import subprocess
import tempfile, os
import ffmpeg
import torchvision.transforms.functional as TF
import torch.nn.functional as F
import cv2
import tempfile
import imageio
import binascii
import torchvision
import torch
from PIL import Image
import os.path as osp
import json
import numpy as np
import soundfile as sf

def rand_name(length=8, suffix=''):
    name = binascii.b2a_hex(os.urandom(length)).decode('utf-8')
    if suffix:
        if not suffix.startswith('.'):
            suffix = '.' + suffix
        name += suffix
    return name


def _prepare_audio_array(audio_data):
    if torch.is_tensor(audio_data):
        audio_data = audio_data.detach().cpu().float().numpy()
    else:
        audio_data = np.asarray(audio_data, dtype=np.float32)
    if audio_data.ndim == 2 and audio_data.shape[0] <= 8 and audio_data.shape[1] > audio_data.shape[0]:
        audio_data = audio_data.T
    return audio_data


def write_wav_file(path, audio_data, sample_rate):
    audio_array = _prepare_audio_array(audio_data)
    sf.write(path, audio_array, int(sample_rate))
    return path


def _get_audio_codec_settings(codec_key):
    if not codec_key:
        codec_key = "wav"
    codec_key = str(codec_key).lower()
    if codec_key == "mp3":
        codec_key = "mp3_192"
    settings = {
        "wav": {"ext": "wav", "format": "wav"},
        "mp3_128": {"ext": "mp3", "format": "mp3", "bitrate": "128k"},
        "mp3_192": {"ext": "mp3", "format": "mp3", "bitrate": "192k"},
        "mp3_320": {"ext": "mp3", "format": "mp3", "bitrate": "320k"},
    }
    return settings.get(codec_key, settings["wav"])


def get_audio_codec_extension(codec_key):
    return _get_audio_codec_settings(codec_key)["ext"]


def _run_ffmpeg_encode(input_path, output_path, codec, bitrate=None, sample_rate=None, drop_video=False):
    cmd = ["ffmpeg", "-y", "-v", "error", "-i", input_path]
    if drop_video:
        cmd.append("-vn")
    cmd += ["-c:a", codec]
    if bitrate:
        cmd += ["-b:a", bitrate]
    if sample_rate:
        cmd += ["-ar", str(int(sample_rate))]
    cmd.append(output_path)
    subprocess.run(cmd, check=True, capture_output=True, text=True)


def save_audio_file(path, audio_data, sample_rate, codec_key="wav"):
    settings = _get_audio_codec_settings(codec_key)
    ext = settings["ext"]
    if not path.lower().endswith(f".{ext}"):
        path = osp.splitext(path)[0] + f".{ext}"
    if settings["format"] == "wav":
        return write_wav_file(path, audio_data, sample_rate)
    fd, tmp_path = tempfile.mkstemp(suffix=".wav", prefix="audio_")
    os.close(fd)
    try:
        write_wav_file(tmp_path, audio_data, sample_rate)
        _run_ffmpeg_encode(tmp_path, path, "libmp3lame", bitrate=settings.get("bitrate"), sample_rate=sample_rate)
    finally:
        try:
            os.remove(tmp_path)
        except OSError:
            pass
    return path


def extract_audio_track_to_wav(video_path, output_path):
    if not video_path:
        return None
    video_path = os.fspath(video_path)
    import ffmpeg
    ffmpeg.input(video_path).output(output_path, **{"map": "0:a:0", "acodec": "pcm_s16le"}).overwrite_output().run(quiet=True)
    return output_path



def extract_audio_tracks(source_video, verbose=False, query_only=False):
    """
    Extract all audio tracks from a source video into temporary AAC files.

    Returns:
        Tuple:
          - List of temp file paths for extracted audio tracks
          - List of corresponding metadata dicts:
              {'codec', 'sample_rate', 'channels', 'duration', 'language'}
              where 'duration' is set to container duration (for consistency).
    """
    if not os.path.exists(source_video):
        msg = f"ffprobe skipped; file not found: {source_video}"
        if verbose:
            print(msg)
        raise FileNotFoundError(msg)

    try:
        probe = ffmpeg.probe(source_video)
    except ffmpeg.Error as err:
        stderr = getattr(err, 'stderr', b'')
        if isinstance(stderr, (bytes, bytearray)):
            stderr = stderr.decode('utf-8', errors='ignore')
        stderr = (stderr or str(err)).strip()
        message = f"ffprobe failed for {source_video}: {stderr}"
        if verbose:
            print(message)
        raise RuntimeError(message) from err
    audio_streams = [s for s in probe['streams'] if s['codec_type'] == 'audio']
    container_duration = float(probe['format'].get('duration', 0.0))

    if not audio_streams:
        if query_only: return 0
        if verbose: print(f"No audio track found in {source_video}")
        return [], []

    if query_only:
        return len(audio_streams)

    if verbose:
        print(f"Found {len(audio_streams)} audio track(s), container duration = {container_duration:.3f}s")

    file_paths = []
    metadata = []

    for i, stream in enumerate(audio_streams):
        fd, temp_path = tempfile.mkstemp(suffix=f'_track{i}.aac', prefix='audio_')
        os.close(fd)

        file_paths.append(temp_path)
        metadata.append({
            'codec': stream.get('codec_name'),
            'sample_rate': int(stream.get('sample_rate', 0)),
            'channels': int(stream.get('channels', 0)),
            'duration': container_duration,
            'language': stream.get('tags', {}).get('language', None)
        })

        ffmpeg.input(source_video).output(
            temp_path,
            **{f'map': f'0:a:{i}', 'acodec': 'aac', 'b:a': '128k'}
        ).overwrite_output().run(quiet=not verbose)

    return file_paths, metadata



def combine_and_concatenate_video_with_audio_tracks(
    save_path_tmp, video_path,
    source_audio_tracks, new_audio_tracks,
    source_audio_duration, audio_sampling_rate,
    new_audio_from_start=False,
    source_audio_metadata=None,
    audio_bitrate='128k',
    audio_codec='aac',
    verbose = False
):
    inputs, filters, maps, idx = ['-i', video_path], [], ['-map', '0:v'], 1
    metadata_args = []
    sources = source_audio_tracks or []
    news = new_audio_tracks or []

    duplicate_source = len(sources) == 1 and len(news) > 1
    N = len(news) if source_audio_duration == 0 else max(len(sources), len(news)) or 1

    for i in range(N):
        s = (sources[i] if i < len(sources)
             else sources[0] if duplicate_source else None)
        n = news[i] if len(news) == N else (news[0] if news else None)

        if source_audio_duration == 0:
            if n:
                inputs += ['-i', n]
                filters.append(f'[{idx}:a]apad=pad_dur=100[aout{i}]')
                idx += 1
            else:
                filters.append(f'anullsrc=r={audio_sampling_rate}:cl=mono,apad=pad_dur=100[aout{i}]')
        else:
            if s:
                inputs += ['-i', s]
                meta = source_audio_metadata[i] if source_audio_metadata and i < len(source_audio_metadata) else {}
                needs_filter = (
                    meta.get('codec') != audio_codec or
                    meta.get('sample_rate') != audio_sampling_rate or
                    meta.get('channels') != 1 or
                    meta.get('duration', 0) < source_audio_duration
                )
                if needs_filter:
                    filters.append(
                        f'[{idx}:a]aresample={audio_sampling_rate},aformat=channel_layouts=mono,'
                        f'apad=pad_dur={source_audio_duration},atrim=0:{source_audio_duration},asetpts=PTS-STARTPTS[s{i}]')
                else:
                    filters.append(
                        f'[{idx}:a]apad=pad_dur={source_audio_duration},atrim=0:{source_audio_duration},asetpts=PTS-STARTPTS[s{i}]')
                if lang := meta.get('language'):
                    metadata_args += ['-metadata:s:a:' + str(i), f'language={lang}']
                idx += 1
            else:
                filters.append(
                    f'anullsrc=r={audio_sampling_rate}:cl=mono,atrim=0:{source_audio_duration},asetpts=PTS-STARTPTS[s{i}]')

            if n:
                inputs += ['-i', n]
                start = '0' if new_audio_from_start else source_audio_duration
                filters.append(
                    f'[{idx}:a]aresample={audio_sampling_rate},aformat=channel_layouts=mono,'
                    f'atrim=start={start},asetpts=PTS-STARTPTS[n{i}]')
                filters.append(f'[s{i}][n{i}]concat=n=2:v=0:a=1[aout{i}]')
                idx += 1
            else:
                filters.append(f'[s{i}]apad=pad_dur=100[aout{i}]')

        maps += ['-map', f'[aout{i}]']

    cmd = ['ffmpeg', '-y', *inputs,
           '-filter_complex', ';'.join(filters),  # ✅ Only change made
           *maps, *metadata_args,
           '-c:v', 'copy',
           '-c:a', audio_codec,
           '-b:a', audio_bitrate,
           '-ar', str(audio_sampling_rate),
           '-ac', '1',
           '-shortest', save_path_tmp]

    if verbose:
        print(f"ffmpeg command: {cmd}")
    try:
        subprocess.run(cmd, check=True, capture_output=True, text=True)
    except subprocess.CalledProcessError as e:
        raise Exception(f"FFmpeg error: {e.stderr}")


def combine_video_with_audio_tracks(target_video, audio_tracks, output_video,
                                     audio_metadata=None, verbose=False):
    if not audio_tracks:
        if verbose: print("No audio tracks to combine."); return False

    dur = float(next(s for s in ffmpeg.probe(target_video)['streams']
                     if s['codec_type'] == 'video')['duration'])
    if verbose: print(f"Video duration: {dur:.3f}s")

    cmd = ['ffmpeg', '-y', '-i', target_video]
    for path in audio_tracks:
        cmd += ['-i', path]

    cmd += ['-map', '0:v']
    for i in range(len(audio_tracks)):
        cmd += ['-map', f'{i+1}:a']

    for i, meta in enumerate(audio_metadata or []):
        if (lang := meta.get('language')):
            cmd += ['-metadata:s:a:' + str(i), f'language={lang}']

    cmd += ['-c:v', 'copy', '-c:a', 'copy', '-t', str(dur), output_video]

    result = subprocess.run(cmd, capture_output=not verbose, text=True)
    if result.returncode != 0:
        raise Exception(f"FFmpeg error:\n{result.stderr}")
    if verbose:
        print(f"Created {output_video} with {len(audio_tracks)} audio track(s)")
    return True


def cleanup_temp_audio_files(audio_tracks, verbose=False):
    """
    Clean up temporary audio files.
    
    Args:
        audio_tracks: List of audio file paths to delete
        verbose: Enable verbose output (default: False)
        
    Returns:
        Number of files successfully deleted
    """
    deleted_count = 0
    
    for audio_path in audio_tracks:
        try:
            if os.path.exists(audio_path):
                os.unlink(audio_path)
                deleted_count += 1
                if verbose:
                    print(f"Cleaned up {audio_path}")
        except PermissionError:
            print(f"Warning: Could not delete {audio_path} (file may be in use)")
        except Exception as e:
            print(f"Warning: Error deleting {audio_path}: {e}")
    
    if verbose and deleted_count > 0:
        print(f"Successfully deleted {deleted_count} temporary audio file(s)")
    
    return deleted_count


def save_video(tensor,
                save_file=None,
                fps=30,
                codec_type='libx264_8',
                container='mp4',
                nrow=8,
                normalize=True,
                value_range=(-1, 1),
                retry=5):
    """Save tensor as video with configurable codec and container options."""
        
    if torch.is_tensor(tensor) and len(tensor.shape) == 4:
        tensor = tensor.unsqueeze(0)
        
    suffix = f'.{container}'
    cache_file = osp.join('/tmp', rand_name(suffix=suffix)) if save_file is None else save_file
    if not cache_file.endswith(suffix):
        cache_file = osp.splitext(cache_file)[0] + suffix
    
    # Configure codec parameters
    codec_params = _get_codec_params(codec_type, container)
    
    # Process and save
    error = None
    for _ in range(retry):
        try:
            # Write video (silence ffmpeg logs)
            writer = imageio.get_writer(cache_file, fps=fps, ffmpeg_log_level='error', **codec_params)
            try:
                if torch.is_tensor(tensor):
                    # Stream frames to avoid materializing the full video on CPU.
                    if tensor.dtype == torch.uint8 and tensor.ndim == 5 and tensor.shape[0] == 1 and nrow == 1:
                        frames = tensor[0].permute(1, 2, 3, 0)
                        for frame in frames:
                            writer.append_data(frame.cpu().numpy())
                    else:
                        if tensor.dtype == torch.uint8:
                            tensor = tensor.float().div_(127.5).sub_(1.0)
                        for u in tensor.unbind(2):
                            u = u.clamp(min(value_range), max(value_range))
                            grid = torchvision.utils.make_grid(
                                u, nrow=nrow, normalize=normalize, value_range=value_range
                            )
                            frame = grid.mul(255).type(torch.uint8).permute(1, 2, 0).cpu().numpy()
                            writer.append_data(frame)
                elif isinstance(tensor, (list, tuple)) and tensor and torch.is_tensor(tensor[0]):
                    for chunk in tensor:
                        if chunk is None:
                            continue
                        if chunk.ndim == 4:
                            if chunk.shape[-1] in (1, 3, 4):
                                frames = chunk
                            else:
                                frames = chunk.permute(1, 2, 3, 0)
                            for frame in frames:
                                writer.append_data(frame.cpu().numpy())
                        else:
                            writer.append_data(chunk)
                else:
                    for frame in tensor:
                        writer.append_data(frame)
            finally:
                writer.close()

            return cache_file

        except Exception as e:
            error = e
            print(f"error saving {save_file}: {e}")


def _get_codec_params(codec_type, container):
    """Get codec parameters based on codec type and container."""
    if codec_type == 'libx264_8':
        return {'codec': 'libx264', 'quality': 8, 'pixelformat': 'yuv420p'}
    elif codec_type == 'libx264_10':
        return {'codec': 'libx264', 'quality': 10, 'pixelformat': 'yuv420p'}
    elif codec_type == 'libx265_28':
        return {'codec': 'libx265', 'pixelformat': 'yuv420p', 'output_params': ['-crf', '28', '-x265-params', 'log-level=none','-hide_banner', '-nostats']}
    elif codec_type == 'libx265_8':
        return {'codec': 'libx265', 'pixelformat': 'yuv420p', 'output_params': ['-crf', '8', '-x265-params', 'log-level=none','-hide_banner', '-nostats']}
    elif codec_type == 'libx264_lossless':
        if container == 'mkv':
            return {'codec': 'ffv1', 'pixelformat': 'rgb24'}
        else:  # mp4
            return {'codec': 'libx264', 'output_params': ['-crf', '0'], 'pixelformat': 'yuv444p'}
    else:  # libx264
        return {'codec': 'libx264', 'pixelformat': 'yuv420p'}




def save_image(tensor,
                save_file,
                nrow=8,
                normalize=True,
                value_range=(-1, 1),
                quality='jpeg_95',  # 'jpeg_95', 'jpeg_85', 'jpeg_70', 'jpeg_50', 'webp_95', 'webp_85', 'webp_70', 'webp_50', 'png', 'webp_lossless'
                retry=5):
    """Save tensor as image with configurable format and quality."""

    RGBA = tensor.shape[0] == 4
    if RGBA:
        quality = "png"

    # Get format and quality settings
    format_info = _get_format_info(quality)
    
    # Rename file extension to match requested format
    save_file = osp.splitext(save_file)[0] + format_info['ext']
    
    # Save image
    error = None
                         
    for _ in range(retry):
        try:
            tensor = tensor.clamp(min(value_range), max(value_range))
            
            if format_info['use_pil'] or RGBA:
                # Use PIL for WebP and advanced options
                grid = torchvision.utils.make_grid(tensor, nrow=nrow, normalize=normalize, value_range=value_range)
                # Convert to PIL Image
                grid = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
                mode = 'RGBA' if RGBA else 'RGB'
                img = Image.fromarray(grid, mode=mode)
                img.save(save_file, **format_info['params'])
            else:
                # Use torchvision for JPEG and PNG
                torchvision.utils.save_image(
                    tensor, save_file, nrow=nrow, normalize=normalize, 
                    value_range=value_range, **format_info['params']
                )
            break
        except Exception as e:
            error = e
            continue
    else:
        print(f'cache_image failed, error: {error}', flush=True)
    
    return save_file


def _get_format_info(quality):
    """Get format extension and parameters."""
    formats = {
        # JPEG with PIL (so 'quality' works)
        'jpeg_95': {'ext': '.jpg', 'params': {'quality': 95}, 'use_pil': True},
        'jpeg_85': {'ext': '.jpg', 'params': {'quality': 85}, 'use_pil': True},
        'jpeg_70': {'ext': '.jpg', 'params': {'quality': 70}, 'use_pil': True},
        'jpeg_50': {'ext': '.jpg', 'params': {'quality': 50}, 'use_pil': True},

        # PNG with torchvision
        'png': {'ext': '.png', 'params': {}, 'use_pil': False},

        # WebP with PIL (for quality control)
        'webp_95': {'ext': '.webp', 'params': {'quality': 95}, 'use_pil': True},
        'webp_85': {'ext': '.webp', 'params': {'quality': 85}, 'use_pil': True},
        'webp_70': {'ext': '.webp', 'params': {'quality': 70}, 'use_pil': True},
        'webp_50': {'ext': '.webp', 'params': {'quality': 50}, 'use_pil': True},
        'webp_lossless': {'ext': '.webp', 'params': {'lossless': True}, 'use_pil': True},
    }
    return formats.get(quality, formats['jpeg_95'])


from PIL import Image, PngImagePlugin

def _enc_uc(s):
    try: return b"ASCII\0\0\0" + s.encode("ascii")
    except UnicodeEncodeError: return b"UNICODE\0" + s.encode("utf-16le")

def _dec_uc(b):
    if not isinstance(b, (bytes, bytearray)):
        try: b = bytes(b)
        except Exception: return None
    if b.startswith(b"ASCII\0\0\0"): return b[8:].decode("ascii", "ignore")
    if b.startswith(b"UNICODE\0"):   return b[8:].decode("utf-16le", "ignore")
    return b.decode("utf-8", "ignore")

def save_image_metadata(image_path, metadata_dict, **save_kwargs):
    try:
        j = json.dumps(metadata_dict, ensure_ascii=False)
        ext = os.path.splitext(image_path)[1].lower()
        with Image.open(image_path) as im:
            if ext == ".png":
                pi = PngImagePlugin.PngInfo(); pi.add_text("comment", j)
                im.save(image_path, pnginfo=pi, **save_kwargs); return True
            if ext in (".jpg", ".jpeg"):
                im.save(image_path, comment=j.encode("utf-8"), **save_kwargs); return True
            if ext == ".webp":
                import piexif
                exif = {"0th":{}, "Exif":{piexif.ExifIFD.UserComment:_enc_uc(j)}, "GPS":{}, "1st":{}, "thumbnail":None}
                im.save(image_path, format="WEBP", exif=piexif.dump(exif), **save_kwargs); return True
            raise ValueError("Unsupported format")
    except Exception as e:
        print(f"Error saving metadata: {e}"); return False

def read_image_metadata(image_path):
    try:
        ext = os.path.splitext(image_path)[1].lower()
        with Image.open(image_path) as im:
            if ext == ".png":
                val = (getattr(im, "text", {}) or {}).get("comment") or im.info.get("comment")
                return json.loads(val) if val else None
            if ext in (".jpg", ".jpeg"):
                val = im.info.get("comment")
                if isinstance(val, (bytes, bytearray)): val = val.decode("utf-8", "ignore")
                if val:
                    try: return json.loads(val)
                    except Exception: pass
                exif = getattr(im, "getexif", lambda: None)()
                if exif:
                    uc = exif.get(37510)  # UserComment
                    s = _dec_uc(uc) if uc else None
                    if s:
                        try: return json.loads(s)
                        except Exception: pass
                return None
            if ext == ".webp":
                exif_bytes = Image.open(image_path).info.get("exif")
                if not exif_bytes: return None
                import piexif
                uc = piexif.load(exif_bytes).get("Exif", {}).get(piexif.ExifIFD.UserComment)
                s = _dec_uc(uc) if uc else None
                return json.loads(s) if s else None
            return None
    except Exception as e:
        print(f"Error reading metadata: {e}"); return None