# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
#   LICENSE is in incl_licenses directory.

# Modified by Yiwei Guo, 2024
# including conditioned snakebeta activation

import torch
from torch import nn, sin, pow
from torch.nn import Parameter


class Snake(nn.Module):
    '''
    Implementation of a sine-based periodic activation function
    Shape:
        - Input: (B, C, T)
        - Output: (B, C, T), same shape as the input
    Parameters:
        - alpha - trainable parameter
    References:
        - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
        https://arxiv.org/abs/2006.08195
    Examples:
        >>> a1 = snake(256)
        >>> x = torch.randn(256)
        >>> x = a1(x)
    '''
    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
        '''
        Initialization.
        INPUT:
            - in_features: shape of the input
            - alpha: trainable parameter
            alpha is initialized to 1 by default, higher values = higher-frequency.
            alpha will be trained along with the rest of your model.
        '''
        super(Snake, self).__init__()
        self.in_features = in_features

        # initialize alpha
        self.alpha_logscale = alpha_logscale
        if self.alpha_logscale: # log scale alphas initialized to zeros
            self.alpha = Parameter(torch.zeros(in_features) * alpha)
        else: # linear scale alphas initialized to ones
            self.alpha = Parameter(torch.ones(in_features) * alpha)

        self.alpha.requires_grad = alpha_trainable

        self.no_div_by_zero = 0.000000001

    def forward(self, x):
        '''
        Forward pass of the function.
        Applies the function to the input elementwise.
        Snake := x + 1/a * sin^2 (xa)
        '''
        alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
        if self.alpha_logscale:
            alpha = torch.exp(alpha)
        x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)

        return x


class SnakeBeta(nn.Module):
    '''
    A modified Snake function which uses separate parameters for the magnitude of the periodic components
    Shape:
        - Input: (B, C, T)
        - Output: (B, C, T), same shape as the input
    Parameters:
        - alpha - trainable parameter that controls frequency
        - beta - trainable parameter that controls magnitude
    References:
        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
        https://arxiv.org/abs/2006.08195
    Examples:
        >>> a1 = snakebeta(256)
        >>> x = torch.randn(256)
        >>> x = a1(x)
    '''
    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
        '''
        Initialization.
        INPUT:
            - in_features: shape of the input
            - alpha - trainable parameter that controls frequency
            - beta - trainable parameter that controls magnitude
            alpha is initialized to 1 by default, higher values = higher-frequency.
            beta is initialized to 1 by default, higher values = higher-magnitude.
            alpha will be trained along with the rest of your model.
        '''
        super(SnakeBeta, self).__init__()
        self.in_features = in_features

        # initialize alpha
        self.alpha_logscale = alpha_logscale
        if self.alpha_logscale: # log scale alphas initialized to zeros
            self.alpha = Parameter(torch.zeros(in_features) * alpha)
            self.beta = Parameter(torch.zeros(in_features) * alpha)
        else: # linear scale alphas initialized to ones
            self.alpha = Parameter(torch.ones(in_features) * alpha)
            self.beta = Parameter(torch.ones(in_features) * alpha)

        self.alpha.requires_grad = alpha_trainable
        self.beta.requires_grad = alpha_trainable

        self.no_div_by_zero = 0.000000001

    def forward(self, x, cond=None):
        '''
        Forward pass of the function.
        Applies the function to the input elementwise.
        SnakeBeta ∶= x + 1/b * sin^2 (xa)
        '''
        alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
        beta = self.beta.unsqueeze(0).unsqueeze(-1)
        if self.alpha_logscale:
            alpha = torch.exp(alpha)
            beta = torch.exp(beta)
        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)

        return x
    

class SnakeBetaWithCondition(nn.Module):
    '''
    A modified Snake function which uses separate parameters for the magnitude of the periodic components
    Shape:
        - Input: (B, C, T)
        - Condition: (B, D), where D-dimension will be mapped to C dimensions
        - Output: (B, C, T), same shape as the input
    Parameters:
        - alpha - trainable parameter that controls frequency
        - beta - trainable parameter that controls magnitude
        - condition_alpha_prenet - trainable parameter that controls alpha and beta using condition
    References:
        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
        https://arxiv.org/abs/2006.08195
    Examples:
        >>> a1 = snakebeta(256, 128)
        >>> x = torch.randn(256)
        >>> cond = torch.randn(128)
        >>> x = a1(x, cond)
    '''
    def __init__(self, in_features, condition_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
        '''
        Initialization.
        INPUT:
            - in_features: dimension of the input
            - condition_features: dimension of the condition vectors
            - alpha - trainable parameter that controls frequency
            - beta - trainable parameter that controls magnitude
            alpha is initialized to 1 by default, higher values = higher-frequency.
            beta is initialized to 1 by default, higher values = higher-magnitude.
            alpha, beta will be trained along with the rest of your model.
        '''
        super(SnakeBetaWithCondition, self).__init__()
        self.in_features = in_features
        
        self.condition_alpha_prenet = torch.nn.Linear(condition_features, in_features)
        # self.condition_beta_prenet = torch.nn.Linear(condition_features, in_features)

        # initialize alpha
        self.alpha_logscale = alpha_logscale
        if self.alpha_logscale: # log scale alphas initialized to zeros
            self.alpha = Parameter(torch.zeros(in_features) * alpha)
            self.beta = Parameter(torch.zeros(in_features) * alpha)
        else: # linear scale alphas initialized to ones
            self.alpha = Parameter(torch.ones(in_features) * alpha)
            self.beta = Parameter(torch.ones(in_features) * alpha)

        self.alpha.requires_grad = alpha_trainable
        self.beta.requires_grad = alpha_trainable

        self.no_div_by_zero = 0.000000001

    def forward(self, x, condition):
        '''
        condition: [B, D]
        Forward pass of the function.
        Applies the function to the input elementwise.
        SnakeBeta := x + 1/b * sin^2 (xa)
        '''
        alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
        beta = self.beta.unsqueeze(0).unsqueeze(-1)
        if self.alpha_logscale:
            alpha = torch.exp(alpha)
            beta = torch.exp(beta)
        
        condition = torch.tanh(self.condition_alpha_prenet(condition).unsqueeze(-1))  # Same prenet for both alpha and beta, to save parameters
        alpha = alpha + condition
        beta = beta + 0.5 * condition  # multiply 0.5 for avoiding beta being too small
        
        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)

        return x