class RotaryPositionalEmbedding(layers.Layer):  
    def __init__(self, dim):  
        super().__init__()  
        inv_freq = 1.0 / (10000 ** (np.arange(0, dim, 2) / dim))  
        self.inv_freq = tf.constant(inv_freq, dtype=tf.float32)  
  
    def call(self, x):  
        batch, heads, seq_len, depth = tf.unstack(tf.shape(x))  
        t = tf.range(seq_len, dtype=tf.float32)  
        freqs = tf.einsum('i,j->ij', t, self.inv_freq)  
        emb_sin = tf.sin(freqs)  
        emb_cos = tf.cos(freqs)  
        emb_cos = tf.reshape(emb_cos, [1, 1, seq_len, -1])  
        emb_sin = tf.reshape(emb_sin, [1, 1, seq_len, -1])  
        x1 = x[..., ::2]  
        x2 = x[..., 1::2]  
        x_rotated = tf.stack([  
            x1 * emb_cos - x2 * emb_sin,  
            x1 * emb_sin + x2 * emb_cos  
        ], axis=-1)  
        x_rotated = tf.reshape(x_rotated, tf.shape(x))  
        return x_rotated

class SwiGLU(tf.keras.layers.Layer):
    def __init__(self, d_model, d_ff):
        super().__init__()
        self.proj = tf.keras.layers.Dense(d_ff * 2)
        self.out = tf.keras.layers.Dense(d_model)

    def call(self, x):
        x_proj = self.proj(x)
        x_val, x_gate = tf.split(x_proj, 2, axis=-1)
        return self.out(x_val * tf.nn.silu(x_gate))
        
class GPTBlock(tf.keras.layers.Layer):
    def __init__(self, d_model, d_ff, num_heads=16, dropout_rate=0.1, adapter_dim=64):  
        super().__init__()  
        self.ln1 = tf.keras.layers.LayerNormalization(epsilon=1e-5)  
        self.mha = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model // num_heads)  
        self.dropout1 = tf.keras.layers.Dropout(dropout_rate) 
        self.adapter_down = tf.keras.layers.Dense(adapter_dim, activation='gelu') 
        self.adapter_up = tf.keras.layers.Dense(d_model)  
  
        self.ln2 = tf.keras.layers.LayerNormalization(epsilon=1e-5)  
        self.ffn = SwiGLU(d_model, d_ff)  
        self.dropout2 = tf.keras.layers.Dropout(dropout_rate) 
        self.rope = RotaryPositionalEmbedding(d_model // num_heads)  
  
    def call(self, x, training=False):  
        x_norm = self.ln1(x)  
        b, s, _ = tf.shape(x_norm)[0], tf.shape(x_norm)[1], tf.shape(x_norm)[2]  
        h = self.mha.num_heads  
        d = x_norm.shape[-1] // h  
  
        qkv = tf.reshape(x_norm, [b, s, h, d])  
        qkv = tf.transpose(qkv, [0, 2, 1, 3])  
        q = self.rope(qkv)  
        k = self.rope(qkv)  
        q = tf.reshape(tf.transpose(q, [0, 2, 1, 3]), [b, s, h * d])  
        k = tf.reshape(tf.transpose(k, [0, 2, 1, 3]), [b, s, h * d])  
  
        attn_out = self.mha(query=q, value=x_norm, key=k, use_causal_mask=True, training=training)  
        attn_out = self.dropout1(attn_out, training=training)  

        adapter_out = self.adapter_up(self.adapter_down(attn_out))
        attn_out = attn_out + adapter_out  
  
        x = x + attn_out  
        ffn_out = self.ffn(self.ln2(x))  
        x = x + self.dropout2(ffn_out, training=training)  
        return x

class InteractGPT(tf.keras.Model):  
    def __init__(self, vocab_size, seq_len, d_model, d_ff, n_layers, num_heads=16, dropout_rate=0.1):  
        super().__init__()  
        self.token_embedding = tf.keras.layers.Embedding(vocab_size, d_model)  
        self.blocks = [GPTBlock(d_model, d_ff, num_heads, dropout_rate) for _ in range(n_layers)]  
        self.ln_f = tf.keras.layers.LayerNormalization(epsilon=1e-5)  
  
    def call(self, x, training=False):  
        x = self.token_embedding(x)  
        for block in self.blocks:  
            x = block(x, training=training)  
        x = self.ln_f(x)  
        logits = tf.matmul(x, self.token_embedding.embeddings, transpose_b=True)  
        return logits  
        
model = InteractGPT(vocab_size=vocab_size, seq_len=max_len, d_model=128, d_ff=480, n_layers=8)