Connection between Embedding and LSTM and Dense layer

I am building a "predict next word" model using the following model architecture. The codes fine, but I have a few questions:

# define model
model = Sequential()
print(model.summary())


Between the Embedding and LSTM layers, are there 50 connection weights, or 50 * 100 connection weights? Based on the documentation, this LSTM block contains 100 cells. So I am not sure how exactly does the Embedding layer connect to the LSTM layer?

Also, between LSTM(100) layer and the Dense(100, activation='relu') layer, are there 100 connection weights or 100 * 100 connection weights?

Thanks!