"""Contains various CTC decoders."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from itertools import groupby
import numpy as np
import multiprocessing


def ctc_best_path_decoder(probs_seq, vocabulary):
    """Best path decoder, also called argmax decoder or greedy decoder.
    Path consisting of the most probable tokens are further post-processed to
    remove consecutive repetitions and all blanks.

    :param probs_seq: 2-D list of probabilities over the vocabulary for each
                      character. Each element is a list of float probabilities
                      for one character.
    :type probs_seq: list
    :param vocabulary: Vocabulary list.
    :type vocabulary: list
    :return: Decoding result string.
    :rtype: baseline
    """
    # dimension verification
    for probs in probs_seq:
        if not len(probs) == len(vocabulary) + 1:
            raise ValueError("probs_seq dimension mismatchedd with vocabulary")
    # argmax to get the best index for each time step
    max_index_list = list(np.array(probs_seq).argmax(axis=1))
    # remove consecutive duplicate indexes
    index_list = [index_group[0] for index_group in groupby(max_index_list)]
    # remove blank indexes
    blank_index = len(vocabulary)
    index_list = [index for index in index_list if index != blank_index]
    # convert index list to string
    return ''.join([vocabulary[index] for index in index_list])


def ctc_beam_search_decoder(probs_seq,
                            beam_size,
                            vocabulary,
                            blank_id,
                            cutoff_prob=1.0,
                            ext_scoring_func=None,
                            nproc=False):
    """Beam search decoder for CTC-trained network. It utilizes beam search
    to approximately select top best decoding labels and returning results
    in the descending order. The implementation is based on Prefix
    Beam Search (https://arxiv.org/abs/1408.2873), and the unclear part is
    redesigned. Two important modifications: 1) in the iterative computation
    of probabilities, the assignment operation is changed to accumulation for
    one prefix may comes from different paths; 2) the if condition "if l^+ not
    in A_prev then" after probabilities' computation is deprecated for it is
    hard to understand and seems unnecessary.

    :param probs_seq: 2-D list of probability distributions over each time
                      step, with each element being a list of normalized
                      probabilities over vocabulary and blank.
    :type probs_seq: 2-D list
    :param beam_size: Width for beam search.
    :type beam_size: int
    :param vocabulary: Vocabulary list.
    :type vocabulary: list
    :param blank_id: ID of blank.
    :type blank_id: int
    :param cutoff_prob: Cutoff probability in pruning,
                        default 1.0, no pruning.
    :type cutoff_prob: float
    :param ext_scoring_func: External scoring function for
                            partially decoded sentence, e.g. word count
                            or language model.
    :type external_scoring_func: callable
    :param nproc: Whether the decoder used in multiprocesses.
    :type nproc: bool
    :return: List of tuples of log probability and sentence as decoding
             results, in descending order of the probability.
    :rtype: list
    """
    # dimension check
    for prob_list in probs_seq:
        if not len(prob_list) == len(vocabulary) + 1:
            raise ValueError("The shape of prob_seq does not match with the "
                             "shape of the vocabulary.")

    # blank_id check
    if not blank_id < len(probs_seq[0]):
        raise ValueError("blank_id shouldn't be greater than probs dimension")

    # If the decoder called in the multiprocesses, then use the global scorer
    # instantiated in ctc_beam_search_decoder_batch().
    if nproc is True:
        global ext_nproc_scorer
        ext_scoring_func = ext_nproc_scorer

    ## initialize
    # prefix_set_prev: the set containing selected prefixes
    # probs_b_prev: prefixes' probability ending with blank in previous step
    # probs_nb_prev: prefixes' probability ending with non-blank in previous step
    prefix_set_prev, probs_b_prev, probs_nb_prev = {
        '\t': 1.0
    }, {
        '\t': 1.0
    }, {
        '\t': 0.0
    }

    ## extend prefix in loop
    for time_step in xrange(len(probs_seq)):
        # prefix_set_next: the set containing candidate prefixes
        # probs_b_cur: prefixes' probability ending with blank in current step
        # probs_nb_cur: prefixes' probability ending with non-blank in current step
        prefix_set_next, probs_b_cur, probs_nb_cur = {}, {}, {}

        prob_idx = list(enumerate(probs_seq[time_step]))
        cutoff_len = len(prob_idx)
        #If pruning is enabled
        if cutoff_prob < 1.0:
            prob_idx = sorted(prob_idx, key=lambda asd: asd[1], reverse=True)
            cutoff_len, cum_prob = 0, 0.0
            for i in xrange(len(prob_idx)):
                cum_prob += prob_idx[i][1]
                cutoff_len += 1
                if cum_prob >= cutoff_prob:
                    break
            prob_idx = prob_idx[0:cutoff_len]

        for l in prefix_set_prev:
            if not prefix_set_next.has_key(l):
                probs_b_cur[l], probs_nb_cur[l] = 0.0, 0.0

            # extend prefix by travering prob_idx
            for index in xrange(cutoff_len):
                c, prob_c = prob_idx[index][0], prob_idx[index][1]

                if c == blank_id:
                    probs_b_cur[l] += prob_c * (
                        probs_b_prev[l] + probs_nb_prev[l])
                else:
                    last_char = l[-1]
                    new_char = vocabulary[c]
                    l_plus = l + new_char
                    if not prefix_set_next.has_key(l_plus):
                        probs_b_cur[l_plus], probs_nb_cur[l_plus] = 0.0, 0.0

                    if new_char == last_char:
                        probs_nb_cur[l_plus] += prob_c * probs_b_prev[l]
                        probs_nb_cur[l] += prob_c * probs_nb_prev[l]
                    elif new_char == ' ':
                        if (ext_scoring_func is None) or (len(l) == 1):
                            score = 1.0
                        else:
                            prefix = l[1:]
                            score = ext_scoring_func(prefix)
                        probs_nb_cur[l_plus] += score * prob_c * (
                            probs_b_prev[l] + probs_nb_prev[l])
                    else:
                        probs_nb_cur[l_plus] += prob_c * (
                            probs_b_prev[l] + probs_nb_prev[l])
                    # add l_plus into prefix_set_next
                    prefix_set_next[l_plus] = probs_nb_cur[
                        l_plus] + probs_b_cur[l_plus]
            # add l into prefix_set_next
            prefix_set_next[l] = probs_b_cur[l] + probs_nb_cur[l]
        # update probs
        probs_b_prev, probs_nb_prev = probs_b_cur, probs_nb_cur

        ## store top beam_size prefixes
        prefix_set_prev = sorted(
            prefix_set_next.iteritems(), key=lambda asd: asd[1], reverse=True)
        if beam_size < len(prefix_set_prev):
            prefix_set_prev = prefix_set_prev[:beam_size]
        prefix_set_prev = dict(prefix_set_prev)

    beam_result = []
    for seq, prob in prefix_set_prev.items():
        if prob > 0.0 and len(seq) > 1:
            result = seq[1:]
            # score last word by external scorer
            if (ext_scoring_func is not None) and (result[-1] != ' '):
                prob = prob * ext_scoring_func(result)
            log_prob = np.log(prob)
            beam_result.append((log_prob, result))

    ## output top beam_size decoding results
    beam_result = sorted(beam_result, key=lambda asd: asd[0], reverse=True)
    return beam_result


def ctc_beam_search_decoder_batch(probs_split,
                                  beam_size,
                                  vocabulary,
                                  blank_id,
                                  num_processes,
                                  cutoff_prob=1.0,
                                  ext_scoring_func=None):
    """CTC beam search decoder using multiple processes.

    :param probs_seq: 3-D list with each element as an instance of 2-D list
                      of probabilities used by ctc_beam_search_decoder().
    :type probs_seq: 3-D list
    :param beam_size: Width for beam search.
    :type beam_size: int
    :param vocabulary: Vocabulary list.
    :type vocabulary: list
    :param blank_id: ID of blank.
    :type blank_id: int
    :param num_processes: Number of parallel processes.
    :type num_processes: int
    :param cutoff_prob: Cutoff probability in pruning,
                        default 1.0, no pruning.
    :param num_processes: Number of parallel processes.
    :type num_processes: int
    :type cutoff_prob: float
    :param ext_scoring_func: External scoring function for
                            partially decoded sentence, e.g. word count
                            or language model.
    :type external_scoring_function: callable
    :return: List of tuples of log probability and sentence as decoding
             results, in descending order of the probability.
    :rtype: list
    """
    if not num_processes > 0:
        raise ValueError("Number of processes must be positive!")

    # use global variable to pass the externnal scorer to beam search decoder
    global ext_nproc_scorer
    ext_nproc_scorer = ext_scoring_func
    nproc = True

    pool = multiprocessing.Pool(processes=num_processes)
    results = []
    for i, probs_list in enumerate(probs_split):
        args = (probs_list, beam_size, vocabulary, blank_id, cutoff_prob, None,
                nproc)
        results.append(pool.apply_async(ctc_beam_search_decoder, args))

    pool.close()
    pool.join()
    beam_search_results = [result.get() for result in results]
    return beam_search_results