"""SpecAugment module."""
from typing import Optional, Sequence, Union

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


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 = "bicubic",
        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: Optional[Union[int, Sequence[int]]] = None,
        time_mask_width_ratio_range: Optional[Union[float, Sequence[float]]] = None,
        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"
            )
        if (
            apply_time_mask
            and (time_mask_width_range is not None)
            and (time_mask_width_ratio_range is not None)
        ):
            raise ValueError(
                'Either one of "time_mask_width_range" or '
                '"time_mask_width_ratio_range" can be used'
            )
        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:
            if time_mask_width_range is not None:
                self.time_mask = MaskAlongAxis(
                    dim="time",
                    mask_width_range=time_mask_width_range,
                    num_mask=num_time_mask,
                )
            elif time_mask_width_ratio_range is not None:
                self.time_mask = MaskAlongAxisVariableMaxWidth(
                    dim="time",
                    mask_width_ratio_range=time_mask_width_ratio_range,
                    num_mask=num_time_mask,
                )
            else:
                raise ValueError(
                    'Either one of "time_mask_width_range" or '
                    '"time_mask_width_ratio_range" should be used.'
                )
        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