# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional

import torch
from torch import Tensor
from typing_extensions import Literal

from torchmetrics.functional.pairwise.helpers import _check_input, _reduce_distance_matrix
from torchmetrics.utilities.compute import _safe_matmul


def _pairwise_cosine_similarity_update(
    x: Tensor, y: Optional[Tensor] = None, zero_diagonal: Optional[bool] = None
) -> Tensor:
    """Calculates the pairwise cosine similarity matrix.

    Args:
        x: tensor of shape ``[N,d]``
        y: tensor of shape ``[M,d]``
        zero_diagonal: determines if the diagonal of the distance matrix should be set to zero
    """
    x, y, zero_diagonal = _check_input(x, y, zero_diagonal)

    norm = torch.norm(x, p=2, dim=1)
    x /= norm.unsqueeze(1)
    norm = torch.norm(y, p=2, dim=1)
    y /= norm.unsqueeze(1)

    distance = _safe_matmul(x, y)
    if zero_diagonal:
        distance.fill_diagonal_(0)
    return distance


def pairwise_cosine_similarity(
    x: Tensor,
    y: Optional[Tensor] = None,
    reduction: Literal["mean", "sum", "none", None] = None,
    zero_diagonal: Optional[bool] = None,
) -> Tensor:
    r"""Calculates pairwise cosine similarity:

    .. math::
        s_{cos}(x,y) = \frac{<x,y>}{||x|| \cdot ||y||}
                     = \frac{\sum_{d=1}^D x_d \cdot y_d }{\sqrt{\sum_{d=1}^D x_i^2} \cdot \sqrt{\sum_{d=1}^D y_i^2}}

    If both :math:`x` and :math:`y` are passed in, the calculation will be performed pairwise
    between the rows of :math:`x` and :math:`y`.
    If only :math:`x` is passed in, the calculation will be performed between the rows of :math:`x`.

    Args:
        x: Tensor with shape ``[N, d]``
        y: Tensor with shape ``[M, d]``, optional
        reduction: reduction to apply along the last dimension. Choose between `'mean'`, `'sum'`
            (applied along column dimension) or  `'none'`, `None` for no reduction
        zero_diagonal: if the diagonal of the distance matrix should be set to 0. If only :math:`x` is given
            this defaults to ``True`` else if :math:`y` is also given it defaults to ``False``

    Returns:
        A ``[N,N]`` matrix of distances if only ``x`` is given, else a ``[N,M]`` matrix

    Example:
        >>> import torch
        >>> from torchmetrics.functional import pairwise_cosine_similarity
        >>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)
        >>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)
        >>> pairwise_cosine_similarity(x, y)
        tensor([[0.5547, 0.8682],
                [0.5145, 0.8437],
                [0.5300, 0.8533]])
        >>> pairwise_cosine_similarity(x)
        tensor([[0.0000, 0.9989, 0.9996],
                [0.9989, 0.0000, 0.9998],
                [0.9996, 0.9998, 0.0000]])
    """
    distance = _pairwise_cosine_similarity_update(x, y, zero_diagonal)
    return _reduce_distance_matrix(distance, reduction)

src/torchmetrics/functional/pairwise/cosine.py
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional

import torch
from torch import Tensor
from typing_extensions import Literal

from torchmetrics.functional.pairwise.helpers import _check_input, _reduce_distance_matrix
from torchmetrics.utilities.compute import _safe_matmul


def _pairwise_cosine_similarity_update(
    x: Tensor, y: Optional[Tensor] = None, zero_diagonal: Optional[bool] = None
) -> Tensor:
    """Calculates the pairwise cosine similarity matrix.

    Args:
        x: tensor of shape ``[N,d]``
        y: tensor of shape ``[M,d]``
        zero_diagonal: determines if the diagonal of the distance matrix should be set to zero
    """
    x, y, zero_diagonal = _check_input(x, y, zero_diagonal)

    norm = torch.norm(x, p=2, dim=1)
    x = x / norm.unsqueeze(1)
    norm = torch.norm(y, p=2, dim=1)
    y = y / norm.unsqueeze(1)

    distance = _safe_matmul(x, y)
    if zero_diagonal:
        distance.fill_diagonal_(0)
    return distance


def pairwise_cosine_similarity(
    x: Tensor,
    y: Optional[Tensor] = None,
    reduction: Literal["mean", "sum", "none", None] = None,
    zero_diagonal: Optional[bool] = None,
) -> Tensor:
    r"""Calculates pairwise cosine similarity:

    .. math::
        s_{cos}(x,y) = \frac{<x,y>}{||x|| \cdot ||y||}
                     = \frac{\sum_{d=1}^D x_d \cdot y_d }{\sqrt{\sum_{d=1}^D x_i^2} \cdot \sqrt{\sum_{d=1}^D y_i^2}}

    If both :math:`x` and :math:`y` are passed in, the calculation will be performed pairwise
    between the rows of :math:`x` and :math:`y`.
    If only :math:`x` is passed in, the calculation will be performed between the rows of :math:`x`.

    Args:
        x: Tensor with shape ``[N, d]``
        y: Tensor with shape ``[M, d]``, optional
        reduction: reduction to apply along the last dimension. Choose between `'mean'`, `'sum'`
            (applied along column dimension) or  `'none'`, `None` for no reduction
        zero_diagonal: if the diagonal of the distance matrix should be set to 0. If only :math:`x` is given
            this defaults to ``True`` else if :math:`y` is also given it defaults to ``False``

    Returns:
        A ``[N,N]`` matrix of distances if only ``x`` is given, else a ``[N,M]`` matrix

    Example:
        >>> import torch
        >>> from torchmetrics.functional import pairwise_cosine_similarity
        >>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)
        >>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)
        >>> pairwise_cosine_similarity(x, y)
        tensor([[0.5547, 0.8682],
                [0.5145, 0.8437],
                [0.5300, 0.8533]])
        >>> pairwise_cosine_similarity(x)
        tensor([[0.0000, 0.9989, 0.9996],
                [0.9989, 0.0000, 0.9998],
                [0.9996, 0.9998, 0.0000]])
    """
    distance = _pairwise_cosine_similarity_update(x, y, zero_diagonal)
    return _reduce_distance_matrix(distance, reduction)

src/torchmetrics/functional/pairwise/cosine.py
diff --git a/CHANGELOG.md b/CHANGELOG.md
index c7f1ba96656..353486370ce 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -22,6 +22,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Changed minimum Pytorch version to be 1.8 ([#1263](https://github.com/Lightning-AI/metrics/pull/1263))
 
 
+- Changed in-place operation to out-of-place operation in `pairwise_cosine_similarity` ([#1288](https://github.com/Lightning-AI/metrics/pull/1288))
+
+
 ### Deprecated
 
 -
diff --git a/src/torchmetrics/functional/pairwise/cosine.py b/src/torchmetrics/functional/pairwise/cosine.py
index 5027007ca1e..44f96f4f48c 100644
--- a/src/torchmetrics/functional/pairwise/cosine.py
+++ b/src/torchmetrics/functional/pairwise/cosine.py
@@ -34,9 +34,9 @@ def _pairwise_cosine_similarity_update(
     x, y, zero_diagonal = _check_input(x, y, zero_diagonal)
 
     norm = torch.norm(x, p=2, dim=1)
-    x /= norm.unsqueeze(1)
+    x = x / norm.unsqueeze(1)
     norm = torch.norm(y, p=2, dim=1)
-    y /= norm.unsqueeze(1)
+    y = y / norm.unsqueeze(1)
 
     distance = _safe_matmul(x, y)
     if zero_diagonal:

inplace operation in pairwise_cosine_similarity
## 🐛 Bug
Hello !
The x, y values are modified inplace in the `pairwise_cosine_similarity` function. 
This is not documented and may cause bugs that are difficult to find. 
Thank you.

### To Reproduce

```python
import torch
from torchmetrics.functional import pairwise_cosine_similarity
x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)
y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)
print("Result:", pairwise_cosine_similarity(x, y))
print("X:", x)
print("Y:", y)
"""Out[0]
Result: tensor([[0.5547, 0.8682],
        [0.5145, 0.8437],
        [0.5300, 0.8533]])
X: tensor([[0.5547, 0.8321],
        [0.5145, 0.8575],
        [0.5300, 0.8480]])
Y: tensor([[1.0000, 0.0000],
        [0.8944, 0.4472]])
"""
```

### Environment
torchmetrics==0.10.0


