# video_compression_model.py

import tensorflow as tf

PRESET_SPEED_CATEGORIES = ["ultrafast", "superfast", "veryfast", "faster", "fast", "medium", "slow", "slower", "veryslow"]
NUM_PRESET_SPEEDS = len(PRESET_SPEED_CATEGORIES)
NUM_CHANNELS = 3

class VideoCompressionModel(tf.keras.Model):
    def __init__(self):
        super(VideoCompressionModel, self).__init__()

        # Inputs
        self.crf_input = tf.keras.layers.InputLayer(name='crf', input_shape=(1,))
        self.preset_speed_input = tf.keras.layers.InputLayer(name='preset_speed', input_shape=(1,))
        self.uncompressed_frame_input = tf.keras.layers.InputLayer(name='uncompressed_frame', input_shape=(None, None, NUM_CHANNELS))
        self.compressed_frame_input = tf.keras.layers.InputLayer(name='compressed_frame', input_shape=(None, None, NUM_CHANNELS))
        
        # Embedding for speed preset and FC layer for CRF and preset speed
        self.embedding = tf.keras.layers.Embedding(NUM_PRESET_SPEEDS, 16)
        self.fc = tf.keras.layers.Dense(32, activation='relu')

        # Encoder layers
        self.encoder = tf.keras.Sequential([
            tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(None, None, 2 * NUM_CHANNELS + 32)),
            tf.keras.layers.BatchNormalization(),
            tf.keras.layers.Conv2D(128, (3, 3), activation='relu', padding='same'),
            tf.keras.layers.BatchNormalization(),
            tf.keras.layers.MaxPooling2D((2, 2)),
            tf.keras.layers.Dropout(0.3)
        ])

        # Decoder layers
        self.decoder = tf.keras.Sequential([
            tf.keras.layers.Conv2DTranspose(128, (3, 3), activation='relu', padding='same'),
            tf.keras.layers.BatchNormalization(),
            tf.keras.layers.Conv2DTranspose(64, (3, 3), activation='relu', padding='same'),
            tf.keras.layers.BatchNormalization(),
            tf.keras.layers.UpSampling2D((2, 2)),
            tf.keras.layers.Dropout(0.3),
            tf.keras.layers.Conv2D(NUM_CHANNELS, (3, 3), activation='sigmoid', padding='same')  # Output layer for video frames
        ])

    def call(self, inputs):
        uncompressed_frame, compressed_frame, crf, preset_speed = inputs['uncompressed_frame'], inputs['compressed_frame'], inputs['crf'], inputs['preset_speed']

        # Convert frames to float32
        uncompressed_frame = tf.cast(uncompressed_frame, tf.float32)
        compressed_frame = tf.cast(compressed_frame, tf.float32)

        # Integrate CRF and preset speed into the network
        preset_speed_embedded = self.embedding(preset_speed)
        crf_expanded = tf.expand_dims(crf, -1)
        integrated_info = tf.keras.layers.Concatenate(axis=-1)([crf_expanded, tf.keras.layers.Flatten()(preset_speed_embedded)])
        integrated_info = self.fc(integrated_info)

        # Integrate the CRF and preset speed information into the frames as additional channels (features)
        _, height, width, _ = uncompressed_frame.shape
        integrated_info_repeated = tf.tile(tf.reshape(integrated_info, [-1, 1, 1, 32]), [1, height, width, 1])

        # Merge uncompressed and compressed frames
        frames_merged = tf.keras.layers.Concatenate(axis=-1)([uncompressed_frame, compressed_frame, integrated_info_repeated])

        compressed_representation = self.encoder(frames_merged)
        reconstructed_frame = self.decoder(compressed_representation)

        return reconstructed_frame