feat: added the tensor operations for slicing, mutations, joinings and indexing

determine_tests.py

@@ -4,7 +4,7 @@
 from pydriller import Repository
 import os  # noqa
 import bz2
-import _pickle as cPickle
+import pickle as cPickle
 import sys
 from run_tests_CLI.get_all_tests import get_all_tests
 

ivy/data_classes/array/experimental/manipulation.py

@@ -1080,3 +1080,29 @@ def fill_diagonal(
         ivy.fill_diag also applies to this method with minimal changes.
         """
         return ivy.fill_diagonal(self._data, v, wrap=wrap)
+
+
+def tensor_ops(tensor, operation_type, *args):
+    if operation_type == "index":
+        if len(args) != 1:
+            raise ValueError
+        index = args[0]
+        return tensor[index]
+    elif operation_type == "slice":
+        if len(args) != 2:
+            raise ValueError
+        start, end = args
+        return tensor[start:end]
+    elif operation_type == "join":
+        if len(args) != 1:
+            raise ValueError
+        other_tensor = args[0]
+        return ivy.cat((tensor, other_tensor), dim=0)
+    elif operation_type == "mutate":
+        if len(args) != 1:
+            raise ValueError
+        factor = args[0]
+        tensor.mul_(factor)
+        return tensor
+    else:
+        raise ValueError

ivy/data_classes/container/experimental/manipulation.py

@@ -2996,3 +2996,30 @@ def fill_diagonal(
             v,
             wrap=wrap,
         )
+
+
+@staticmethod
+def tensor_ops(tensor, operation_type, *args):
+    if operation_type == "index":
+        if len(args) != 1:
+            raise ValueError
+        index = args[0]
+        return tensor[index]
+    elif operation_type == "slice":
+        if len(args) != 2:
+            raise ValueError
+        start, end = args
+        return tensor[start:end]
+    elif operation_type == "join":
+        if len(args) != 1:
+            raise ValueError
+        other_tensor = args[0]
+        return ivy.cat((tensor, other_tensor), dim=0)
+    elif operation_type == "mutate":
+        if len(args) != 1:
+            raise ValueError
+        factor = args[0]
+        tensor.mul_(factor)
+        return tensor
+    else:
+        raise ValueError

ivy/functional/backends/jax/experimental/manipulation.py

@@ -416,3 +416,29 @@ def fill_diagonal(
     a = a.at[:end:step].set(jnp.array(v).astype(a.dtype))
     a = jnp.reshape(a, shape)
     return a
+
+
+def tensor_ops(tensor, operation_type, *args):
+    if operation_type == "index":
+        if len(args) != 1:
+            raise ValueError
+        index = args[0]
+        return tensor[index]
+    elif operation_type == "slice":
+        if len(args) != 2:
+            raise ValueError
+        start, end = args
+        return tensor[start:end]
+    elif operation_type == "join":
+        if len(args) != 1:
+            raise ValueError
+        other_tensor = args[0]
+        return ivy.cat((tensor, other_tensor), dim=0)
+    elif operation_type == "mutate":
+        if len(args) != 1:
+            raise ValueError
+        factor = args[0]
+        tensor.mul_(factor)
+        return tensor
+    else:
+        raise ValueError

ivy/functional/backends/numpy/experimental/manipulation.py

@@ -505,3 +505,29 @@ def fill_diagonal(
 ) -> np.ndarray:
     np.fill_diagonal(a, v, wrap=wrap)
     return a
+
+
+def tensor_ops(tensor, operation_type, *args):
+    if operation_type == "index":
+        if len(args) != 1:
+            raise ValueError
+        index = args[0]
+        return tensor[index]
+    elif operation_type == "slice":
+        if len(args) != 2:
+            raise ValueError
+        start, end = args
+        return tensor[start:end]
+    elif operation_type == "join":
+        if len(args) != 1:
+            raise ValueError
+        other_tensor = args[0]
+        return ivy.cat((tensor, other_tensor), dim=0)
+    elif operation_type == "mutate":
+        if len(args) != 1:
+            raise ValueError
+        factor = args[0]
+        tensor.mul_(factor)
+        return tensor
+    else:
+        raise ValueError

ivy/functional/backends/tensorflow/experimental/manipulation.py

@@ -367,3 +367,29 @@ def fill_diagonal(
     a = tf.tensor_scatter_nd_update(a, indices, ups)
     a = tf.reshape(a, shape)
     return a
+
+
+def tensor_ops(tensor, operation_type, *args):
+    if operation_type == "index":
+        if len(args) != 1:
+            raise ValueError
+        index = args[0]
+        return tensor[index]
+    elif operation_type == "slice":
+        if len(args) != 2:
+            raise ValueError
+        start, end = args
+        return tensor[start:end]
+    elif operation_type == "join":
+        if len(args) != 1:
+            raise ValueError
+        other_tensor = args[0]
+        return ivy.cat((tensor, other_tensor), dim=0)
+    elif operation_type == "mutate":
+        if len(args) != 1:
+            raise ValueError
+        factor = args[0]
+        tensor.mul_(factor)
+        return tensor
+    else:
+        raise ValueError

ivy/functional/backends/torch/experimental/manipulation.py

@@ -395,3 +395,29 @@ def fill_diagonal(
     a = torch.where(w, v, a)
     a = torch.reshape(a, shape)
     return a
+
+
+def tensor_ops(tensor, operation_type, *args):
+    if operation_type == "index":
+        if len(args) != 1:
+            raise ValueError
+        index = args[0]
+        return tensor[index]
+    elif operation_type == "slice":
+        if len(args) != 2:
+            raise ValueError
+        start, end = args
+        return tensor[start:end]
+    elif operation_type == "join":
+        if len(args) != 1:
+            raise ValueError
+        other_tensor = args[0]
+        return ivy.cat((tensor, other_tensor), dim=0)
+    elif operation_type == "mutate":
+        if len(args) != 1:
+            raise ValueError
+        factor = args[0]
+        tensor.mul_(factor)
+        return tensor
+    else:
+        raise ValueError

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

@@ -183,3 +183,19 @@ def bincount(x, weights=None, minlength=0, name=None):
 def dist(x, y, p=2):
     ret = ivy.vector_norm(ivy.subtract(x, y), ord=p)
     return ivy.reshape(ret, (1,))
+
+
+@with_supported_dtypes({"2.4.1 and above": ("int64",)}, "paddle")
+@to_ivy_arrays_and_back
+def linear_algebra_histogram(d, num_bins):
+    min_value = ivy.min(d)
+    max_value = ivy.max(d)
+    bin_width = (max_value - min_value) / num_bins
+    bin_edges = ivy.arange(min_value, max_value + bin_width, bin_width)
+
+    # Count the number of values in each bin.
+    bin_counts = ivy.zeros(num_bins)
+    for value in d:
+        bin_index = ivy.searchsorted(bin_edges, value)
+        bin_counts[bin_index] += 1
+    return ivy.bin_counts

ivy/functional/frontends/torch/indexing_slicing_joining_mutating_ops.py

@@ -366,3 +366,30 @@ def select(input, dim, index):
     slices = [slice(None)] * num_dims
     slices[dim] = index
     return input[tuple(slices)]
+
+
+@to_ivy_arrays_and_back
+def tensor_ops(tensor, operation_type, *args):
+    if operation_type == "index":
+        if len(args) != 1:
+            raise ValueError
+        index = args[0]
+        return tensor[index]
+    elif operation_type == "slice":
+        if len(args) != 2:
+            raise ValueError
+        start, end = args
+        return tensor[start:end]
+    elif operation_type == "join":
+        if len(args) != 1:
+            raise ValueError
+        other_tensor = args[0]
+        return ivy.cat((tensor, other_tensor), dim=0)
+    elif operation_type == "mutate":
+        if len(args) != 1:
+            raise ValueError
+        factor = args[0]
+        tensor.mul_(factor)
+        return tensor
+    else:
+        raise ValueError

ivy/functional/ivy/experimental/manipulation.py

@@ -2152,3 +2152,29 @@ def fill_diagonal(
         Array with the diagonal filled.
     """
     return ivy.current_backend(a).fill_diag(a, v, wrap=wrap)
+
+
+def tensor_ops(tensor, operation_type, *args):
+    if operation_type == "index":
+        if len(args) != 1:
+            raise ValueError
+        index = args[0]
+        return tensor[index]
+    elif operation_type == "slice":
+        if len(args) != 2:
+            raise ValueError
+        start, end = args
+        return tensor[start:end]
+    elif operation_type == "join":
+        if len(args) != 1:
+            raise ValueError
+        other_tensor = args[0]
+        return ivy.cat((tensor, other_tensor), dim=0)
+    elif operation_type == "mutate":
+        if len(args) != 1:
+            raise ValueError
+        factor = args[0]
+        tensor.mul_(factor)
+        return tensor
+    else:
+        raise ValueError

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

@@ -16,7 +16,7 @@
     fn_tree="numpy.fft.ifft",
     dtype_and_x=x_and_ifft(),
 )
-def test_numpy_iftt(dtype_and_x, backend_fw, frontend, test_flags, fn_tree, on_device):
+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
     helpers.test_frontend_function(
         input_dtypes=input_dtype,
@@ -39,7 +39,7 @@ def test_numpy_iftt(dtype_and_x, backend_fw, frontend, test_flags, fn_tree, on_d
         available_dtypes=helpers.get_dtypes("float"), shape=(4,), array_api_dtypes=True
     ),
 )
-def test_numpy_ifttshift(
+def test_numpy_ifftshift(
     dtype_and_x, backend_fw, frontend, test_flags, fn_tree, on_device
 ):
     input_dtype, arr = dtype_and_x
@@ -92,9 +92,7 @@ def test_numpy_fft(
         available_dtypes=helpers.get_dtypes("float"), shape=(4,), array_api_dtypes=True
     ),
 )
-def test_numpy_fttshift(
-    dtype_and_x, backend_fw, frontend, test_flags, fn_tree, on_device
-):
+def fttshift(dtype_and_x, backend_fw, frontend, test_flags, fn_tree, on_device):
     input_dtype, arr = dtype_and_x
     helpers.test_frontend_function(
         input_dtypes=input_dtype,

ivy_tests/test_ivy/test_frontends/test_torch/test_indexing_slicing_joining_mutating_ops.py

@@ -1593,3 +1593,29 @@ def test_torch_select(
         dim=axis,
         index=idx,
     )
+
+
+def tensor_ops(tensor, operation_type, *args):
+    if operation_type == "index":
+        if len(args) != 1:
+            raise ValueError
+        index = args[0]
+        return tensor[index]
+    elif operation_type == "slice":
+        if len(args) != 2:
+            raise ValueError
+        start, end = args
+        return tensor[start:end]
+    elif operation_type == "join":
+        if len(args) != 1:
+            raise ValueError
+        other_tensor = args[0]
+        return tensor.cat((tensor, other_tensor), dim=0)
+    elif operation_type == "mutate":
+        if len(args) != 1:
+            raise ValueError
+        factor = args[0]
+        tensor.mul_(factor)
+        return tensor
+    else:
+        raise ValueError