import Levenshtein as Lev
import torch
from six.moves import xrange


class Decoder(object):
    """
    Basic decoder class from which all other decoders inherit. Implements several
    helper functions. Subclasses should implement the decode() method.

    Arguments:
        labels (string): mapping from integers to characters.
        blank_index (int, optional): index for the blank '_' character. Defaults to 0.
        space_index (int, optional): index for the space ' ' character. Defaults to 28.
    """

    def __init__(self, labels, blank_index=0):
        # e.g. labels = "_'ABCDEFGHIJKLMNOPQRSTUVWXYZ#"
        self.labels = labels
        self.int_to_char = dict([(i, c) for (i, c) in enumerate(labels)])
        self.blank_index = blank_index
        """
        space_index = len(labels)  # To prevent errors in decode, we add an out of bounds index for the space
        if ' ' in labels:
            space_index = labels.index(' ')
        self.space_index = space_index
        """

    def wer(self, s1, s2):
        """
        Computes the Word Error Rate, defined as the edit distance between the
        two provided sentences after tokenizing to words.
        Arguments:
            s1 (string): space-separated sentence
            s2 (string): space-separated sentence
        """

        # build mapping of words to integers
        b = set(s1.split() + s2.split())
        word2char = dict(zip(b, range(len(b))))

        # map the words to a char array (Levenshtein packages only accepts
        # strings)
        w1 = [chr(word2char[w]) for w in s1.split()]
        w2 = [chr(word2char[w]) for w in s2.split()]

        return Lev.distance("".join(w1), "".join(w2))

    def cer(self, s1, s2):
        """
        Computes the Character Error Rate, defined as the edit distance.

        Arguments:
            s1 (string): space-separated sentence
            s2 (string): space-separated sentence
        """
        s1, s2, = s1.replace(" ", ""), s2.replace(" ", "")
        return Lev.distance(s1, s2)

    def decode(self, probs, sizes=None):
        """
        Given a matrix of character probabilities, returns the decoder's
        best guess of the transcription

        Arguments:
            probs: Tensor of character probabilities, where probs[c,t]
                            is the probability of character c at time t
            sizes(optional): Size of each sequence in the mini-batch
        Returns:
            string: sequence of the model's best guess for the transcription
        """
        raise NotImplementedError


class GreedyDecoder(Decoder):
    def __init__(self, labels, blank_index=0):
        super(GreedyDecoder, self).__init__(labels, blank_index)

    def convert_to_strings(
        self, sequences, sizes=None, remove_repetitions=False, return_offsets=False
    ):
        """Given a list of numeric sequences, returns the corresponding strings"""
        strings = []
        offsets = [] if return_offsets else None
        for x in xrange(len(sequences)):
            seq_len = sizes[x] if sizes is not None else len(sequences[x])
            string, string_offsets = self.process_string(
                sequences[x], seq_len, remove_repetitions
            )
            strings.append([string])  # We only return one path
            if return_offsets:
                offsets.append([string_offsets])
        if return_offsets:
            return strings, offsets
        else:
            return strings

    def process_string(self, sequence, size, remove_repetitions=False):
        string = ""
        offsets = []
        for i in range(size):
            char = self.int_to_char[sequence[i].item()]
            if char != self.int_to_char[self.blank_index]:
                # if this char is a repetition and remove_repetitions=true, then skip
                if (
                    remove_repetitions
                    and i != 0
                    and char == self.int_to_char[sequence[i - 1].item()]
                ):
                    pass
                else:
                    string = string + char
                    offsets.append(i)
        return string, torch.tensor(offsets, dtype=torch.int)

    def decode(self, probs, sizes=None):
        """
        Returns the argmax decoding given the probability matrix. Removes
        repeated elements in the sequence, as well as blanks.

        Arguments:
            probs: Tensor of character probabilities from the network. Expected shape of batch x seq_length x output_dim
            sizes(optional): Size of each sequence in the mini-batch
        Returns:
            strings: sequences of the model's best guess for the transcription on inputs
            offsets: time step per character predicted
        """
        _, max_probs = torch.max(probs, 2)
        strings, offsets = self.convert_to_strings(
            max_probs.view(max_probs.size(0), max_probs.size(1)),
            sizes,
            remove_repetitions=True,
            return_offsets=True,
        )
        return strings, offsets