refactor(tests): Added new pre-commit hook for ordering functions (iv…

…y-llc#22600)

.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: 956eb831454d5a4fc3138467b85aedbf634140f0
+    rev: a90659d806c6d65f20ec41095a2da8e8920cc96f
     hooks:
       - id: ivy-lint

ivy/functional/frontends/numpy/fft/discrete_fourier_transform.py

@@ -7,10 +7,6 @@
 # --------------- #
 
 
-# --- Helpers --- #
-# --------------- #
-
-
 def _swap_direction(norm):
     try:
         return _SWAP_DIRECTION_MAP[norm]

ivy/functional/frontends/numpy/ma/MaskedArray.py

@@ -2,7 +2,6 @@
 import ivy.functional.frontends.numpy as np_frontend
 import numpy as np
 
-masked = True
 masked_print_options = "--"
 nomask = False
 
@@ -194,10 +193,12 @@ def _array_in_str(self):
 def _is_masked_array(x):
     return isinstance(x, (np.ma.MaskedArray, np_frontend.ma.MaskedArray))
 
-    # Instance Methods #
-    # ---------------- #
 
-    # TODO
+masked = True
+# Instance Methods #
+# ---------------- #
+
+# TODO
 
 
 # masked_array (alias)

ivy/functional/frontends/tensorflow/raw_ops.py

@@ -14,7 +14,6 @@
 
 Acos = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.acos))
 Acosh = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.acosh))
-Add = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.add))
 AddN = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.add_n))
 AddV2 = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.add))
 ArgMax = to_ivy_arrays_and_back(
@@ -220,7 +219,6 @@
 )
 Sin = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.sin))
 Size = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.general_functions.size))
-Slice = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.slice))
 Softmax = to_ivy_arrays_and_back(
     with_unsupported_dtypes(
         {
@@ -253,7 +251,6 @@
 Squeeze = to_ivy_arrays_and_back(
     map_raw_ops_alias(tf_frontend.general_functions.squeeze)
 )
-Sub = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.subtract))
 Tan = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.tan))
 Tanh = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.tanh))
 Xlogy = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.xlogy))
@@ -831,3 +828,8 @@ def Xlog1py(*, x, y, name="Xlog1py"):
 @to_ivy_arrays_and_back
 def ZerosLike(*, x, name="ZerosLike"):
     return ivy.zeros_like(x)
+
+
+Add = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.add))
+Slice = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.slice))
+Sub = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.math.subtract))

ivy/functional/frontends/torch/comparison_ops.py

@@ -289,8 +289,8 @@ def topk(input, k, dim=None, largest=True, sorted=True, *, out=None):
     return ivy.top_k(input, k, axis=dim, largest=largest, sorted=sorted, out=out)
 
 
-ge = greater_equal
 gt = greater
+ne = not_equal
+ge = greater_equal
 le = less_equal
 lt = less
-ne = not_equal

ivy_tests/test_ivy/test_frontends/test_jax/test_nn/test_non_linear_activations.py

@@ -106,9 +106,11 @@ def test_jax_elu(
     fn_tree="jax.nn.gelu",
     dtype_and_x=helpers.dtype_and_values(
         available_dtypes=helpers.get_dtypes("float_and_complex"),
+        large_abs_safety_factor=1,
+        small_abs_safety_factor=1,
+        safety_factor_scale="linear",
         min_value=-1e4,
         max_value=1e4,
-        abs_smallest_val=1e-3,
     ),
     approximate=st.booleans(),
     test_with_out=st.just(False),
@@ -125,7 +127,7 @@ def test_jax_gelu(
 ):
     input_dtype, x = dtype_and_x
     # As erf function doesn't support complex dtype
-    if "complex" in input_dtype[0]:
+    if "complex" in str(x[0].dtype):
         approximate = True
     helpers.test_frontend_function(
         input_dtypes=input_dtype,
@@ -134,8 +136,8 @@ def test_jax_gelu(
         test_flags=test_flags,
         fn_tree=fn_tree,
         on_device=on_device,
-        rtol=1e-2,
-        atol=1e-2,
+        rtol=1e-02,
+        atol=1e-02,
         x=x[0],
         approximate=approximate,
     )
@@ -315,9 +317,7 @@ def test_jax_hard_tanh(
         small_abs_safety_factor=2,
         safety_factor_scale="linear",
     ),
-    negative_slope=helpers.floats(
-        min_value=0.0, max_value=1.0, small_abs_safety_factor=16
-    ),
+    negative_slope=helpers.floats(min_value=0.0, max_value=1.0),
     test_with_out=st.just(False),
 )
 def test_jax_leaky_relu(
@@ -735,9 +735,9 @@ def test_jax_soft_sign(
 @handle_frontend_test(
     fn_tree="jax.nn.softmax",
     dtype_x_axis=helpers.dtype_values_axis(
-        available_dtypes=helpers.get_dtypes("float_and_complex"),
+        available_dtypes=helpers.get_dtypes("float"),
         min_num_dims=2,
-        max_axes_size=2,
+        max_axes_size=1,
         force_int_axis=True,
         valid_axis=True,
     ),

ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_mathematical_functions.py

@@ -543,46 +543,6 @@ def test_jax_ceil(
     )
 
 
-# clip
-@handle_frontend_test(
-    fn_tree="jax.numpy.clip",
-    dtype_and_x=helpers.dtype_and_values(
-        available_dtypes=helpers.get_dtypes("float_and_integer"),
-        min_value=-1e3,
-        max_value=1e3,
-        max_dim_size=10,
-        max_num_dims=4,
-        min_dim_size=1,
-        min_num_dims=1,
-    ),
-    a_min=st.integers(min_value=0, max_value=5),
-    a_max=st.integers(min_value=5, max_value=10),
-)
-def test_jax_clip(
-    *,
-    dtype_and_x,
-    a_min,
-    a_max,
-    on_device,
-    fn_tree,
-    frontend,
-    backend_fw,
-    test_flags,
-):
-    input_dtype, x = dtype_and_x
-    helpers.test_frontend_function(
-        input_dtypes=input_dtype,
-        frontend=frontend,
-        backend_to_test=backend_fw,
-        test_flags=test_flags,
-        fn_tree=fn_tree,
-        on_device=on_device,
-        a=x[0],
-        a_min=a_min,
-        a_max=a_max,
-    )
-
-
 # conj
 @handle_frontend_test(
     fn_tree="jax.numpy.conj",
@@ -1486,49 +1446,6 @@ def test_jax_gcd(
     )
 
 
-# gradient
-@handle_frontend_test(
-    fn_tree="jax.numpy.gradient",
-    dtype_input_axis=helpers.dtype_values_axis(
-        available_dtypes=("float32", "float16", "float64"),
-        min_num_dims=1,
-        max_num_dims=3,
-        min_dim_size=2,
-        max_dim_size=4,
-        valid_axis=True,
-        force_int_axis=True,
-    ),
-    varargs=helpers.ints(
-        min_value=-3,
-        max_value=3,
-    ),
-)
-def test_jax_gradient(
-    dtype_input_axis,
-    varargs,
-    frontend,
-    backend_fw,
-    test_flags,
-    fn_tree,
-    on_device,
-):
-    input_dtype, x, axis = dtype_input_axis
-    test_flags.num_positional_args = 2
-    kw = {}
-    kw["varargs"] = varargs
-    kw["axis"] = axis
-    helpers.test_frontend_function(
-        input_dtypes=input_dtype,
-        frontend=frontend,
-        backend_to_test=backend_fw,
-        test_flags=test_flags,
-        fn_tree=fn_tree,
-        on_device=on_device,
-        f=x[0],
-        **kw,
-    )
-
-
 # heaviside
 @handle_frontend_test(
     fn_tree="jax.numpy.heaviside",

ivy_tests/test_ivy/test_frontends/test_jax/test_random.py

@@ -820,62 +820,6 @@ def call():
         assert u.shape == v.shape
 
 
-@pytest.mark.xfail
-@handle_frontend_test(
-    fn_tree="jax.random.logistic",
-    dtype_key=helpers.dtype_and_values(
-        available_dtypes=["uint32"],
-        min_value=0,
-        max_value=2000,
-        min_num_dims=1,
-        max_num_dims=1,
-        min_dim_size=2,
-        max_dim_size=2,
-    ),
-    shape=helpers.get_shape(allow_none=False, min_num_dims=1, min_dim_size=1),
-    dtype=helpers.get_dtypes("float", full=False),
-    test_with_out=st.just(False),
-)
-def test_jax_logistic(
-    *,
-    dtype_key,
-    shape,
-    dtype,
-    on_device,
-    fn_tree,
-    frontend,
-    backend_fw,
-    test_flags,
-):
-    input_dtype, key = dtype_key
-
-    def call():
-        return helpers.test_frontend_function(
-            input_dtypes=input_dtype,
-            frontend=frontend,
-            backend_to_test=backend_fw,
-            test_flags=test_flags,
-            fn_tree=fn_tree,
-            on_device=on_device,
-            key=key[0],
-            shape=shape,
-            dtype=dtype[0],
-            test_values=False,
-        )
-
-    ret = call()
-
-    if not ivy.exists(ret):
-        return
-
-    ret_np, ret_from_np = ret
-    ret_np = helpers.flatten_and_to_np(ret=ret_np, backend=backend_fw)
-    ret_from_np = helpers.flatten_and_to_np(ret=ret_from_np, backend=backend_fw)
-    for u, v in zip(ret_np, ret_from_np):
-        assert u.dtype == v.dtype
-        assert u.shape == v.shape
-
-
 @pytest.mark.xfail
 @handle_frontend_test(
     fn_tree="jax.random.maxwell",

ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py

@@ -665,52 +665,6 @@ def test_mindspore_log_softmax(
 #     )
 
 
-# max_pool3d
-@pytest.mark.skip("Testing pipeline not yet implemented")
-@handle_frontend_test(
-    fn_tree="mindspore.ops.function.nn_func.max_pool3d",
-    x_k_s_p=helpers.arrays_for_pooling(
-        min_dims=5,
-        max_dims=5,
-        min_side=1,
-        max_side=4,
-        only_explicit_padding=True,
-        return_dilation=True,
-        data_format="channel_first",
-    ),
-    test_with_out=st.just(False),
-    ceil_mode=st.sampled_from([True, False]),
-)
-def test_mindspore_max_pool3d(
-    x_k_s_p,
-    ceil_mode,
-    *,
-    test_flags,
-    frontend,
-    backend_fw,
-    fn_tree,
-    on_device,
-):
-    input_dtypes, x, kernel_size, stride, padding, dilation = x_k_s_p
-
-    padding = (padding[0][0], padding[1][0], padding[2][0])
-
-    helpers.test_frontend_function(
-        input_dtypes=input_dtypes,
-        backend_to_test=backend_fw,
-        test_flags=test_flags,
-        frontend=frontend,
-        fn_tree=fn_tree,
-        on_device=on_device,
-        input=x[0],
-        kernel_size=kernel_size,
-        stride=stride,
-        padding=padding,
-        dilation=dilation,
-        ceil_mode=ceil_mode,
-    )
-
-
 # pad
 @pytest.mark.skip("Testing pipeline not yet implemented")
 @handle_frontend_test(

ivy_tests/test_ivy/test_frontends/test_numpy/test_fft/test_discrete_fourier_transform.py

@@ -5,8 +5,8 @@
 import ivy_tests.test_ivy.helpers as helpers
 from ivy_tests.test_ivy.helpers import handle_frontend_test
 from ivy_tests.test_ivy.test_functional.test_experimental.test_nn.test_layers import (
-    _x_and_ifft,
-    _x_and_rfftn,
+    x_and_ifft,
+    x_and_rfftn,
 )
 
 
@@ -90,7 +90,7 @@ def test_numpy_fftshift(
 
 @handle_frontend_test(
     fn_tree="numpy.fft.ifft",
-    dtype_and_x=_x_and_ifft(),
+    dtype_and_x=x_and_ifft(),
 )
 def test_numpy_ifft(dtype_and_x, backend_fw, frontend, test_flags, fn_tree, on_device):
     input_dtype, x, dim, norm, n = dtype_and_x
@@ -111,7 +111,7 @@ def test_numpy_ifft(dtype_and_x, backend_fw, frontend, test_flags, fn_tree, on_d
 
 @handle_frontend_test(
     fn_tree="numpy.fft.ifftn",
-    dtype_and_x=_x_and_ifft(),
+    dtype_and_x=x_and_ifft(),
 )
 def test_numpy_ifftn(dtype_and_x, backend_fw, frontend, test_flags, fn_tree, on_device):
     input_dtype, x, dim, norm, n = dtype_and_x
@@ -237,7 +237,7 @@ def test_numpy_rfftfreq(
 
 @handle_frontend_test(
     fn_tree="numpy.fft.rfftn",
-    dtype_and_x=_x_and_rfftn(),
+    dtype_and_x=x_and_rfftn(),
 )
 def test_numpy_rfftn(dtype_and_x, frontend, backend_fw, test_flags, fn_tree, on_device):
     dtype, x, s, axes, norm = dtype_and_x