import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np


# Constants

class Constants:
    PAD_WORD = '<blank>'
    UNK_WORD = '<unk>'
    BOS_WORD = '<s>'
    EOS_WORD = '</s>'

# Layers


class EncoderLayer(nn.Module):
    ''' Compose with two layers '''

    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1):
        super(EncoderLayer, self).__init__()
        self.slf_attn = MultiHeadAttention(
            n_head, d_model, d_k, d_v, dropout=dropout)
        self.pos_ffn = PositionwiseFeedForward(
            d_model, d_inner, dropout=dropout)

    def forward(self, enc_input, slf_attn_mask=None):
        enc_output, enc_slf_attn = self.slf_attn(
            enc_input, enc_input, enc_input, mask=slf_attn_mask)
        enc_output = self.pos_ffn(enc_output)
        return enc_output, enc_slf_attn


class DecoderLayer(nn.Module):
    ''' Compose with three layers '''

    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1):
        super(DecoderLayer, self).__init__()
        self.slf_attn = MultiHeadAttention(
            n_head, d_model, d_k, d_v, dropout=dropout)
        self.enc_attn = MultiHeadAttention(
            n_head, d_model, d_k, d_v, dropout=dropout)
        self.pos_ffn = PositionwiseFeedForward(
            d_model, d_inner, dropout=dropout)

    def forward(
            self, dec_input, enc_output,
            slf_attn_mask=None, dec_enc_attn_mask=None):
        dec_output, dec_slf_attn = self.slf_attn(
            dec_input, dec_input, dec_input, mask=slf_attn_mask)
        dec_output, dec_enc_attn = self.enc_attn(
            dec_output, enc_output, enc_output, mask=dec_enc_attn_mask)
        dec_output = self.pos_ffn(dec_output)
        return dec_output, dec_slf_attn, dec_enc_attn


# Models

def get_pad_mask(seq, pad_idx):
    return (seq != pad_idx).unsqueeze(-2)


def get_subsequent_mask(seq):
    ''' For masking out the subsequent info. '''
    sz_b, len_s = seq.size()
    subsequent_mask = (1 - torch.triu(
        torch.ones((1, len_s, len_s), device=seq.device), diagonal=1)).bool()
    return subsequent_mask


class PositionalEncoding(nn.Module):

    def __init__(self, d_hid, n_position=200):
        super(PositionalEncoding, self).__init__()

        # Not a parameter
        self.register_buffer(
            'pos_table', self._get_sinusoid_encoding_table(n_position, d_hid))

    def _get_sinusoid_encoding_table(self, n_position, d_hid):
        ''' Sinusoid position encoding table '''
        # TODO: make it with torch instead of numpy

        def get_position_angle_vec(position):
            return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]

        sinusoid_table = np.array([get_position_angle_vec(pos_i)
                                  for pos_i in range(n_position)])
        sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
        sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1

        return torch.FloatTensor(sinusoid_table).unsqueeze(0)

    def forward(self, x):
        return x + self.pos_table[:, :x.size(1)].clone().detach()


class Encoder(nn.Module):
    ''' A encoder model with self attention mechanism. '''

    def __init__(
            self, n_src_vocab, d_word_vec, n_layers, n_head, d_k, d_v,
            d_model, d_inner, pad_idx, dropout=0.1, n_position=200):

        super().__init__()

        self.src_word_emb = nn.Embedding(
            n_src_vocab, d_word_vec, padding_idx=pad_idx)
        self.position_enc = PositionalEncoding(
            d_word_vec, n_position=n_position)
        self.dropout = nn.Dropout(p=dropout)
        self.layer_stack = nn.ModuleList([
            EncoderLayer(d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
            for _ in range(n_layers)])
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)

    def forward(self, src_seq, src_mask, return_attns=False):

        enc_slf_attn_list = []

        # -- Forward

        enc_output = self.dropout(
            self.position_enc(self.src_word_emb(src_seq)))
        enc_output = self.layer_norm(enc_output)

        for enc_layer in self.layer_stack:
            enc_output, enc_slf_attn = enc_layer(
                enc_output, slf_attn_mask=src_mask)
            enc_slf_attn_list += [enc_slf_attn] if return_attns else []

        if return_attns:
            return enc_output, enc_slf_attn_list
        return enc_output,


class Decoder(nn.Module):
    ''' A decoder model with self attention mechanism. '''

    def __init__(
            self, n_trg_vocab, d_word_vec, n_layers, n_head, d_k, d_v,
            d_model, d_inner, pad_idx, n_position=200, dropout=0.1):

        super().__init__()

        self.trg_word_emb = nn.Embedding(
            n_trg_vocab, d_word_vec, padding_idx=pad_idx)
        self.position_enc = PositionalEncoding(
            d_word_vec, n_position=n_position)
        self.dropout = nn.Dropout(p=dropout)
        self.layer_stack = nn.ModuleList([
            DecoderLayer(d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
            for _ in range(n_layers)])
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)

    def forward(self, trg_seq, trg_mask, enc_output, src_mask, return_attns=False):

        dec_slf_attn_list, dec_enc_attn_list = [], []

        # -- Forward
        dec_output = self.dropout(
            self.position_enc(self.trg_word_emb(trg_seq)))
        dec_output = self.layer_norm(dec_output)

        for dec_layer in self.layer_stack:
            dec_output, dec_slf_attn, dec_enc_attn = dec_layer(
                dec_output, enc_output, slf_attn_mask=trg_mask, dec_enc_attn_mask=src_mask)
            dec_slf_attn_list += [dec_slf_attn] if return_attns else []
            dec_enc_attn_list += [dec_enc_attn] if return_attns else []

        if return_attns:
            return dec_output, dec_slf_attn_list, dec_enc_attn_list
        return dec_output,


class Transformer(nn.Module):
    ''' A sequence to sequence model with attention mechanism. '''

    def __init__(
            self, n_src_vocab, n_trg_vocab, src_pad_idx, trg_pad_idx,
            d_word_vec=512, d_model=512, d_inner=2048,
            n_layers=6, n_head=8, d_k=64, d_v=64, dropout=0.1, n_position=200,
            trg_emb_prj_weight_sharing=True, emb_src_trg_weight_sharing=True):

        super().__init__()

        self.src_pad_idx, self.trg_pad_idx = src_pad_idx, trg_pad_idx

        self.encoder = Encoder(
            n_src_vocab=n_src_vocab, n_position=n_position,
            d_word_vec=d_word_vec, d_model=d_model, d_inner=d_inner,
            n_layers=n_layers, n_head=n_head, d_k=d_k, d_v=d_v,
            pad_idx=src_pad_idx, dropout=dropout)

        self.decoder = Decoder(
            n_trg_vocab=n_trg_vocab, n_position=n_position,
            d_word_vec=d_word_vec, d_model=d_model, d_inner=d_inner,
            n_layers=n_layers, n_head=n_head, d_k=d_k, d_v=d_v,
            pad_idx=trg_pad_idx, dropout=dropout)

        self.trg_word_prj = nn.Linear(d_model, n_trg_vocab, bias=False)

        for p in self.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)

        assert d_model == d_word_vec, \
            'To facilitate the residual connections, \
         the dimensions of all module outputs shall be the same.'

        self.x_logit_scale = 1.
        if trg_emb_prj_weight_sharing:
            # Share the weight between target word embedding & last dense layer
            self.trg_word_prj.weight = self.decoder.trg_word_emb.weight
            self.x_logit_scale = (d_model ** -0.5)

        if emb_src_trg_weight_sharing:
            self.encoder.src_word_emb.weight = self.decoder.trg_word_emb.weight

    def forward(self, src_seq, trg_seq):

        src_mask = get_pad_mask(src_seq, self.src_pad_idx)
        trg_mask = get_pad_mask(
            trg_seq, self.trg_pad_idx) & get_subsequent_mask(trg_seq)

        enc_output, *_ = self.encoder(src_seq, src_mask)
        dec_output, *_ = self.decoder(trg_seq, trg_mask, enc_output, src_mask)
        seq_logit = self.trg_word_prj(dec_output) * self.x_logit_scale

        return seq_logit.view(-1, seq_logit.size(2))


# Modules

class ScaledDotProductAttention(nn.Module):
    ''' Scaled Dot-Product Attention '''

    def __init__(self, temperature, attn_dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = nn.Dropout(attn_dropout)

    def forward(self, q, k, v, mask=None):

        attn = torch.matmul(q / self.temperature, k.transpose(2, 3))

        if mask is not None:
            attn = attn.masked_fill(mask == 0, -1e9)

        attn = self.dropout(F.softmax(attn, dim=-1))
        output = torch.matmul(attn, v)

        return output, attn


# Optim

class ScheduledOptim():
    '''A simple wrapper class for learning rate scheduling'''

    def __init__(self, optimizer, init_lr, d_model, n_warmup_steps):
        self._optimizer = optimizer
        self.init_lr = init_lr
        self.d_model = d_model
        self.n_warmup_steps = n_warmup_steps
        self.n_steps = 0

    def step_and_update_lr(self):
        "Step with the inner optimizer"
        self._update_learning_rate()
        self._optimizer.step()

    def zero_grad(self):
        "Zero out the gradients with the inner optimizer"
        self._optimizer.zero_grad()

    def _get_lr_scale(self):
        d_model = self.d_model
        n_steps, n_warmup_steps = self.n_steps, self.n_warmup_steps
        return (d_model ** -0.5) * min(n_steps ** (-0.5), n_steps * n_warmup_steps ** (-1.5))

    def _update_learning_rate(self):
        ''' Learning rate scheduling per step '''

        self.n_steps += 1
        lr = self.init_lr * self._get_lr_scale()

        for param_group in self._optimizer.param_groups:
            param_group['lr'] = lr


# SubLayers

class MultiHeadAttention(nn.Module):
    ''' Multi-Head Attention module '''

    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
        super().__init__()

        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v

        self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
        self.fc = nn.Linear(n_head * d_v, d_model, bias=False)

        self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)

        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)

    def forward(self, q, k, v, mask=None):

        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)

        residual = q

        # Pass through the pre-attention projection: b x lq x (n*dv)
        # Separate different heads: b x lq x n x dv
        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)

        # Transpose for attention dot product: b x n x lq x dv
        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)

        if mask is not None:
            mask = mask.unsqueeze(1)   # For head axis broadcasting.

        q, attn = self.attention(q, k, v, mask=mask)

        # Transpose to move the head dimension back: b x lq x n x dv
        # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
        q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
        q = self.dropout(self.fc(q))
        q += residual

        q = self.layer_norm(q)

        return q, attn


class PositionwiseFeedForward(nn.Module):
    ''' A two-feed-forward-layer module '''

    def __init__(self, d_in, d_hid, dropout=0.1):
        super().__init__()
        self.w_1 = nn.Linear(d_in, d_hid)  # position-wise
        self.w_2 = nn.Linear(d_hid, d_in)  # position-wise
        self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):

        residual = x

        x = self.w_2(F.relu(self.w_1(x)))
        x = self.dropout(x)
        x += residual

        x = self.layer_norm(x)

        return x


# Translator

class Translator(nn.Module):
    ''' Load a trained model and translate in beam search fashion. '''

    def __init__(
            self, model, beam_size, max_seq_len,
            src_pad_idx, trg_pad_idx, trg_bos_idx, trg_eos_idx):

        super(Translator, self).__init__()

        self.alpha = 0.7
        self.beam_size = beam_size
        self.max_seq_len = max_seq_len
        self.src_pad_idx = src_pad_idx
        self.trg_bos_idx = trg_bos_idx
        self.trg_eos_idx = trg_eos_idx

        self.model = model
        self.model.eval()

        self.register_buffer('init_seq', torch.LongTensor([[trg_bos_idx]]))
        self.register_buffer(
            'blank_seqs',
            torch.full((beam_size, max_seq_len), trg_pad_idx, dtype=torch.long))
        self.blank_seqs[:, 0] = self.trg_bos_idx
        self.register_buffer(
            'len_map',
            torch.arange(1, max_seq_len + 1, dtype=torch.long).unsqueeze(0))

    def _model_decode(self, trg_seq, enc_output, src_mask):
        trg_mask = get_subsequent_mask(trg_seq)
        dec_output, * \
            _ = self.model.decoder(trg_seq, trg_mask, enc_output, src_mask)
        return F.softmax(self.model.trg_word_prj(dec_output), dim=-1)

    def _get_init_state(self, src_seq, src_mask):
        beam_size = self.beam_size

        enc_output, *_ = self.model.encoder(src_seq, src_mask)
        dec_output = self._model_decode(self.init_seq, enc_output, src_mask)

        best_k_probs, best_k_idx = dec_output[:, -1, :].topk(beam_size)

        scores = torch.log(best_k_probs).view(beam_size)
        gen_seq = self.blank_seqs.clone().detach()
        gen_seq[:, 1] = best_k_idx[0]
        enc_output = enc_output.repeat(beam_size, 1, 1)
        return enc_output, gen_seq, scores

    def _get_the_best_score_and_idx(self, gen_seq, dec_output, scores, step):
        assert len(scores.size()) == 1

        beam_size = self.beam_size

        # Get k candidates for each beam, k^2 candidates in total.
        best_k2_probs, best_k2_idx = dec_output[:, -1, :].topk(beam_size)

        # Include the previous scores.
        scores = torch.log(best_k2_probs).view(
            beam_size, -1) + scores.view(beam_size, 1)

        # Get the best k candidates from k^2 candidates.
        scores, best_k_idx_in_k2 = scores.view(-1).topk(beam_size)

        # Get the corresponding positions of the best k candidiates.
        best_k_r_idxs, best_k_c_idxs = best_k_idx_in_k2 // beam_size, best_k_idx_in_k2 % beam_size
        best_k_idx = best_k2_idx[best_k_r_idxs, best_k_c_idxs]

        # Copy the corresponding previous tokens.
        gen_seq[:, :step] = gen_seq[best_k_r_idxs, :step]
        # Set the best tokens in this beam search step
        gen_seq[:, step] = best_k_idx

        return gen_seq, scores

    def translate_sentence(self, src_seq):
        # Only accept batch size equals to 1 in this function.
        # TODO: expand to batch operation.
        assert src_seq.size(0) == 1

        src_pad_idx, trg_eos_idx = self.src_pad_idx, self.trg_eos_idx
        max_seq_len, beam_size, alpha = self.max_seq_len, self.beam_size, self.alpha

        with torch.no_grad():
            src_mask = get_pad_mask(src_seq, src_pad_idx)
            enc_output, gen_seq, scores = self._get_init_state(
                src_seq, src_mask)

            ans_idx = 0   # default
            for step in range(2, max_seq_len):    # decode up to max length
                dec_output = self._model_decode(
                    gen_seq[:, :step], enc_output, src_mask)
                gen_seq, scores = self._get_the_best_score_and_idx(
                    gen_seq, dec_output, scores, step)

                # Check if all path finished
                # -- locate the eos in the generated sequences
                eos_locs = gen_seq == trg_eos_idx
                # -- replace the eos with its position for the length penalty use
                seq_lens, _ = self.len_map.masked_fill(
                    ~eos_locs, max_seq_len).min(1)
                # -- check if all beams contain eos
                if (eos_locs.sum(1) > 0).sum(0).item() == beam_size:
                    # TODO: Try different terminate conditions.
                    _, ans_idx = scores.div(seq_lens.float() ** alpha).max(0)
                    ans_idx = ans_idx.item()
                    break
        return gen_seq[ans_idx][:seq_lens[ans_idx]].tolist()