''' This module will handle the text generation with beam search. '''

import torch
import torch.nn as nn
import torch.nn.functional as F

from transformer.Models import Transformer
from transformer.Beam import Beam
import transformer.Constants as Constants

class Translator(object):
    ''' Load with trained model and handle the beam search '''

    def __init__(self, opt=None, model=None, beam_size=None, max_seq_len=None, n_best=1):
        assert opt is not None or model is not None
        self.opt = opt
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

        if beam_size is None:
            self.beam_size = opt.beam_size
        else:
            self.beam_size = beam_size

        self.max_seq_len = max_seq_len
        self.n_best = n_best

        if opt is not None:
            self.n_best = opt.n_best

        if model is None:
            checkpoint = torch.load(opt.model)
            model_opt = checkpoint['settings']
            self.model_opt = model_opt

            if self.max_seq_len is None:
                self.max_seq_len = self.model_opt.max_token_seq_len

            model = Transformer(
                model_opt.src_vocab_size,
                model_opt.tgt_vocab_size,
                model_opt.max_token_seq_len,
                tgt_emb_prj_weight_sharing=model_opt.proj_share_weight,
                emb_src_tgt_weight_sharing=model_opt.embs_share_weight,
                d_k=model_opt.d_k,
                d_v=model_opt.d_v,
                d_model=model_opt.d_model,
                d_word_vec=model_opt.d_word_vec,
                d_inner=model_opt.d_inner_hid,
                n_layers=model_opt.n_layers,
                n_head=model_opt.n_head,
                dropout=model_opt.dropout)

            model.load_state_dict(checkpoint['model'])
            print('[Info] Trained model state loaded.')

        model.word_prob_prj = nn.LogSoftmax(dim=1)

        model = model.to(self.device)

        self.model = model

    def sample_topk_batch(self, src_seq, src_pos, src_segs=None, k=20):

        def predict_word(dec_seq, dec_pos, src_seq, enc_output):
            dec_output, *_ = self.model.decoder(dec_seq, dec_pos, src_seq, enc_output)
            dec_output = dec_output[:, -1, :]  # Pick the last step: (bh * bm) * d_h
            word_prob = F.softmax(self.model.tgt_word_prj(dec_output), dim=1)

            return word_prob

        with torch.no_grad():
            #-- Encode
            src_seq, src_pos = src_seq.to(self.device), src_pos.to(self.device)

            if src_segs is not None:
                src_segs.to(self.device)

            src_enc, *_ = self.model.encoder(src_seq, src_pos, src_segs=src_segs)

            batch_size = src_seq.shape[0]

            dec_seq= torch.LongTensor([Constants.BOS] * batch_size).view(batch_size, 1).to(src_seq.device)
            dec_pos = torch.LongTensor([[i for i in range(self.max_seq_len)] for _ in range(batch_size)]).to(src_seq.device)

            for i in range(self.max_seq_len):
                # assume word_prob shape [batch_size, vocab_size]
                word_prob = predict_word(dec_seq, dec_pos[:, :i+1], src_seq, src_enc)

                if k is None:
                    k = word_prob.shape[-1]
                # [batch_size, k]
                topk, _ = torch.topk(word_prob, k, dim=1)
                # [batch_size,]
                kth_value = topk[:, -1].unsqueeze(1)
                word_topk = word_prob * (word_prob >= kth_value).float()
                # normalize
                word_prob_topk = word_topk / word_topk.norm(dim=1).unsqueeze(1)
                next_word = word_prob_topk.multinomial(1)
                dec_seq = torch.cat([dec_seq, next_word], 1)

            # remove go token before returning sampled sequence
        return dec_seq[:, 1:]

    def translate_batch(self, src_seq, src_pos, src_segs=None):
        ''' Translation work in one batch '''

        self.model.eval()

        def get_inst_idx_to_tensor_position_map(inst_idx_list):
            ''' Indicate the position of an instance in a tensor. '''
            return {inst_idx: tensor_position for tensor_position, inst_idx in enumerate(inst_idx_list)}

        def collect_active_part(beamed_tensor, curr_active_inst_idx, n_prev_active_inst, n_bm):
            ''' Collect tensor parts associated to active instances. '''

            _, *d_hs = beamed_tensor.size()
            n_curr_active_inst = len(curr_active_inst_idx)
            new_shape = (n_curr_active_inst * n_bm, *d_hs)

            beamed_tensor = beamed_tensor.view(n_prev_active_inst, -1)
            beamed_tensor = beamed_tensor.index_select(0, curr_active_inst_idx)
            beamed_tensor = beamed_tensor.view(*new_shape)

            return beamed_tensor

        def collate_active_info(
                src_seq, src_enc, inst_idx_to_position_map, active_inst_idx_list):
            # Sentences which are still active are collected,
            # so the decoder will not run on completed sentences.
            n_prev_active_inst = len(inst_idx_to_position_map)
            active_inst_idx = [inst_idx_to_position_map[k] for k in active_inst_idx_list]
            active_inst_idx = torch.LongTensor(active_inst_idx).to(self.device)

            active_src_seq = collect_active_part(src_seq, active_inst_idx, n_prev_active_inst, n_bm)
            active_src_enc = collect_active_part(src_enc, active_inst_idx, n_prev_active_inst, n_bm)
            active_inst_idx_to_position_map = get_inst_idx_to_tensor_position_map(active_inst_idx_list)

            return active_src_seq, active_src_enc, active_inst_idx_to_position_map

        def beam_decode_step(
                inst_dec_beams, len_dec_seq, src_seq, enc_output, inst_idx_to_position_map, n_bm):
            ''' Decode and update beam status, and then return active beam idx '''

            def prepare_beam_dec_seq(inst_dec_beams, len_dec_seq):
                dec_partial_seq = [b.get_current_state() for b in inst_dec_beams if not b.done]
                dec_partial_seq = torch.stack(dec_partial_seq).to(self.device)
                dec_partial_seq = dec_partial_seq.view(-1, len_dec_seq)
                return dec_partial_seq

            def prepare_beam_dec_pos(len_dec_seq, n_active_inst, n_bm):
                dec_partial_pos = torch.arange(1, len_dec_seq + 1, dtype=torch.long, device=self.device)
                dec_partial_pos = dec_partial_pos.unsqueeze(0).repeat(n_active_inst * n_bm, 1)
                return dec_partial_pos

            def predict_word(dec_seq, dec_pos, src_seq, enc_output, n_active_inst, n_bm):
                dec_output, *_ = self.model.decoder(dec_seq, dec_pos, src_seq, enc_output)
                dec_output = dec_output[:, -1, :]  # Pick the last step: (bh * bm) * d_h
                word_prob = F.log_softmax(self.model.tgt_word_prj(dec_output), dim=1)
                word_prob = word_prob.view(n_active_inst, n_bm, -1)

                return word_prob

            def collect_active_inst_idx_list(inst_beams, word_prob, inst_idx_to_position_map):
                active_inst_idx_list = []
                for inst_idx, inst_position in inst_idx_to_position_map.items():
                    is_inst_complete = inst_beams[inst_idx].advance(word_prob[inst_position])
                    if not is_inst_complete:
                        active_inst_idx_list += [inst_idx]

                return active_inst_idx_list

            n_active_inst = len(inst_idx_to_position_map)

            dec_seq = prepare_beam_dec_seq(inst_dec_beams, len_dec_seq)
            dec_pos = prepare_beam_dec_pos(len_dec_seq, n_active_inst, n_bm)
            word_prob = predict_word(dec_seq, dec_pos, src_seq, enc_output, n_active_inst, n_bm)

            # Update the beam with predicted word prob information and collect incomplete instances
            active_inst_idx_list = collect_active_inst_idx_list(
                inst_dec_beams, word_prob, inst_idx_to_position_map)

            return active_inst_idx_list

        def collect_hypothesis_and_scores(inst_dec_beams, n_best):
            all_hyp, all_scores = [], []
            for inst_idx in range(len(inst_dec_beams)):
                scores, tail_idxs = inst_dec_beams[inst_idx].sort_scores()
                all_scores += [scores[:n_best]]

                hyps = [inst_dec_beams[inst_idx].get_hypothesis(i) for i in tail_idxs[:n_best]]
                all_hyp += [hyps]
            return all_hyp, all_scores

        with torch.no_grad():
            #-- Encode
            src_seq, src_pos = src_seq.to(self.device), src_pos.to(self.device)

            if src_segs is not None:
                src_segs.to(self.device)

            src_enc, *_ = self.model.encoder(src_seq, src_pos, src_segs=src_segs)

            #-- Repeat data for beam search
            n_bm = self.beam_size
            n_inst, len_s, d_h = src_enc.size()
            src_seq = src_seq.repeat(1, n_bm).view(n_inst * n_bm, len_s)
            src_enc = src_enc.repeat(1, n_bm, 1).view(n_inst * n_bm, len_s, d_h)

            #-- Prepare beams
            inst_dec_beams = [Beam(n_bm, device=self.device) for _ in range(n_inst)]

            #-- Bookkeeping for active or not
            active_inst_idx_list = list(range(n_inst))
            inst_idx_to_position_map = get_inst_idx_to_tensor_position_map(active_inst_idx_list)

            #-- Decode
            for len_dec_seq in range(1, self.max_seq_len + 1):

                active_inst_idx_list = beam_decode_step(
                    inst_dec_beams, len_dec_seq, src_seq, src_enc, inst_idx_to_position_map, n_bm)

                if not active_inst_idx_list:
                    break  # all instances have finished their path to <EOS>

                src_seq, src_enc, inst_idx_to_position_map = collate_active_info(
                    src_seq, src_enc, inst_idx_to_position_map, active_inst_idx_list)

        batch_hyp, batch_scores = collect_hypothesis_and_scores(inst_dec_beams, self.n_best)

        return batch_hyp, batch_scores
