#!/usr/bin/env python3
"""
Test transcription of full audio file with progress monitoring.
"""

import sys
import time
import threading
import warnings
import psutil
from pathlib import Path

# Capture warnings (disabled due to urllib3 warning)
# warnings.filterwarnings("error")  # Convert warnings to exceptions


def monitor_memory(pid, interval=1, stop_event=None):
    """Monitor memory usage of process."""
    samples = []
    while not stop_event or not stop_event.is_set():
        try:
            proc = psutil.Process(pid)
            mem = proc.memory_info().rss / 1024 / 1024
            samples.append((time.time(), mem))
        except:
            pass
        time.sleep(interval)
    return samples


def transcribe_full(
    audio_path, model_size="tiny", compute_type="int8", timeout_per_segment=30
):
    """Transcribe full audio with timeout per segment."""
    from faster_whisper import WhisperModel

    print(f"Loading model {model_size} ({compute_type})...")
    start = time.time()
    model = WhisperModel(model_size, device="cpu", compute_type=compute_type)
    print(f"Model loaded in {time.time() - start:.1f}s")

    print(f"Starting transcription of {audio_path}...")
    print(f"File size: {Path(audio_path).stat().st_size / 1024 / 1024:.1f} MB")

    segments, info = model.transcribe(audio_path, beam_size=5)

    # Start memory monitoring in background
    import threading

    stop_event = threading.Event()
    mem_samples = []

    def monitor():
        nonlocal mem_samples
        while not stop_event.is_set():
            try:
                proc = psutil.Process()
                mem = proc.memory_info().rss / 1024 / 1024
                mem_samples.append((time.time(), mem))
            except:
                pass
            time.sleep(2)

    monitor_thread = threading.Thread(target=monitor, daemon=True)
    monitor_thread.start()

    results = []
    segment_times = []
    start_time = time.time()
    last_segment_time = start_time

    try:
        for i, segment in enumerate(segments):
            segment_time = time.time()
            elapsed = segment_time - last_segment_time
            last_segment_time = segment_time
            segment_times.append(elapsed)

            results.append(
                {
                    "start": segment.start,
                    "end": segment.end,
                    "text": segment.text.strip(),
                }
            )

            # Print progress
            if len(mem_samples) > 0:
                current_mem = mem_samples[-1][1]
            else:
                current_mem = 0

            print(
                f"[{i + 1}] {segment.start:.1f}-{segment.end:.1f} ({elapsed:.1f}s, mem: {current_mem:.1f} MB): {segment.text[:80]}..."
            )

            # Reset timeout for next segment
            # If segment takes too long, maybe something is wrong
            if elapsed > timeout_per_segment:
                print(
                    f"WARNING: Segment {i + 1} took {elapsed:.1f}s > {timeout_per_segment}s timeout"
                )
                # Continue anyway

        total_time = time.time() - start_time
        print(f"Transcription completed in {total_time:.1f}s")
        print(f"Total segments: {len(results)}")
        print(
            f"Average time per segment: {total_time / len(results) if results else 0:.2f}s"
        )

    except Exception as e:
        print(f"Error during transcription: {e}")
        import traceback

        traceback.print_exc()
    finally:
        stop_event.set()
        monitor_thread.join(timeout=5)

        if mem_samples:
            peak_mem = max(m[1] for m in mem_samples)
            avg_mem = sum(m[1] for m in mem_samples) / len(mem_samples)
            print(f"Memory usage: peak {peak_mem:.1f} MB, average {avg_mem:.1f} MB")

    return results, info


def main():
    audio_path = "/tmp/test_audio.wav"
    if not Path(audio_path).exists():
        print(f"Audio file not found: {audio_path}")
        sys.exit(1)

    print(f"Testing full audio transcription")
    print(f"Audio duration: 1:54:39 (approx)")

    # Set a total timeout of 10 minutes
    start = time.time()
    results = None
    info = None

    def run_transcribe():
        nonlocal results, info
        results, info = transcribe_full(audio_path, timeout_per_segment=60)

    thread = threading.Thread(target=run_transcribe)
    thread.start()
    thread.join(timeout=600)  # 10 minutes

    if thread.is_alive():
        print("\nTIMEOUT: Transcription took longer than 10 minutes")
        # Can't interrupt, but we can exit
        sys.exit(1)

    if results is not None:
        print(f"\nSuccessfully transcribed {len(results)} segments")
        print(f"Language: {info.language} (prob {info.language_probability:.2f})")
        # Save results
        output_path = Path("test_output/full_audio_transcription.json")
        output_path.parent.mkdir(exist_ok=True)
        import json

        with open(output_path, "w") as f:
            json.dump(
                {
                    "language": info.language,
                    "language_probability": info.language_probability,
                    "segments": results,
                },
                f,
                indent=2,
            )
        print(f"Results saved to {output_path}")

    print(f"Total execution time: {time.time() - start:.1f}s")


if __name__ == "__main__":
    main()