# train_model.py

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'

import json
import numpy as np
import cv2
import argparse
import tensorflow as tf
from video_compression_model import NUM_CHANNELS, VideoCompressionModel, PRESET_SPEED_CATEGORIES, VideoDataGenerator
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint

from global_train import LOGGER

# Constants
BATCH_SIZE = 4
EPOCHS = 100
LEARNING_RATE = 0.000001
TRAIN_SAMPLES = 100
MODEL_SAVE_FILE = "models/model.tf"
MODEL_CHECKPOINT_DIR = "checkpoints"
EARLY_STOP = 10
WIDTH = 638
HEIGHT = 360

def load_video_metadata(list_path):
    LOGGER.trace(f"Entering: load_video_metadata({list_path})")
    try:
        with open(list_path, "r") as json_file:
            file = json.load(json_file)
            LOGGER.trace(f"load_video_metadata returning: {file}")
            return file
    except FileNotFoundError:
        LOGGER.error(f"Metadata file {list_path} not found.")
        raise
    except json.JSONDecodeError:
        LOGGER.error(f"Error decoding JSON from {list_path}.")
        raise

def load_video_samples(list_path, samples=TRAIN_SAMPLES):
    """
    Load video samples from the metadata list.

    Args:
    - list_path (str): Path to the metadata JSON file.
    - samples (int): Number of total samples to be extracted.

    Returns:
    - list: Extracted video samples.
    """
    LOGGER.trace(f"Entering: load_video_samples({list_path}, {samples})" )
    
    details_list = load_video_metadata(list_path)
    all_samples = []
    num_videos = len(details_list)
    frames_per_video = int(samples / num_videos)

    LOGGER.info(f"Loading {frames_per_video} frames from {num_videos} videos")

    for video_details in details_list:
        video_file = video_details["video_file"]
        uncompressed_video_file = video_details["uncompressed_video_file"]
        crf = video_details['crf'] / 63.0
        preset_speed = PRESET_SPEED_CATEGORIES.index(video_details['preset_speed'])
        video_details['preset_speed'] = preset_speed

        compressed_frames, uncompressed_frames = [], []
        
        try:
            cap = cv2.VideoCapture(os.path.join(os.path.dirname(list_path), video_file))
            cap_uncompressed = cv2.VideoCapture(os.path.join(os.path.dirname(list_path), uncompressed_video_file))
            
            if not cap.isOpened() or not cap_uncompressed.isOpened():
                raise RuntimeError(f"Could not open video files {video_file} or {uncompressed_video_file}, searched under: {os.path.dirname(list_path)}")
    
            for _ in range(frames_per_video):
                ret, frame_compressed = cap.read()
                ret_uncompressed, frame = cap_uncompressed.read()

                if not ret or not ret_uncompressed:
                    continue
                
                 # Check frame dimensions and resize if necessary
                if frame.shape[:2] != (WIDTH, HEIGHT):
                    LOGGER.warn(f"Resizing video: {video_file}")
                    frame = cv2.resize(frame, (WIDTH, HEIGHT), interpolation=cv2.INTER_AREA)
                if frame_compressed.shape[:2] != (WIDTH, HEIGHT):
                    LOGGER.warn(f"Resizing video: {uncompressed_video_file}")
                    frame_compressed = cv2.resize(frame_compressed, (WIDTH, HEIGHT), interpolation=cv2.INTER_AREA)

                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                frame_compressed = cv2.cvtColor(frame_compressed, cv2.COLOR_BGR2RGB)

                uncompressed_frames.append(normalize(frame))
                compressed_frames.append(normalize(frame_compressed))

            all_samples.extend({
                "frame": frame,
                "compressed_frame": frame_compressed,
                "crf": crf,
                "preset_speed": preset_speed,
                "video_file": video_file
            } for frame, frame_compressed in zip(uncompressed_frames, compressed_frames))
        
        except Exception as e:
            LOGGER.error(f"Error during video sample loading: {e}")
            raise

        finally:
            cap.release()
            cap_uncompressed.release()

    return all_samples

def normalize(frame):
    """
    Normalize pixel values of the frame to range [0, 1].

    Args:
    - frame (ndarray): Image frame.

    Returns:
    - ndarray: Normalized frame.
    """
    LOGGER.trace(f"Normalizing frame")
    return frame / 255.0

def save_model(model):
    try:
        LOGGER.debug("Attempting to save the model.")
        os.makedirs("models", exist_ok=True)
        model.save(MODEL_SAVE_FILE, save_format='tf')
        LOGGER.info("Model saved successfully!")
    except Exception as e:
        LOGGER.error(f"Error saving the model: {e}")
        raise

def main():
    # Argument parsing
    parser = argparse.ArgumentParser(description="Train the video compression model.")
    parser.add_argument('-b', '--batch_size', type=int, default=BATCH_SIZE, help='Batch size for training.')
    parser.add_argument('-e', '--epochs', type=int, default=EPOCHS, help='Number of epochs for training.')
    parser.add_argument('-s', '--training_samples', type=int, default=TRAIN_SAMPLES, help='Number of training samples.')
    parser.add_argument('-l', '--learning_rate', type=float, default=LEARNING_RATE, help='Learning rate for training.')
    parser.add_argument('-c', '--continue_training', type=str, nargs='?', const=MODEL_SAVE_FILE, default=None, help='Path to the saved model to continue training. If used without a value, defaults to the MODEL_SAVE_FILE.')

    args = parser.parse_args()

    # Display training configuration
    LOGGER.info("Starting the training with the given configuration.")
    LOGGER.info("Training configuration:")
    LOGGER.info(f"Batch size: {args.batch_size}")
    LOGGER.info(f"Epochs: {args.epochs}")
    LOGGER.info(f"Training samples: {args.training_samples}")
    LOGGER.info(f"Learning rate: {args.learning_rate}")
    LOGGER.info(f"Continue training from: {args.continue_training}")

    # Load training and validation samples
    LOGGER.debug("Loading training and validation samples.")
    training_samples = load_video_samples("test_data/training/training.json")
    validation_samples = load_video_samples("test_data/validation/validation.json", args.training_samples // 2)

    train_generator = VideoDataGenerator(training_samples, args.batch_size)
    val_generator = VideoDataGenerator(validation_samples, args.batch_size)

    # Load or initialize model
    if args.continue_training:
        model = tf.keras.models.load_model(args.continue_training)
    else:
        model = VideoCompressionModel()

    # Set optimizer and compile the model
    optimizer = tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
    model.compile(loss='mean_squared_error', optimizer=optimizer)

    # Define checkpoints and early stopping
    checkpoint_callback = ModelCheckpoint(
        filepath=os.path.join(MODEL_CHECKPOINT_DIR, "epoch-{epoch:02d}.tf"),
        save_weights_only=False,
        save_best_only=False,
        verbose=1,
        save_format="tf"
    )
    early_stop = EarlyStopping(monitor='val_loss', patience=EARLY_STOP, verbose=1, restore_best_weights=True)

    # Train the model
    LOGGER.info("Starting model training.")
    model.fit(
        train_generator,
        steps_per_epoch=len(train_generator),
        epochs=args.epochs,
        validation_data=val_generator,
        validation_steps=len(val_generator),
        callbacks=[early_stop, checkpoint_callback]
    )
    LOGGER.info("Model training completed.")

    save_model(model)

if __name__ == "__main__":
    try:
        main()
    except Exception as e:
        LOGGER.error(f"Unexpected error during training: {e}")
        raise