import os
import torch
import json
from io import BytesIO
import torch.distributed as dist
import numpy as np
import kaldiio
import librosa
try:
    import torchaudio
except ImportError:
    torchaudio = None
import time
import logging
from torch.nn.utils.rnn import pad_sequence

try:
    from funasr.download.file import download_from_url
except:
    print("urllib is not installed, if you infer from url, please install it first.")
import subprocess
from subprocess import CalledProcessError, run

try:
    from pydub import AudioSegment
except:
    pass


def is_ffmpeg_installed():
    """Is ffmpeg installed."""
    try:
        output = subprocess.check_output(["ffmpeg", "-version"], stderr=subprocess.STDOUT)
        return "ffmpeg version" in output.decode("utf-8")
    except (subprocess.CalledProcessError, FileNotFoundError):
        return False


use_ffmpeg = False
if is_ffmpeg_installed():
    use_ffmpeg = True
else:
    print(
        "Notice: ffmpeg is not installed. torchaudio is used to load audio\n"
        "If you want to use ffmpeg backend to load audio, please install it by:"
        "\n\tsudo apt install ffmpeg # ubuntu"
        "\n\t# brew install ffmpeg # mac"
    )


def load_audio_text_image_video(
    data_or_path_or_list,
    fs: int = 16000,
    audio_fs: int = 16000,
    data_type="sound",
    tokenizer=None,
    **kwargs,
):
    """Load audio/text/image/video data from various input formats.

    Args:
        data_or_path_or_list: File path, URL, numpy array, torch Tensor, bytes, or list.
        fs (int): Target sample rate (default 16000).
        audio_fs (int): Source audio sample rate.
        data_type (str): Input type ("sound", "text", "fbank").

    Returns:
        torch.Tensor or list: Loaded and resampled audio tensor(s).
    """
    if isinstance(data_or_path_or_list, (list, tuple)):
        if data_type is not None and isinstance(data_type, (list, tuple)):
            data_types = [data_type] * len(data_or_path_or_list)
            data_or_path_or_list_ret = [[] for d in data_type]
            for i, (data_type_i, data_or_path_or_list_i) in enumerate(
                zip(data_types, data_or_path_or_list)
            ):
                for j, (data_type_j, data_or_path_or_list_j) in enumerate(
                    zip(data_type_i, data_or_path_or_list_i)
                ):
                    data_or_path_or_list_j = load_audio_text_image_video(
                        data_or_path_or_list_j,
                        fs=fs,
                        audio_fs=audio_fs,
                        data_type=data_type_j,
                        tokenizer=tokenizer,
                        **kwargs,
                    )
                    data_or_path_or_list_ret[j].append(data_or_path_or_list_j)

            return data_or_path_or_list_ret
        else:
            return [
                load_audio_text_image_video(
                    audio, fs=fs, audio_fs=audio_fs, data_type=data_type, **kwargs
                )
                for audio in data_or_path_or_list
            ]
    if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith(
        ("http://", "https://")
    ):  # download url to local file
        data_or_path_or_list = download_from_url(data_or_path_or_list)

    # Fail fast with a clear error if an audio file path does not exist, instead of
    # silently passing the string downstream (which later crashes with a cryptic
    # "expected Tensor ... but got str" deep inside the model).
    if (
        isinstance(data_or_path_or_list, str)
        and data_type in (None, "sound")
        and not data_or_path_or_list.startswith(("http://", "https://"))
        and not os.path.exists(data_or_path_or_list)
    ):
        raise FileNotFoundError(
            f"Audio file not found: {data_or_path_or_list!r}. Pass a valid local file "
            f"path, URL, numpy array, torch.Tensor, or bytes."
        )
    if (isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list)) or hasattr(data_or_path_or_list, 'read'):  # local file or bytes io
        if data_type is None or data_type == "sound":
            if hasattr(data_or_path_or_list, "read") and hasattr(data_or_path_or_list, "seek"):
                data_or_path_or_list.seek(0)
            # if use_ffmpeg:
            #     data_or_path_or_list = _load_audio_ffmpeg(data_or_path_or_list, sr=fs)
            #     data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze()  # [n_samples,]
            # else:
            #     data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
            #     if kwargs.get("reduce_channels", True):
            #         data_or_path_or_list = data_or_path_or_list.mean(0)
            try:
                data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
                if kwargs.get("reduce_channels", True):
                    data_or_path_or_list = data_or_path_or_list.mean(0)
            except:
                try:
                    if hasattr(data_or_path_or_list, "seek"):
                        data_or_path_or_list.seek(0)
                    import soundfile as sf
                    data_np, audio_fs = sf.read(data_or_path_or_list, dtype="float32")
                    data_or_path_or_list = torch.from_numpy(data_np).squeeze()
                    if data_or_path_or_list.ndim > 1 and kwargs.get("reduce_channels", True):
                        data_or_path_or_list = data_or_path_or_list.mean(-1)
                except:
                    if hasattr(data_or_path_or_list, "seek"):
                        data_or_path_or_list.seek(0)
                    data_or_path_or_list = _load_audio_ffmpeg(data_or_path_or_list, sr=fs)
                    data_or_path_or_list = torch.from_numpy(
                        data_or_path_or_list
                    ).squeeze()  # [n_samples,]
        elif data_type == "text" and tokenizer is not None:
            with open(data_or_path_or_list, "r") as f:
                data_or_path_or_list = tokenizer.encode(f.read().strip())
        elif data_type == "image":  # undo
            pass
        elif data_type == "video":  # undo
            pass

        # if data_in is a file or url, set is_final=True
        if "cache" in kwargs:
            kwargs["cache"]["is_final"] = True
            kwargs["cache"]["is_streaming_input"] = False
    elif isinstance(data_or_path_or_list, str) and data_type == "text" and tokenizer is not None:
        data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
    elif isinstance(data_or_path_or_list, np.ndarray):  # audio sample point
        data_or_path_or_list = torch.from_numpy(data_or_path_or_list)  # .squeeze()  # [n_samples,]
    elif isinstance(data_or_path_or_list, str) and data_type == "kaldi_ark":
        data_mat = kaldiio.load_mat(data_or_path_or_list)
        if isinstance(data_mat, tuple):
            audio_fs, mat = data_mat
        else:
            mat = data_mat
        if mat.dtype == "int16" or mat.dtype == "int32":
            mat = mat.astype(np.float64)
            mat = mat / 32768
        if mat.ndim == 2:
            mat = mat[:, 0]
        data_or_path_or_list = mat
    else:
        pass
        # print(f"unsupport data type: {data_or_path_or_list}, return raw data")

    if audio_fs != fs and data_type != "text":
        if torchaudio is not None:
            resampler = torchaudio.transforms.Resample(audio_fs, fs)
            data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :]
        else:
            y = librosa.resample(
                data_or_path_or_list.cpu().numpy(), orig_sr=int(audio_fs), target_sr=int(fs)
            )
            data_or_path_or_list = torch.from_numpy(y)
    return data_or_path_or_list


def _mp3_header_fields(data: bytes, offset: int):
    """Return MPEG audio header fields, including free-format bitrate index zero."""
    if offset + 4 > len(data):
        return None

    header = int.from_bytes(data[offset : offset + 4], "big")
    if header & 0xFFE00000 != 0xFFE00000:
        return None

    version_id = (header >> 19) & 0x3
    layer_id = (header >> 17) & 0x3
    bitrate_index = (header >> 12) & 0xF
    sample_rate_index = (header >> 10) & 0x3
    padding = (header >> 9) & 0x1
    if (
        version_id == 1
        or layer_id == 0
        or bitrate_index == 15
        or sample_rate_index == 3
    ):
        return None
    return version_id, layer_id, bitrate_index, sample_rate_index, padding


def _mp3_frame_length(data: bytes, offset: int) -> int:
    """Return a fixed-bitrate MPEG audio frame length, or zero if unavailable."""
    fields = _mp3_header_fields(data, offset)
    if fields is None:
        return 0
    version_id, layer_id, bitrate_index, sample_rate_index, padding = fields
    if bitrate_index == 0:
        return 0

    mpeg1_bitrates = {
        3: (32, 64, 96, 128, 160, 192, 224, 256, 288, 320, 352, 384, 416, 448),
        2: (32, 48, 56, 64, 80, 96, 112, 128, 160, 192, 224, 256, 320, 384),
        1: (32, 40, 48, 56, 64, 80, 96, 112, 128, 160, 192, 224, 256, 320),
    }
    mpeg2_bitrates = {
        3: (32, 48, 56, 64, 80, 96, 112, 128, 144, 160, 176, 192, 224, 256),
        2: (8, 16, 24, 32, 40, 48, 56, 64, 80, 96, 112, 128, 144, 160),
        1: (8, 16, 24, 32, 40, 48, 56, 64, 80, 96, 112, 128, 144, 160),
    }
    sample_rates = {
        3: (44100, 48000, 32000),
        2: (22050, 24000, 16000),
        0: (11025, 12000, 8000),
    }
    bitrate_table = mpeg1_bitrates if version_id == 3 else mpeg2_bitrates
    bitrate = bitrate_table[layer_id][bitrate_index - 1] * 1000
    sample_rate = sample_rates[version_id][sample_rate_index]

    if layer_id == 3:
        return (12 * bitrate // sample_rate + padding) * 4
    coefficient = 144 if version_id == 3 or layer_id == 2 else 72
    return coefficient * bitrate // sample_rate + padding


def _has_consecutive_mp3_frames(data: bytes) -> bool:
    """Avoid mistaking raw PCM that starts with one sync-like sample for MP3."""
    fields = _mp3_header_fields(data, 0)
    if fields is None:
        return False
    version_id, layer_id, bitrate_index, sample_rate_index, _ = fields
    if bitrate_index == 0:
        signature = version_id, layer_id, sample_rate_index
        padding_slot = 4 if layer_id == 3 else 1
        first_padding = fields[4] * padding_slot
        for second_offset in range(24, min(len(data) - 3, 8192)):
            next_fields = _mp3_header_fields(data, second_offset)
            if next_fields is not None and (
                next_fields[0], next_fields[1], next_fields[3]
            ) == signature and next_fields[2] == 0:
                base_frame_length = second_offset - first_padding
                third_offset = (
                    second_offset
                    + base_frame_length
                    + next_fields[4] * padding_slot
                )
                third_fields = _mp3_header_fields(data, third_offset)
                if third_fields is not None and (
                    third_fields[0], third_fields[1], third_fields[3]
                ) == signature and third_fields[2] == 0:
                    return True
        return False

    first_frame_length = _mp3_frame_length(data, 0)
    return first_frame_length > 0 and _mp3_frame_length(data, first_frame_length) > 0


def _is_audio_container(data: bytes) -> bool:
    """Return True if *data* starts with a recognised container-format magic header.

    Raw PCM byte streams have no header, so they will return False and the
    expensive pydub/ffmpeg validation round-trip can be skipped entirely.
    """
    if len(data) < 4:
        return False
    # WAV  – RIFF....WAVE
    if (
        len(data) >= 12
        and data[:4] in (b"RIFF", b"RIFX", b"RF64", b"BW64")
        and data[8:12] == b"WAVE"
    ):
        return True
    # MP3  – ID3 tag or at least two structurally valid MPEG audio frames
    has_mpeg_sync = data[0] == 0xFF and (data[1] & 0xE0) == 0xE0
    if data[:3] == b"ID3" or (has_mpeg_sync and _has_consecutive_mp3_frames(data)):
        return True
    # OGG
    if data[:4] == b"OggS":
        return True
    # FLAC
    if data[:4] == b"fLaC":
        return True
    # MP4 / M4A / AAC  – 'ftyp' box at offset 4
    if len(data) >= 8 and data[4:8] == b"ftyp":
        return True
    # WebM / MKV
    if data[:4] == b"\x1a\x45\xdf\xa3":
        return True
    return False


def load_bytes(input, input_format="auto"):
    """Convert raw PCM or container-formatted audio bytes to a waveform.

    Args:
        input (bytes): Raw int16 PCM or encoded audio-file bytes.
        input_format (str): "auto" preserves format detection. "pcm_s16le"
            reads mono signed little-endian PCM16 without probing or decoding.

    Returns:
        numpy.ndarray: Normalized float32 samples. Containers are resampled to
        16 kHz; raw PCM keeps its source rate and is not resampled here.
    """
    if input_format not in ("auto", "pcm_s16le"):
        raise ValueError("input_format must be 'auto' or 'pcm_s16le'")
    if input_format == "pcm_s16le":
        if len(input) % 2:
            raise ValueError("PCM16 input must contain complete 2-byte samples")
        return np.frombuffer(input, dtype="<i2").astype(np.float32) / 32768.0

    if _is_audio_container(input):
        try:
            waveform = load_audio_text_image_video(BytesIO(input), fs=16000)
        except Exception as exc:
            raise RuntimeError(
                "Failed to decode container-formatted audio bytes. Verify that the input is "
                "a complete supported audio file and that torchaudio, soundfile, or ffmpeg "
                "is available."
            ) from exc
        else:
            if isinstance(waveform, torch.Tensor):
                waveform = waveform.detach().cpu().numpy()
            return np.asarray(waveform, dtype=np.float32)

    middle_data = np.frombuffer(input, dtype=np.int16)
    middle_data = np.asarray(middle_data)
    if middle_data.dtype.kind not in "iu":
        raise TypeError("'middle_data' must be an array of integers")
    dtype = np.dtype("float32")
    if dtype.kind != "f":
        raise TypeError("'dtype' must be a floating point type")

    i = np.iinfo(middle_data.dtype)
    abs_max = 2 ** (i.bits - 1)
    offset = i.min + abs_max
    array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
    return array


def validate_frame_rate(
    input,
    fs: int = 16000,
):

    # 将文件读取为字节流
    """Validate frame rate.
    
        Args:
            input: Input audio/text data.
            fs: TODO.
        """
    byte_data = BytesIO(input)

    # 使用 pydub 加载音频
    try:
        audio = AudioSegment.from_file(byte_data)
    except:
        raise RuntimeError(
            "You are decoding the pcm data, please install pydub first. via `pip install pydub`."
        )

    # 确保采样率为 16000 Hz
    if audio.frame_rate != fs:
        audio = audio.set_frame_rate(fs)

        # 将重新采样后的音频导出为字节流
        output = BytesIO()
        audio.export(output, format="wav")
        output.seek(0)

        # 获取重新采样后的字节流数据
        input = output.read()

    return input


def extract_fbank(data, data_len=None, data_type: str = "sound", frontend=None, **kwargs):
    """Extract filter-bank features from audio data.

    Args:
        data: Audio samples (list of numpy arrays or tensors).
        data_len: Lengths of each sample.
        data_type (str): Input type ("sound", "fbank").
        frontend: Frontend instance for feature extraction.

    Returns:
        tuple: (features_tensor, feature_lengths, feature_times)
    """
    if isinstance(data, np.ndarray):
        data = torch.from_numpy(data)
        if len(data.shape) < 2:
            data = data[None, :]  # data: [batch, N]
        elif data.shape[0] > 1:
            data = data.mean(dim=0, keepdim=True)  # convert stereo/multi-channel to mono
        data_len = [data.shape[1]] if data_len is None else data_len
    elif isinstance(data, torch.Tensor):
        if len(data.shape) < 2:
            data = data[None, :]  # data: [batch, N]
        elif data.shape[0] > 1:
            data = data.mean(dim=0, keepdim=True)  # convert stereo/multi-channel to mono
        data_len = [data.shape[1]] if data_len is None else data_len
    elif isinstance(data, (list, tuple)):
        data_list, data_len = [], []
        for data_i in data:
            if isinstance(data_i, np.ndarray):
                data_i = torch.from_numpy(data_i)
            data_list.append(data_i)
            data_len.append(data_i.shape[0])
        data = pad_sequence(data_list, batch_first=True)  # data: [batch, N]

    data, data_len = frontend(data, data_len, **kwargs)

    if isinstance(data_len, (list, tuple)):
        data_len = torch.tensor([data_len])
    return data.to(torch.float32), data_len.to(torch.int32)


def _load_audio_ffmpeg(file, sr: int = 16000):
    """
    Open an audio file and read as mono waveform, resampling as necessary

    Parameters
    ----------
    file: str or file-like object
        The audio file or byte stream to open

    sr: int
        The sample rate to resample the audio if necessary

    Returns
    -------
    A NumPy array containing the audio waveform, in float32 dtype.
    """

    # This launches a subprocess to decode audio while down-mixing
    # and resampling as necessary.  Requires the ffmpeg CLI in PATH.
    # fmt: off
    pcm_params = []
    stdin_data = None
    if hasattr(file, "read"):
        if hasattr(file, "seek"):
            file.seek(0)
        stdin_data = file.read()
        input_source = "pipe:0"
    else:
        input_source = os.fspath(file)
    if isinstance(input_source, str) and input_source.lower().endswith('.pcm'):
        pcm_params = [
            "-f", "s16le",
            "-ar", str(sr),
            "-ac", "1"
        ]

    cmd = [
        "ffmpeg",
        "-nostdin",
        "-threads", "0",
        *pcm_params,  # PCM files need input format specified before -i since PCM is raw data without headers
        "-i", input_source,
        "-f", "s16le",
        "-ac", "1",
        "-acodec", "pcm_s16le",
        "-ar", str(sr),
        "-"
    ]
    # fmt: on
    try:
        out = run(cmd, input=stdin_data, capture_output=True, check=True).stdout
    except CalledProcessError as e:
        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e

    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0