"""
ChatterboxTTS File Management & Media Processing Module
======================================================

OVERVIEW:
This module handles all file system operations, media format conversions, and
metadata management for ChatterboxTTS. It manages the complex directory structure
for audiobook production and handles conversion to final distribution formats.

MAIN COMPONENTS:
1. DIRECTORY MANAGEMENT: Creates and maintains audiobook processing directories
2. AUDIO DISCOVERY: Locates and validates audio files across directory structures
3. M4B CONVERSION: Converts WAV chunks to M4B audiobook format using FFmpeg
4. METADATA HANDLING: Adds cover art, chapters, and book information to audiobooks
5. FILE VALIDATION: Ensures audio file compatibility and format requirements
6. VOICE SAMPLE MANAGEMENT: Handles voice sample discovery and validation

KEY OPERATIONS:
- Directory structure setup for new audiobooks
- Audio chunk discovery and organization
- WAV to M4B conversion with chapter markers
- Cover art integration and metadata embedding
- File compatibility checking (24kHz requirement for voice samples)
- Final audiobook packaging and organization

DIRECTORY STRUCTURE MANAGED:
```
Audiobook/[book_name]/
├── TTS/
│   ├── text_chunks/     # Individual text chunk files
│   └── audio_chunks/    # Generated WAV audio chunks
├── [book_name].m4b      # Final audiobook file
├── processing.log       # Processing logs
└── metadata files       # Cover art, chapter info
```

TECHNICAL FEATURES:
- FFmpeg integration for media processing
- Automatic cover art detection and integration
- Chapter marker generation from chunk structure
- Metadata preservation across format conversions
- File system safety with validation and error handling
- Cross-platform file operations (Windows/Linux/Mac)

PERFORMANCE CONSIDERATIONS:
Handles large audio files efficiently with streaming processing
and manages disk space through temporary file cleanup.
"""

import subprocess
import soundfile as sf
import os
import re
import time
import logging
from pathlib import Path
from config.config import *

# ============================================================================
# VOICE SAMPLE MANAGEMENT
# ============================================================================

def list_voice_samples():
    """List available voice samples"""
    return sorted(VOICE_SAMPLES_DIR.glob("*.wav"), key=lambda x: x.stem.lower())

def ensure_voice_sample_compatibility(input_path, output_dir=None):
    """Ensure voice sample is compatible with TTS (24kHz mono)"""
    input_path = str(input_path)
    ext = os.path.splitext(input_path)[1].lower()
    basename = os.path.splitext(os.path.basename(input_path))[0]
    output_dir = output_dir or os.path.dirname(input_path)
    output_path = os.path.join(output_dir, basename + "_ttsready.wav")

    try:
        info = sf.info(input_path)
        if (ext == '.wav' and info.samplerate == 24000 and info.channels == 1):
            return input_path
    except Exception:
        pass

    cmd = [
        "ffmpeg", "-y",
        "-i", input_path,
        "-ar", "24000",
        "-ac", "1",
        output_path
    ]
    subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
    return output_path

# ============================================================================
# FFMPEG OPERATIONS
# ============================================================================

def run_ffmpeg(cmd):
    """Run FFmpeg command with error handling"""
    try:
        subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
    except subprocess.CalledProcessError as e:
        logging.info(f"FFmpeg command failed: {' '.join(cmd)}")
        logging.info(f"Error: {e}")
        subprocess.run(cmd)
        raise

# ============================================================================
# M4B CONVERSION WITH NORMALIZATION
# ============================================================================

def convert_to_m4b_with_peak_normalization(wav_path, temp_m4b_path, target_db=-3.0, custom_speed=None):
    """Convert WAV to M4B with peak normalization"""
    print("🚀 Converting to m4b with peak normalization...")

    # Build audio filter chain
    speed_to_use = custom_speed if custom_speed is not None else ATEMPO_SPEED
    audio_filters = [f"loudnorm=I=-16:TP={target_db}:LRA=11"]
    if speed_to_use != 1.0:
        audio_filters.append(f"atempo={speed_to_use}")
    
    print(f"🚀 Converting to m4b with peak normalization and speed {speed_to_use}x...")
    
    cmd = [
        "ffmpeg", "-y",
        "-i", str(wav_path),
        "-af", ",".join(audio_filters),
        "-c:a", "aac",
        str(temp_m4b_path)
    ]

    start_time = time.time()
    process = subprocess.Popen(cmd, stderr=subprocess.PIPE, text=True)

    audio_secs = 0.0
    for line in process.stderr:
        match = re.search(r"time=(\d{2}):(\d{2}):(\d{2})\.(\d{2})", line)
        if match:
            h, m, s, ms = map(int, match.groups())
            audio_secs = h * 3600 + m * 60 + s + ms / 100
            elapsed = time.time() - start_time
            factor = audio_secs / elapsed if elapsed > 0 else 0.0
            print(f"📼 FFmpeg (normalizing): {match.group(0)} | {factor:.2f}x realtime", end='\r')

    process.wait()
    print("\n✅ Conversion with normalization complete.")

def convert_to_m4b_with_loudness_normalization(wav_path, temp_m4b_path, custom_speed=None):
    """Convert WAV to M4B with two-pass loudness normalization"""
    import json

    print("🚀 Converting to m4b with loudness normalization...")

    # Step 1: Analyze audio loudness
    print("📊 Analyzing audio loudness...")
    analyze_cmd = [
        "ffmpeg", "-y",
        "-i", str(wav_path),
        "-af", "loudnorm=I=-16:TP=-1.5:LRA=11:print_format=json",
        "-f", "null", "-"
    ]

    result = subprocess.run(analyze_cmd, capture_output=True, text=True)

    # Extract loudness measurements from stderr
    loudness_data = None
    for line in result.stderr.split('\n'):
        if line.strip().startswith('{'):
            try:
                loudness_data = json.loads(line.strip())
                break
            except:
                continue

    if not loudness_data:
        print("⚠️ Could not analyze loudness, falling back to single-pass...")
        return convert_to_m4b_with_peak_normalization(wav_path, temp_m4b_path)

    # Step 2: Apply normalization with measured values
    print("🔧 Applying normalization...")
    
    # Build audio filter chain  
    speed_to_use = custom_speed if custom_speed is not None else ATEMPO_SPEED
    audio_filters = [f"loudnorm=I=-16:TP=-1.5:LRA=11:measured_I={loudness_data['input_i']}:measured_LRA={loudness_data['input_lra']}:measured_TP={loudness_data['input_tp']}:measured_thresh={loudness_data['input_thresh']}:offset={loudness_data['target_offset']}:linear=true:print_format=summary"]
    if speed_to_use != 1.0:
        audio_filters.append(f"atempo={speed_to_use}")
    
    cmd = [
        "ffmpeg", "-y",
        "-i", str(wav_path),
        "-af", ",".join(audio_filters),
        "-c:a", "aac",
        str(temp_m4b_path)
    ]

    start_time = time.time()
    process = subprocess.Popen(cmd, stderr=subprocess.PIPE, text=True)

    audio_secs = 0.0
    for line in process.stderr:
        match = re.search(r"time=(\d{2}):(\d{2}):(\d{2})\.(\d{2})", line)
        if match:
            h, m, s, ms = map(int, match.groups())
            audio_secs = h * 3600 + m * 60 + s + ms / 100
            elapsed = time.time() - start_time
            factor = audio_secs / elapsed if elapsed > 0 else 0.0
            print(f"📼 FFmpeg (normalizing): {match.group(0)} | {factor:.2f}x realtime", end='\r')

    process.wait()
    print("\n✅ Two-pass normalization complete.")

def convert_to_m4b_with_simple_normalization(wav_path, temp_m4b_path, target_db=-6.0, custom_speed=None):
    """Convert WAV to M4B with simple peak normalization"""
    print("🚀 Converting to m4b with simple normalization...")

    # Build audio filter chain
    speed_to_use = custom_speed if custom_speed is not None else ATEMPO_SPEED
    audio_filters = [f"volume={target_db}dB"]
    if speed_to_use != 1.0:
        audio_filters.append(f"atempo={speed_to_use}")

    cmd = [
        "ffmpeg", "-y",
        "-i", str(wav_path),
        "-af", ",".join(audio_filters),
        "-c:a", "aac",
        str(temp_m4b_path)
    ]

    start_time = time.time()
    process = subprocess.Popen(cmd, stderr=subprocess.PIPE, text=True)

    audio_secs = 0.0
    for line in process.stderr:
        match = re.search(r"time=(\d{2}):(\d{2}):(\d{2})\.(\d{2})", line)
        if match:
            h, m, s, ms = map(int, match.groups())
            audio_secs = h * 3600 + m * 60 + s + ms / 100
            elapsed = time.time() - start_time
            factor = audio_secs / elapsed if elapsed > 0 else 0.0
            print(f"📼 FFmpeg (normalizing): {match.group(0)} | {factor:.2f}x realtime", end='\r')

    process.wait()
    print("\n✅ Simple normalization complete.")

def convert_to_m4b(wav_path, temp_m4b_path, custom_speed=None):
    """Convert WAV to M4B with configurable normalization and optional custom speed"""
    # Determine speed to use (custom speed overrides config)
    speed_to_use = custom_speed if custom_speed is not None else ATEMPO_SPEED
    
    if not ENABLE_NORMALIZATION or NORMALIZATION_TYPE == "none":
        # Original function without normalization
        print(f"🚀 Converting to m4b with speed {speed_to_use}x...")

        # Build audio filter for atempo if needed
        audio_filter = []
        if speed_to_use != 1.0:
            audio_filter = ["-filter:a", f"atempo={speed_to_use}"]

        cmd = [
            "ffmpeg", "-y",
            "-i", str(wav_path)
        ] + audio_filter + [
            "-c:a", "aac",
            str(temp_m4b_path)
        ]

    elif NORMALIZATION_TYPE == "loudness":
        # EBU R128 loudness normalization (recommended for audiobooks)
        return convert_to_m4b_with_loudness_normalization(wav_path, temp_m4b_path, custom_speed)

    elif NORMALIZATION_TYPE == "peak":
        # Peak normalization
        return convert_to_m4b_with_peak_normalization(wav_path, temp_m4b_path, TARGET_PEAK_DB, custom_speed)

    elif NORMALIZATION_TYPE == "simple":
        # Simple volume adjustment
        return convert_to_m4b_with_simple_normalization(wav_path, temp_m4b_path, TARGET_PEAK_DB, custom_speed)

    else:
        # Fallback to no normalization
        # Build audio filter for atempo if needed
        audio_filter = []
        if speed_to_use != 1.0:
            audio_filter = ["-filter:a", f"atempo={speed_to_use}"]

        cmd = [
            "ffmpeg", "-y",
            "-i", str(wav_path)
        ] + audio_filter + [
            "-c:a", "aac",
            str(temp_m4b_path)
        ]

    # Run the conversion (if not handled by specialized functions above)
    start_time = time.time()
    process = subprocess.Popen(cmd, stderr=subprocess.PIPE, text=True)

    audio_secs = 0.0
    for line in process.stderr:
        match = re.search(r"time=(\d{2}):(\d{2}):(\d{2})\.(\d{2})", line)
        if match:
            h, m, s, ms = map(int, match.groups())
            audio_secs = h * 3600 + m * 60 + s + ms / 100
            elapsed = time.time() - start_time
            factor = audio_secs / elapsed if elapsed > 0 else 0.0
            print(f"📼 FFmpeg: {match.group(0)} | {factor:.2f}x realtime", end='\r')

    process.wait()
    print("\n✅ Conversion complete.")

def add_metadata_to_m4b(temp_m4b_path, final_m4b_path, cover_path=None, nfo_path=None):
    """Add metadata and cover to M4B"""
    cmd = ["ffmpeg", "-y", "-i", str(temp_m4b_path)]

    if cover_path and cover_path.exists():
        cmd.extend(["-i", str(cover_path), "-map", "0", "-map", "1", "-c", "copy", "-disposition:v:0", "attached_pic"])
    else:
        cmd.extend(["-map", "0", "-c", "copy"])

    if nfo_path and nfo_path.exists():
        with open(nfo_path, 'r', encoding='utf-8') as f:
            for line in f:
                if ':' in line:
                    key, val = line.strip().split(':', 1)
                    cmd.extend(["-metadata", f"{key.strip()}={val.strip()}"])

    cmd.append(str(final_m4b_path))
    run_ffmpeg(cmd)
    temp_m4b_path.unlink(missing_ok=True)

# ============================================================================
# FILE UTILITIES
# ============================================================================

def chunk_sort_key(f):
    """Extracts the chunk number for natural sorting"""
    m = re.match(r"chunk_(\d+)\.wav", f.name)
    return int(m.group(1)) if m else 0

def create_concat_file(chunk_paths, output_path):
    """Create FFmpeg concat file for audio chunks"""
    with open(output_path, 'w') as f:
        for p in chunk_paths:
            # Use absolute path to ensure FFmpeg can find the files
            f.write(f"file '{str(p.resolve())}'\n")

    logging.info(f"concat.txt written with {len(chunk_paths)} chunks.")
    return output_path

def cleanup_temp_files(directory, patterns):
    """Clean up temporary files matching patterns"""
    files_cleaned = 0
    for pattern in patterns:
        for temp_file in directory.glob(pattern):
            temp_file.unlink(missing_ok=True)
            files_cleaned += 1

    return files_cleaned

# ============================================================================
# DIRECTORY MANAGEMENT
# ============================================================================

def setup_book_directories(book_dir):
    """Set up directory structure for book processing"""
    basename = book_dir.name
    output_root = AUDIOBOOK_ROOT / basename
    tts_dir = output_root / "TTS"
    text_chunks_dir = tts_dir / "text_chunks"
    audio_chunks_dir = tts_dir / "audio_chunks"

    # Create directories
    for d in [output_root, tts_dir, text_chunks_dir, audio_chunks_dir]:
        d.mkdir(parents=True, exist_ok=True)

    return output_root, tts_dir, text_chunks_dir, audio_chunks_dir

def find_book_files(book_dir):
    """Find text files, cover, and metadata for a book"""
    text_files = sorted(book_dir.glob("*.txt"))
    nfo_file = book_dir / "book.nfo"
    cover_jpg = book_dir / "cover.jpg"
    cover_png = book_dir / "cover.png"
    cover_file = cover_jpg if cover_jpg.exists() else cover_png if cover_png.exists() else None

    return {
        'text': text_files[0] if text_files else None,
        'cover': cover_file,
        'nfo': nfo_file if nfo_file.exists() else None
    }

# ============================================================================
# AUDIO FILE OPERATIONS
# ============================================================================

def combine_audio_chunks(chunk_paths, output_path):
    """Combine audio chunks into single file using FFmpeg"""
    concat_list_path = output_path.parent / "concat.txt"
    create_concat_file(chunk_paths, concat_list_path)

    run_ffmpeg([
        "ffmpeg", "-y", "-f", "concat", "-safe", "0",
        "-i", str(concat_list_path.resolve()),
        "-c", "copy", str(output_path.resolve())
    ])

    return output_path

def get_audio_files_in_directory(directory, pattern="chunk_*.wav"):
    """Get sorted list of audio files matching pattern"""
    chunk_paths = sorted([f for f in directory.glob(pattern)
                         if re.fullmatch(r'chunk_\d{3,}\.wav', f.name)],
                        key=chunk_sort_key)
    return chunk_paths

# ============================================================================
# VALIDATION AND VERIFICATION
# ============================================================================

def verify_audio_file(wav_path):
    """Verify audio file is valid and readable"""
    try:
        info = sf.info(str(wav_path))
        return info.frames > 0 and info.samplerate > 0
    except Exception as e:
        logging.error(f"Invalid audio file {wav_path}: {e}")
        return False

def verify_chunk_completeness(audio_chunks_dir, expected_count):
    """Verify all expected chunks exist and are valid"""
    missing_chunks = []
    invalid_chunks = []

    for i in range(1, expected_count + 1):
        chunk_path = audio_chunks_dir / f"chunk_{i:05}.wav"

        if not chunk_path.exists():
            missing_chunks.append(i)
        elif not verify_audio_file(chunk_path):
            invalid_chunks.append(i)

    return missing_chunks, invalid_chunks

# ============================================================================
# EXPORT AND IMPORT FUNCTIONS
# ============================================================================

def export_processing_log(output_dir, processing_info):
    """Export comprehensive processing log"""
    log_path = output_dir / "processing_complete.log"

    with open(log_path, 'w', encoding='utf-8') as f:
        f.write("GenTTS Processing Complete\n")
        f.write("=" * 50 + "\n\n")

        for key, value in processing_info.items():
            f.write(f"{key}: {value}\n")

    return log_path

def save_chunk_info(text_chunks_dir, chunks_info):
    """Save chunk information for debugging/resume"""
    info_path = text_chunks_dir / "chunks_info.json"

    import json
    with open(info_path, 'w', encoding='utf-8') as f:
        json.dump(chunks_info, f, indent=2, ensure_ascii=False)

    return info_path

def load_chunk_info(text_chunks_dir):
    """Load chunk information if available"""
    info_path = text_chunks_dir / "chunks_info.json"

    if not info_path.exists():
        return None

    import json
    try:
        with open(info_path, 'r', encoding='utf-8') as f:
            return json.load(f)
    except Exception as e:
        logging.warning(f"Could not load chunk info: {e}")
        return None