`silhouette_score` fails whit `"k-means++"` init on very sparse data

### Describe the bug

Not sure this is a bug or something that just need to be better documented.

The `"k-means++"` init on very sparse data can select initial centroids than never get updated and this cause problems in the silhouette clustering evaluation with `sample_size=1000`.

The problem disappears when `sample_size` is increased or by setting particular random states.

### Steps/Code to Reproduce

```python
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from sklearn import metrics

categories = [
    "alt.atheism",
    "talk.religion.misc",
    "comp.graphics",
    "sci.space",
]

dataset = fetch_20newsgroups(
    remove=("headers", "footers", "quotes"),
    subset="all",
    categories=categories,
    shuffle=True,
    random_state=42,
)

vectorizer = TfidfVectorizer(
    max_df=0.5,
    min_df=2,
    stop_words="english",
)

X = vectorizer.fit_transform(dataset.data)

km = KMeans(
    n_clusters=4,
    init="k-means++",
    max_iter=100,
    n_init=1,
    random_state=0,  #notice fixed random state
).fit(X)


metrics.silhouette_score(X, km.labels_, sample_size=1000, random_state=3) # works

metrics.silhouette_score(X, km.labels_, sample_size=1000, random_state=4) # fails
```

### Expected Results

Always return silhouette score.

### Actual Results

```python-traceback
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Untitled-1 in <module>
----> <a href='untitled:Untitled-1?line=37'>38</a> metrics.silhouette_score(X, km.labels_, sample_size=1000, random_state=43)

File ~/scikit-learn/sklearn/metrics/cluster/_unsupervised.py:117, in silhouette_score(X, labels, metric, sample_size, random_state, **kwds)
    [115](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=114)     else:
    [116](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=115)         X, labels = X[indices], labels[indices]
--> [117](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=116) return np.mean(silhouette_samples(X, labels, metric=metric, **kwds))

File ~/scikit-learn/sklearn/metrics/cluster/_unsupervised.py:231, in silhouette_samples(X, labels, metric, **kwds)
    [229](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=228) n_samples = len(labels)
    [230](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=229) label_freqs = np.bincount(labels)
--> [231](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=230) check_number_of_labels(len(le.classes_), n_samples)
    [233](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=232) kwds["metric"] = metric
    [234](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=233) reduce_func = functools.partial(
    [235](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=234)     _silhouette_reduce, labels=labels, label_freqs=label_freqs
    [236](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=235) )

File ~/scikit-learn/sklearn/metrics/cluster/_unsupervised.py:33, in check_number_of_labels(n_labels, n_samples)
     [22](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=21) """Check that number of labels are valid.
     [23](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=22) 
     [24](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=23) Parameters
   (...)
     [30](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=29)     Number of samples.
     [31](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=30) """
     [32](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=31) if not 1 < n_labels < n_samples:
---> [33](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=32)     raise ValueError(
     [34](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=33)         "Number of labels is %d. Valid values are 2 to n_samples - 1 (inclusive)"
     [35](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=34)         % n_labels
     [36](file:///home/arturoamor/scikit-learn/sklearn/metrics/cluster/_unsupervised.py?line=35)     )

ValueError: Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive)
```

### Versions

```shell
System:
    python: 3.9.9 | packaged by conda-forge | (main, Dec 20 2021, 02:40:17)  [GCC 9.4.0]
executable: /home/arturoamor/miniforge3/envs/dev-scikit-learn/bin/python
   machine: Linux-5.14.0-1038-oem-x86_64-with-glibc2.31

Python dependencies:
      sklearn: 1.2.dev0
          pip: 21.3.1
   setuptools: 60.5.0
        numpy: 1.22.1
        scipy: 1.7.3
       Cython: 0.29.26
       pandas: 1.3.5
   matplotlib: 3.5.1
       joblib: 1.1.0
threadpoolctl: 3.0.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /home/arturoamor/miniforge3/envs/dev-scikit-learn/lib/python3.9/site-packages/numpy.libs/libopenblas64_p-r0-2f7c42d4.3.18.so
        version: 0.3.18
threading_layer: pthreads
   architecture: SkylakeX
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /home/arturoamor/miniforge3/envs/dev-scikit-learn/lib/python3.9/site-packages/scipy.libs/libopenblasp-r0-8b9e111f.3.17.so
        version: 0.3.17
threading_layer: pthreads
   architecture: SkylakeX
    num_threads: 8

       user_api: openmp
   internal_api: openmp
         prefix: libgomp
       filepath: /home/arturoamor/miniforge3/envs/dev-scikit-learn/lib/libgomp.so.1.0.0
        version: None
    num_threads: 8
```

COMMENT:
In your examples, the clustering is highly non balanced: 
```py
np.unique(km.labels_, return_counts=True)
>>> (array([0, 1, 2, 3], dtype=int32), array([   1,    1, 3382,    3]))
```
Almost all points are in the same cluster, hence more chance to only pick points corresponding to a same label.

Labels: Bug, module:cluster