import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, optimizers

# Custom Layer for Boolformer, with added threshold parameter
class BoolformerLayer(layers.Layer):
    def __init__(self, threshold=0.5, **kwargs):
        super(BoolformerLayer, self).__init__(**kwargs)
        self.threshold = threshold

    def build(self, input_shape):
        self.dense_layer = layers.Dense(input_shape[-1], activation='relu')

    def call(self, inputs):
        logic_and = tf.math.logical_and(inputs, inputs > self.threshold)
        logic_transformed = self.dense_layer(logic_and)
        return logic_transformed

# Updated positional encoding function with improved efficiency
def positional_encoding(seq_length, d_model):
    position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]
    div_term = tf.exp(tf.range(0, d_model, 2, dtype=tf.float32) * -(tf.math.log(10000.0) / d_model))
    pos_encoding = position * div_term
    pos_encoding = tf.concat([tf.sin(pos_encoding[:, 0::2]), tf.cos(pos_encoding[:, 1::2])], axis=-1)
    return pos_encoding[tf.newaxis, ...]

# Enhanced transformer encoder with parameter flexibility
def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout_rate=0.1):
    attention_output = layers.MultiHeadAttention(key_dim=head_size, num_heads=num_heads, dropout=dropout_rate)(inputs, inputs)
    attention_output = layers.Dropout(dropout_rate)(attention_output)
    attention_output = layers.LayerNormalization(epsilon=1e-6)(inputs + attention_output)

    ffn_output = layers.Dense(ff_dim, activation="relu")(attention_output)
    ffn_output = layers.Dense(inputs.shape[-1])(ffn_output)
    ffn_output = layers.Dropout(dropout_rate)(ffn_output)
    return layers.LayerNormalization(epsilon=1e-6)(attention_output + ffn_output)

# Improved QLearningLayer with additional functionality
class QLearningLayer(layers.Layer):
    def __init__(self, action_space_size, learning_rate=0.01, gamma=0.95, **kwargs):
        super(QLearningLayer, self).__init__(**kwargs)
        self.action_space_size = action_space_size
        self.learning_rate = learning_rate
        self.gamma = gamma

    def build(self, input_shape):
        self.q_table = tf.Variable(initial_value=tf.random.uniform([input_shape[-1], self.action_space_size], 0, 1), trainable=True)

    def call(self, state, action=None, reward=None, next_state=None):
        if action is not None and reward is not None and next_state is not None:
            q_update = reward + self.gamma * tf.reduce_max(self.q_table[next_state])
            self.q_table[state, action].assign((1 - self.learning_rate) * self.q_table[state, action] + self.learning_rate * q_update)
        return tf.argmax(self.q_table[state], axis=1)

# Function to create and compile the neural network model
def create_neural_network_model(seq_length, d_model, action_space_size):
    input_layer = keras.Input(shape=(seq_length, d_model))

    pos_encoded = positional_encoding(seq_length, d_model) + input_layer
    transformer_output = transformer_encoder(pos_encoded, head_size=32, num_heads=2, ff_dim=64)

    x_bool = BoolformerLayer()(transformer_output)
    rl_layer = QLearningLayer(action_space_size=action_space_size)(x_bool)

    output_layer = layers.Dense(action_space_size, activation='softmax', name='Output')(rl_layer)
    reward_layer = layers.Dense(1, name='Reward')(rl_layer)

    model = keras.Model(inputs=input_layer, outputs=[output_layer, reward_layer])
    opt = optimizers.Adam(learning_rate=0.001)
    model.compile(optimizer=opt, loss={'Output': 'categorical_crossentropy', 'Reward': 'mean_squared_error'},
                  metrics={'Output': 'accuracy'})

    return model

# Example of creating and compiling the model
seq_length = 128  # Example sequence length
d_model = 512     # Example dimension
action_space_size = 10  # Example action space size

model = create_neural_network_model(seq_length, d_model, action_space_size)