import tensorflow as tf
from .layers import EncoderLayer

@tf.keras.utils.register_keras_serializable()
class Encoder(tf.keras.layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, 
                 max_tokens, dropout_rate, **kwargs):
        super(Encoder, self).__init__(**kwargs)
        self.d_model = d_model
        self.num_layers = num_layers
        self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
        self.pos_encoding = self.positional_encoding(max_tokens, d_model)
        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, dropout_rate) 
                          for _ in range(num_layers)]
        self.dropout = tf.keras.layers.Dropout(dropout_rate)

    def call(self, x, training=None, mask=None):
        seq_len = tf.shape(x)[1]
        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :seq_len, :]
        x = self.dropout(x, training=training)
        for i in range(self.num_layers):
            x = self.enc_layers[i](x, training=training, mask=mask)
        return x

    def positional_encoding(self, max_len, d_model):
        angle_rads = self.get_angles(tf.range(max_len, dtype=tf.float32)[:, tf.newaxis],
                                    tf.range(d_model, dtype=tf.float32)[tf.newaxis, :], 
                                    d_model)
        sines = tf.math.sin(angle_rads[:, 0::2])
        cosines = tf.math.cos(angle_rads[:, 1::2])
        pos_encoding = tf.concat([sines, cosines], axis=-1)
        return pos_encoding[tf.newaxis, ...]

    def get_angles(self, pos, i, d_model):
        angle_rates = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(d_model, tf.float32))
        return pos * angle_rates

    def get_config(self):
        config = super().get_config()
        config.update({
            'num_layers': self.num_layers,
            'd_model': self.d_model,
            'num_heads': self.num_heads,
            'dff': self.dff,
            'input_vocab_size': self.embedding.input_dim,
            'max_tokens': self.pos_encoding.shape[1],
            'dropout_rate': self.dropout.rate
        })
        return config