# Source code for pennylane.math.utils

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
"""Utility functions"""
# pylint: disable=import-outside-toplevel
import autoray as ar
from autoray import numpy as np
import numpy as _np

from . import single_dispatch  # pylint:disable=unused-import

[docs]def allequal(tensor1, tensor2, **kwargs):
"""Returns True if two tensors are element-wise equal along a given axis.

This function is equivalent to calling np.all(tensor1 == tensor2, **kwargs),
but allows for tensor1 and tensor2 to differ in type.

Args:
tensor1 (tensor_like): tensor to compare
tensor2 (tensor_like): tensor to compare
**kwargs: Accepts any keyword argument that is accepted by np.all,
such as axis, out, and keepdims. See the NumPy documentation
<https://numpy.org/doc/stable/reference/generated/numpy.all.html>__ for
more details.

Returns:
ndarray, bool: If axis=None, a logical AND reduction is applied to all elements
and a boolean will be returned, indicating if all elements evaluate to True. Otherwise,
a boolean NumPy array will be returned.

**Example**

>>> a = torch.tensor([1, 2])
>>> b = np.array([1, 2])
>>> allequal(a, b)
True
"""
t1 = ar.to_numpy(tensor1)
t2 = ar.to_numpy(tensor2)
return np.all(t1 == t2, **kwargs)

[docs]def allclose(a, b, rtol=1e-05, atol=1e-08, **kwargs):
"""Wrapper around np.allclose, allowing tensors a and b
to differ in type"""
try:
# Some frameworks may provide their own allclose implementation.
# Try and use it if available.
res = np.allclose(a, b, rtol=rtol, atol=atol, **kwargs)
except (TypeError, AttributeError):
# Otherwise, convert the input to NumPy arrays.
#
# TODO: replace this with a bespoke, framework agnostic
# low-level implementation to avoid the NumPy conversion:
#
#    np.abs(a - b) <= atol + rtol * np.abs(b)
#
t1 = ar.to_numpy(a)
t2 = ar.to_numpy(b)
res = np.allclose(t1, t2, rtol=rtol, atol=atol, **kwargs)

return res

allclose.__doc__ = _np.allclose.__doc__

[docs]def cast(tensor, dtype):
"""Casts the given tensor to a new type.

Args:
tensor (tensor_like): tensor to cast
dtype (str, np.dtype): Any supported NumPy dtype representation; this can be
a string ("float64"), a np.dtype object (np.dtype("float64")), or
a dtype class (np.float64). If tensor is not a NumPy array, the
**equivalent** dtype in the dispatched framework is used.

Returns:
tensor_like: a tensor with the same shape and values as tensor and the
same dtype as dtype

**Example**

We can use NumPy dtype specifiers:

>>> x = torch.tensor([1, 2])
>>> cast(x, np.float64)
tensor([1., 2.], dtype=torch.float64)

We can also use strings:

>>> x = tf.Variable([1, 2])
>>> cast(x, "complex128")
<tf.Tensor: shape=(2,), dtype=complex128, numpy=array([1.+0.j, 2.+0.j])>
"""
if isinstance(tensor, (list, tuple)):
tensor = np.asarray(tensor)

if not isinstance(dtype, str):
try:
dtype = np.dtype(dtype).name
except (AttributeError, TypeError):
dtype = getattr(dtype, "name", dtype)

return ar.astype(tensor, ar.to_backend_dtype(dtype, like=ar.infer_backend(tensor)))

[docs]def cast_like(tensor1, tensor2):
"""Casts a tensor to the same dtype as another.

Args:
tensor1 (tensor_like): tensor to cast
tensor2 (tensor_like): tensor with corresponding dtype to cast to

Returns:
tensor_like: a tensor with the same shape and values as tensor1 and the
same dtype as tensor2

**Example**

>>> x = torch.tensor([1, 2])
>>> y = torch.tensor([3., 4.])
>>> cast_like(x, y)
tensor([1., 2.])
"""
dtype = ar.to_numpy(tensor2).dtype.type
return cast(tensor1, dtype)

[docs]def convert_like(tensor1, tensor2):
"""Convert a tensor to the same type as another.

Args:
tensor1 (tensor_like): tensor to convert
tensor2 (tensor_like): tensor with corresponding type to convert to

Returns:
tensor_like: a tensor with the same shape, values, and dtype as tensor1 and the
same type as tensor2.

**Example**

>>> x = np.array([1, 2])
>>> y = tf.Variable([3, 4])
>>> convert_like(x, y)
<tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2])>
"""
interface = get_interface(tensor2)

if interface == "torch":
dev = tensor2.device
return np.asarray(tensor1, device=dev, like=interface)

return np.asarray(tensor1, like=interface)

[docs]def get_interface(tensor):
"""Returns the name of the package that any array/tensor manipulations
will dispatch to. The returned strings correspond to those used for PennyLane
:doc:interfaces </introduction/interfaces>.

Args:
tensor (tensor_like): tensor input

Returns:
str: name of the interface

**Example**

>>> x = torch.tensor([1., 2.])
>>> get_interface(x)
'torch'
>>> from pennylane import numpy as np
>>> x = np.array([4, 5], requires_grad=True)
>>> get_interface(x)
"""
namespace = tensor.__class__.__module__.split(".")

res = ar.infer_backend(tensor)

if res == "builtins":
return "numpy"

return res

"""Returns True if the tensor is considered trainable.

.. warning::

The implemetation depends on the contained tensor type, and
may be context dependent.

For example, Torch tensors and PennyLane tensors track trainability
as a property of the tensor itself. TensorFlow, on the other hand,
only tracks trainability if being watched by a gradient tape.

Args:
tensor (tensor_like): input tensor
interface (str): The name of the interface. Will be determined automatically
if not provided.

**Example**

Calling this function on a PennyLane NumPy array:

>>> x = np.array([1., 5.], requires_grad=True)
True
False

PyTorch has similar behaviour.

With TensorFlow, the output is dependent on whether the tensor
is currently being watched by a gradient tape:

>>> x = tf.Variable([0.6, 0.1])
False
True

While TensorFlow constants are by default not trainable, they can be
manually watched by the gradient tape:

>>> x = tf.constant([0.6, 0.1])
False
...     tape.watch([x])
True
"""
interface = interface or get_interface(tensor)

if interface == "tensorflow":
import tensorflow as tf

try:
from tensorflow.python.eager.tape import should_record_backprop
except ImportError:  # pragma: no cover
from tensorflow.python.eager.tape import should_record as should_record_backprop

return should_record_backprop([tf.convert_to_tensor(tensor)])

if isinstance(tensor, ArrayBox):
return True

# Currently, in the Autograd interface, we assume
# that all objects are differentiable by default.

if interface == "torch":