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

Issue:
ldexp



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/numpy/__init__.py b/ivy/functional/frontends/numpy/__init__.py
--- a/ivy/functional/frontends/numpy/__init__.py
+++ b/ivy/functional/frontends/numpy/__init__.py
@@ -581,6 +581,7 @@ def promote_types_of_numpy_inputs(
     _log2,
     _logaddexp,
     _logaddexp2,
+    _ldexp,
 )
 
 from ivy.functional.frontends.numpy.logic.array_type_testing import (
@@ -673,3 +674,5 @@ def promote_types_of_numpy_inputs(
 real = ufunc("_real")
 divmod = ufunc("_divmod")
 fmax = ufunc("_fmax")
+ldexp = ufunc("_ldexp")
+
diff --git a/ivy/functional/frontends/numpy/mathematical_functions/exponents_and_logarithms.py b/ivy/functional/frontends/numpy/mathematical_functions/exponents_and_logarithms.py
--- a/ivy/functional/frontends/numpy/mathematical_functions/exponents_and_logarithms.py
+++ b/ivy/functional/frontends/numpy/mathematical_functions/exponents_and_logarithms.py
@@ -214,3 +214,26 @@ def _logaddexp2(
 @to_ivy_arrays_and_back
 def i0(x):
     return ivy.i0(x)
+
+
+@handle_numpy_out
+@handle_numpy_dtype
+@to_ivy_arrays_and_back
+@handle_numpy_casting
+@from_zero_dim_arrays_to_scalar
+def _ldexp(
+    x1,
+    x2,
+    /,
+    out=None,
+    *,
+    where=True,
+    casting="same_kind",
+    order="k",
+    dtype=None,
+    subok=True,
+):
+    ret = ivy.ldexp(x1, x2, out=out)
+    if ivy.is_array(where):
+        ret = ivy.where(where, ret, ivy.default(out, ivy.zeros_like(ret)), out=out)
+    return ret


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_numpy/test_mathematical_functions/test_exponents_and_logarithms.py b/ivy_tests/test_ivy/test_frontends/test_numpy/test_mathematical_functions/test_exponents_and_logarithms.py
--- a/ivy_tests/test_ivy/test_frontends/test_numpy/test_mathematical_functions/test_exponents_and_logarithms.py
+++ b/ivy_tests/test_ivy/test_frontends/test_numpy/test_mathematical_functions/test_exponents_and_logarithms.py
@@ -5,6 +5,7 @@
 import ivy_tests.test_ivy.test_frontends.test_numpy.helpers as np_frontend_helpers
 from ivy_tests.test_ivy.helpers import handle_frontend_test
 
+from ivy_tests.test_ivy.test_functional.test_experimental.test_core.test_elementwise import ldexp_args
 
 # exp
 @handle_frontend_test(
@@ -468,3 +469,30 @@ def test_numpy_i0(
         on_device=on_device,
         x=x[0],
     )
+
+# ldexp
+@handle_frontend_test(
+    fn_tree="numpy.ldexp",
+    dtype_and_x=ldexp_args(),
+    number_positional_args=np_frontend_helpers.get_num_positional_args_ufunc(
+        fn_name="ldexp"
+    ),
+)
+def test_numpy_ldexp(
+    *,
+    dtype_and_x,
+    test_flags,
+    on_device,
+    fn_tree,
+    frontend,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        test_flags=test_flags,
+        frontend=frontend,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x1=x[0],
+        x2=x[1],
+    )
Repository: ivy-llc/ivy

Issue:
reverse



Discussion:
- [ ] #9964

Resolving patch:
diff --git a/ivy/functional/frontends/tensorflow/general_functions.py b/ivy/functional/frontends/tensorflow/general_functions.py
--- a/ivy/functional/frontends/tensorflow/general_functions.py
+++ b/ivy/functional/frontends/tensorflow/general_functions.py
@@ -376,3 +376,8 @@ def repeat(
 @to_ivy_arrays_and_back
 def unstack(value: ivy.array, axis=0, num=None, name=None):
     return ivy.unstack(value, axis=axis)
+
+
+@to_ivy_arrays_and_back
+def reverse(tensor, axis, name=None):
+    return ivy.flip(tensor, axis=axis)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_general_functions.py b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_general_functions.py
--- a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_general_functions.py
+++ b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_general_functions.py
@@ -1617,3 +1617,52 @@ def test_tensorflow_unstack(
         value=x[0],
         axis=axis,
     )
+
+
+# reverse    
+@st.composite
+def reverse_helper(draw):
+    dtype, x, shape = draw(
+        helpers.dtype_and_values(
+            available_dtypes=helpers.get_dtypes("numeric"),
+            min_num_dims=1,
+            max_num_dims=8,
+            ret_shape=True,
+        )
+    )
+    axis_dtype, axis = draw(
+        helpers.dtype_and_values(
+            available_dtypes=["int32", "int64"],
+            min_num_dims=1,
+            max_num_dims=1,
+            min_value=-(len(shape) - 1),
+            max_value=len(shape) - 1,
+            shape=(1,),
+        )
+    )
+    return dtype, x, axis_dtype, axis
+
+
+@handle_frontend_test(
+    fn_tree="tensorflow.reverse",
+    dtype_x_axis=reverse_helper(),
+)
+def test_tensorflow_reverse(
+    *,
+    dtype_x_axis,
+    frontend,
+    fn_tree,
+    test_flags,
+    on_device,
+):
+
+    dtype, x, axis_dtype, axis = dtype_x_axis
+    helpers.test_frontend_function(
+        input_dtypes=dtype + axis_dtype,
+        test_flags=test_flags,
+        frontend=frontend,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        tensor=x[0],
+        axis=axis[0],
+    )
Repository: ivy-llc/ivy

Issue:
to_dev



Discussion:

Resolving patch:
diff --git a/ivy/functional/ivy/device.py b/ivy/functional/ivy/device.py
--- a/ivy/functional/ivy/device.py
+++ b/ivy/functional/ivy/device.py
@@ -826,12 +826,13 @@ def to_device(
     stream: Optional[Union[int, Any]] = None,
     out: Optional[ivy.Array] = None,
 ) -> ivy.Array:
-    """Move the input array x to the desired device, specified by device string.
+    """
+    Move the input array x to the desired device, specified by device string.
 
     Parameters
     ----------
     x
-       input array to be moved to the desired device
+        input array to be moved to the desired device
     device
         device to move the input array `x` to
     stream


Test patch:
Repository: ivy-llc/ivy

Issue:
moveaxis



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/indexing_slicing_joining_mutating_ops.py b/ivy/functional/frontends/torch/indexing_slicing_joining_mutating_ops.py
--- a/ivy/functional/frontends/torch/indexing_slicing_joining_mutating_ops.py
+++ b/ivy/functional/frontends/torch/indexing_slicing_joining_mutating_ops.py
@@ -161,6 +161,11 @@ def movedim(input, source, destination):
     return ivy.moveaxis(input, source, destination)
 
 
+@to_ivy_arrays_and_back
+def moveaxis(input, source, destination):
+    return ivy.moveaxis(input, source, destination)
+
+
 @to_ivy_arrays_and_back
 def hstack(tensors, *, out=None):
     return ivy.hstack(tensors, out=out)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_indexing_slicing_joining_mutating_ops.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_indexing_slicing_joining_mutating_ops.py
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_indexing_slicing_joining_mutating_ops.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_indexing_slicing_joining_mutating_ops.py
@@ -704,6 +704,78 @@ def test_torch_movedim(
     )
 
 
+# moveaxis
+@handle_frontend_test(
+    fn_tree="torch.moveaxis",
+    dtype_and_input=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_value=-100,
+        max_value=100,
+        shape=st.shared(
+            helpers.get_shape(
+                min_num_dims=1,
+                max_num_dims=3,
+                min_dim_size=1,
+                max_dim_size=3,
+            ),
+            key="a_s_d",
+        ),
+    ),
+    source=helpers.get_axis(
+        allow_none=False,
+        unique=True,
+        shape=st.shared(
+            helpers.get_shape(
+                min_num_dims=1,
+                max_num_dims=3,
+                min_dim_size=1,
+                max_dim_size=3,
+            ),
+            key="a_s_d",
+        ),
+        min_size=1,
+        force_int=True,
+    ),
+    destination=helpers.get_axis(
+        allow_none=False,
+        unique=True,
+        shape=st.shared(
+            helpers.get_shape(
+                min_num_dims=1,
+                max_num_dims=3,
+                min_dim_size=1,
+                max_dim_size=3,
+            ),
+            key="a_s_d",
+        ),
+        min_size=1,
+        force_int=True,
+    ),
+    test_with_out=st.just(False),
+)
+def test_torch_moveaxis(
+    *,
+    dtype_and_input,
+    source,
+    destination,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+):
+    input_dtype, value = dtype_and_input
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        input=value[0],
+        source=source,
+        destination=destination,
+    )
+
+
 # hstack
 @handle_frontend_test(
     fn_tree="torch.hstack",
Repository: ivy-llc/ivy

Issue:
all



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/tensor.py b/ivy/functional/frontends/torch/tensor.py
--- a/ivy/functional/frontends/torch/tensor.py
+++ b/ivy/functional/frontends/torch/tensor.py
@@ -85,8 +85,8 @@ def chunk(self, chunks, dim=0):
     def any(self, dim=None, keepdim=False, *, out=None):
         return torch_frontend.any(self._ivy_array, dim=dim, keepdim=keepdim, out=out)
 
-    def all(self, dim=None, keepdim=False, *, out=None):
-        return torch_frontend.all(self._ivy_array, dim=dim, keepdim=keepdim, out=out)
+    def all(self, dim=None, keepdim=False):
+        return torch_frontend.all(self._ivy_array, dim=dim, keepdim=keepdim)
 
     @with_unsupported_dtypes({"1.11.0 and below": ("bfloat16",)}, "torch")
     def add_(self, other, *, alpha=1):


Test patch:
Repository: ivy-llc/ivy

Issue:
bitwise_xor



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/tensor.py b/ivy/functional/frontends/torch/tensor.py
--- a/ivy/functional/frontends/torch/tensor.py
+++ b/ivy/functional/frontends/torch/tensor.py
@@ -869,3 +869,6 @@ def __and__(self, other):
     # Method aliases
     absolute, absolute_ = abs, abs_
     ndimension = dim
+
+    def bitwise_xor(self, other, *, out=None):
+        return torch_frontend.bitwise_xor(self._ivy_array, other)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
@@ -5734,3 +5734,39 @@ def test_torch_special_and(
         frontend=frontend,
         on_device=on_device,
     )
+
+
+# bitwise_xor
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="bitwise_xor",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=st.one_of(st.just(("bool",)), helpers.get_dtypes("integer")),
+        num_arrays=2,
+    ),
+)
+def test_torch_instance_bitwise_xor(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_all_as_kwargs_np={
+            "data": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "other": x[1],
+        },
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )
Repository: ivy-llc/ivy

Issue:
vander



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/numpy/creation_routines/building_matrices.py b/ivy/functional/frontends/numpy/creation_routines/building_matrices.py
--- a/ivy/functional/frontends/numpy/creation_routines/building_matrices.py
+++ b/ivy/functional/frontends/numpy/creation_routines/building_matrices.py
@@ -27,3 +27,12 @@ def tri(N, M=None, k=0, dtype="float64", *, like=None):
 @to_ivy_arrays_and_back
 def diag(v, k=0):
     return ivy.diag(v, k=k)
+
+
+@to_ivy_arrays_and_back
+def vander(x, N=None, increasing=False):
+    if ivy.is_float_dtype(x):
+        x = x.astype(ivy.float64)
+    elif ivy.is_bool_dtype or ivy.is_int_dtype(x):
+        x = x.astype(ivy.int64)
+    return ivy.vander(x, N=N, increasing=increasing)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_numpy/test_creation_routines/test_building_matrices.py b/ivy_tests/test_ivy/test_frontends/test_numpy/test_creation_routines/test_building_matrices.py
--- a/ivy_tests/test_ivy/test_frontends/test_numpy/test_creation_routines/test_building_matrices.py
+++ b/ivy_tests/test_ivy/test_frontends/test_numpy/test_creation_routines/test_building_matrices.py
@@ -130,3 +130,42 @@ def test_numpy_diag(
         v=x[0],
         k=k,
     )
+
+
+@handle_frontend_test(
+    fn_tree="numpy.vander",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        shape=st.tuples(
+            helpers.ints(min_value=1, max_value=10),
+        ),
+        large_abs_safety_factor=15,
+        small_abs_safety_factor=15,
+        safety_factor_scale="log",
+    ),
+    N=st.integers(min_value=1, max_value=10) | st.none(),
+    increasing=st.booleans(),
+    test_with_out=st.just(False),
+)
+def test_numpy_vander(
+    *,
+    fn_tree,
+    dtype_and_x,
+    N,
+    increasing,
+    test_flags,
+    frontend,
+    on_device
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        test_flags=test_flags,
+        on_device=on_device,
+        frontend=frontend,
+        fn_tree=fn_tree,
+        x=x[0],
+        N=N,
+        increasing=increasing,
+    )
+
Repository: ivy-llc/ivy

Issue:
geomspace



Discussion:
This issue has been labelled as stale. If you would like to continue working on this issue, then please add another comment or create a PR . If a PR has already been created which refers to this issue, then you should explicitly mention this issue in the relevant PR. For more information please refer to this page in our documentation: https://lets-unify.ai/ivy/contributing/1_the_basics.html#todo-list-issues

Resolving patch:
diff --git a/ivy/functional/frontends/jax/numpy/creation.py b/ivy/functional/frontends/jax/numpy/creation.py
--- a/ivy/functional/frontends/jax/numpy/creation.py
+++ b/ivy/functional/frontends/jax/numpy/creation.py
@@ -1,3 +1,4 @@
+
 import ivy
 from ivy.func_wrapper import with_unsupported_dtypes
 from ivy.functional.frontends.jax.devicearray import DeviceArray
@@ -159,3 +160,15 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
 @to_ivy_arrays_and_back
 def single(x):
     return ivy.astype(x, ivy.float32)
+
+
+@to_ivy_arrays_and_back
+def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0):
+    cr = ivy.log(stop / start) / (num - 1 if endpoint else num)
+    x = ivy.linspace(0, cr * (num - 1 if endpoint else num), num, endpoint=endpoint, axis=axis)
+    x = ivy.exp(x)
+    x = start * x
+    x[0] = (start*cr)/cr
+    if endpoint:
+        x[-1] = stop
+    return x.asarray(dtype=dtype)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_jax/test_jax_numpy_creation.py b/ivy_tests/test_ivy/test_frontends/test_jax/test_jax_numpy_creation.py
--- a/ivy_tests/test_ivy/test_frontends/test_jax/test_jax_numpy_creation.py
+++ b/ivy_tests/test_ivy/test_frontends/test_jax/test_jax_numpy_creation.py
@@ -714,3 +714,35 @@ def test_jax_numpy_single(
         on_device=on_device,
         x=x[0]
     )
+
+
+@handle_frontend_test(
+    fn_tree="jax.numpy.geomspace",
+    dtype_start_stop=_get_dtype_and_range(),
+    num=helpers.ints(min_value=5, max_value=50),
+    endpoint=st.booleans(),
+    test_with_out=st.just(False),
+)
+def test_geomspace(
+    dtype_start_stop,
+    num,
+    endpoint,
+    frontend,
+    test_flags,
+    fn_tree,
+    on_device,
+):
+    input_dtypes, start, stop = dtype_start_stop
+    helpers.test_frontend_function(
+        input_dtypes=input_dtypes,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        rtol=1e-1,
+        start=start,
+        stop=stop,
+        num=num,
+        endpoint=endpoint,
+        dtype=input_dtypes[0],
+    )
Repository: ivy-llc/ivy

Issue:
geomspace



Discussion:
This issue has been labelled as stale. If you would like to continue working on this issue, then please add another comment or create a PR . If a PR has already been created which refers to this issue, then you should explicitly mention this issue in the relevant PR. For more information please refer to this page in our documentation: https://lets-unify.ai/ivy/contributing/1_the_basics.html#todo-list-issues

Resolving patch:
diff --git a/ivy/functional/frontends/numpy/creation_routines/numerical_ranges.py b/ivy/functional/frontends/numpy/creation_routines/numerical_ranges.py
--- a/ivy/functional/frontends/numpy/creation_routines/numerical_ranges.py
+++ b/ivy/functional/frontends/numpy/creation_routines/numerical_ranges.py
@@ -41,6 +41,19 @@ def meshgrid(*xi, copy=True, sparse=False, indexing="xy"):
     if copy:
         return [ivy.copy_array(x) for x in ret]
     return ret
+    
+
+@handle_numpy_dtype
+@to_ivy_arrays_and_back
+def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0):
+    cr = ivy.log(stop / start) / (num - 1 if endpoint else num)
+    x = ivy.linspace(0, cr * (num - 1 if endpoint else num), num, endpoint=endpoint, axis=axis)
+    x = ivy.exp(x)
+    x = start * x
+    x[0] = start
+    if endpoint:
+        x[-1] = stop
+    return x.asarray(dtype=dtype)
 
 
 class nd_grid:


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_numpy/test_creation_routines/test_numerical_ranges.py b/ivy_tests/test_ivy/test_frontends/test_numpy/test_creation_routines/test_numerical_ranges.py
--- a/ivy_tests/test_ivy/test_frontends/test_numpy/test_creation_routines/test_numerical_ranges.py
+++ b/ivy_tests/test_ivy/test_frontends/test_numpy/test_creation_routines/test_numerical_ranges.py
@@ -261,3 +261,35 @@ def test_numpy_ogrid(range, class_, method_name):
     ret = helpers.flatten_and_to_np(ret=ret)
     ret_np = helpers.flatten_and_to_np(ret=ret_np)
     helpers.value_test(ret_np_flat=ret, ret_np_from_gt_flat=ret_np, rtol=1e-03)
+
+
+@handle_frontend_test(
+    fn_tree="numpy.geomspace",
+    dtype_start_stop=_get_dtype_and_range(),
+    num=helpers.ints(min_value=5, max_value=50),
+    endpoint=st.booleans(),
+    test_with_out=st.just(False),
+)
+def test_geomspace(
+        dtype_start_stop,
+        num,
+        endpoint,
+        frontend,
+        test_flags,
+        fn_tree,
+        on_device,
+):
+    input_dtypes, start, stop = dtype_start_stop
+    helpers.test_frontend_function(
+        input_dtypes=input_dtypes,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        rtol=1e-1,
+        start=start,
+        stop=stop,
+        num=num,
+        endpoint=endpoint,
+        dtype=input_dtypes[0],
+    )
Repository: ivy-llc/ivy

Issue:
floor



Discussion:
Hey @pillarxyz , 
Since you closed this issue, someone else seemed to have created another issue and a PR aswell, feel free to pick another function 🙂 https://github.com/unifyai/ivy/issues/12085
@sreegandh804 I was confused because I saw that there was a PR about the same function https://github.com/unifyai/ivy/pull/11894 so that's why I closed it haha, but it's okay

Resolving patch:
diff --git a/ivy/functional/frontends/numpy/mathematical_functions/rounding.py b/ivy/functional/frontends/numpy/mathematical_functions/rounding.py
--- a/ivy/functional/frontends/numpy/mathematical_functions/rounding.py
+++ b/ivy/functional/frontends/numpy/mathematical_functions/rounding.py
@@ -19,8 +19,8 @@
 def _ceil(
     x,
     /,
-    out=None,
     *,
+    out=None,
     where=True,
     casting="same_kind",
     order="k",
@@ -33,6 +33,28 @@ def _ceil(
     return ret
 
 
+@handle_numpy_out
+@handle_numpy_dtype
+@to_ivy_arrays_and_back
+@handle_numpy_casting
+@from_zero_dim_arrays_to_scalar
+def floor(
+    x,
+    /,
+    out=None,
+    *,
+    where=True,
+    casting="same_kind",
+    order="k",
+    dtype=None,
+    subok=True,
+):
+    ret = ivy.floor(x, out=out)
+    if ivy.is_array(where):
+        ret = ivy.where(where, ret, ivy.default(out, ivy.zeros_like(ret)), out=out)
+    return ret
+
+
 @handle_numpy_out
 @to_ivy_arrays_and_back
 def fix(


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_numpy/test_mathematical_functions/test_rounding.py b/ivy_tests/test_ivy/test_frontends/test_numpy/test_mathematical_functions/test_rounding.py
--- a/ivy_tests/test_ivy/test_frontends/test_numpy/test_mathematical_functions/test_rounding.py
+++ b/ivy_tests/test_ivy/test_frontends/test_numpy/test_mathematical_functions/test_rounding.py
@@ -50,6 +50,52 @@ def test_numpy_ceil(
     )
 
 
+# ceil
+@handle_frontend_test(
+    fn_tree="numpy.floor",
+    dtypes_values_casting=np_frontend_helpers.dtypes_values_casting_dtype(
+        arr_func=[
+            lambda: helpers.dtype_and_values(
+                available_dtypes=helpers.get_dtypes("float"),
+            )
+        ],
+        get_dtypes_kind="float",
+    ),
+    where=np_frontend_helpers.where(),
+    number_positional_args=np_frontend_helpers.get_num_positional_args_ufunc(
+        fn_name="floor"
+    ),
+)
+def test_numpy_floor(
+    dtypes_values_casting,
+    where,
+    frontend,
+    test_flags,
+    fn_tree,
+    on_device,
+):
+    input_dtypes, x, casting, dtype = dtypes_values_casting
+    where, input_dtypes, test_flags = np_frontend_helpers.handle_where_and_array_bools(
+        where=where,
+        input_dtype=input_dtypes,
+        test_flags=test_flags,
+    )
+    np_frontend_helpers.test_frontend_function(
+        input_dtypes=input_dtypes,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+        out=None,
+        where=where,
+        casting=casting,
+        order="K",
+        dtype=dtype,
+        subok=True,
+    )
+
+
 # fix
 @handle_frontend_test(
     fn_tree="numpy.fix",