import gc
import math
import re
import warnings
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import torch

from . import _video_opt
from ._video_opt import VideoMetaData


try:
    import av

    av.logging.set_level(av.logging.ERROR)
    if not hasattr(av.video.frame.VideoFrame, "pict_type"):
        av = ImportError(
            """\
Your version of PyAV is too old for the necessary video operations in torchvision.
If you are on Python 3.5, you will have to build from source (the conda-forge
packages are not up-to-date).  See
https://github.com/mikeboers/PyAV#installation for instructions on how to
install PyAV on your system.
"""
        )
except ImportError:
    av = ImportError(
        """\
PyAV is not installed, and is necessary for the video operations in torchvision.
See https://github.com/mikeboers/PyAV#installation for instructions on how to
install PyAV on your system.
"""
    )


def _check_av_available() -> None:
    if isinstance(av, Exception):
        raise av


def _av_available() -> bool:
    return not isinstance(av, Exception)


# PyAV has some reference cycles
_CALLED_TIMES = 0
_GC_COLLECTION_INTERVAL = 10


def write_video(
    filename: str,
    video_array: torch.Tensor,
    fps: float,
    video_codec: str = "libx264",
    options: Optional[Dict[str, Any]] = None,
    audio_array: Optional[torch.Tensor] = None,
    audio_fps: Optional[float] = None,
    audio_codec: Optional[str] = None,
    audio_options: Optional[Dict[str, Any]] = None,
) -> None:
    """
    Writes a 4d tensor in [T, H, W, C] format in a video file

    Args:
        filename (str): path where the video will be saved
        video_array (Tensor[T, H, W, C]): tensor containing the individual frames,
            as a uint8 tensor in [T, H, W, C] format
        fps (Number): video frames per second
        video_codec (str): the name of the video codec, i.e. "libx264", "h264", etc.
        options (Dict): dictionary containing options to be passed into the PyAV video stream
        audio_array (Tensor[C, N]): tensor containing the audio, where C is the number of channels
            and N is the number of samples
        audio_fps (Number): audio sample rate, typically 44100 or 48000
        audio_codec (str): the name of the audio codec, i.e. "mp3", "aac", etc.
        audio_options (Dict): dictionary containing options to be passed into the PyAV audio stream
    """
    _check_av_available()
    video_array = torch.as_tensor(video_array, dtype=torch.uint8).numpy()

    # PyAV does not support floating point numbers with decimal point
    # and will throw OverflowException in case this is not the case
    if isinstance(fps, float):
        fps = np.round(fps)

    with av.open(filename, mode="w") as container:
        stream = container.add_stream(video_codec, rate=fps)
        stream.width = video_array.shape[2]
        stream.height = video_array.shape[1]
        stream.pix_fmt = "yuv420p" if video_codec != "libx264rgb" else "rgb24"
        stream.options = options or {}

        if audio_array is not None:
            audio_format_dtypes = {
                'dbl': '<f8',
                'dblp': '<f8',
                'flt': '<f4',
                'fltp': '<f4',
                's16': '<i2',
                's16p': '<i2',
                's32': '<i4',
                's32p': '<i4',
                'u8': 'u1',
                'u8p': 'u1',
            }
            a_stream = container.add_stream(audio_codec, rate=audio_fps)
            a_stream.options = audio_options or {}

            num_channels = audio_array.shape[0]
            audio_layout = "stereo" if num_channels > 1 else "mono"
            audio_sample_fmt = container.streams.audio[0].format.name

            format_dtype = np.dtype(audio_format_dtypes[audio_sample_fmt])
            audio_array = torch.as_tensor(audio_array).numpy().astype(format_dtype)

            frame = av.AudioFrame.from_ndarray(
                audio_array, format=audio_sample_fmt, layout=audio_layout
            )

            frame.sample_rate = audio_fps

            for packet in a_stream.encode(frame):
                container.mux(packet)

            for packet in a_stream.encode():
                container.mux(packet)

        for img in video_array:
            frame = av.VideoFrame.from_ndarray(img, format="rgb24")
            frame.pict_type = "NONE"
            for packet in stream.encode(frame):
                container.mux(packet)

        # Flush stream
        for packet in stream.encode():
            container.mux(packet)


def _read_from_stream(
    container: "av.container.Container",
    start_offset: float,
    end_offset: float,
    pts_unit: str,
    stream: "av.stream.Stream",
    stream_name: Dict[str, Optional[Union[int, Tuple[int, ...], List[int]]]],
) -> List["av.frame.Frame"]:
    global _CALLED_TIMES, _GC_COLLECTION_INTERVAL
    _CALLED_TIMES += 1
    if _CALLED_TIMES % _GC_COLLECTION_INTERVAL == _GC_COLLECTION_INTERVAL - 1:
        gc.collect()

    if pts_unit == "sec":
        start_offset = int(math.floor(start_offset * (1 / stream.time_base)))
        if end_offset != float("inf"):
            end_offset = int(math.ceil(end_offset * (1 / stream.time_base)))
    else:
        warnings.warn(
            "The pts_unit 'pts' gives wrong results and will be removed in a "
            + "follow-up version. Please use pts_unit 'sec'."
        )

    frames = {}
    should_buffer = True
    max_buffer_size = 5
    if stream.type == "video":
        # DivX-style packed B-frames can have out-of-order pts (2 frames in a single pkt)
        # so need to buffer some extra frames to sort everything
        # properly
        extradata = stream.codec_context.extradata
        # overly complicated way of finding if `divx_packed` is set, following
        # https://github.com/FFmpeg/FFmpeg/commit/d5a21172283572af587b3d939eba0091484d3263
        if extradata and b"DivX" in extradata:
            # can't use regex directly because of some weird characters sometimes...
            pos = extradata.find(b"DivX")
            d = extradata[pos:]
            o = re.search(br"DivX(\d+)Build(\d+)(\w)", d)
            if o is None:
                o = re.search(br"DivX(\d+)b(\d+)(\w)", d)
            if o is not None:
                should_buffer = o.group(3) == b"p"
    seek_offset = start_offset
    # some files don't seek to the right location, so better be safe here
    seek_offset = max(seek_offset - 1, 0)
    if should_buffer:
        # FIXME this is kind of a hack, but we will jump to the previous keyframe
        # so this will be safe
        seek_offset = max(seek_offset - max_buffer_size, 0)
    try:
        # TODO check if stream needs to always be the video stream here or not
        container.seek(seek_offset, any_frame=False, backward=True, stream=stream)
    except av.AVError:
        # TODO add some warnings in this case
        # print("Corrupted file?", container.name)
        return []
    buffer_count = 0
    try:
        for _idx, frame in enumerate(container.decode(**stream_name)):
            frames[frame.pts] = frame
            if frame.pts >= end_offset:
                if should_buffer and buffer_count < max_buffer_size:
                    buffer_count += 1
                    continue
                break
    except av.AVError:
        # TODO add a warning
        pass
    # ensure that the results are sorted wrt the pts
    result = [
        frames[i] for i in sorted(frames) if start_offset <= frames[i].pts <= end_offset
    ]
    if len(frames) > 0 and start_offset > 0 and start_offset not in frames:
        # if there is no frame that exactly matches the pts of start_offset
        # add the last frame smaller than start_offset, to guarantee that
        # we will have all the necessary data. This is most useful for audio
        preceding_frames = [i for i in frames if i < start_offset]
        if len(preceding_frames) > 0:
            first_frame_pts = max(preceding_frames)
            result.insert(0, frames[first_frame_pts])
    return result


def _align_audio_frames(
    aframes: torch.Tensor, audio_frames: List["av.frame.Frame"], ref_start: int, ref_end: float
) -> torch.Tensor:
    start, end = audio_frames[0].pts, audio_frames[-1].pts
    total_aframes = aframes.shape[1]
    step_per_aframe = (end - start + 1) / total_aframes
    s_idx = 0
    e_idx = total_aframes
    if start < ref_start:
        s_idx = int((ref_start - start) / step_per_aframe)
    if end > ref_end:
        e_idx = int((ref_end - end) / step_per_aframe)
    return aframes[:, s_idx:e_idx]


def read_video(
    filename: str, start_pts: int = 0, end_pts: Optional[float] = None, pts_unit: str = "pts"
) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any]]:
    """
    Reads a video from a file, returning both the video frames as well as
    the audio frames

    Args:
        filename (str): path to the video file
        start_pts (int if pts_unit = 'pts', float / Fraction if pts_unit = 'sec', optional):
            The start presentation time of the video
        end_pts (int if pts_unit = 'pts', float / Fraction if pts_unit = 'sec', optional):
            The end presentation time
        pts_unit (str, optional): unit in which start_pts and end_pts values will be interpreted,
            either 'pts' or 'sec'. Defaults to 'pts'.

    Returns:
        vframes (Tensor[T, H, W, C]): the `T` video frames
        aframes (Tensor[K, L]): the audio frames, where `K` is the number of channels and `L` is the number of points
        info (Dict): metadata for the video and audio. Can contain the fields video_fps (float) and audio_fps (int)
    """

    from torchvision import get_video_backend

    if get_video_backend() != "pyav":
        return _video_opt._read_video(filename, start_pts, end_pts, pts_unit)

    _check_av_available()

    if end_pts is None:
        end_pts = float("inf")

    if end_pts < start_pts:
        raise ValueError(
            "end_pts should be larger than start_pts, got "
            "start_pts={} and end_pts={}".format(start_pts, end_pts)
        )

    info = {}
    video_frames = []
    audio_frames = []

    try:
        with av.open(filename, metadata_errors="ignore") as container:
            if container.streams.video:
                video_frames = _read_from_stream(
                    container,
                    start_pts,
                    end_pts,
                    pts_unit,
                    container.streams.video[0],
                    {"video": 0},
                )
                video_fps = container.streams.video[0].average_rate
                # guard against potentially corrupted files
                if video_fps is not None:
                    info["video_fps"] = float(video_fps)

            if container.streams.audio:
                audio_frames = _read_from_stream(
                    container,
                    start_pts,
                    end_pts,
                    pts_unit,
                    container.streams.audio[0],
                    {"audio": 0},
                )
                info["audio_fps"] = container.streams.audio[0].rate

    except av.AVError:
        # TODO raise a warning?
        pass

    vframes_list = [frame.to_rgb().to_ndarray() for frame in video_frames]
    aframes_list = [frame.to_ndarray() for frame in audio_frames]

    if vframes_list:
        vframes = torch.as_tensor(np.stack(vframes_list))
    else:
        vframes = torch.empty((0, 1, 1, 3), dtype=torch.uint8)

    if aframes_list:
        aframes = np.concatenate(aframes_list, 1)
        aframes = torch.as_tensor(aframes)
        aframes = _align_audio_frames(aframes, audio_frames, start_pts, end_pts)
    else:
        aframes = torch.empty((1, 0), dtype=torch.float32)

    return vframes, aframes, info


def _can_read_timestamps_from_packets(container: "av.container.Container") -> bool:
    extradata = container.streams[0].codec_context.extradata
    if extradata is None:
        return False
    if b"Lavc" in extradata:
        return True
    return False


def _decode_video_timestamps(container: "av.container.Container") -> List[int]:
    if _can_read_timestamps_from_packets(container):
        # fast path
        return [x.pts for x in container.demux(video=0) if x.pts is not None]
    else:
        return [x.pts for x in container.decode(video=0) if x.pts is not None]


def read_video_timestamps(filename: str, pts_unit: str = "pts") -> Tuple[List[int], Optional[float]]:
    """
    List the video frames timestamps.

    Note that the function decodes the whole video frame-by-frame.

    Args:
        filename (str): path to the video file
        pts_unit (str, optional): unit in which timestamp values will be returned
            either 'pts' or 'sec'. Defaults to 'pts'.

    Returns:
        pts (List[int] if pts_unit = 'pts', List[Fraction] if pts_unit = 'sec'):
            presentation timestamps for each one of the frames in the video.
        video_fps (float, optional): the frame rate for the video

    """
    from torchvision import get_video_backend

    if get_video_backend() != "pyav":
        return _video_opt._read_video_timestamps(filename, pts_unit)

    _check_av_available()

    video_fps = None
    pts = []

    try:
        with av.open(filename, metadata_errors="ignore") as container:
            if container.streams.video:
                video_stream = container.streams.video[0]
                video_time_base = video_stream.time_base
                try:
                    pts = _decode_video_timestamps(container)
                except av.AVError:
                    warnings.warn(f"Failed decoding frames for file {filename}")
                video_fps = float(video_stream.average_rate)
    except av.AVError:
        # TODO add a warning
        pass

    pts.sort()

    if pts_unit == "sec":
        pts = [x * video_time_base for x in pts]

    return pts, video_fps
