import time
import random
import logging
import json

# Mock AIVA Modules (replace with actual imports in a real environment)
class VideoStream:
    def __init__(self, resolution=(1920, 1080), frame_rate=30):
        self.resolution = resolution
        self.frame_rate = frame_rate
        self.current_resolution = resolution # Start at original resolution
        self.jitter_aware_threshold = 0.1 # Example, tune this based on experimentation

    def get_resolution(self):
        return self.current_resolution

    def simulate_network_jitter(self, jitter_level):
        # Simulate network jitter by introducing random delays
        delay = random.uniform(0, jitter_level) # Delay in seconds
        time.sleep(delay)

    def downscale(self):
        # Simulate downscaling logic.  In reality, this will involve codecs.
        current_width, current_height = self.current_resolution
        self.current_resolution = (int(current_width * 0.5), int(current_height * 0.5))
        logging.info(f"Downscaling to {self.current_resolution}")

    def upscale(self):
        # Simulate upscaling logic
        current_width, current_height = self.current_resolution
        self.current_resolution = (int(current_width * 2), int(current_height * 2))
        logging.info(f"Upscaling to {self.current_resolution}")

    def should_downscale(self, jitter_level):
        # Jitter-aware downscale decision.  More sophisticated models can be used.
        # This is a simple example.
        if jitter_level > self.jitter_aware_threshold:
            return True
        return False


class QualityMetrics:
    def calculate_psnr(self, original_resolution, current_resolution):
        # Placeholder for PSNR calculation.  Requires image data in reality.
        psnr = 20 * random.random() + 20 # Simulate a PSNR value
        return psnr

    def calculate_vmaf(self, original_resolution, current_resolution):
        # Placeholder for VMAF calculation.  Requires video data in reality.
        vmaf = 50 * random.random() + 50 # Simulate a VMAF value
        return vmaf



def run_downscale_test(jitter_levels, num_frames=100):
    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
    video_stream = VideoStream()
    quality_metrics = QualityMetrics()
    downscale_events = 0
    upscale_events = 0
    original_resolution = video_stream.get_resolution()

    results = []

    for jitter_level in jitter_levels:
        logging.info(f"Testing with jitter level: {jitter_level}")
        downscale_count = 0
        upscale_count = 0
        psnr_values = []
        vmaf_values = []

        for frame in range(num_frames):
            video_stream.simulate_network_jitter(jitter_level)

            if video_stream.should_downscale(jitter_level):
                video_stream.downscale()
                downscale_count += 1

            # Simulate a recovery condition - upscale after a few downscaled frames.
            if downscale_count > 5 and video_stream.get_resolution() != original_resolution:
                video_stream.upscale()
                upscale_count += 1
                downscale_count = 0  # Reset downscale count after upscale

            psnr = quality_metrics.calculate_psnr(original_resolution, video_stream.get_resolution())
            vmaf = quality_metrics.calculate_vmaf(original_resolution, video_stream.get_resolution())
            psnr_values.append(psnr)
            vmaf_values.append(vmaf)

        average_psnr = sum(psnr_values) / len(psnr_values)
        average_vmaf = sum(vmaf_values) / len(vmaf_values)

        results.append({
            "jitter_level": jitter_level,
            "downscale_events": downscale_count,
            "upscale_events": upscale_count,
            "average_psnr": average_psnr,
            "average_vmaf": average_vmaf
        })

        downscale_events += downscale_count
        upscale_events += upscale_count

    logging.info(f"Total downscale events: {downscale_events}")
    logging.info(f"Total upscale events: {upscale_events}")

    return results


if __name__ == "__main__":
    jitter_levels = [0.01, 0.05, 0.1, 0.2, 0.3]  # Example jitter levels
    test_results = run_downscale_test(jitter_levels)

    # Save results to JSON for later analysis
    with open("downscale_test_results.json", "w") as f:
        json.dump(test_results, f, indent=4)

    print("Test completed. Results saved to downscale_test_results.json")