import os.path
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union

import torch
from torch import Tensor
from torchaudio import (
    compliance,
    datasets,
    kaldi_io,
    sox_effects,
    transforms
)
from torchaudio._backend import (
    check_input,
    _audio_backend_guard,
    _get_audio_backend_module,
    get_audio_backend,
    set_audio_backend,
)
from torchaudio._soundfile_backend import SignalInfo, EncodingInfo

try:
    from .version import __version__, git_version  # noqa: F401
except ImportError:
    pass


def load(filepath: Union[str, Path],
         out: Optional[Tensor] = None,
         normalization: Union[bool, float, Callable] = True,
         channels_first: bool = True,
         num_frames: int = 0,
         offset: int = 0,
         signalinfo: Optional[SignalInfo] = None,
         encodinginfo: Optional[EncodingInfo] = None,
         filetype: Optional[str] = None) -> Tuple[Tensor, int]:
    r"""Loads an audio file from disk into a tensor

    Args:
        filepath (str or Path): Path to audio file
        out (Tensor, optional): An output tensor to use instead of creating one. (Default: ``None``)
        normalization (bool, float, or callable, optional): If boolean `True`, then output is divided by `1 << 31`
            (assumes signed 32-bit audio), and normalizes to `[-1, 1]`.
            If `float`, then output is divided by that number
            If `Callable`, then the output is passed as a parameter
            to the given function, then the output is divided by
            the result. (Default: ``True``)
        channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``)
        num_frames (int, optional): Number of frames to load.  0 to load everything after the offset.
            (Default: ``0``)
        offset (int, optional): Number of frames from the start of the file to begin data loading.
            (Default: ``0``)
        signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the
            audio type cannot be automatically determined. (Default: ``None``)
        encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the
            audio type cannot be automatically determined. (Default: ``None``)
        filetype (str, optional): A filetype or extension to be set if sox cannot determine it
            automatically. (Default: ``None``)

    Returns:
        (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number
        of audio frames and C is the number of channels. An integer which is the sample rate of the
        audio (as listed in the metadata of the file)

    Example
        >>> data, sample_rate = torchaudio.load('foo.mp3')
        >>> print(data.size())
        torch.Size([2, 278756])
        >>> print(sample_rate)
        44100
        >>> data_vol_normalized, _ = torchaudio.load('foo.mp3', normalization=lambda x: torch.abs(x).max())
        >>> print(data_vol_normalized.abs().max())
        1.

    """

    return _get_audio_backend_module().load(
        filepath,
        out=out,
        normalization=normalization,
        channels_first=channels_first,
        num_frames=num_frames,
        offset=offset,
        signalinfo=signalinfo,
        encodinginfo=encodinginfo,
        filetype=filetype,
    )


def load_wav(filepath: Union[str, Path], **kwargs: Any) -> Tuple[Tensor, int]:
    r""" Loads a wave file. It assumes that the wav file uses 16 bit per sample that needs normalization by shifting
    the input right by 16 bits.

    Args:
        filepath (str or Path): Path to audio file

    Returns:
        (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number
        of audio frames and C is the number of channels. An integer which is the sample rate of the
        audio (as listed in the metadata of the file)
    """
    kwargs['normalization'] = 1 << 16
    return load(filepath, **kwargs)


def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None:
    r"""Convenience function for `save_encinfo`.

    Args:
        filepath (str): Path to audio file
        src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is
            the number of audio frames, C is the number of channels
        sample_rate (int): An integer which is the sample rate of the
            audio (as listed in the metadata of the file)
        precision (int, optional): Bit precision (Default: ``16``)
        channels_first (bool, optional): Set channels first or length first in result. (
            Default: ``True``)
    """

    return _get_audio_backend_module().save(
        filepath, src, sample_rate, precision=precision, channels_first=channels_first
    )


@_audio_backend_guard("sox")
def save_encinfo(filepath: str,
                 src: Tensor,
                 channels_first: bool = True,
                 signalinfo: Optional[SignalInfo] = None,
                 encodinginfo: Optional[EncodingInfo] = None,
                 filetype: Optional[str] = None) -> None:
    r"""Saves a tensor of an audio signal to disk as a standard format like mp3, wav, etc.

    Args:
        filepath (str): Path to audio file
        src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is
            the number of audio frames, C is the number of channels
        channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``)
        signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the
            audio type cannot be automatically determined (Default: ``None``).
        encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the
            audio type cannot be automatically determined (Default: ``None``).
        filetype (str, optional): A filetype or extension to be set if sox cannot determine it
            automatically. (Default: ``None``)

    Example
        >>> data, sample_rate = torchaudio.load('foo.mp3')
        >>> torchaudio.save('foo.wav', data, sample_rate)

    """
    ch_idx, len_idx = (0, 1) if channels_first else (1, 0)

    # check if save directory exists
    abs_dirpath = os.path.dirname(os.path.abspath(filepath))
    if not os.path.isdir(abs_dirpath):
        raise OSError("Directory does not exist: {}".format(abs_dirpath))
    # check that src is a CPU tensor
    check_input(src)
    # Check/Fix shape of source data
    if src.dim() == 1:
        # 1d tensors as assumed to be mono signals
        src.unsqueeze_(ch_idx)
    elif src.dim() > 2 or src.size(ch_idx) > 16:
        # assumes num_channels < 16
        raise ValueError(
            "Expected format where C < 16, but found {}".format(src.size()))
    # sox stores the sample rate as a float, though practically sample rates are almost always integers
    # convert integers to floats
    if signalinfo:
        if not isinstance(signalinfo.rate, float):
            if float(signalinfo.rate) == signalinfo.rate:
                signalinfo.rate = float(signalinfo.rate)
            else:
                raise TypeError('Sample rate should be a float or int')
        # check if the bit precision (i.e. bits per sample) is an integer
        if not isinstance(signalinfo.precision, int):
            if int(signalinfo.precision) == signalinfo.precision:
                signalinfo.precision = int(signalinfo.precision)
            else:
                raise TypeError('Bit precision should be an integer')
    # programs such as librosa normalize the signal, unnormalize if detected
    if src.min() >= -1.0 and src.max() <= 1.0:
        src = src * (1 << 31)
        src = src.long()
    # set filetype and allow for files with no extensions
    extension = os.path.splitext(filepath)[1]
    filetype = extension[1:] if len(extension) > 0 else filetype
    # transpose from C x L -> L x C
    if channels_first:
        src = src.transpose(1, 0)
    # save data to file
    src = src.contiguous()

    import _torch_sox
    _torch_sox.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype)


def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]:
    r"""Gets metadata from an audio file without loading the signal.

     Args:
        filepath (str): Path to audio file

     Returns:
        (sox_signalinfo_t, sox_encodinginfo_t): A si (sox_signalinfo_t) signal
        info as a python object. An ei (sox_encodinginfo_t) encoding info

     Example
         >>> si, ei = torchaudio.info('foo.wav')
         >>> rate, channels, encoding = si.rate, si.channels, ei.encoding
     """

    return _get_audio_backend_module().info(filepath)


@_audio_backend_guard("sox")
def sox_signalinfo_t() -> SignalInfo:
    r"""Create a sox_signalinfo_t object. This object can be used to set the sample
    rate, number of channels, length, bit precision and headroom multiplier
    primarily for effects

    Returns: sox_signalinfo_t(object)
        - rate (float), sample rate as a float, practically will likely be an integer float
        - channel (int), number of audio channels
        - precision (int), bit precision
        - length (int), length of audio in samples * channels, 0 for unspecified and -1 for unknown
        - mult (float, optional), headroom multiplier for effects and ``None`` for no multiplier

    Example
        >>> si = torchaudio.sox_signalinfo_t()
        >>> si.channels = 1
        >>> si.rate = 16000.
        >>> si.precision = 16
        >>> si.length = 0
    """

    import _torch_sox
    return _torch_sox.sox_signalinfo_t()


@_audio_backend_guard("sox")
def sox_encodinginfo_t() -> EncodingInfo:
    r"""Create a sox_encodinginfo_t object.  This object can be used to set the encoding
    type, bit precision, compression factor, reverse bytes, reverse nibbles,
    reverse bits and endianness.  This can be used in an effects chain to encode the
    final output or to save a file with a specific encoding.  For example, one could
    use the sox ulaw encoding to do 8-bit ulaw encoding.  Note in a tensor output
    the result will be a 32-bit number, but number of unique values will be determined by
    the bit precision.

    Returns: sox_encodinginfo_t(object)
        - encoding (sox_encoding_t), output encoding
        - bits_per_sample (int), bit precision, same as `precision` in sox_signalinfo_t
        - compression (float), compression for lossy formats, 0.0 for default compression
        - reverse_bytes (sox_option_t), reverse bytes, use sox_option_default
        - reverse_nibbles (sox_option_t), reverse nibbles, use sox_option_default
        - reverse_bits (sox_option_t), reverse bytes, use sox_option_default
        - opposite_endian (sox_bool), change endianness, use sox_false

    Example
        >>> ei = torchaudio.sox_encodinginfo_t()
        >>> ei.encoding = torchaudio.get_sox_encoding_t(1)
        >>> ei.bits_per_sample = 16
        >>> ei.compression = 0
        >>> ei.reverse_bytes = torchaudio.get_sox_option_t(2)
        >>> ei.reverse_nibbles = torchaudio.get_sox_option_t(2)
        >>> ei.reverse_bits = torchaudio.get_sox_option_t(2)
        >>> ei.opposite_endian = torchaudio.get_sox_bool(0)

    """

    import _torch_sox
    ei = _torch_sox.sox_encodinginfo_t()
    sdo = get_sox_option_t(2)  # sox_default_option
    ei.reverse_bytes = sdo
    ei.reverse_nibbles = sdo
    ei.reverse_bits = sdo
    return ei


@_audio_backend_guard("sox")
def get_sox_encoding_t(i: int = None) -> EncodingInfo:
    r"""Get enum of sox_encoding_t for sox encodings.

    Args:
        i (int, optional): Choose type or get a dict with all possible options
            use ``__members__`` to see all options when not specified. (Default: ``None``)

    Returns:
        sox_encoding_t: A sox_encoding_t type for output encoding
    """

    import _torch_sox
    if i is None:
        # one can see all possible values using the .__members__ attribute
        return _torch_sox.sox_encoding_t
    else:
        return _torch_sox.sox_encoding_t(i)


@_audio_backend_guard("sox")
def get_sox_option_t(i: int = 2) -> Any:
    r"""Get enum of sox_option_t for sox encodinginfo options.

    Args:
        i (int, optional): Choose type or get a dict with all possible options
            use ``__members__`` to see all options when not specified.
            (Default: ``sox_option_default`` or ``2``)
    Returns:
        sox_option_t: A sox_option_t type
    """

    import _torch_sox
    if i is None:
        return _torch_sox.sox_option_t
    else:
        return _torch_sox.sox_option_t(i)


@_audio_backend_guard("sox")
def get_sox_bool(i: int = 0) -> Any:
    r"""Get enum of sox_bool for sox encodinginfo options.

    Args:
        i (int, optional): Choose type or get a dict with all possible options
            use ``__members__`` to see all options when not specified. (Default:
            ``sox_false`` or ``0``)

    Returns:
        sox_bool: A sox_bool type
    """

    import _torch_sox
    if i is None:
        return _torch_sox.sox_bool
    else:
        return _torch_sox.sox_bool(i)


@_audio_backend_guard("sox")
def initialize_sox() -> int:
    """Initialize sox for use with effects chains.  This is not required for simple
    loading.  Importantly, only run `initialize_sox` once and do not shutdown
    after each effect chain, but rather once you are finished with all effects chains.
    """

    import _torch_sox
    return _torch_sox.initialize_sox()


@_audio_backend_guard("sox")
def shutdown_sox() -> int:
    """Showdown sox for effects chain.  Not required for simple loading.  Importantly,
    only call once.  Attempting to re-initialize sox will result in seg faults.
    """

    import _torch_sox
    return _torch_sox.shutdown_sox()


def _audio_normalization(signal: Tensor, normalization: Union[bool, float, Callable]) -> None:
    """Audio normalization of a tensor in-place.  The normalization can be a bool,
    a number, or a callable that takes the audio tensor as an input. SoX uses
    32-bit signed integers internally, thus bool normalizes based on that assumption.
    """

    if not normalization:
        return

    if isinstance(normalization, bool):
        normalization = 1 << 31

    if isinstance(normalization, (float, int)):
        # normalize with custom value
        a = normalization
        signal /= a
    elif callable(normalization):
        a = normalization(signal)
        signal /= a
