'''
Code me a large language model in python.
'''
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM, Embedding

# Prepare input sequences
data = "the quick brown fox jumps over the lazy dog"
tokens = data.split()
vocab_size = len(set(tokens))
seq_length = 3
sequences = []
for i in range(seq_length, len(tokens)):
    seq = tokens[i-seq_length:i]
    sequences.append(' '.join(seq))
    
# Prepare data for training
X = []
y = []
for seq in sequences:
    X.append(seq[:-1])
    y.append(seq[-1])
    
# Convert data to numerical form
X_num = np.zeros((len(X), seq_length))
y_num = np.zeros(len(y))
word_to_num = {}
num_to_word = {}
idx = 0
for word in set(tokens):
    word_to_num[word] = idx
    num_to_word[idx] = word
    idx += 1
for i in range(len(X)):
    for j in range(seq_length):
        X_num[i][j] = word_to_num[X[i].split()[j]]
    y_num[i] = word_to_num[y[i]]

# Build the model
model = Sequential()
model.add(Embedding(vocab_size, 50, input_length=seq_length))
model.add(LSTM(100, return_sequences=True))
model.add(LSTM(100))
model.add(Dense(100, activation='relu'))
model.add(Dense(vocab_size, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')

# Train the model
model.fit(X_num, y_num, epochs=500)

# Generate new text
seed_text = "brown fox jumps"
generated_text = seed_text
for i in range(20):
    x = np.zeros((1, seq_length))
    for j in range(seq_length):
        x[0][j] = word_to_num[seed_text.split()[j]]
    prediction = model.predict(x, verbose=0)
    idx = np.argmax(prediction)
    word = num_to_word[idx]
    generated_text += ' ' + word
    seed_text = seed_text + ' ' + word
    seed_text = ' '.join(seed_text.split()[1:])
    
print(generated_text)