import tensorflow as tf

def sdp_attention(query, key, value, mask):
    matmul_qk = tf.matmul(query, key, transpose_b=True)
    depth = tf.cast(tf.shape(key)[-1], tf.float32)
    logits = matmul_qk / tf.math.sqrt(depth)
    if mask is not None:
        logits += mask * -1e9
    attention_weights = tf.nn.softmax(logits, axis=-1)
    output = tf.matmul(attention_weights, value)
    return output

class MultiHeadAttention(tf.keras.layers.Layer):
    def __init__(self, num_heads, d_model, **kwargs):
        assert d_model % num_heads == 0
        super(MultiHeadAttention, self).__init__(**kwargs)
        self.num_heads = num_heads
        self.d_model = d_model

        self.depth = self.d_model // self.num_heads

        self.query_dense = tf.keras.layers.Dense(self.d_model)
        self.key_dense = tf.keras.layers.Dense(self.d_model)
        self.value_dense = tf.keras.layers.Dense(self.d_model)

        self.dense = tf.keras.layers.Dense(self.d_model)

        
    def get_config(self):
        config = super(MultiHeadAttention, self).get_config()
        config.update({"num_heads": self.num_heads, "d_model": self.d_model})
        return config
    
    def split_heads(self, inputs: tf.Tensor, batch_size: int):
        inputs = tf.keras.layers.Lambda(
            lambda inputs: tf.reshape(
                inputs, shape=(batch_size, -1, self.num_heads, self.depth))
        )(inputs)
        return tf.keras.layers.Lambda(
            lambda inputs: tf.transpose(inputs, perm=[0, 2, 1, 3])
        )(inputs)
    
    def call(self, inputs: tf.Tensor):
        query, key, value, mask = (
            inputs["query"],
            inputs["key"],
            inputs["value"],
            inputs["mask"],
        )
        batch_size = tf.shape(query)[0]

        query = self.query_dense(query)
        key = self.key_dense(key)
        value = self.value_dense(value)

        query = self.split_heads(query, batch_size)
        key = self.split_heads(key, batch_size)
        value = self.split_heads(value, batch_size)

        scaled_attention = sdp_attention(query, key, value, mask)
        scaled_attention = tf.keras.layers.Lambda(
            lambda scaled_attention: tf.transpose(scaled_attention, perm=[0, 2, 1, 3])
        )(scaled_attention)

        concat_attention = tf.keras.layers.Lambda(
            lambda scaled_attention: tf.reshape(
                scaled_attention, (batch_size, -1, self.d_model)
            )
        )(scaled_attention)

        outputs = self.dense(concat_attention)

        return outputs
    
    
def create_padding_mask(x):
    mask = tf.cast(tf.math.equal(x, 0), dtype=tf.float32)
    return mask[:, tf.newaxis, tf.newaxis, :]


def create_look_ahead_mask(x):
    seq_len = tf.shape(x)[1]
    look_ahead_mask = 1 - tf.linalg.band_part(
        tf.ones((seq_len, seq_len), dtype=tf.float32), -1, 0
    )
    padding_mask = create_padding_mask(x)
    return tf.maximum(look_ahead_mask, padding_mask)
    

class PositionalEncoding(tf.keras.layers.Layer):
    def __init__(self, position: int, d_model: int, **kwargs):
        super(PositionalEncoding, self).__init__(**kwargs)
        self.position = position
        self.d_model = d_model
        self.pos_encoding = self.positional_encoding(position, d_model)
        
    def get_config(self):
        config = super(PositionalEncoding, self).get_config()
        config.update({"position": self.position, "d_model": self.d_model})
        return config
    
    def get_angles(self, position: tf.Tensor, i: tf.Tensor, d_model: tf.Tensor):
        angles = 1 / tf.pow(10000, (2 * (i // 2)) / d_model)
        return position * angles
    
    def positional_encoding(self, position: int, d_model: int):
        angle_rads = self.get_angles(
            position=tf.cast(tf.range(position)[:, tf.newaxis], dtype=tf.float32),
            i=tf.cast(tf.range(d_model)[tf.newaxis, :], dtype=tf.float32),
            d_model=tf.cast(d_model, dtype=tf.float32),
        )
        sines = tf.math.sin(angle_rads[:, 0::2])
        cosines = tf.math.cos(angle_rads[:, 1::2])

        pos_encoding = tf.concat([sines, cosines], axis=-1)
        pos_encoding = pos_encoding[tf.newaxis, ...]
        return pos_encoding

    def call(self, inputs: tf.Tensor):
        return inputs + self.pos_encoding[:, : tf.shape(inputs)[1], :]
    
def encoder_layer(hparams, name: str = "encoder_layer"):
    inputs = tf.keras.Input(shape=(None, hparams.d_model), name="inputs")
    padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask")

    attention = MultiHeadAttention(
        num_heads=hparams.num_heads, d_model=hparams.d_model, name="attention"
    )({"query": inputs, "key": inputs, "value": inputs, "mask": padding_mask})
    attention = tf.keras.layers.Dropout(hparams.dropout)(attention)
    attention += tf.cast(inputs, dtype=tf.float32)
    attention = tf.keras.layers.LayerNormalization(epsilon=1e-6)(attention)

    outputs = tf.keras.layers.Dense(hparams.num_units, activation=hparams.activation)(
        attention
    )
    outputs = tf.keras.layers.Dense(hparams.d_model)(outputs)
    outputs = tf.keras.layers.Dropout(hparams.dropout)(outputs)
    outputs += attention
    outputs = tf.keras.layers.LayerNormalization(epsilon=1e-6)(outputs)

    return tf.keras.Model(inputs=[inputs, padding_mask], outputs=outputs, name=name)

def encoder(hparams, name: str = "encoder"):
    inputs = tf.keras.Input(shape=(None,), name="inputs")
    padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask")

    embeddings = tf.keras.layers.Embedding(hparams.vocab_size, hparams.d_model)(inputs)
    embeddings *= tf.math.sqrt(tf.cast(hparams.d_model, dtype=tf.float32))
    embeddings = PositionalEncoding(
        position=hparams.vocab_size, d_model=hparams.d_model
    )(embeddings)

    outputs = tf.keras.layers.Dropout(hparams.dropout)(embeddings)

    for i in range(hparams.num_layers):
        outputs = encoder_layer(hparams, name=f"encoder_layer_{i}")(
            [outputs, padding_mask]
        )

    return tf.keras.Model(inputs=[inputs, padding_mask], outputs=outputs, name=name)

def decoder_layer(hparams, name: str = "decoder_layer"):
    inputs = tf.keras.Input(shape=(None, hparams.d_model), name="inputs")
    enc_outputs = tf.keras.Input(shape=(None, hparams.d_model), name="encoder_outputs")
    look_ahead_mask = tf.keras.Input(shape=(1, None, None), name="look_ahead_mask")
    padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask")

    attention1 = MultiHeadAttention(
        num_heads=hparams.num_heads, d_model=hparams.d_model, name="attention_1"
    )(
        inputs={
            "query": inputs,
            "key": inputs,
            "value": inputs,
            "mask": look_ahead_mask,
        }
    )
    attention1 += tf.cast(inputs, dtype=tf.float32)
    attention1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)(attention1)

    attention2 = MultiHeadAttention(
        num_heads=hparams.num_heads, d_model=hparams.d_model, name="attention_2"
    )(
        inputs={
            "query": attention1,
            "key": enc_outputs,
            "value": enc_outputs,
            "mask": padding_mask,
        }
    )
    attention2 = tf.keras.layers.Dropout(hparams.dropout)(attention2)
    attention2 += attention1
    attention2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)(
        attention2 + attention1
    )

    outputs = tf.keras.layers.Dense(hparams.num_units, activation=hparams.activation)(
        attention2
    )
    outputs = tf.keras.layers.Dense(hparams.d_model)(outputs)
    outputs = tf.keras.layers.Dropout(hparams.dropout)(outputs)
    outputs += attention2
    outputs = tf.keras.layers.LayerNormalization(epsilon=1e-6)(outputs)

    return tf.keras.Model(
        inputs=[inputs, enc_outputs, look_ahead_mask, padding_mask],
        outputs=outputs,
        name=name,
    )

def decoder(hparams, name: str = "decoder"):
    inputs = tf.keras.Input(shape=(None,), name="inputs")
    enc_outputs = tf.keras.Input(shape=(None, hparams.d_model), name="encoder_outputs")
    look_ahead_mask = tf.keras.Input(shape=(1, None, None), name="look_ahead_mask")
    padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask")

    embeddings = tf.keras.layers.Embedding(hparams.vocab_size, hparams.d_model)(inputs)
    embeddings *= tf.math.sqrt(tf.cast(hparams.d_model, dtype=tf.float32))
    embeddings = PositionalEncoding(
        position=hparams.vocab_size, d_model=hparams.d_model
    )(embeddings)

    outputs = tf.keras.layers.Dropout(hparams.dropout)(embeddings)

    for i in range(hparams.num_layers):
        outputs = decoder_layer(
            hparams,
            name="decoder_layer_{}".format(i),
        )(inputs=[outputs, enc_outputs, look_ahead_mask, padding_mask])

    return tf.keras.Model(
        inputs=[inputs, enc_outputs, look_ahead_mask, padding_mask],
        outputs=outputs,
        name=name,
    )



def transformer(hparams, name: str = "transformer"):
    inputs = tf.keras.Input(shape=(None,), name="inputs")
    dec_inputs = tf.keras.Input(shape=(None,), name="dec_inputs")

    enc_padding_mask = tf.keras.layers.Lambda(
        create_padding_mask, output_shape=(1, 1, None), name="enc_padding_mask"
    )(inputs)

    look_ahead_mask = tf.keras.layers.Lambda(
        create_look_ahead_mask, output_shape=(1, None, None), name="look_ahead_mask"
    )(dec_inputs)

    dec_padding_mask = tf.keras.layers.Lambda(
        create_padding_mask, output_shape=(1, 1, None), name="dec_padding_mask"
    )(inputs)

    enc_outputs = encoder(hparams)(inputs=[inputs, enc_padding_mask])

    dec_outputs = decoder(hparams)(
        inputs=[dec_inputs, enc_outputs, look_ahead_mask, dec_padding_mask]
    )

    outputs = tf.keras.layers.Dense(hparams.vocab_size, name="outputs")(dec_outputs)

    return tf.keras.Model(inputs=[inputs, dec_inputs], outputs=outputs, name=name)