"""
Utility functions
"""
from __future__ import annotations

from src.logger_config import logger, setup_logger
import sys
from pathlib import Path
import subprocess
import os
import uuid
import re
import shutil
import tempfile
from src.config import get_config_value
import json
import traceback
import cv2
import numpy as np
import imagehash
from PIL import Image
from moviepy.editor import VideoFileClip
import tempfile
import librosa

def get_temp_dir(prefix: str = "tmp_") -> Path:
    """
    Creates a temp directory.
    Uses fixed path during test automation if configured.
    """
    if get_config_value("test_automation"):
        base_dir = get_config_value("test_data_directory")
        if not base_dir:
            raise RuntimeError("TEST_DATA_DIRECTORY must be set when TEST_AUTOMATION=true")

        # Ensure base dir exists
        Path(base_dir).mkdir(parents=True, exist_ok=True)

        sub_dir = "output"
        if "download" in prefix:
            sub_dir = "downloads"
            
        path = Path(base_dir) / sub_dir
        path.mkdir(parents=True, exist_ok=True)
        return path

    return Path(tempfile.mkdtemp(prefix=prefix))

def get_video_duration(path: str) -> float:
    """
    Returns the duration of a video file in seconds as a float.
    Uses ffprobe (very fast and accurate).
    """
    cmd = [
        "ffprobe", "-v", "error",
        "-select_streams", "v:0",
        "-show_entries", "format=duration",
        "-of", "json",
        path
    ]
    result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
    info = json.loads(result.stdout)
    return float(info["format"]["duration"])

def calculate_video_durations(selected_videos, all_tts_script_segment, word_level_segment, total_duration: float) -> None:
    """
    Calculate and update duration for each video based on word-level segments.
    Uses three approaches in order of preference:
    1. Simple word count matching (if counts align exactly)
    2. Text matching with cleaning (if counts differ slightly)
    3. Fuzzy matching (if words are missing or mismatched)
    """
    try:
        # Get word counts
        all_script_words = all_tts_script_segment.split()

        def clean_word(word: str) -> str:
            return re.sub(r'[^a-zA-Z]', '', word).lower()
        
        cleaned_script_words = [clean_word(w) for w in all_script_words if clean_word(w)]
        cleaned_segment_words = [clean_word(seg.get("word", "")) for seg in word_level_segment if clean_word(seg.get("word", ""))]
        
        logger.debug(f"📊 Original: Script={len(all_script_words)} words, Segments={len(word_level_segment)} words")
        logger.debug(f"📊 Cleaned: Script={len(cleaned_script_words)} words, Segments={len(cleaned_segment_words)} words")
        logger.debug(f"⏱️ Total audio duration: {total_duration}s (starting from 0)")
        
        # APPROACH 1: Exact match (original word counts)
        if len(all_script_words) == len(word_level_segment):
            logger.debug("✅ Using APPROACH 1: Simple word count matching")
            calculate_durations_simple(selected_videos, word_level_segment, total_duration)
        
        # APPROACH 2: Cleaned match (cleaned word counts)
        elif len(cleaned_script_words) == len(cleaned_segment_words):
            logger.debug("✅ Using APPROACH 2: Text matching with cleaning")
            calculate_durations_with_text_matching(selected_videos, word_level_segment, total_duration)
        
        # APPROACH 3: Fuzzy match
        else:
            diff = abs(len(cleaned_script_words) - len(cleaned_segment_words))
            logger.debug(f"⚠️ Word count mismatch after cleaning (diff: {diff})")
            logger.debug("🔍 Using APPROACH 3: Fuzzy matching")
            calculate_durations_with_fuzzy_matching(selected_videos, word_level_segment, total_duration)
            
    except Exception as e:
        logger.error(f"❌ Failed to calculate video durations: {e}")
        traceback.print_exc()
        # Fallback: set equal durations
        equal_duration = total_duration / len(selected_videos)
        for video in selected_videos:
            video["duration"] = round(equal_duration, 2)


def calculate_durations_simple(selected_videos, word_level_segment, total_duration: float) -> None:
    """
    APPROACH 1: Simple sequential matching when word counts align exactly.
    First video always starts at 0, last video always ends at total_duration.
    """
    current_word_index = 0
    
    for i, video in enumerate(selected_videos):
        tts_text = video.get("tts_script_segment", "").strip()
        
        if not tts_text:
            video["duration"] = 0
            continue
            
        word_count = len(tts_text.split())
        
        # Get start time for this segment
        if i == 0:
            start_time = 0.0
        else:
            start_time = word_level_segment[current_word_index]["start_time"]
        
        # Calculate next word index
        next_word_index = current_word_index + word_count
        
        # Get end time
        if i + 1 == len(selected_videos):
            end_time = total_duration
        else:
            if next_word_index < len(word_level_segment):
                end_time = word_level_segment[next_word_index]["start_time"]
            else:
                end_time = total_duration
        
        # Calculate duration
        video["duration"] = round(end_time - start_time, 2)
        logger.debug(f"  Video {i}: [{start_time:.2f}s - {end_time:.2f}s] = {video['duration']}s | '{tts_text[:40]}...'")
        
        # Move to next segment
        current_word_index = next_word_index
    
    # Verify total
    total_calculated = sum(v.get("duration", 0) for v in selected_videos)
    logger.debug(f"✅ Total calculated duration: {total_calculated:.2f}s (expected: {total_duration:.2f}s)")


def calculate_durations_with_text_matching(selected_videos, word_level_segment, total_duration: float) -> None:
    """
    APPROACH 2: Text matching with cleaned/normalized text.
    Handles cases where word counts don't align due to numbers, punctuation, etc.
    First video always starts at 0, last video always ends at total_duration.
    """
    def clean_word(word: str) -> str:
        """Clean a single word - remove numbers, special chars, keep only alpha"""
        return re.sub(r'[^a-zA-Z]', '', word).lower()
    
    # Build cleaned word list from word_level_segment
    cleaned_word_segments = []
    for seg in word_level_segment:
        word = seg.get("word", "")
        cleaned = clean_word(word)
        if cleaned:
            cleaned_word_segments.append({
                "cleaned": cleaned,
                "original": word,
                "start_time": seg.get("start_time", 0),
                "end_time": seg.get("end_time", 0)
            })
    
    logger.debug(f"📝 Cleaned word segments: {len(cleaned_word_segments)} words")
    
    # Track current position in cleaned_word_segments
    current_word_index = 0
    
    for i, video in enumerate(selected_videos):
        tts_text = video.get("tts_script_segment", "").strip()
        
        if not tts_text:
            video["duration"] = 0
            continue
        
        # Clean the video's script segment
        video_words = tts_text.split()
        cleaned_video_words = [clean_word(w) for w in video_words if clean_word(w)]
        
        if not cleaned_video_words:
            video["duration"] = 0
            continue
        
        word_count = len(cleaned_video_words)
        logger.debug(f"  Video {i}: Looking for {word_count} cleaned words starting at index {current_word_index}")
        
        # Get start time
        if i == 0:
            start_time = 0.0
        elif current_word_index < len(cleaned_word_segments):
            start_time = cleaned_word_segments[current_word_index]["start_time"]
        else:
            logger.warning(f"    ⚠️ Out of word segments, using remaining time")
            remaining_videos = len(selected_videos) - i
            remaining_time = total_duration - (cleaned_word_segments[-1]["end_time"] if cleaned_word_segments else 0)
            video["duration"] = round(remaining_time / remaining_videos, 2)
            continue
        
        # Calculate next word index
        next_word_index = current_word_index + word_count
        
        # Get end time
        if i + 1 == len(selected_videos):
            end_time = total_duration
        else:
            if next_word_index < len(cleaned_word_segments):
                end_time = cleaned_word_segments[next_word_index]["start_time"]
            else:
                end_time = total_duration
        
        # Calculate duration
        video["duration"] = round(end_time - start_time, 2)
        logger.debug(f"    ✅ [{start_time:.2f}s - {end_time:.2f}s] = {video['duration']}s | {word_count} words")
        
        # Move to next segment
        current_word_index = next_word_index
    
    # Verify total
    total_calculated = sum(v.get("duration", 0) for v in selected_videos)
    logger.debug(f"✅ Total calculated duration: {total_calculated:.2f}s (expected: {total_duration:.2f}s)")


def calculate_durations_with_fuzzy_matching(selected_videos, word_level_segment, total_duration: float) -> None:
    """
    APPROACH 3: Fuzzy matching with flexible word alignment.
    Handles cases where words are missing, misspelled, or slightly different.
    First video always starts at 0, last video always ends at total_duration.
    """
    from difflib import SequenceMatcher
    
    def clean_word(word: str) -> str:
        """Clean a single word - remove numbers, special chars, keep only alpha"""
        return re.sub(r'[^a-zA-Z]', '', word).lower()
    
    def similarity_ratio(word1: str, word2: str) -> float:
        """Calculate similarity between two words (0.0 to 1.0)"""
        if not word1 or not word2:
            return 0.0
        return SequenceMatcher(None, word1, word2).ratio()
    
    # Build cleaned word list from word_level_segment
    cleaned_word_segments = []
    for seg in word_level_segment:
        word = seg.get("word", "")
        cleaned = clean_word(word)
        if cleaned:
            cleaned_word_segments.append({
                "cleaned": cleaned,
                "original": word,
                "start_time": seg.get("start_time", 0),
                "end_time": seg.get("end_time", 0)
            })
    
    logger.debug(f"📝 Cleaned word segments: {len(cleaned_word_segments)} words")
    
    # Track current position in cleaned_word_segments
    current_word_index = 0
    
    for i, video in enumerate(selected_videos):
        tts_text = video.get("tts_script_segment", "").strip()
        
        if not tts_text:
            video["duration"] = 0
            continue
        
        # Clean the video's script segment
        video_words = tts_text.split()
        cleaned_video_words = [clean_word(w) for w in video_words if clean_word(w)]
        
        if not cleaned_video_words:
            video["duration"] = 0
            continue
        
        word_count = len(cleaned_video_words)
        logger.debug(f"  Video {i}: Fuzzy matching {word_count} words starting at index {current_word_index}")
        
        # Get start time
        if i == 0:
            start_time = 0.0
        elif current_word_index < len(cleaned_word_segments):
            start_time = cleaned_word_segments[current_word_index]["start_time"]
        else:
            logger.warning(f"    ⚠️ Out of word segments")
            remaining_videos = len(selected_videos) - i
            remaining_time = total_duration - (cleaned_word_segments[-1]["end_time"] if cleaned_word_segments else 0)
            video["duration"] = round(remaining_time / remaining_videos, 2)
            continue
        
        # FUZZY MATCHING: Match words with flexibility
        matched_count = 0
        search_index = current_word_index
        last_matched_index = current_word_index - 1
        
        for video_word in cleaned_video_words:
            found = False
            
            # Search within a window (next 5 words to avoid jumping too far)
            search_end = min(search_index + 5, len(cleaned_word_segments))
            
            for j in range(search_index, search_end):
                segment_word = cleaned_word_segments[j]["cleaned"]
                
                # Exact match
                if video_word == segment_word:
                    matched_count += 1
                    last_matched_index = j
                    search_index = j + 1
                    found = True
                    break
                
                # Substring match
                if video_word in segment_word or segment_word in video_word:
                    matched_count += 1
                    last_matched_index = j
                    search_index = j + 1
                    found = True
                    logger.debug(f"    Substring match: '{video_word}' ≈ '{segment_word}'")
                    break
                
                # Fuzzy similarity match
                similarity = similarity_ratio(video_word, segment_word)
                if similarity >= 0.75:  # 75% similarity threshold
                    matched_count += 1
                    last_matched_index = j
                    search_index = j + 1
                    found = True
                    logger.debug(f"    Fuzzy match: '{video_word}' ≈ '{segment_word}' (sim: {similarity:.2f})")
                    break
            
            if not found:
                logger.debug(f"    No match for '{video_word}'")
        
        # Determine end index
        if matched_count > 0:
            next_word_index = last_matched_index + 1
            logger.debug(f"    ✓ Matched {matched_count}/{word_count} words")
        else:
            logger.warning(f"    ⚠️ No matches, estimating position")
            next_word_index = min(current_word_index + word_count, len(cleaned_word_segments))
        
        # Get end time
        if i + 1 == len(selected_videos):
            end_time = total_duration
        else:
            if next_word_index < len(cleaned_word_segments):
                end_time = cleaned_word_segments[next_word_index]["start_time"]
            else:
                end_time = total_duration
        
        # Calculate duration
        video["duration"] = round(end_time - start_time, 2)
        logger.debug(f"    ✅ [{start_time:.2f}s - {end_time:.2f}s] = {video['duration']}s")
        
        # Move to next segment
        current_word_index = next_word_index
    
    # Verify total
    total_calculated = sum(v.get("duration", 0) for v in selected_videos)
    logger.debug(f"✅ Total calculated duration: {total_calculated:.2f}s (expected: {total_duration:.2f}s)")

def is_video_loopable(video_path, frame_check_window=10, threshold=15.0):
    if not video_path:
        return False
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        raise ValueError(f"Cannot open video: {video_path}")

    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    if frame_count <= frame_check_window * 2:
        cap.release()
        return False  # Too short to judge loopability

    frame_indices = list(range(frame_check_window)) + \
                    list(range(frame_count - frame_check_window, frame_count))

    frames = []
    for idx in frame_indices:
        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
        ret, frame = cap.read()
        if not ret or frame is None:
            continue
        frame = cv2.resize(frame, (128, 128))
        frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY))

    cap.release()

    if len(frames) < 2 * frame_check_window:
        return False

    start_frames = np.array(frames[:frame_check_window])
    end_frames = np.array(frames[-frame_check_window:])

    diff = np.mean(np.abs(start_frames.astype(np.float32) - end_frames.astype(np.float32)))
    logger.debug(f"🔍 Mean frame difference: {diff:.2f}")

    return diff < threshold


def is_loopable_phash(video_path, hash_diff_threshold=8):
    if not video_path:
        return False
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        raise ValueError(f"Cannot open video: {video_path}")

    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    if frame_count < 2:
        cap.release()
        return False

    # Read first frame
    cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
    ret, start = cap.read()
    if not ret or start is None:
        cap.release()
        return False

    # Try to read last valid frame (with fallback)
    last_frame_index = frame_count - 1
    ret, end = False, None
    while not ret and last_frame_index > frame_count - 10:
        cap.set(cv2.CAP_PROP_POS_FRAMES, last_frame_index)
        ret, end = cap.read()
        last_frame_index -= 1

    cap.release()

    if end is None or not ret:
        return False

    start_hash = imagehash.phash(Image.fromarray(cv2.cvtColor(start, cv2.COLOR_BGR2RGB)))
    end_hash = imagehash.phash(Image.fromarray(cv2.cvtColor(end, cv2.COLOR_BGR2RGB)))

    diff = abs(start_hash - end_hash)
    logger.debug(f"🧩 pHash difference: {diff}")

    return diff < hash_diff_threshold

def is_video_zoomable_tail(video_path, tail_seconds=1, sample_frames=15, motion_threshold=1.5):
    """
    Checks only the *last few seconds* of the video to see if it's already zooming.
    Returns True if mostly static (safe to add zoom), False if motion already exists.
    """
    return False
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        raise ValueError(f"Cannot open video: {video_path}")

    fps = cap.get(cv2.CAP_PROP_FPS)
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    duration = total_frames / fps if fps > 0 else 0

    # Only analyze the last N seconds
    start_frame = max(total_frames - int(tail_seconds * fps), 0)
    cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)

    frames = []
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY))
    cap.release()

    if len(frames) < 2:
        return True  # too few frames → assume static

    # Sample frames evenly
    step = max(len(frames) // sample_frames, 1)
    total_motion = 0
    motion_samples = 0

    for i in range(0, len(frames) - step, step):
        prev_gray = frames[i]
        gray = frames[i + step]
        flow = cv2.calcOpticalFlowFarneback(
            prev_gray, gray, None,
            pyr_scale=0.5, levels=3, winsize=15,
            iterations=3, poly_n=5, poly_sigma=1.2, flags=0
        )
        mag, _ = cv2.cartToPolar(flow[..., 0], flow[..., 1])
        total_motion += np.mean(mag)
        motion_samples += 1

    avg_motion = total_motion / motion_samples if motion_samples else 0
    logger.debug(f"🎥 Tail motion magnitude: {avg_motion:.3f}")

    # If low optical flow in tail section → it's safe to add zoom
    return avg_motion < motion_threshold

def selective_update_with_keymaps(source: dict, modified: dict, source_keys: list, modified_keys: list) -> dict:
    """
    Update 'source' dict by copying values from 'modified' dict.
    Each pair (source_key, modified_key) defines a mapping.

    Example:
      source_keys = ["url", "description"]
      modified_keys = ["video_url", "desc_text"]
    """
    updated = source.copy()

    for s_key, m_key in zip(source_keys, modified_keys):
        if m_key in modified:
            updated[s_key] = modified[m_key]

    return updated

def clean_tts_script(tts_script: str) -> str:
    """Split and clean TTS script joined by '-'."""
    if tts_script:
        # Split by hyphen and strip spaces
        parts = [part.strip() for part in tts_script.split('-') if part.strip()]
        return " ".join(parts).rstrip(".")
    return ""

def reverse_clip(path_or_clip) -> str:
    input_path = ""
    # ✅ Handle any MoviePy clip (VideoFileClip, CompositeVideoClip, etc.)
    if hasattr(path_or_clip, "write_videofile"):
        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_input:
            input_path = temp_input.name
        path_or_clip.write_videofile(
            input_path,
            codec="libx264",
            audio_codec="aac",
            verbose=False,
            logger=None,
            fps=25
        )

    elif isinstance(path_or_clip, str):
        input_path = path_or_clip

    """Reverse both video and audio using ffmpeg."""
    out_path = os.path.join(tempfile.gettempdir(), f"{uuid.uuid4().hex[:8]}_reversed.mp4")
    
    subprocess.run([
        "ffmpeg", "-hide_banner", "-loglevel", "error",
        "-y", "-i", input_path,
        "-vf", "reverse",
        "-af", "areverse",
        out_path
    ], check=True)
    
    return out_path

def interpolate_video(input_path: str, target_duration: float = 4.0, fps: int = 60) -> str:
    """
    Smoothly extend a short video using motion interpolation.
    Works entirely on CPU (no GPU required).

    Args:
        input_path: path to input video
        target_duration: desired output length (seconds)
        fps: target output framerate (default 60)
    """
    return None
    # Get actual duration
    cmd = ["ffprobe", "-v", "error", "-show_entries", "format=duration",
           "-of", "default=noprint_wrappers=1:nokey=1", input_path]
    duration_str = subprocess.check_output(cmd).decode().strip()
    duration = float(duration_str)

    # Calculate how much we need to stretch
    stretch_factor = target_duration / duration

    # Output file
    base = os.path.splitext(os.path.basename(input_path))[0]
    output_path = f"/tmp/{base}_interp.mp4"

    # FFmpeg motion interpolation command
    cmd = [
        "ffmpeg",
        "-i", input_path,
        "-filter_complex",
        f"[0:v]setpts={stretch_factor}*PTS,"
        f"minterpolate='mi_mode=mci:mc_mode=aobmc:vsbmc=1:fps={fps}'[v]",
        "-map", "[v]",
        "-an",  # remove audio
        "-c:v", "libx264",
        "-preset", "fast",
        "-crf", "18",
        "-y", output_path
    ]

    subprocess.run(cmd, check=True)
    return output_path

def _get_video_resolution(path: str) -> tuple[int, int]:
    cmd = [
        "ffprobe",
        "-v", "error",
        "-select_streams", "v:0",
        "-show_entries", "stream=width,height",
        "-of", "json",
        path,
    ]
    result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    if result.returncode != 0:
        raise RuntimeError(f"ffprobe failed:\n{result.stderr.decode()}")

    info = json.loads(result.stdout)
    stream = info["streams"][0]
    return stream["width"], stream["height"]

def _get_pixel_format(path: str) -> str:
    cmd = [
        "ffprobe",
        "-v", "error",
        "-select_streams", "v:0",
        "-show_entries", "stream=pix_fmt",
        "-of", "json",
        path,
    ]
    result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    if result.returncode != 0:
        return ""

    info = json.loads(result.stdout)
    return info["streams"][0].get("pix_fmt", "")

def resize_video(input_path: str, target_width: int = 1080, target_height: int = 1920, overwrite: bool = False, force: bool = False) -> str:
    """
    Resize a video to the given resolution (default 1080x1920) using FFmpeg.
    If overwrite=True, replaces the original file safely after successful conversion.
    If force=True, re-encodes even if the resolution already matches.
    """
    if not os.path.exists(input_path):
        raise FileNotFoundError(f"Input video not found: {input_path}")

    # 🔍 Probe resolution
    width, height = _get_video_resolution(input_path)
    pix_fmt = _get_pixel_format(input_path)

    # Check if we can skip:
    # 1. Force is False
    # 2. Dimensions match
    # 3. Pixel format is yuv420p (required for broad compatibility)
    if not force and width == target_width and height == target_height and pix_fmt == "yuv420p":
        logger.debug(
            f"Skipping resize (already {width}x{height}, {pix_fmt}): {os.path.basename(input_path)}"
        )
        return input_path

    logger.debug(
        f"Resizing/Re-encoding {os.path.basename(input_path)} "
        f"({width}x{height}, {pix_fmt}) → ({target_width}x{target_height}, yuv420p)"
    )

    temp_output = os.path.join("/tmp", f"{uuid.uuid4().hex}.mp4")

    # FFmpeg resize command (output goes to /tmp first)
    # FFmpeg command for Crop-to-Fill (Strict 9:16 enforcement)
    # scale=1080:1920:force_original_aspect_ratio=increase ensures min dim fits
    # crop=1080:1920 crops the excess
    cmd = [
        "ffmpeg", "-y", "-hide_banner", "-loglevel", "error",
        "-i", input_path,
        "-vf", f"scale={target_width}:{target_height}:force_original_aspect_ratio=increase,crop={target_width}:{target_height},setsar=1",
        "-c:v", "libx264", "-crf", "18", "-preset", "slow",
        "-pix_fmt", "yuv420p",
        "-c:a", "copy",
        temp_output
    ]

    # Run FFmpeg process
    result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    if result.returncode != 0:
        if os.path.exists(temp_output):
            os.remove(temp_output)
        raise RuntimeError(f"FFmpeg failed:\n{result.stderr.decode('utf-8', errors='ignore')}")

    # Overwrite original safely if requested
    if overwrite:
        shutil.move(temp_output, input_path)
        return input_path

    return temp_output

def remove_black_padding(input_path: str, overwrite: bool = False, threshold_pct: float = 0.1) -> str:
    """
    Automatically detect and remove black padding (crop only) using FFmpeg.
    Saves to /tmp with a unique UUID filename unless overwrite=True.

    Args:
        input_path (str): Path to the input video.
        overwrite (bool): If True, safely replace the original file.
        threshold_pct (float): Only crop if black padding > threshold_pct (0.0 to 1.0).
                              0.0 = always crop if any padding detected.

    Returns:
        str: Path to the cropped video (or original if no crop needed).
    """
    if get_config_value("test_automation"):
        return input_path
    if not os.path.exists(input_path):
        raise FileNotFoundError(f"Input video not found: {input_path}")

    # Step 1: Detect crop parameters using cropdetect
    detect_cmd = [
        "ffmpeg", "-i", input_path, "-vf", "cropdetect=24:16:0",
        "-frames:v", "500", "-f", "null", "-"
    ]
    result = subprocess.run(detect_cmd, stderr=subprocess.PIPE, text=True)
    matches = re.findall(r"crop=\S+", result.stderr)

    if not matches:
        logger.debug("No black padding detected.")
        return input_path

    # Get most frequent crop value
    crop_value = max(set(matches), key=matches.count)
    
    # Parse crop string: crop=w:h:x:y
    # Example: crop=1080:1520:0:200
    try:
        match = re.search(r"crop=(\d+):(\d+):(\d+):(\d+)", crop_value)
        if match:
            c_w, c_h, _, _ = map(int, match.groups())
            
            # Get original resolution
            orig_w, orig_h = _get_video_resolution(input_path)
            
            orig_area = orig_w * orig_h
            crop_area = c_w * c_h
            padding_area = orig_area - crop_area
            padding_pct = padding_area / orig_area if orig_area > 0 else 0
            
            if padding_pct < threshold_pct:
                logger.debug(f"Skipping crop: Padding {padding_pct:.1%} < Threshold {threshold_pct:.1%}")
                return input_path
            
            logger.debug(f"Detected crop: {crop_value} (Padding: {padding_pct:.1%})")
            
    except Exception as e:
        logger.warning(f"Could not parse crop value '{crop_value}' for threshold check: {e}")
        # Proceed with cropping if parsing fails, or return? 
        # Safest is to proceed as before or log and continue. 
        # Let's proceed to maintain existing behavior on failure unless explicitly stopped.
        logger.debug(f"Proceeding with crop: {crop_value}")

    # Step 2: Create temp output file
    tmp_output = os.path.join("/tmp", f"{uuid.uuid4().hex}_cropped.mp4")

    # Step 3: Run FFmpeg crop command
    crop_cmd = ["ffmpeg", "-y", "-i", input_path, "-vf", crop_value, "-c:a", "copy", tmp_output]
    crop_proc = subprocess.run(crop_cmd, stderr=subprocess.PIPE, text=True)

    if crop_proc.returncode != 0:
        raise RuntimeError(f"FFmpeg crop failed:\n{crop_proc.stderr}")

    # Step 4: Handle overwrite safely
    if overwrite:
        shutil.move(tmp_output, input_path)
        return input_path

    return tmp_output

def trim_black_frames(
    input_path: str, 
    overwrite: bool = False, 
    black_threshold: int = 20,
    min_frames_to_trim: int = 1,
    max_frames_to_trim: int = 30
) -> str:
    """
    Detect and remove solid black frames from the start and end of a video.
    
    Uses FFmpeg showinfo filter to analyze frame luminance (Y channel mean).
    A frame is considered black if its Y mean is <= black_threshold.
    
    Args:
        input_path: Path to the input video
        overwrite: If True, replace the original file
        black_threshold: Maximum Y luminance value to consider a frame as black (0-255)
                        Default 20 catches pure black (16) with some tolerance
        min_frames_to_trim: Minimum black frames at start/end to trigger trimming
        max_frames_to_trim: Maximum frames to check at start/end
        
    Returns:
        Path to the trimmed video, or original path if no trimming needed
    """
    if not os.path.exists(input_path):
        raise FileNotFoundError(f"Input video not found: {input_path}")
    
    # Get video info
    probe_cmd = [
        "ffprobe", "-v", "error",
        "-select_streams", "v:0",
        "-show_entries", "stream=nb_frames,r_frame_rate,duration",
        "-show_entries", "format=duration",
        "-of", "json", input_path
    ]
    probe_result = subprocess.run(probe_cmd, capture_output=True, text=True)
    
    if probe_result.returncode != 0:
        logger.warning(f"Failed to probe video: {input_path}")
        return input_path
    
    probe_data = json.loads(probe_result.stdout)
    
    # Get FPS
    fps_str = probe_data.get("streams", [{}])[0].get("r_frame_rate", "25/1")
    fps_parts = fps_str.split("/")
    fps = float(fps_parts[0]) / float(fps_parts[1]) if len(fps_parts) == 2 else float(fps_parts[0])
    
    # Get total duration
    duration = float(probe_data.get("format", {}).get("duration", 0))
    if duration == 0:
        duration = float(probe_data.get("streams", [{}])[0].get("duration", 0))
    
    if duration <= 0:
        logger.warning(f"Could not determine video duration: {input_path}")
        return input_path
    
    # Analyze first N frames for black frames at start
    start_black_frames = _count_black_frames_at_position(
        input_path, "start", max_frames_to_trim, black_threshold, fps
    )
    
    # Analyze last N frames for black frames at end  
    end_black_frames = _count_black_frames_at_position(
        input_path, "end", max_frames_to_trim, black_threshold, fps, duration
    )
    
    logger.debug(f"🎬 Black frame analysis: start={start_black_frames}, end={end_black_frames}")
    
    # Check if trimming is needed
    if start_black_frames < min_frames_to_trim and end_black_frames < min_frames_to_trim:
        logger.debug(f"✅ No black frames to trim in: {os.path.basename(input_path)}")
        return input_path
    
    # Calculate trim times
    start_trim_time = start_black_frames / fps if start_black_frames >= min_frames_to_trim else 0
    end_trim_time = end_black_frames / fps if end_black_frames >= min_frames_to_trim else 0
    
    # New duration after trimming
    new_duration = duration - start_trim_time - end_trim_time
    
    if new_duration <= 0.1:
        logger.warning(f"⚠️ Trimming would remove entire video, skipping: {input_path}")
        return input_path
    
    logger.debug(
        f"✂️ Trimming black frames: {os.path.basename(input_path)} "
        f"(start: {start_trim_time:.3f}s, end: {end_trim_time:.3f}s)"
    )
    
    # Generate output path
    temp_output = os.path.join("/tmp", f"{uuid.uuid4().hex}_trimmed.mp4")
    
    # Build FFmpeg command
    cmd = [
        "ffmpeg", "-y", "-hide_banner", "-loglevel", "error",
        "-ss", str(start_trim_time),
        "-i", input_path,
        "-t", str(new_duration),
        "-c:v", "libx264", "-preset", "fast", "-crf", "18",
        "-pix_fmt", "yuv420p",
        "-c:a", "copy",
        temp_output
    ]
    
    result = subprocess.run(cmd, capture_output=True, text=True)
    
    if result.returncode != 0:
        logger.error(f"FFmpeg trim failed: {result.stderr}")
        return input_path
    
    logger.debug(f"✅ Trimmed video saved: {temp_output}")
    
    # Handle overwrite
    if overwrite:
        shutil.move(temp_output, input_path)
        return input_path
    
    return temp_output


def _count_black_frames_at_position(
    video_path: str,
    position: str,  # "start" or "end"
    max_frames: int,
    black_threshold: int,
    fps: float,
    duration: float = 0
) -> int:
    """
    Count consecutive black frames at the start or end of a video.
    
    Args:
        video_path: Path to video file
        position: "start" or "end"
        max_frames: Maximum frames to analyze
        black_threshold: Y luminance threshold for black detection
        fps: Video frame rate
        duration: Video duration (required for "end" position)
        
    Returns:
        Number of consecutive black frames at the specified position
    """
    # For start: analyze first max_frames frames
    # For end: seek to near end and analyze last max_frames frames
    if position == "end" and duration > 0:
        seek_time = max(0, duration - (max_frames / fps) - 0.5)
        ss_arg = ["-ss", str(seek_time)]
    else:
        ss_arg = []
    
    # Use showinfo filter to get frame luminance
    cmd = [
        "ffmpeg", "-hide_banner",
        *ss_arg,
        "-i", video_path,
        "-vf", f"select='lte(n,{max_frames})',showinfo",
        "-frames:v", str(max_frames + 5),
        "-f", "null", "-"
    ]
    
    result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
    
    if result.returncode != 0:
        return 0
    
    # Parse showinfo output for mean values
    # Format: mean:[Y U V] where Y is luminance
    # A pure black frame has Y=16 in YUV (limited range)
    frame_means = []
    for line in result.stderr.split('\n'):
        match = re.search(r'mean:\[(\d+)\s+\d+\s+\d+\]', line)
        if match:
            y_mean = int(match.group(1))
            frame_means.append(y_mean)
    
    if not frame_means:
        return 0
    
    # Count consecutive black frames
    if position == "start":
        # Count from beginning
        black_count = 0
        for y_mean in frame_means:
            if y_mean <= black_threshold:
                black_count += 1
            else:
                break
        return black_count
    else:
        # Count from end (reverse)
        black_count = 0
        for y_mean in reversed(frame_means):
            if y_mean <= black_threshold:
                black_count += 1
            else:
                break
        return black_count


def ratio_1x1_to9x16(video_path, overwrite=False):
    """
    Convert a 1:1 video to 9:16 by adding blurred padding using FFmpeg.
    Saves to /tmp with a unique UUID filename unless overwrite=True.

    Args:
        video_path (str): Path to the input video.
        overwrite (bool): If True, safely replace the original file.

    Returns:
        str: Path to the converted video.
    """
    if not os.path.exists(video_path):
        raise FileNotFoundError(f"Input video not found: {video_path}")

    tmp_output = os.path.join("/tmp", f"{uuid.uuid4().hex}_9x16.mp4")

    cmd = [
        "ffmpeg",
        "-i", video_path,
        "-vf", "crop=min(iw\\,ih):min(iw\\,ih),scale=1080:1080,pad=1080:1920:0:(1920-1080)/2:black",
        "-c:a", "copy",
        "-y",
        tmp_output
    ]

    result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    if result.returncode != 0:
        raise RuntimeError(f"FFmpeg failed:\n{result.stderr.decode('utf-8', errors='ignore')}")

    if overwrite:
        shutil.move(tmp_output, video_path)
        return video_path

    return tmp_output

def get_best_beat_method(audio_path: str, min_interval: float = 1.0, target_beats: int = 10) -> tuple[np.ndarray, str]:
    """
    Try all beat detection methods and return the one with closest to target number of beats.
    
    Args:
        audio_path: Path to audio file
        min_interval: Minimum time between beats in seconds
        target_beats: Desired number of beats (default 10 for 10-15 sec videos)
    
    Returns:
        Tuple of (beat_times, method_name)
    """
    methods = ["kick", "snare", "downbeat", "general"]
    results = {}
    
    logger.debug(f"Testing all beat detection methods (target: ~{target_beats} beats)...")
    
    for method in methods:
        try:
            beat_times = get_beat_times(audio_path, beat_type=method, min_interval=min_interval)
            results[method] = beat_times
            logger.debug(f"{method:12s}: {len(beat_times):2d} beats detected")
        except Exception as e:
            logger.debug(f"{method:12s}: ERROR - {e}")
            results[method] = np.array([])
    
    # Filter out empty results
    valid_results = {k: v for k, v in results.items() if len(v) > 0}
    
    if not valid_results:
        return None, None
    
    # Find the method closest to target
    best_method = min(valid_results.keys(), key=lambda k: abs(len(valid_results[k]) - target_beats))
    best_beats = valid_results[best_method]
    
    logger.debug(f"Selected: {best_method} with {len(best_beats)} beats (closest to target)")
    
    return best_beats, best_method


def get_kick_times(audio_path: str, min_interval: float = 1.0) -> np.ndarray:
    """
    Detect kick drum hits (low frequency emphasis).
    Kicks are the "boom" - usually the strongest low-end hits.
    
    Args:
        audio_path: Path to audio file
        min_interval: Minimum time between kicks in seconds
    
    Returns:
        Array of kick drum timestamps in seconds
    """
    y, sr = librosa.load(audio_path)
    
    # Use percussive component separation
    y_harmonic, y_percussive = librosa.effects.hpss(y, margin=2.0)
    
    # Apply low-pass filter to focus on bass frequencies
    y_bass = librosa.effects.percussive(y_percussive, margin=4.0)
    
    # Get onset strength emphasizing low frequencies
    onset_env = librosa.onset.onset_strength(
        y=y_bass, 
        sr=sr,
        aggregate=np.median,
        fmax=200,  # Focus on frequencies below 200Hz
        n_mels=128
    )
    
    # Detect onsets with lower threshold for kicks
    onset_frames = librosa.onset.onset_detect(
        onset_envelope=onset_env,
        sr=sr,
        backtrack=False,
        pre_max=3,
        post_max=3,
        pre_avg=3,
        post_avg=5,
        delta=0.15,  # Very low threshold to catch more kicks
        wait=8
    )
    
    kick_times = librosa.frames_to_time(onset_frames, sr=sr)
    
    logger.debug(f"Raw kick detections: {len(kick_times)}")
    
    # Filter to minimum interval
    return _filter_by_min_interval(kick_times, min_interval)


def get_snare_times(audio_path: str, min_interval: float = 1.0) -> np.ndarray:
    """
    Detect snare/clap hits (mid-high frequency emphasis).
    Snares are the "crack" - sharp, crisp hits.
    
    Args:
        audio_path: Path to audio file
        min_interval: Minimum time between snares in seconds
    
    Returns:
        Array of snare hit timestamps in seconds
    """
    y, sr = librosa.load(audio_path)
    
    # Use percussive component
    y_harmonic, y_percussive = librosa.effects.hpss(y, margin=2.0)
    
    # Get onset strength emphasizing mid-high frequencies  
    onset_env = librosa.onset.onset_strength(
        y=y_percussive,
        sr=sr,
        aggregate=np.median,
        fmin=150,  # Focus on frequencies above 150Hz
        fmax=4000,
        n_mels=128
    )
    
    # Detect onsets
    onset_frames = librosa.onset.onset_detect(
        onset_envelope=onset_env,
        sr=sr,
        backtrack=False,
        pre_max=3,
        post_max=3,
        pre_avg=3,
        post_avg=5,
        delta=0.15,  # Very low threshold
        wait=8
    )
    
    snare_times = librosa.frames_to_time(onset_frames, sr=sr)
    
    logger.debug(f"Raw snare detections: {len(snare_times)}")
    
    # Filter to minimum interval
    return _filter_by_min_interval(snare_times, min_interval)


def get_downbeats(audio_path: str, min_interval: float = 1.0) -> np.ndarray:
    """
    Detect downbeats - every Nth beat based on tempo.
    More reliable than frequency filtering for finding the "1" count.
    
    Args:
        audio_path: Path to audio file
        min_interval: Minimum time between downbeats in seconds
    
    Returns:
        Array of downbeat timestamps in seconds
    """
    y, sr = librosa.load(audio_path)
    
    # Get all beats first
    tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr, units='frames')
    beat_times = librosa.frames_to_time(beat_frames, sr=sr)
    
    # Handle tempo being array or scalar
    tempo_val = tempo[0] if isinstance(tempo, np.ndarray) else tempo
    logger.debug(f"Detected {len(beat_times)} total beats at {tempo_val:.1f} BPM")
    
    if len(beat_times) == 0:
        return np.array([])
    
    # Most music is in 4/4 time, so take every 4th beat as downbeat
    # Or every 2nd beat for half-time feel
    beats_per_bar = 4
    
    # If we have very few beats, use every 2nd
    if len(beat_times) < 8:
        beats_per_bar = 2
    
    # Select every Nth beat
    downbeat_indices = np.arange(0, len(beat_times), beats_per_bar)
    downbeat_times = beat_times[downbeat_indices]
    
    logger.debug(f"Selected {len(downbeat_times)} downbeats (every {beats_per_bar} beats)")
    
    # Filter to minimum interval
    return _filter_by_min_interval(downbeat_times, min_interval)


def get_general_beats(audio_path: str, min_interval: float = 1.0) -> np.ndarray:
    """
    Fallback: Get general beat times (original method).
    
    Args:
        audio_path: Path to audio file
        min_interval: Minimum time between beats in seconds
    
    Returns:
        Array of beat timestamps in seconds
    """
    y, sr = librosa.load(audio_path)
    
    tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
    beat_times = librosa.frames_to_time(beat_frames, sr=sr)
    
    logger.debug(f"Tempo: {tempo} BPM")
    logger.debug(f"Beat times: {beat_times}")
    
    return _filter_by_min_interval(beat_times, min_interval)


def _filter_by_min_interval(times: np.ndarray, min_interval: float) -> np.ndarray:
    """Filter timestamps to ensure minimum interval between them."""
    if len(times) == 0:
        return times
    
    filtered = [times[0]]
    for t in times[1:]:
        if t - filtered[-1] >= min_interval:
            filtered.append(t)
    
    return np.array(filtered)


def get_beat_times(audio_path: str, beat_type: str = "downbeat", min_interval: float = 1.0) -> np.ndarray:
    """
    Get beat times based on specified drum element.
    
    Args:
        audio_path: Path to audio file
        beat_type: One of "kick", "snare", "downbeat", or "general"
        min_interval: Minimum time between beats in seconds
    
    Returns:
        Array of beat timestamps in seconds
    
    Recommendation: Start with "downbeat" - it's the most reliable!
    """
    logger.debug(f"Detecting {beat_type} beats with min_interval={min_interval}s...")
    
    if beat_type == "kick":
        result = get_kick_times(audio_path, min_interval)
    elif beat_type == "snare":
        result = get_snare_times(audio_path, min_interval)
    elif beat_type == "downbeat":
        result = get_downbeats(audio_path, min_interval)
    elif beat_type == "general":
        result = get_general_beats(audio_path, min_interval)
    else:
        raise ValueError(f"Unknown beat_type: {beat_type}. Use 'kick', 'snare', 'downbeat', or 'general'")
    
    logger.debug(f"Final result: {len(result)} {beat_type} beats detected")
    
    return result

def repeat_audio_ffmpeg(input_audio, output_audio, repeat: int):
    """
    Repeat audio multiple times, removing leading/trailing silence before repeating.
    Automatically determines the correct output format based on the file extension.
    
    Args:
        input_audio: Path to input audio file
        output_audio: Path to output audio file (extension determines format)
        repeat: Number of times to repeat the audio
        
    Returns:
        str: Path to the output file (may be modified if extension was incompatible)
    """
    
    # Determine output format and codec from extension
    output_ext = os.path.splitext(output_audio)[1].lower()
    output_base = os.path.splitext(output_audio)[0]
    
    # Map extensions to appropriate codec and container
    format_map = {
        '.mp3': {'codec': 'libmp3lame', 'bitrate': '192k'},
        '.m4a': {'codec': 'aac', 'bitrate': '192k'},
        '.aac': {'codec': 'aac', 'bitrate': '192k'},
        '.opus': {'codec': 'libopus', 'bitrate': '128k'},
        '.ogg': {'codec': 'libvorbis', 'bitrate': '192k'},
        '.wav': {'codec': 'pcm_s16le', 'bitrate': None},
    }
    
    # Default to m4a if extension not recognized or if using aac with mp3
    if output_ext not in format_map:
        output_ext = '.m4a'
        output_audio = output_base + output_ext
        logger.debug(f"Unknown format, defaulting to: {output_audio}")
    
    audio_config = format_map[output_ext]
    
    # Create a temporary file for the silence-trimmed audio (use same format)
    with tempfile.NamedTemporaryFile(suffix=output_ext, delete=False) as tmp:
        temp_trimmed = tmp.name
    
    try:
        # Step 1: Remove leading AND trailing silence from the original audio
        trim_cmd = [
            "ffmpeg", "-y",
            "-i", input_audio,
            "-af", "silenceremove=start_periods=1:start_threshold=-50dB:start_duration=0:stop_periods=-1:stop_threshold=-50dB:stop_duration=0",
            "-c:a", audio_config['codec']
        ]
        
        # Add bitrate if applicable (not for WAV)
        if audio_config['bitrate']:
            trim_cmd.extend(["-b:a", audio_config['bitrate']])
            
        trim_cmd.append(temp_trimmed)
        
        result = subprocess.run(trim_cmd, check=True, capture_output=True, text=True)
        
        # Step 2: Repeat the trimmed audio
        repeat_cmd = [
            "ffmpeg", "-y",
            "-stream_loop", str(repeat - 1),
            "-i", temp_trimmed,
            "-c:a", audio_config['codec']
        ]
        
        # Add bitrate if applicable
        if audio_config['bitrate']:
            repeat_cmd.extend(["-b:a", audio_config['bitrate']])
            
        repeat_cmd.append(output_audio)
        
        result = subprocess.run(repeat_cmd, check=True, capture_output=True, text=True)
        
        logger.debug(f"Successfully repeated audio {repeat} times, output: {output_audio}")
        
        return output_audio
        
    except subprocess.CalledProcessError as e:
        logger.error(f"FFmpeg error: STDOUT={e.stdout}, STDERR={e.stderr}")
        raise
        
    finally:
        # Clean up temporary file
        if os.path.exists(temp_trimmed):
            os.remove(temp_trimmed)

def clean_and_drop_empty(
    df: pd.DataFrame,
    column: str,
    extra_nulls: list[str] | None = None,
) -> pd.DataFrame:
    """
    Normalize Google Sheets empty values and drop rows
    where `column` is effectively empty.

    Handles:
    - NaN
    - ""
    - " "
    - "nan", "None", "NULL", "N/A"

    Args:
        df: Input DataFrame
        column: Column to validate (e.g. "VIDEO_LINK")
        extra_nulls: Optional extra string values to treat as null

    Returns:
        Cleaned DataFrame with valid rows only
    """

    if column not in df.columns:
        raise KeyError(f"Column '{column}' not found in DataFrame")

    null_values = ["", "nan", "none", "null", "n/a"]
    if extra_nulls:
        null_values.extend([v.lower() for v in extra_nulls])

    df = df.copy()

    df[column] = (
        df[column]
        .astype(str)
        .str.strip()
        # .str.lower()
        .replace(null_values, np.nan)
    )

    return df.dropna(subset=[column])

def is_valid_video(path: str) -> bool:
    if not os.path.exists(path):
        return False
    if os.path.getsize(path) < 100 * 1024:  # <100KB = almost certainly invalid
        return False
    return True