Is there a reason you need to use one-hot vectors? If there’s no explicit constraint, you can just convert the vectors to integer tokens:
import tensorflow as tf
from tensorflow.keras import backend as K

# Create random integer tensor, 128 tokens with vocab size of 100
integer_embed = tf.random.uniform((32, 128), minval=0, maxval=100, dtype=tf.int64)
# Create one-hot encoding
one_hot = tf.keras.utils.to_categorical(integer_embed)

model = tf.keras.layers.Embedding(100, 32, input_shape=(128,))
model(K.argmax(one_hot, axis=-1))

If you do have a constraint, here’s an example One-Hot Embedding using Layer subclassing (from: Is there anyway to pass one-hot binary vector as input and embed it? · Issue #2505 · keras-team/keras · GitHub 1):
class OnehotEmbedding(tf.keras.layers.Layer):
    def __init__(self, Nembeddings, **kwargs):
        self.Nembeddings = Nembeddings
        super(OnehotEmbedding, self).__init__(**kwargs)

    def build(self, input_shape):
        # Create a trainable weight variable for this layer.
        self.kernel = self.add_weight(name='kernel',
                                      shape=(input_shape[2], self.Nembeddings),
                                      initializer='uniform',
                                      trainable=True)
        super(OnehotEmbedding, self).build(input_shape)  # Be sure to call this at the end

    def call(self, x):
        return K.dot(x, self.kernel)

    def compute_output_shape(self, input_shape):
        return (input_shape[0], input_shape[1], self.Nembeddings)

model = OnehotEmbedding(32, input_shape=(32, 100, 128))
model(one_hot)