import torch
from torch import nn


class AdaptiveEmbedding(nn.Module):
    def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, sample_softmax=False):
        super().__init__()

        self.n_token = n_token
        self.d_embed = d_embed

        self.cutoffs = cutoffs + [n_token]
        self.div_val = div_val
        self.d_proj = d_proj

        self.emb_scale = d_proj**0.5

        self.cutoff_ends = [0] + self.cutoffs

        self.emb_layers = nn.ModuleList()
        self.emb_projs = nn.ParameterList()
        if div_val == 1:
            self.emb_layers.append(nn.Embedding(n_token, d_embed, sparse=sample_softmax > 0))
            if d_proj != d_embed:
                self.emb_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed)))
        else:
            for i in range(len(self.cutoffs)):
                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
                d_emb_i = d_embed // (div_val**i)
                self.emb_layers.append(nn.Embedding(r_idx - l_idx, d_emb_i))
                self.emb_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_emb_i)))

    def forward(self, inp):
        if self.div_val == 1:
            embed = self.emb_layers[0](inp)
            if self.d_proj != self.d_embed:
                embed = nn.functional.linear(embed, self.emb_projs[0])
        else:
            param = next(self.parameters())
            inp_flat = inp.view(-1)
            emb_flat = torch.zeros([inp_flat.size(0), self.d_proj], dtype=param.dtype, device=param.device)
            for i in range(len(self.cutoffs)):
                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]

                mask_i = (inp_flat >= l_idx) & (inp_flat < r_idx)
                indices_i = mask_i.nonzero().squeeze()

                if indices_i.numel() == 0:
                    continue

                inp_i = inp_flat.index_select(0, indices_i) - l_idx
                emb_i = self.emb_layers[i](inp_i)
                emb_i = nn.functional.linear(emb_i, self.emb_projs[i])

                emb_flat.index_copy_(0, indices_i, emb_i)

            embed_shape = inp.size() + (self.d_proj,)
            embed = emb_flat.view(embed_shape)

        embed.mul_(self.emb_scale)

        return embed


class PositionalEmbeddingAux(nn.Module):
    def __init__(self, demb):
        super().__init__()

        self.demb = demb

        inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
        self.register_buffer("inv_freq", inv_freq)

    def forward(self, pos_seq, bsz=None):
        sinusoid_inp = torch.outer(pos_seq, self.inv_freq)
        pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1)

        if bsz is not None:
            return pos_emb[:, None, :].expand(-1, bsz, -1)
        else:
            return pos_emb[:, None, :]


class PositionalEmbedding(PositionalEmbeddingAux):
    def forward(self, pos_seq, bsz=None):
        return super().forward(pos_seq.squeeze(0), bsz=bsz).squeeze(1)