feat(frontend): adding __truediv__ to paddle.tensor.tensor.Tensor

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

@@ -1,7 +1,7 @@
 # global
 from numbers import Number
 from typing import Union, Tuple, Iterable
-
+import jax.numpy as jnp
 
 # local
 import ivy
@@ -19,101 +19,123 @@
 _uint16 = ivy.UintDtype("uint16")
 _uint32 = ivy.UintDtype("uint32")
 _uint64 = ivy.UintDtype("uint64")
-_bfloat16 = ivy.FloatDtype("bfloat16")
-_float16 = ivy.FloatDtype("float16")
-_float32 = ivy.FloatDtype("float32")
-_float64 = ivy.FloatDtype("float64")
-_complex64 = ivy.ComplexDtype("complex64")
-_complex128 = ivy.ComplexDtype("complex128")
-_bool = ivy.Dtype("bool")
+_bfloat16 = jnp.bfloat16
+_float16 = jnp.float16
+_float32 = jnp.float32
+_float64 = jnp.float64
+_complex64 = jnp.complex64
+_complex128 = jnp.complex128
+_bool = jnp.bool_
 
 # jax-numpy casting table
 jax_numpy_casting_table = {
     _bool: [
         _bool,
-        _int8,
-        _int16,
-        _int32,
-        _int64,
-        _uint8,
-        _uint16,
-        _uint32,
-        _uint64,
+        jnp.int8,
+        jnp.int16,
+        jnp.int32,
+        jnp.int64,
+        jnp.uint8,
+        jnp.uint16,
+        jnp.uint32,
+        jnp.uint64,
         _float16,
         _float32,
         _float64,
         _complex64,
         _complex128,
         _bfloat16,
     ],
-    _int8: [
-        _int8,
-        _int16,
-        _int32,
-        _int64,
+    jnp.int8: [
+        jnp.int8,
+        jnp.int16,
+        jnp.int32,
+        jnp.int64,
         _float16,
         _float32,
         _float64,
         _complex64,
         _complex128,
         _bfloat16,
     ],
-    _int16: [
-        _int16,
-        _int32,
-        _int64,
+    jnp.int16: [
+        jnp.int16,
+        jnp.int32,
+        jnp.int64,
+        _float16,
         _float32,
         _float64,
         _complex64,
         _complex128,
+        _bfloat16,
     ],
-    _int32: [
-        _int32,
-        _int64,
+    jnp.int32: [
+        jnp.int32,
+        jnp.int64,
+        _float16,
+        _float32,
         _float64,
+        _complex64,
         _complex128,
+        _bfloat16,
     ],
-    _int64: [
-        _int64,
+    jnp.int64: [
+        jnp.int64,
+        _float16,
+        _float32,
         _float64,
+        _complex64,
         _complex128,
+        _bfloat16,
     ],
-    _uint8: [
-        _int16,
-        _int32,
-        _int64,
-        _uint8,
-        _uint16,
-        _uint32,
-        _uint64,
+    jnp.uint8: [
+        jnp.uint8,
+        jnp.uint16,
+        jnp.uint32,
+        jnp.uint64,
         _float16,
         _float32,
         _float64,
         _complex64,
         _complex128,
         _bfloat16,
     ],
-    _uint16: [
-        _int32,
-        _int64,
-        _uint16,
-        _uint32,
-        _uint64,
+    jnp.uint16: [
+        jnp.uint16,
+        jnp.uint32,
+        jnp.uint64,
+        _float16,
         _float32,
         _float64,
         _complex64,
         _complex128,
+        _bfloat16,
     ],
-    _uint32: [
-        _int64,
-        _uint32,
-        _uint64,
+    jnp.uint32: [
+        jnp.uint32,
+        jnp.uint64,
+        _float16,
+        _float32,
         _float64,
+        _complex64,
         _complex128,
+        _bfloat16,
     ],
-    _uint64: [
-        _uint64,
+    jnp.uint64: [
+        jnp.uint64,
+        _float16,
+        _float32,
         _float64,
+        _complex64,
+        _complex128,
+        _bfloat16,
+    ],
+    _bfloat16: [
+        _bfloat16,
+        _float16,
+        _float32,
+        _float64,
+        _complex64,
         _complex128,
     ],
     _float16: [
@@ -131,17 +153,16 @@
     ],
     _float64: [
         _float64,
+        _complex64,
         _complex128,
     ],
-    _complex64: [_complex64, ivy.complex128],
-    _complex128: [_complex128],
-    _bfloat16: [
-        _bfloat16,
-        _float32,
-        _float64,
+    _complex64: [
         _complex64,
         _complex128,
     ],
+    _complex128: [
+        _complex128,
+    ],
 }
 
 
@@ -384,6 +405,16 @@
 }
 
 
+def array_repr(arr):
+    shape = arr.shape
+    dtype = arr.dtype
+    device = arr.device
+    data = ivy.to_numpy(arr)
+
+    repr_str = f"Ivy Array (shape={shape}, dtype={dtype}, device={device}):\n{data}" # takes an Ivy array as input and generates a string representation
+
+    return repr_str
+
 @handle_exceptions
 def promote_types_jax(
     type1: Union[ivy.Dtype, ivy.NativeDtype],

ivy/functional/frontends/paddle/tensor/tensor.py

@@ -65,6 +65,12 @@ def ivy_array(self, array):
         {"2.6.0 and below": ("bool", "unsigned", "int8", "float16", "bfloat16")},
         "paddle",
     )
+    def __truediv__(self, other):
+        if isinstance(other, Tensor):
+            return Tensor(self.ivy_array / other.ivy_array)
+        else:
+            return Tensor(self.ivy_array / other)
+
     def __add__(self, y, /, name=None):
         return paddle_frontend.add(self, y)