from __future__ import division, print_function

import unicodedata
from functools import partial, lru_cache
from typing import Sequence, Tuple

import numpy as np
from uniseg.graphemecluster import grapheme_clusters


def levenshtein_matrix(seq1: Sequence, seq2: Sequence):
    """Compute the matrix commonly computed to produce the Levenshtein distance.
    This is also known as the Wagner-Fischer algorithm. The matrix element at the bottom right contains the desired
    edit distance.

    This algorithm is implemented here because we need an implementation that can work with sequences other than
    strings, e.g. lists of grapheme clusters or lists of word strings.
    """

    # Internally, we use a cached version. As the cache only works on hashable parameters, we convert the input
    # sequences to tuples to make them hashable.
    return _levenshtein_matrix(tuple(seq1), tuple(seq2))


@lru_cache(maxsize=10)
def _levenshtein_matrix(seq1: Tuple, seq2: Tuple):
    """Compute the matrix commonly computed to produce the Levenshtein distance.

    This is a LRU cached function not meant to be used directly. Use levenshtein_matrix() instead.
    """
    m = len(seq1)
    n = len(seq2)

    def from_to(start, stop):
        return range(start, stop + 1, 1)

    D = np.zeros((m + 1, n + 1), np.int)
    D[0, 0] = 0
    for i in from_to(1, m):
        D[i, 0] = i
    for j in from_to(1, n):
        D[0, j] = j
    for i in from_to(1, m):
        for j in from_to(1, n):
            D[i, j] = min(
                D[i - 1, j - 1] + 1 * (seq1[i - 1] != seq2[j - 1]),  # Same or Substitution
                D[i, j - 1] + 1,  # Insertion
                D[i - 1, j] + 1   # Deletion
            )

    return D


def levenshtein(seq1, seq2):
    """Compute the Levenshtein edit distance between two sequences"""
    m = len(seq1)
    n = len(seq2)

    D = levenshtein_matrix(seq1, seq2)
    return D[m, n]


def levenshtein_matrix_cache_clear():
    """Clear internal Levenshtein matrix cache.

    You want to do this between different input file pairs to decrease memory
    usage by not caching results from prior input files.
    """
    _levenshtein_matrix.cache_clear()


def distance(s1, s2):
    """Compute the Levenshtein edit distance between two Unicode strings

    Note that this is different from levenshtein() as this function knows about Unicode normalization and grapheme
    clusters. This should be the correct way to compare two Unicode strings.
    """
    s1 = list(grapheme_clusters(unicodedata.normalize('NFC', s1)))
    s2 = list(grapheme_clusters(unicodedata.normalize('NFC', s2)))
    return levenshtein(s1, s2)


def seq_editops(seq1, seq2):
    """
    Return sequence of edit operations transforming one sequence to another.

    This aims to return the same/similar results as python-Levenshtein's editops(), just generalized to arbitrary
    sequences.
    """
    seq1 = list(seq1)
    seq2 = list(seq2)
    m = len(seq1)
    n = len(seq2)
    D = levenshtein_matrix(seq1, seq2)

    def _tail_backtrace(i, j, accumulator):
        if i > 0 and D[i - 1, j] + 1 == D[i, j]:
            return partial(_tail_backtrace, i - 1, j, [('delete', i-1, j)] + accumulator)
        if j > 0 and D[i, j - 1] + 1 == D[i, j]:
            return partial(_tail_backtrace, i, j - 1, [('insert', i, j-1)] + accumulator)
        if i > 0 and j > 0 and D[i - 1, j - 1] + 1 == D[i, j]:
            return partial(_tail_backtrace, i - 1, j - 1, [('replace', i-1, j-1)] + accumulator)
        if i > 0 and j > 0 and D[i - 1, j - 1] == D[i, j]:
            return partial(_tail_backtrace, i - 1, j - 1, accumulator)  # NOP
        return accumulator

    def backtrace(i, j):
        result = partial(_tail_backtrace, i, j, [])
        while isinstance(result, partial):
            result = result()

        return result

    b = backtrace(m, n)
    return b


def editops(word1, word2):
    # XXX Note that this returns indices to the _grapheme clusters_, not characters!
    word1 = list(grapheme_clusters(unicodedata.normalize('NFC', word1)))
    word2 = list(grapheme_clusters(unicodedata.normalize('NFC', word2)))
    return seq_editops(word1, word2)