refactor(All APIs): Added new pre-commit hook for ordering functions

.pre-commit-config.yaml

@@ -26,6 +26,6 @@ repos:
         # Exclude everything in frontends except __init__.py, and func_wrapper.py
         exclude: 'ivy/functional/(frontends|backends)/(?!.*/func_wrapper\.py$).*(?!__init__\.py$)'
   - repo: https://github.com/unifyai/lint-hook
-    rev: 27646397c5390f644a645f439535b1061b9c0105
+    rev: 2e2dc6c06475b5ec47e4b97c30b23dbb4bd01891
     hooks:
       - id: ivy-lint

ivy/functional/backends/jax/activations.py

@@ -23,6 +23,10 @@ def gelu(
     return jax.nn.gelu(x, approximate)
 
 
+def hardswish(x: JaxArray, /, *, out: Optional[JaxArray] = None) -> JaxArray:
+    return jax.nn.hard_swish(x)
+
+
 def leaky_relu(
     x: JaxArray,
     /,
@@ -34,6 +38,18 @@ def leaky_relu(
     return jnp.asarray(jnp.where(x > 0, x, jnp.multiply(x, alpha)), x.dtype)
 
 
+def log_softmax(
+    x: JaxArray, /, *, axis: Optional[int] = None, out: Optional[JaxArray] = None
+):
+    if axis is None:
+        axis = -1
+    return jax.nn.log_softmax(x, axis)
+
+
+def mish(x: JaxArray, /, *, out: Optional[JaxArray] = None):
+    return x * jnp.tanh(jax.nn.softplus(x))
+
+
 def relu(
     x: JaxArray, /, *, complex_mode="jax", out: Optional[JaxArray] = None
 ) -> JaxArray:
@@ -78,19 +94,3 @@ def softplus(
     if threshold is not None:
         return jnp.where(x_beta > threshold, x, res).astype(x.dtype)
     return res.astype(x.dtype)
-
-
-def log_softmax(
-    x: JaxArray, /, *, axis: Optional[int] = None, out: Optional[JaxArray] = None
-):
-    if axis is None:
-        axis = -1
-    return jax.nn.log_softmax(x, axis)
-
-
-def mish(x: JaxArray, /, *, out: Optional[JaxArray] = None):
-    return x * jnp.tanh(jax.nn.softplus(x))
-
-
-def hardswish(x: JaxArray, /, *, out: Optional[JaxArray] = None) -> JaxArray:
-    return jax.nn.hard_swish(x)

ivy/functional/backends/jax/creation.py

@@ -79,6 +79,19 @@ def asarray(
         return jnp.asarray(obj, dtype=dtype)
 
 
+def copy_array(
+    x: JaxArray, *, to_ivy_array: bool = True, out: Optional[JaxArray] = None
+) -> JaxArray:
+    x = (
+        jax.core.ShapedArray(x.shape, x.dtype)
+        if isinstance(x, jax.core.ShapedArray)
+        else jnp.array(x)
+    )
+    if to_ivy_array:
+        return ivy.to_ivy(x)
+    return x
+
+
 def empty(
     shape: Union[ivy.NativeShape, Sequence[int]],
     *,
@@ -127,6 +140,15 @@ def from_dlpack(x, /, *, out: Optional[JaxArray] = None) -> JaxArray:
     return jax.dlpack.from_dlpack(capsule)
 
 
+def frombuffer(
+    buffer: bytes,
+    dtype: Optional[jnp.dtype] = float,
+    count: Optional[int] = -1,
+    offset: Optional[int] = 0,
+) -> JaxArray:
+    return jnp.frombuffer(buffer, dtype=dtype, count=count, offset=offset)
+
+
 def full(
     shape: Union[ivy.NativeShape, Sequence[int]],
     fill_value: Union[int, float, bool],
@@ -234,6 +256,42 @@ def meshgrid(
     return jnp.meshgrid(*arrays, sparse=sparse, indexing=indexing)
 
 
+def one_hot(
+    indices: JaxArray,
+    depth: int,
+    /,
+    *,
+    on_value: Optional[Number] = None,
+    off_value: Optional[Number] = None,
+    axis: Optional[int] = None,
+    dtype: Optional[jnp.dtype] = None,
+    device: jaxlib.xla_extension.Device,
+    out: Optional[JaxArray] = None,
+) -> JaxArray:
+    on_none = on_value is None
+    off_none = off_value is None
+
+    if dtype is None:
+        if on_none and off_none:
+            dtype = jnp.float32
+        else:
+            if not on_none:
+                dtype = jnp.array(on_value).dtype
+            elif not off_none:
+                dtype = jnp.array(off_value).dtype
+
+    res = jnp.eye(depth, dtype=dtype)[jnp.array(indices, dtype="int64").reshape(-1)]
+    res = res.reshape(list(indices.shape) + [depth])
+
+    if not on_none and not off_none:
+        res = jnp.where(res == 1, on_value, off_value)
+
+    if axis is not None:
+        res = jnp.moveaxis(res, -1, axis)
+
+    return res
+
+
 def ones(
     shape: Union[ivy.NativeShape, Sequence[int]],
     *,
@@ -263,6 +321,17 @@ def triu(x: JaxArray, /, *, k: int = 0, out: Optional[JaxArray] = None) -> JaxAr
     return jnp.triu(x, k)
 
 
+def triu_indices(
+    n_rows: int,
+    n_cols: Optional[int] = None,
+    k: int = 0,
+    /,
+    *,
+    device: jaxlib.xla_extension.Device,
+) -> Tuple[JaxArray]:
+    return jnp.triu_indices(n=n_rows, k=k, m=n_cols)
+
+
 def zeros(
     shape: Union[ivy.NativeShape, Sequence[int]],
     *,
@@ -289,72 +358,3 @@ def zeros_like(
 
 
 array = asarray
-
-
-def copy_array(
-    x: JaxArray, *, to_ivy_array: bool = True, out: Optional[JaxArray] = None
-) -> JaxArray:
-    x = (
-        jax.core.ShapedArray(x.shape, x.dtype)
-        if isinstance(x, jax.core.ShapedArray)
-        else jnp.array(x)
-    )
-    if to_ivy_array:
-        return ivy.to_ivy(x)
-    return x
-
-
-def one_hot(
-    indices: JaxArray,
-    depth: int,
-    /,
-    *,
-    on_value: Optional[Number] = None,
-    off_value: Optional[Number] = None,
-    axis: Optional[int] = None,
-    dtype: Optional[jnp.dtype] = None,
-    device: jaxlib.xla_extension.Device,
-    out: Optional[JaxArray] = None,
-) -> JaxArray:
-    on_none = on_value is None
-    off_none = off_value is None
-
-    if dtype is None:
-        if on_none and off_none:
-            dtype = jnp.float32
-        else:
-            if not on_none:
-                dtype = jnp.array(on_value).dtype
-            elif not off_none:
-                dtype = jnp.array(off_value).dtype
-
-    res = jnp.eye(depth, dtype=dtype)[jnp.array(indices, dtype="int64").reshape(-1)]
-    res = res.reshape(list(indices.shape) + [depth])
-
-    if not on_none and not off_none:
-        res = jnp.where(res == 1, on_value, off_value)
-
-    if axis is not None:
-        res = jnp.moveaxis(res, -1, axis)
-
-    return res
-
-
-def frombuffer(
-    buffer: bytes,
-    dtype: Optional[jnp.dtype] = float,
-    count: Optional[int] = -1,
-    offset: Optional[int] = 0,
-) -> JaxArray:
-    return jnp.frombuffer(buffer, dtype=dtype, count=count, offset=offset)
-
-
-def triu_indices(
-    n_rows: int,
-    n_cols: Optional[int] = None,
-    k: int = 0,
-    /,
-    *,
-    device: jaxlib.xla_extension.Device,
-) -> Tuple[JaxArray]:
-    return jnp.triu_indices(n=n_rows, k=k, m=n_cols)