from __future__ import division, print_function
import os.path

import torch
import _torch_sox

from torchaudio import transforms, datasets, sox_effects, legacy


def check_input(src):
    if not torch.is_tensor(src):
        raise TypeError('Expected a tensor, got %s' % type(src))
    if src.is_cuda:
        raise TypeError('Expected a CPU based tensor, got %s' % type(src))


def load(filepath,
         out=None,
         normalization=True,
         channels_first=True,
         num_frames=0,
         offset=0,
         signalinfo=None,
         encodinginfo=None,
         filetype=None):
    """Loads an audio file from disk into a Tensor

    Args:
        filepath (string or pathlib.Path): path to audio file
        out (Tensor, optional): an output Tensor to use instead of creating one
        normalization (bool, number, or callable, optional): If boolean `True`, then output is divided by `1 << 31`
                                                             (assumes signed 32-bit audio), and normalizes to `[0, 1]`.
                                                             If `number`, 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.
        channels_first (bool): 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.
        offset (int, optional): number of frames from the start of the file to begin data loading.
        signalinfo (sox_signalinfo_t, optional): a sox_signalinfo_t type, which could be helpful if the
                                                 audio type cannot be automatically determined
        encodinginfo (sox_encodinginfo_t, optional): a sox_encodinginfo_t type, which could be set if the
                                                     audio type cannot be automatically determined
        filetype (str, optional): a filetype or extension to be set if sox cannot determine it automatically

    Returns: tuple(Tensor, int)
       - Tensor: 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
       - int: 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.

    """
    # stringify if `pathlib.Path` (noop if already `str`)
    filepath = str(filepath)
    # check if valid file
    if not os.path.isfile(filepath):
        raise OSError("{} not found or is a directory".format(filepath))

    # initialize output tensor
    if out is not None:
        check_input(out)
    else:
        out = torch.FloatTensor()

    if num_frames < -1:
        raise ValueError("Expected value for num_samples -1 (entire file) or >=0")
    if offset < 0:
        raise ValueError("Expected positive offset value")

    sample_rate = _torch_sox.read_audio_file(filepath,
                                             out,
                                             channels_first,
                                             num_frames,
                                             offset,
                                             signalinfo,
                                             encodinginfo,
                                             filetype)

    # normalize if needed
    _audio_normalization(out, normalization)

    return out, sample_rate


def save(filepath, src, sample_rate, precision=16, channels_first=True):
    """Convenience function for `save_encinfo`.

    """
    si = sox_signalinfo_t()
    ch_idx = 0 if channels_first else 1
    si.rate = sample_rate
    si.channels = 1 if src.dim() == 1 else src.size(ch_idx)
    si.length = src.numel()
    si.precision = precision
    return save_encinfo(filepath, src, channels_first, si)


def save_encinfo(filepath,
                 src,
                 channels_first=True,
                 signalinfo=None,
                 encodinginfo=None,
                 filetype=None):
    """Saves a Tensor of an audio signal to disk as a standard format like mp3, wav, etc.

    Args:
        filepath (string): 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): Set channels first or length first in result.  Default: ``True``
        signalinfo (sox_signalinfo_t): a sox_signalinfo_t type, which could be helpful if the
                                       audio type cannot be automatically determined
        encodinginfo (sox_encodinginfo_t, optional): a sox_encodinginfo_t type, which could be set if the
                                                     audio type cannot be automatically determined
        filetype (str, optional): a filetype or extension to be set if sox cannot determine it automatically

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


def info(filepath):
    """Gets metadata from an audio file without loading the signal.

     Args:
        filepath (string): path to audio file

     Returns: tuple(si, ei)
       - si (sox_signalinfo_t): signal info as a python object
       - ei (sox_encodinginfo_t): encoding info as a python object

     Example::
         >>> si, ei = torchaudio.info('foo.wav')
         >>> rate, channels, encoding = si.rate, si.channels, ei.encoding
     """
    return _torch_sox.get_info(filepath)


def sox_signalinfo_t():
    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
    """
    return _torch_sox.sox_signalinfo_t()


def sox_encodinginfo_t():
    """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)

    """
    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


def get_sox_encoding_t(i=None):
    """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
    Returns:
        sox_encoding_t: a sox_encoding_t type for output encoding
    """
    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)


def get_sox_option_t(i=2):
    """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.
                           Defaults to sox_option_default.
    Returns:
        sox_option_t: a sox_option_t type
    """
    if i is None:
        return _torch_sox.sox_option_t
    else:
        return _torch_sox.sox_option_t(i)


def get_sox_bool(i=0):
    """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.
                           Defaults to sox_false.
    Returns:
        sox_bool: a sox_bool type
    """
    if i is None:
        return _torch_sox.sox_bool
    else:
        return _torch_sox.sox_bool(i)


def initialize_sox():
    """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.
    """
    return _torch_sox.initialize_sox()


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


def _audio_normalization(signal, normalization):
    """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
