real()
         im = x.imag()
         return paddle.complex(
@@ -687,7 +687,7 @@ def tanh(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.
         paddle.float16,
     ]:
         return paddle.tanh(x.astype("float32")).astype(x.dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         tanh_a = paddle.tanh(paddle.real(x))
         tan_b = paddle.tan(paddle.imag(x))
         return (tanh_a + 1j * tan_b) / (1 + 1j * (tanh_a * tan_b))
@@ -733,7 +733,7 @@ def sinh(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.
     ]:
         ret_dtype = x.dtype
         return paddle.sinh(x.astype("float32")).astype(ret_dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         re = x.real()
         im = x.imag()
         return paddle.complex(
@@ -757,7 +757,7 @@ def square(
 ) -> paddle.Tensor:
     if x.dtype in [paddle.int32, paddle.int64, paddle.float32, paddle.float64]:
         return paddle.square(x)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         return paddle.complex(
             paddle.square(paddle.real(x)) - paddle.square(paddle.imag(x)),
             2.0 * paddle.real(x) * paddle.imag(x),
@@ -979,7 +979,7 @@ def tan(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.T
     ]:
         ret_dtype = x.dtype
         return paddle.tan(x.astype("float32")).astype(ret_dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         tanh_ix = paddle_backend.tanh(paddle.complex(-x.imag(), x.real()))
         return paddle.complex(tanh_ix.imag(), -tanh_ix.real())
     return paddle.tan(x)
@@ -1040,7 +1040,7 @@ def log(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.T
 def exp(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.Tensor:
     if x.dtype in [paddle.int32, paddle.int64, paddle.float32, paddle.float64]:
         return paddle.exp(x)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         return paddle.multiply(
             paddle.exp(x.real()),
             paddle.complex(paddle.cos(x.imag()), paddle.sin(x.imag())),
@@ -1118,7 +1118,7 @@ def atanh(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle
     ]:
         ret_dtype = x.dtype
         return paddle.atanh(x.astype("float32")).astype(ret_dtype)
-    if x.dtype in [paddle.complex64, paddle.complex128]:
+    if paddle.is_complex(x):
         return 0.5 * (paddle_backend.log(1 + x) - paddle_backend.log(1 - x))
     return paddle.atanh(x)