from distutils.version import LooseVersion
from typing import Sequence
from typing import Union

import torch

from espnet2.asr.specaug.abs_specaug import AbsSpecAug
from espnet2.layers.mask_along_axis import MaskAlongAxis
from espnet2.layers.time_warp import TimeWarp


if LooseVersion(torch.__version__) >= LooseVersion("1.1"):
    DEFAULT_TIME_WARP_MODE = "bicubic"
else:
    # pytorch1.0 doesn't implement bicubic
    DEFAULT_TIME_WARP_MODE = "bilinear"


class SpecAug(AbsSpecAug):
    """Implementation of SpecAug.

    Reference:
        Daniel S. Park et al.
        "SpecAugment: A Simple Data
         Augmentation Method for Automatic Speech Recognition"

    .. warning::
        When using cuda mode, time_warp doesn't have reproducibility
        due to `torch.nn.functional.interpolate`.

    """

    def __init__(
        self,
        apply_time_warp: bool = True,
        time_warp_window: int = 5,
        time_warp_mode: str = DEFAULT_TIME_WARP_MODE,
        apply_freq_mask: bool = True,
        freq_mask_width_range: Union[int, Sequence[int]] = (0, 20),
        num_freq_mask: int = 2,
        apply_time_mask: bool = True,
        time_mask_width_range: Union[int, Sequence[int]] = (0, 100),
        num_time_mask: int = 2,
    ):
        if not apply_time_warp and not apply_time_mask and not apply_freq_mask:
            raise ValueError(
                "Either one of time_warp, time_mask, or freq_mask should be applied",
            )
        super().__init__()
        self.apply_time_warp = apply_time_warp
        self.apply_freq_mask = apply_freq_mask
        self.apply_time_mask = apply_time_mask

        if apply_time_warp:
            self.time_warp = TimeWarp(window=time_warp_window, mode=time_warp_mode)
        else:
            self.time_warp = None

        if apply_freq_mask:
            self.freq_mask = MaskAlongAxis(
                dim="freq",
                mask_width_range=freq_mask_width_range,
                num_mask=num_freq_mask,
            )
        else:
            self.freq_mask = None

        if apply_time_mask:
            self.time_mask = MaskAlongAxis(
                dim="time",
                mask_width_range=time_mask_width_range,
                num_mask=num_time_mask,
            )
        else:
            self.time_mask = None

    def forward(self, x, x_lengths=None):
        if self.time_warp is not None:
            x, x_lengths = self.time_warp(x, x_lengths)
        if self.freq_mask is not None:
            x, x_lengths = self.freq_mask(x, x_lengths)
        if self.time_mask is not None:
            x, x_lengths = self.time_mask(x, x_lengths)
        return x, x_lengths