import torch
import torch.nn as nn

class WordDropout(nn.Module):
    """ A word dropout layer that's designed for embedded inputs (e.g., any inputs to an LSTM layer).
    Given a batch of embedded inputs, this layer randomly set some of them to be a replacement state.
    Note that this layer assumes the last dimension of the input to be the hidden dimension of a unit.
    """
    def __init__(self, dropprob):
        super().__init__()
        self.dropprob = dropprob

    def forward(self, x, replacement=None):
        if not self.training or self.dropprob == 0:
            return x

        masksize = [y for y in x.size()]
        masksize[-1] = 1
        dropmask = torch.rand(*masksize, device=x.device) < self.dropprob

        res = x.masked_fill(dropmask, 0)
        if replacement is not None:
            res = res + dropmask.float() * replacement

        return res
    
    def extra_repr(self):
        return 'p={}'.format(self.dropprob)

class LockedDropout(nn.Module):
    """
    A variant of dropout layer that consistently drops out the same parameters over time. Also known as the variational dropout. 
    This implementation was modified from the LockedDropout implementation in the flair library (https://github.com/zalandoresearch/flair).
    """
    def __init__(self, dropprob, batch_first=True):
        super().__init__()
        self.dropprob = dropprob
        self.batch_first = batch_first

    def forward(self, x):
        if not self.training or self.dropprob == 0:
            return x

        if not self.batch_first:
            m = x.new_empty(1, x.size(1), x.size(2), requires_grad=False).bernoulli_(1 - self.dropprob)
        else:
            m = x.new_empty(x.size(0), 1, x.size(2), requires_grad=False).bernoulli_(1 - self.dropprob)

        mask = m.div(1 - self.dropprob).expand_as(x)
        return mask * x
    
    def extra_repr(self):
        return 'p={}'.format(self.dropprob)

class SequenceUnitDropout(nn.Module):
    """ A unit dropout layer that's designed for input of sequence units (e.g., word sequence, char sequence, etc.).
    Given a sequence of unit indices, this layer randomly set some of them to be a replacement id (usually set to be <UNK>).
    """
    def __init__(self, dropprob, replacement_id):
        super().__init__()
        self.dropprob = dropprob
        self.replacement_id = replacement_id

    def forward(self, x):
        """ :param: x must be a LongTensor of unit indices. """
        if not self.training or self.dropprob == 0:
            return x
        masksize = [y for y in x.size()]
        dropmask = torch.rand(*masksize, device=x.device) < self.dropprob
        res = x.masked_fill(dropmask, self.replacement_id)
        return res
    
    def extra_repr(self):
        return 'p={}, replacement_id={}'.format(self.dropprob, self.replacement_id)


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