from __future__ import annotations

import logging
import os
import re
import shlex
from shutil import copyfile, rmtree
from tempfile import mkdtemp, mkstemp
from typing import TYPE_CHECKING, Iterable, Iterator, Literal, TypedDict, Union

from mutagen.id3 import ID3, TXXX
from mutagen.mp3 import MP3
from mutagen.mp4 import MP4
from mutagen.oggopus import OggOpus
from mutagen.oggvorbis import OggVorbis
from tqdm import tqdm

from ._cmd_utils import DUR_REGEX, CommandRunner
from ._errors import FFmpegNormalizeError
from ._streams import (
    AudioStream,
    LoudnessStatisticsWithMetadata,
    SubtitleStream,
    VideoStream,
)

if TYPE_CHECKING:
    from ffmpeg_normalize import FFmpegNormalize

_logger = logging.getLogger(__name__)

# Note: this does not contain MP3, see https://github.com/slhck/ffmpeg-normalize/issues/246
# We may need to remove other formats as well, to be checked.
AUDIO_ONLY_FORMATS = {"aac", "ast", "flac", "mka", "oga", "ogg", "opus", "wav"}
ONE_STREAM = {"aac", "ast", "flac", "mp3", "wav"}

TQDM_BAR_FORMAT = "{desc}: {percentage:3.2f}% |{bar}{r_bar}"


def _to_ms(**kwargs: str) -> int:
    hour = int(kwargs.get("hour", 0))
    minute = int(kwargs.get("min", 0))
    sec = int(kwargs.get("sec", 0))
    ms = int(kwargs.get("ms", 0))

    return (hour * 60 * 60 * 1000) + (minute * 60 * 1000) + (sec * 1000) + ms


class StreamDict(TypedDict):
    audio: dict[int, AudioStream]
    video: dict[int, VideoStream]
    subtitle: dict[int, SubtitleStream]


class MediaFile:
    """
    Class that holds a file, its streams and adjustments
    """

    def __init__(
        self, ffmpeg_normalize: FFmpegNormalize, input_file: str, output_file: str
    ):
        """
        Initialize a media file for later normalization by parsing the streams.

        Args:
            ffmpeg_normalize (FFmpegNormalize): reference to overall settings
            input_file (str): Path to input file
            output_file (str): Path to output file
        """
        self.ffmpeg_normalize = ffmpeg_normalize
        self.skip = False
        # Per-file outcome, reported in the --print-stats output: "normalized"
        # (default), "skipped" (already within threshold of target), or "error".
        # "error" is set by FFmpegNormalize.run_normalization when processing
        # fails; on failure, self.error holds the error message.
        self.status: str = "normalized"
        self.error: str | None = None
        self.input_file = input_file
        self.output_file = output_file
        current_ext = os.path.splitext(output_file)[1][1:]
        # we need to check if it's empty, e.g. /dev/null or NUL
        if current_ext == "" or self.output_file == os.devnull:
            _logger.debug(
                f"Current extension is unset, or output file is a null device, using extension: {self.ffmpeg_normalize.extension}"
            )
            self.output_ext = self.ffmpeg_normalize.extension
        else:
            _logger.debug(
                f"Current extension is set from output file, using extension: {current_ext}"
            )
            self.output_ext = current_ext
        self.streams: StreamDict = {"audio": {}, "video": {}, "subtitle": {}}
        self.temp_file: Union[str, None] = None
        self.batch_reference: float | None = None
        # Input (access, modification) times captured before processing, used
        # when the keep_mtime option is enabled.
        self.input_timestamps: tuple[float, float] | None = None

        self.parse_streams()

    def _stream_ids(self) -> list[int]:
        """
        Get all stream IDs of this file.

        Returns:
            list: List of stream IDs
        """
        return (
            list(self.streams["audio"].keys())
            + list(self.streams["video"].keys())
            + list(self.streams["subtitle"].keys())
        )

    def __repr__(self) -> str:
        return os.path.basename(self.input_file)

    def parse_streams(self) -> None:
        """
        Try to parse all input streams from file and set them in self.streams.

        Raises:
            FFmpegNormalizeError: If no audio streams are found
        """
        _logger.debug(f"Parsing streams of {self.input_file}")

        cmd = [
            self.ffmpeg_normalize.ffmpeg_exe,
            "-i",
            self.input_file,
            "-c",
            "copy",
            "-t",
            "0",
            "-map",
            "0",
            "-f",
            "null",
            os.devnull,
        ]

        output = CommandRunner().run_command(cmd).get_output()

        _logger.debug("Stream parsing command output:")
        _logger.debug(output)

        output_lines = [line.strip() for line in output.split("\n")]

        # First pass: parse disposition flags for each stream
        stream_dispositions: dict[int, bool] = {}

        for line in output_lines:
            if line.startswith("Stream"):
                if stream_id_match := re.search(r"#0:([\d]+)", line):
                    stream_id = int(stream_id_match.group(1))
                    # Check if (default) appears on the Stream line
                    is_default = "(default)" in line
                    stream_dispositions[stream_id] = is_default

        # Second pass: parse stream information
        duration = None
        for line in output_lines:
            if "Duration" in line:
                if duration_search := DUR_REGEX.search(line):
                    duration = _to_ms(**duration_search.groupdict()) / 1000
                    _logger.debug(f"Found duration: {duration} s")
                else:
                    _logger.warning("Could not extract duration from input file!")

            if not line.startswith("Stream"):
                continue

            if stream_id_match := re.search(r"#0:([\d]+)", line):
                stream_id = int(stream_id_match.group(1))
                if stream_id in self._stream_ids():
                    continue
            else:
                continue

            is_default = stream_dispositions.get(stream_id, False)

            if "Audio" in line:
                _logger.debug(
                    f"Found audio stream at index {stream_id} (default: {is_default})"
                )
                sample_rate_match = re.search(r"(\d+) Hz", line)
                sample_rate = (
                    int(sample_rate_match.group(1)) if sample_rate_match else None
                )
                bit_depth_match = re.search(r"([sfu])(\d+)(p|le|be)?", line)
                bit_depth = int(bit_depth_match.group(2)) if bit_depth_match else None
                is_float = bit_depth_match.group(1) == "f" if bit_depth_match else False
                self.streams["audio"][stream_id] = AudioStream(
                    self.ffmpeg_normalize,
                    self,
                    stream_id,
                    sample_rate,
                    bit_depth,
                    duration,
                    is_default,
                    is_float,
                )

            elif "Video" in line:
                _logger.debug(f"Found video stream at index {stream_id}")
                self.streams["video"][stream_id] = VideoStream(
                    self.ffmpeg_normalize, self, stream_id
                )

            elif "Subtitle" in line:
                _logger.debug(f"Found subtitle stream at index {stream_id}")
                self.streams["subtitle"][stream_id] = SubtitleStream(
                    self.ffmpeg_normalize, self, stream_id
                )

        if not self.streams["audio"]:
            raise FFmpegNormalizeError(
                f"Input file {self.input_file} does not contain any audio streams"
            )

        if (
            self.output_ext.lower() in ONE_STREAM
            and len(self.streams["audio"].values()) > 1
        ):
            _logger.warning(
                "Output file only supports one stream. Keeping only first audio stream."
            )
            first_stream = list(self.streams["audio"].values())[0]
            self.streams["audio"] = {first_stream.stream_id: first_stream}
            self.streams["video"] = {}
            self.streams["subtitle"] = {}

    def _get_streams_to_normalize(self) -> list[AudioStream]:
        """
        Determine which audio streams to normalize based on configuration.

        Returns:
            list[AudioStream]: List of audio streams to normalize
        """
        all_audio_streams = list(self.streams["audio"].values())

        if self.ffmpeg_normalize.audio_streams is not None:
            # User specified specific stream indices
            selected_streams = [
                stream
                for stream in all_audio_streams
                if stream.stream_id in self.ffmpeg_normalize.audio_streams
            ]
            if not selected_streams:
                _logger.warning(
                    f"No audio streams found matching indices {self.ffmpeg_normalize.audio_streams}. "
                    f"Available streams: {[s.stream_id for s in all_audio_streams]}"
                )
            else:
                _logger.info(
                    f"Normalizing selected audio streams: {[s.stream_id for s in selected_streams]}"
                )
            return selected_streams

        elif self.ffmpeg_normalize.audio_default_only:
            # Only normalize streams with default disposition
            default_streams = [
                stream for stream in all_audio_streams if stream.is_default
            ]
            if not default_streams:
                _logger.warning(
                    "No audio streams with 'default' disposition found. "
                    f"Available streams: {[s.stream_id for s in all_audio_streams]}"
                )
            else:
                _logger.info(
                    f"Normalizing default audio streams: {[s.stream_id for s in default_streams]}"
                )
            return default_streams

        else:
            # Normalize all streams (default behavior)
            return all_audio_streams

    def run_normalization(self, batch_reference: float | None = None) -> None:
        """
        Run the normalization process for this file.

        Args:
            batch_reference (float | None, optional): Reference loudness for batch mode.
                If provided, the first pass is skipped (assumed already done) and this
                reference is used to calculate relative adjustments. Defaults to None.
        """
        _logger.debug(f"Running normalization for {self.input_file}")

        # Store batch reference for use in second pass
        self.batch_reference = batch_reference

        # Capture the input file's timestamps before processing, since an
        # in-place overwrite would otherwise lose the original modification time.
        if self.ffmpeg_normalize.keep_mtime:
            try:
                stat = os.stat(self.input_file)
                self.input_timestamps = (stat.st_atime, stat.st_mtime)
            except OSError as e:
                _logger.warning(
                    f"Could not read timestamps from {self.input_file}: {e}"
                )

        # run the first pass to get loudness stats, unless in dynamic EBU mode or batch mode
        # (in batch mode, first pass is already done in FFmpegNormalize.run_normalization)
        if batch_reference is None:
            if not (
                self.ffmpeg_normalize.dynamic
                and self.ffmpeg_normalize.normalization_type == "ebu"
            ):
                self._first_pass()
            else:
                _logger.debug(
                    "Dynamic EBU mode: First pass will not run, as it is not needed."
                )
        else:
            _logger.debug(
                f"Batch mode: Skipping first pass (already completed), using batch reference = {batch_reference:.2f}"
            )

        # If the file is already within the configured threshold of the target
        # level, skip normalization entirely and copy the input through to the
        # output unchanged. This avoids needless re-encoding of files that are
        # already at the target level.
        if self._is_within_threshold():
            self._handle_skip()
            return

        temp_dir = None

        if self.ffmpeg_normalize.replaygain:
            _logger.debug(
                "ReplayGain mode: Second pass will run with temporary file to get stats."
            )
            temp_dir = mkdtemp()
            self.temp_file = os.path.join(temp_dir, f"out.{self.output_ext}")
            self.output_file = self.temp_file

        # run the second pass as a whole.
        if self.ffmpeg_normalize.progress:
            with tqdm(
                total=100,
                position=1,
                desc="Second Pass",
                bar_format=TQDM_BAR_FORMAT,
            ) as pbar:
                for progress in self._second_pass():
                    pbar.update(progress - pbar.n)
        else:
            for _ in self._second_pass():
                pass

        # remove temp dir; this will remove the temp file as well if it has not been renamed (e.g. for replaygain)
        if temp_dir and os.path.exists(temp_dir):
            rmtree(temp_dir, ignore_errors=True)

        # This will use stats from ebu_pass2 if available (from the main second pass),
        # or fall back to ebu_pass1.
        if self.ffmpeg_normalize.replaygain:
            _logger.debug(
                "ReplayGain tagging is enabled. Proceeding with tag calculation/application."
            )
            self._run_replaygain()
        else:
            # Strip any existing ReplayGain tags from the output file
            # since they are no longer accurate after normalization
            self._strip_replaygain_tags(self.output_file)
            # Copy input timestamps last, after any tag modifications, so the
            # output ends up with the original modification time.
            if self.ffmpeg_normalize.keep_mtime and not self.ffmpeg_normalize.dry_run:
                self._apply_input_timestamps()
            _logger.info(f"Normalized file written to {self.output_file}")

    def _is_within_threshold(self) -> bool:
        """
        Return whether every stream selected for normalization is already
        within the configured threshold of the target level, meaning the file
        can be copied through unchanged instead of being re-normalized.

        The check compares the measured level against the absolute target level
        (integrated loudness for EBU, peak/RMS level otherwise). It is disabled
        when the threshold is zero or less, in batch mode (where files are
        adjusted relative to a shared reference rather than an absolute target),
        in ReplayGain mode (which only writes tags), when a pre/post filter or
        channel downmix is requested (since these change the audio), and when
        the output extension differs from the input (since the file is copied
        verbatim, a container change would otherwise produce an invalid file).

        Returns:
            bool: True if the file should be skipped, False otherwise.
        """
        threshold = self.ffmpeg_normalize.threshold
        if threshold <= 0:
            return False

        if self.ffmpeg_normalize.batch or self.ffmpeg_normalize.replaygain:
            return False

        if (
            self.ffmpeg_normalize.pre_filter
            or self.ffmpeg_normalize.post_filter
            or self.ffmpeg_normalize.audio_channels
        ):
            return False

        # A skipped file is copied verbatim, which only yields a valid file when
        # the container stays the same. If the output extension differs, fall
        # back to normal normalization so the requested format is produced.
        input_ext = os.path.splitext(self.input_file)[1][1:].lower()
        if input_ext != self.output_ext.lower():
            return False

        norm_type = self.ffmpeg_normalize.normalization_type
        target = self.ffmpeg_normalize.target_level
        streams = self._get_streams_to_normalize()
        if not streams:
            return False

        for stream in streams:
            measured: float | None
            if norm_type == "ebu":
                ebu_stats = stream.loudness_statistics["ebu_pass1"]
                if ebu_stats is None:
                    return False
                measured = ebu_stats["input_i"]
            elif norm_type == "peak":
                measured = stream.loudness_statistics["max"]
            else:  # rms
                measured = stream.loudness_statistics["mean"]

            if measured is None:
                return False
            if abs(target - float(measured)) > threshold:
                return False

        return True

    def _handle_skip(self) -> None:
        """
        Mark this file as skipped (already at target) and copy the input
        through to the output unchanged.

        The input is copied verbatim (codec and other output options are not
        applied to skipped files; use a threshold of 0 to always re-encode).
        Stale ReplayGain tags are still stripped from the output, and the input
        modification time is preserved if requested.
        """
        self.status = "skipped"
        _logger.info(
            f"{self.input_file}: already within {self.ffmpeg_normalize.threshold} "
            f"of target level {self.ffmpeg_normalize.target_level}, "
            "skipping normalization"
        )

        if self.ffmpeg_normalize.dry_run:
            _logger.warning("Dry run used, not actually copying the file")
            return

        if self.output_file == os.devnull:
            return

        if os.path.realpath(self.input_file) != os.path.realpath(self.output_file):
            try:
                copyfile(self.input_file, self.output_file)
            except OSError as e:
                raise FFmpegNormalizeError(
                    f"Could not copy {self.input_file} to {self.output_file}: {e}"
                )
        else:
            _logger.debug(
                "Output file is the same as the input file, leaving it unchanged"
            )

        # Remove any existing ReplayGain tags from the output, matching the
        # behavior of a normal run.
        self._strip_replaygain_tags(self.output_file)

        if self.ffmpeg_normalize.keep_mtime:
            self._apply_input_timestamps()

        _logger.info(f"Skipped file copied to {self.output_file}")

    def _apply_input_timestamps(self) -> None:
        """
        Copy the input file's access and modification times to the output file.

        Used when the ``keep_mtime`` option is enabled. Only the access and
        modification times are copied; a file's creation time (which some
        operating systems such as Windows track separately) is not affected.
        """
        if self.input_timestamps is None:
            return
        atime, mtime = self.input_timestamps
        try:
            os.utime(self.output_file, (atime, mtime))
            _logger.debug(
                f"Copied input timestamps to {self.output_file} "
                f"(atime={atime}, mtime={mtime})"
            )
        except OSError as e:
            _logger.warning(f"Could not copy timestamps to {self.output_file}: {e}")

    def _run_replaygain(self) -> None:
        """
        Run the replaygain process for this file.
        """
        _logger.debug(f"Running replaygain for {self.input_file}")

        # get the audio streams
        audio_streams = list(self.streams["audio"].values())

        # Attempt to use EBU pass 2 statistics, which account for pre-filters.
        # These are populated by the main second pass if it runs (not a dry run)
        # and normalization_type is 'ebu'.
        loudness_stats_source = "ebu_pass2"
        loudnorm_stats = audio_streams[0].loudness_statistics.get("ebu_pass2")

        if loudnorm_stats is None:
            _logger.warning(
                "ReplayGain: Second pass EBU statistics (ebu_pass2) not found. "
                "Falling back to first pass EBU statistics (ebu_pass1). "
                "This may not account for pre-filters if any are used."
            )
            loudness_stats_source = "ebu_pass1"
            loudnorm_stats = audio_streams[0].loudness_statistics.get("ebu_pass1")

        if loudnorm_stats is None:
            _logger.error(
                f"ReplayGain: No loudness statistics available from {loudness_stats_source} (and fallback) for stream 0. "
                "Cannot calculate ReplayGain tags."
            )
            return

        _logger.debug(
            f"Using statistics from {loudness_stats_source} for ReplayGain calculation."
        )

        # apply the replaygain tag from the first audio stream (to all audio streams)
        if len(audio_streams) > 1:
            _logger.warning(
                f"Your input file has {len(audio_streams)} audio streams. "
                "Only the first audio stream's replaygain tag will be applied. "
                "All audio streams will receive the same tag."
            )

        target_level = self.ffmpeg_normalize.target_level
        # Use 'input_i' and 'input_tp' from the chosen stats.
        # For ebu_pass2, these are measurements *after* pre-filter but *before* loudnorm adjustment.
        input_i = loudnorm_stats.get("input_i")
        input_tp = loudnorm_stats.get("input_tp")

        if input_i is None or input_tp is None:
            _logger.error(
                f"ReplayGain: 'input_i' or 'input_tp' missing from {loudness_stats_source} statistics. "
                "Cannot calculate ReplayGain tags."
            )
            return

        track_gain = -(input_i - target_level)  # dB
        track_peak = 10 ** (input_tp / 20)  # linear scale

        _logger.debug(f"Calculated Track gain: {track_gain:.2f} dB")
        _logger.debug(f"Calculated Track peak: {track_peak:.2f}")

        if not self.ffmpeg_normalize.dry_run:  # This uses the overall dry_run state
            self._write_replaygain_tags(track_gain, track_peak)
        else:
            _logger.warning(
                "Overall dry_run is enabled, not actually writing ReplayGain tags to the file. "
                "Tag calculation based on available stats was performed."
            )

    def _write_replaygain_tags(self, track_gain: float, track_peak: float) -> None:
        """
        Write the replaygain tags to the input file.

        This is based on the code from bohning/usdb_syncer, licensed under the MIT license.
        See: https://github.com/bohning/usdb_syncer/blob/2fa638c4f487dffe9f5364f91e156ba54cb20233/src/usdb_syncer/resource_dl.py
        """
        _logger.debug(f"Writing ReplayGain tags to {self.input_file}")

        input_file_ext = os.path.splitext(self.input_file)[1]
        if input_file_ext == ".mp3":
            mp3 = MP3(self.input_file, ID3=ID3)
            if not mp3.tags:
                return
            mp3.tags.add(
                TXXX(desc="REPLAYGAIN_TRACK_GAIN", text=[f"{track_gain:.2f} dB"])
            )
            mp3.tags.add(TXXX(desc="REPLAYGAIN_TRACK_PEAK", text=[f"{track_peak:.6f}"]))
            mp3.save()
        elif input_file_ext in [".mp4", ".m4a", ".m4v", ".mov"]:
            mp4 = MP4(self.input_file)
            if not mp4.tags:
                mp4.add_tags()
            if not mp4.tags:
                return
            mp4.tags["----:com.apple.iTunes:REPLAYGAIN_TRACK_GAIN"] = [
                f"{track_gain:.2f} dB".encode()
            ]
            mp4.tags["----:com.apple.iTunes:REPLAYGAIN_TRACK_PEAK"] = [
                f"{track_peak:.6f}".encode()
            ]
            mp4.save()
        elif input_file_ext == ".ogg":
            ogg = OggVorbis(self.input_file)
            ogg["REPLAYGAIN_TRACK_GAIN"] = [f"{track_gain:.2f} dB"]
            ogg["REPLAYGAIN_TRACK_PEAK"] = [f"{track_peak:.6f}"]
            ogg.save()
        elif input_file_ext == ".opus":
            opus = OggOpus(self.input_file)
            # See https://datatracker.ietf.org/doc/html/rfc7845#section-5.2.1
            opus["R128_TRACK_GAIN"] = [str(round(256 * track_gain))]
            opus.save()
        else:
            _logger.error(
                f"Unsupported input file extension: {input_file_ext} for writing replaygain tags. "
                "Only .mp3, .mp4/.m4a, .ogg, .opus are supported. "
                "If you think this should support more formats, please let me know at "
                "https://github.com/slhck/ffmpeg-normalize/issues"
            )
            return

        _logger.info(
            f"Successfully wrote replaygain tags to input file {self.input_file}"
        )

    def _strip_replaygain_tags(self, output_file: str) -> None:
        """
        Strip ReplayGain tags from the output file after normalization.

        This ensures that old ReplayGain tags from the input are removed,
        since they are no longer accurate after normalization.

        Args:
            output_file (str): Path to the output file to strip tags from
        """
        _logger.debug(f"Stripping ReplayGain tags from {output_file}")

        output_file_ext = os.path.splitext(output_file)[1]
        if output_file_ext == ".mp3":
            try:
                mp3 = MP3(output_file, ID3=ID3)
                if not mp3.tags:
                    return
                # Remove REPLAYGAIN_* tags
                tags_to_remove = [
                    key for key in mp3.tags if key.startswith("TXXX:REPLAYGAIN_")
                ]
                for tag in tags_to_remove:
                    del mp3.tags[tag]
                if tags_to_remove:
                    mp3.save()
                    _logger.debug(
                        f"Stripped {len(tags_to_remove)} ReplayGain tag(s) from {output_file}"
                    )
            except Exception as e:
                _logger.warning(
                    f"Could not strip ReplayGain tags from {output_file}: {e}"
                )
        elif output_file_ext in [".mp4", ".m4a", ".m4v", ".mov"]:
            try:
                mp4 = MP4(output_file)
                if not mp4.tags:
                    return
                # Remove REPLAYGAIN_* tags
                tags_to_remove = [
                    key
                    for key in mp4.tags
                    if "REPLAYGAIN_" in key.upper() or "REPLAYGAIN" in key.upper()
                ]
                for tag in tags_to_remove:
                    del mp4.tags[tag]
                if tags_to_remove:
                    mp4.save()
                    _logger.debug(
                        f"Stripped {len(tags_to_remove)} ReplayGain tag(s) from {output_file}"
                    )
            except Exception as e:
                _logger.warning(
                    f"Could not strip ReplayGain tags from {output_file}: {e}"
                )
        elif output_file_ext == ".ogg":
            try:
                ogg = OggVorbis(output_file)
                # Remove REPLAYGAIN_* tags (case-insensitive)
                tags_to_remove = [
                    key for key in ogg.keys() if key.upper().startswith("REPLAYGAIN_")
                ]
                for tag in tags_to_remove:
                    del ogg[tag]
                if tags_to_remove:
                    ogg.save()
                    _logger.debug(
                        f"Stripped {len(tags_to_remove)} ReplayGain tag(s) from {output_file}"
                    )
            except Exception as e:
                _logger.warning(
                    f"Could not strip ReplayGain tags from {output_file}: {e}"
                )
        elif output_file_ext == ".opus":
            try:
                opus = OggOpus(output_file)
                # Remove R128_* tags (Opus uses R128 instead of REPLAYGAIN, case-insensitive)
                tags_to_remove = [
                    key for key in opus.keys() if key.upper().startswith("R128_")
                ]
                for tag in tags_to_remove:
                    del opus[tag]
                if tags_to_remove:
                    opus.save()
                    _logger.debug(
                        f"Stripped {len(tags_to_remove)} R128 tag(s) from {output_file}"
                    )
            except Exception as e:
                _logger.warning(
                    f"Could not strip ReplayGain tags from {output_file}: {e}"
                )
        else:
            # For other formats, we don't need to strip tags
            _logger.debug(
                f"Skipping ReplayGain tag stripping for {output_file_ext} (not supported/needed)"
            )

    def _can_write_output_video(self) -> bool:
        """
        Determine whether the output file can contain video at all.

        Returns:
            bool: True if the output file can contain video, False otherwise
        """
        if self.output_ext.lower() in AUDIO_ONLY_FORMATS:
            return False

        return not self.ffmpeg_normalize.video_disable

    def _first_pass(self) -> None:
        """
        Run the first pass of the normalization process.
        """
        _logger.debug(f"Parsing normalization info for {self.input_file}")

        streams_to_normalize = self._get_streams_to_normalize()

        for index, audio_stream in enumerate(streams_to_normalize):
            if self.ffmpeg_normalize.normalization_type == "ebu":
                fun = getattr(audio_stream, "parse_loudnorm_stats")
            else:
                fun = getattr(audio_stream, "parse_astats")

            if self.ffmpeg_normalize.progress:
                with tqdm(
                    total=100,
                    position=1,
                    desc=f"Stream {index + 1}/{len(streams_to_normalize)}",
                    bar_format=TQDM_BAR_FORMAT,
                ) as pbar:
                    for progress in fun():
                        pbar.update(progress - pbar.n)
            else:
                for _ in fun():
                    pass

    def _get_audio_filter_cmd(self) -> tuple[str, list[str]]:
        """
        Return the audio filter command and output labels needed.

        Returns:
            tuple[str, list[str]]: filter_complex command and the required output labels
        """
        filter_chains = []
        output_labels = []

        streams_to_normalize = self._get_streams_to_normalize()

        for audio_stream in streams_to_normalize:
            skip_normalization = False
            if self.ffmpeg_normalize.lower_only:
                if self.ffmpeg_normalize.normalization_type == "ebu":
                    if (
                        audio_stream.loudness_statistics["ebu_pass1"] is not None
                        and audio_stream.loudness_statistics["ebu_pass1"]["input_i"]
                        < self.ffmpeg_normalize.target_level
                    ):
                        skip_normalization = True
                elif self.ffmpeg_normalize.normalization_type == "peak":
                    if (
                        audio_stream.loudness_statistics["max"] is not None
                        and audio_stream.loudness_statistics["max"]
                        < self.ffmpeg_normalize.target_level
                    ):
                        skip_normalization = True
                elif self.ffmpeg_normalize.normalization_type == "rms":
                    if (
                        audio_stream.loudness_statistics["mean"] is not None
                        and audio_stream.loudness_statistics["mean"]
                        < self.ffmpeg_normalize.target_level
                    ):
                        skip_normalization = True

            if skip_normalization:
                _logger.warning(
                    f"Stream {audio_stream.stream_id} had measured input loudness lower than target, skipping normalization."
                )
                normalization_filter = "acopy"
            else:
                if self.ffmpeg_normalize.normalization_type == "ebu":
                    normalization_filter = audio_stream.get_second_pass_opts_ebu(
                        batch_reference=self.batch_reference
                    )
                else:
                    normalization_filter = audio_stream.get_second_pass_opts_peakrms(
                        batch_reference=self.batch_reference
                    )

            input_label = f"[0:{audio_stream.stream_id}]"
            output_label = f"[norm{audio_stream.stream_id}]"
            output_labels.append(output_label)

            filter_chain = []

            if self.ffmpeg_normalize.pre_filter:
                filter_chain.append(self.ffmpeg_normalize.pre_filter)

            # Apply channel conversion before normalization so that the
            # normalization filter operates on the same channel layout that
            # was measured in the first pass. See issue #316.
            if self.ffmpeg_normalize.audio_channels:
                filter_chain.append(
                    f"aformat=sample_fmts=fltp:channel_layouts={self.ffmpeg_normalize.audio_channels}c"
                )

            filter_chain.append(normalization_filter)

            if self.ffmpeg_normalize.post_filter:
                filter_chain.append(self.ffmpeg_normalize.post_filter)

            filter_chains.append(input_label + ",".join(filter_chain) + output_label)

        filter_complex_cmd = ";".join(filter_chains)

        return filter_complex_cmd, output_labels

    def _second_pass(self) -> Iterator[float]:
        """
        Construct the second pass command and run it.

        FIXME: make this method simpler
        """
        _logger.info(f"Running second pass for {self.input_file}")

        # get the target output stream types depending on the options
        output_stream_types: list[Literal["audio", "video", "subtitle"]] = ["audio"]
        if self._can_write_output_video():
            output_stream_types.append("video")
        if not self.ffmpeg_normalize.subtitle_disable:
            output_stream_types.append("subtitle")

        # base command, here we will add all other options
        cmd = [self.ffmpeg_normalize.ffmpeg_exe, "-hide_banner", "-y"]

        # extra options (if any)
        if self.ffmpeg_normalize.extra_input_options:
            cmd.extend(self.ffmpeg_normalize.extra_input_options)

        # get complex filter command
        audio_filter_cmd, output_labels = self._get_audio_filter_cmd()

        # add input file and basic filter
        cmd.extend(["-i", self.input_file, "-filter_complex", audio_filter_cmd])

        # map metadata, only if needed
        if self.ffmpeg_normalize.metadata_disable:
            cmd.extend(["-map_metadata", "-1"])
        else:
            # map global metadata
            cmd.extend(["-map_metadata", "0"])
            # map per-stream metadata (e.g. language tags)
            for stream_type in output_stream_types:
                stream_key = stream_type[0]
                if stream_type not in self.streams:
                    continue
                for idx, _ in enumerate(self.streams[stream_type].items()):
                    cmd.extend(
                        [
                            f"-map_metadata:s:{stream_key}:{idx}",
                            f"0:s:{stream_key}:{idx}",
                        ]
                    )

        # map chapters if needed
        if self.ffmpeg_normalize.chapters_disable:
            cmd.extend(["-map_chapters", "-1"])
        else:
            cmd.extend(["-map_chapters", "0"])

        # collect all '-map' and codecs needed for output video based on input video
        if self.streams["video"]:
            if self._can_write_output_video():
                for s in self.streams["video"].keys():
                    cmd.extend(["-map", f"0:{s}"])
                # set codec (copy by default)
                cmd.extend(["-c:v", self.ffmpeg_normalize.video_codec])
            else:
                if not self.ffmpeg_normalize.video_disable:
                    _logger.warning(
                        f"The chosen output extension {self.output_ext} does not support video/cover art. It will be disabled."
                    )

        # Determine streams to normalize and passthrough
        streams_to_normalize = self._get_streams_to_normalize()
        all_audio_streams = list(self.streams["audio"].values())

        # Determine which streams to passthrough
        if self.ffmpeg_normalize.keep_other_audio and (
            self.ffmpeg_normalize.audio_streams is not None
            or self.ffmpeg_normalize.audio_default_only
        ):
            streams_to_passthrough = [
                s for s in all_audio_streams if s not in streams_to_normalize
            ]
        else:
            streams_to_passthrough = []

        # ... and map the output of the normalization filters
        for ol in output_labels:
            cmd.extend(["-map", ol])

        # ... and map passthrough audio streams (copy without normalization)
        for stream in streams_to_passthrough:
            cmd.extend(["-map", f"0:{stream.stream_id}"])

        # Track output audio stream index for codec assignment
        output_audio_idx = 0

        # set audio codec for normalized streams
        for audio_stream in streams_to_normalize:
            if self.ffmpeg_normalize.audio_codec:
                codec = self.ffmpeg_normalize.audio_codec
            else:
                codec = audio_stream.get_pcm_codec()
            cmd.extend([f"-c:a:{output_audio_idx}", codec])
            output_audio_idx += 1

        # set audio codec for passthrough streams (always copy)
        for _ in streams_to_passthrough:
            cmd.extend([f"-c:a:{output_audio_idx}", "copy"])
            output_audio_idx += 1

        # other audio options (if any) - only apply to normalized streams
        if self.ffmpeg_normalize.audio_bitrate:
            if self.ffmpeg_normalize.audio_codec == "libvorbis":
                # libvorbis takes just a "-b" option, for some reason
                # https://github.com/slhck/ffmpeg-normalize/issues/277
                cmd.extend(["-b", str(self.ffmpeg_normalize.audio_bitrate)])
            else:
                # Only apply to normalized streams
                for idx in range(len(streams_to_normalize)):
                    cmd.extend(
                        [f"-b:a:{idx}", str(self.ffmpeg_normalize.audio_bitrate)]
                    )
        if self.ffmpeg_normalize.sample_rate:
            # Only apply to normalized streams
            for idx in range(len(streams_to_normalize)):
                cmd.extend([f"-ar:a:{idx}", str(self.ffmpeg_normalize.sample_rate)])
        if self.ffmpeg_normalize.audio_channels:
            # Only apply to normalized streams
            for idx in range(len(streams_to_normalize)):
                cmd.extend([f"-ac:a:{idx}", str(self.ffmpeg_normalize.audio_channels)])

        # carry the input bit depth through to the output encoder, if requested
        if self.ffmpeg_normalize.keep_bit_depth:
            for idx, audio_stream in enumerate(streams_to_normalize):
                sample_fmt = audio_stream.get_output_sample_fmt(
                    self.ffmpeg_normalize.audio_codec
                )
                if sample_fmt is not None:
                    cmd.extend([f"-sample_fmt:a:{idx}", sample_fmt])

        # ... and subtitles
        if not self.ffmpeg_normalize.subtitle_disable:
            for s in self.streams["subtitle"].keys():
                cmd.extend(["-map", f"0:{s}"])
            # copy subtitles
            cmd.extend(["-c:s", "copy"])

        if self.ffmpeg_normalize.keep_original_audio:
            # Map all original audio streams after normalized and passthrough streams
            for index, _ in enumerate(self.streams["audio"].items()):
                cmd.extend(["-map", f"0:a:{index}"])
                cmd.extend([f"-c:a:{output_audio_idx}", "copy"])
                output_audio_idx += 1

        # extra options (if any)
        if self.ffmpeg_normalize.extra_output_options:
            cmd.extend(self.ffmpeg_normalize.extra_output_options)

        # output format (if any)
        if self.ffmpeg_normalize.output_format:
            cmd.extend(["-f", self.ffmpeg_normalize.output_format])

        # if dry run, only show sample command
        if self.ffmpeg_normalize.dry_run:
            cmd.append(self.output_file)
            _logger.warning("Dry run used, not actually running second-pass command")
            CommandRunner(dry=True).run_command(cmd)
            yield 100
            return

        is_in_place_overwrite = os.path.realpath(self.input_file) == os.path.realpath(
            self.output_file
        )

        temp_file: Union[str, None] = None
        if is_in_place_overwrite:
            # need to create a temporary file because we cannot override
            # the same input file
            output_dir = os.path.dirname(self.output_file) or "."
            fd, temp_file = mkstemp(
                suffix=f".{self.output_ext}",
                prefix=f".{os.path.splitext(os.path.basename(self.output_file))[0]}.",
                dir=output_dir,
            )
            os.close(fd)
            _logger.debug(
                f"Output file is the same as the input file, "
                f"encoding to temporary file {temp_file} first"
            )
            cmd.append(temp_file)
        else:
            cmd.append(self.output_file)

        cmd_runner = CommandRunner()
        try:
            yield from cmd_runner.run_ffmpeg_command(cmd)
        except Exception as e:
            _logger.error(f"Error while running command {shlex.join(cmd)}! Error: {e}")
            if temp_file and os.path.exists(temp_file):
                os.remove(temp_file)
            raise e
        else:
            # for in-place normalization, move the finished temp file over the input
            if temp_file:
                _logger.debug(
                    f"Moving temporary file from {temp_file} to {self.output_file}"
                )
                os.replace(temp_file, self.output_file)

        output = cmd_runner.get_output()
        # in the second pass, we do not normalize stream-by-stream, so we set the stats based on the
        # overall output (which includes multiple loudnorm stats)
        if self.ffmpeg_normalize.normalization_type == "ebu":
            ebu_pass_2_stats = list(
                AudioStream.prune_and_parse_loudnorm_output(output).values()
            )
            # Only set second pass stats for streams that were actually normalized
            streams_to_normalize = self._get_streams_to_normalize()
            if len(ebu_pass_2_stats) == len(streams_to_normalize):
                for idx, audio_stream in enumerate(streams_to_normalize):
                    audio_stream.set_second_pass_stats(ebu_pass_2_stats[idx])
            else:
                _logger.debug(
                    f"Expected {len(streams_to_normalize)} EBU pass 2 statistics but got {len(ebu_pass_2_stats)}. "
                    "This can happen when normalization is skipped (e.g., with --lower-only)."
                )

        # warn if dynamic == False and any of the second pass stats contain "normalization_type" == "dynamic"
        if self.ffmpeg_normalize.dynamic is False:
            for audio_stream in self.streams["audio"].values():
                pass2_stats = audio_stream.get_stats()["ebu_pass2"]
                if pass2_stats is None:
                    continue
                if pass2_stats["normalization_type"] == "dynamic":
                    pass1_stats = audio_stream.get_stats()["ebu_pass1"]

                    reason = ""
                    if pass1_stats is not None:
                        linear_gain = (
                            self.ffmpeg_normalize.target_level - pass1_stats["input_i"]
                        )
                        estimated_tp = pass1_stats["input_tp"] + linear_gain
                        if estimated_tp > self.ffmpeg_normalize.true_peak:
                            min_tp = estimated_tp
                            max_target = (
                                self.ffmpeg_normalize.true_peak
                                - pass1_stats["input_tp"]
                                + pass1_stats["input_i"]
                            )
                            reason = (
                                f" Reason: the input true peak ({pass1_stats['input_tp']:.2f} dBTP) is too high — "
                                f"after linear gain of {linear_gain:.2f} dB, "
                                f"the estimated true peak would be {estimated_tp:.2f} dBTP, "
                                f"exceeding the target of {self.ffmpeg_normalize.true_peak} dBTP. "
                                f"To avoid this, raise --true-peak (-tp) to at least {min_tp:.1f}, "
                                f"or lower the target level (-t) to at most {max_target:.1f}."
                            )
                        elif (
                            pass1_stats["input_lra"]
                            > self.ffmpeg_normalize.loudness_range_target
                        ):
                            reason = (
                                f" Reason: the input loudness range ({pass1_stats['input_lra']:.2f} LU) "
                                f"exceeds the target ({self.ffmpeg_normalize.loudness_range_target:.2f} LU). "
                                "Consider raising the target loudness range or using "
                                "--keep-loudness-range-target / --keep-lra-above-loudness-range-target."
                            )

                    _logger.warning(
                        f"{self.input_file}: You specified linear normalization, but the loudnorm filter "
                        f"reverted to dynamic normalization.{reason}"
                    )

        _logger.debug("Normalization finished")

    def get_stats(self) -> Iterable[LoudnessStatisticsWithMetadata]:
        return (
            audio_stream.get_stats() for audio_stream in self.streams["audio"].values()
        )
