#!/usr/bin/python
# -*- coding: utf-8 -*-
#File originally part of moses package: http://www.statmt.org/moses/ (as bleu.py)
#Stripped of unused code to reduce number of libraries used

# $Id$

'''Provides:

cook_refs(refs, n=4): Transform a list of reference sentences as strings into a form usable by cook_test().
cook_test(test, refs, n=4): Transform a test sentence as a string (together with the cooked reference sentences) into a form usable by score_cooked().
score_cooked(alltest, n=4): Score a list of cooked test sentences.

score_set(s, testid, refids, n=4): Interface with dataset.py; calculate BLEU score of testid against refids.

The reason for breaking the BLEU computation into three phases cook_refs(), cook_test(), and score_cooked() is to allow the caller to calculate BLEU scores for multiple test sets as efficiently as possible.
'''

from __future__ import division, print_function
import sys, math, re, xml.sax.saxutils

# Added to bypass NIST-style pre-processing of hyp and ref files -- wade
nonorm = 0

preserve_case = False
eff_ref_len = "shortest"

normalize1 = [
    ('<skipped>', ''),         # strip "skipped" tags
    (r'-\n', ''),              # strip end-of-line hyphenation and join lines
    (r'\n', ' '),              # join lines
#    (r'(\d)\s+(?=\d)', r'\1'), # join digits
]
normalize1 = [(re.compile(pattern), replace) for (pattern, replace) in normalize1]

normalize2 = [
    (r'([\{-\~\[-\` -\&\(-\+\:-\@\/])',r' \1 '), # tokenize punctuation. apostrophe is missing
    (r'([^0-9])([\.,])',r'\1 \2 '),              # tokenize period and comma unless preceded by a digit
    (r'([\.,])([^0-9])',r' \1 \2'),              # tokenize period and comma unless followed by a digit
    (r'([0-9])(-)',r'\1 \2 ')                    # tokenize dash when preceded by a digit
]
normalize2 = [(re.compile(pattern), replace) for (pattern, replace) in normalize2]

#combine normalize2 into a single regex.
normalize3 = re.compile(r'([\{-\~\[-\` -\&\(-\+\:-\@\/])|(?:(?<![0-9])([\.,]))|(?:([\.,])(?![0-9]))|(?:(?<=[0-9])(-))')

def normalize(s):
    '''Normalize and tokenize text. This is lifted from NIST mteval-v11a.pl.'''
    # Added to bypass NIST-style pre-processing of hyp and ref files -- wade
    if (nonorm):
        return s.split()
    try:
        s.split()
    except:
        s = " ".join(s)
    # language-independent part:
    for (pattern, replace) in normalize1:
        s = re.sub(pattern, replace, s)
    s = xml.sax.saxutils.unescape(s, {'&quot;':'"'})
    # language-dependent part (assuming Western languages):
    s = " %s " % s
    if not preserve_case:
        s = s.lower()         # this might not be identical to the original
    return [tok for tok in normalize3.split(s) if tok and tok != ' ']

def count_ngrams(words, n=4):
    counts = {}
    for k in range(1,n+1):
        for i in range(len(words)-k+1):
            ngram = tuple(words[i:i+k])
            counts[ngram] = counts.get(ngram, 0)+1
    return counts

def cook_refs(refs, n=4):
    '''Takes a list of reference sentences for a single segment
    and returns an object that encapsulates everything that BLEU
    needs to know about them.'''
    
    refs = [normalize(ref) for ref in refs]
    maxcounts = {}
    for ref in refs:
        counts = count_ngrams(ref, n)
        for (ngram,count) in list(counts.items()):
            maxcounts[ngram] = max(maxcounts.get(ngram,0), count)
    return ([len(ref) for ref in refs], maxcounts)

def cook_ref_set(ref, n=4):
    '''Takes a reference sentences for a single segment
    and returns an object that encapsulates everything that BLEU
    needs to know about them.  Also provides a set cause bleualign wants it'''
    ref = normalize(ref)
    counts = count_ngrams(ref, n)
    return (len(ref), counts, frozenset(counts))




def cook_test(test, args, n=4):
    '''Takes a test sentence and returns an object that
    encapsulates everything that BLEU needs to know about it.'''
    
    reflens, refmaxcounts = args
    test = normalize(test)
    result = {}
    result["testlen"] = len(test)

    # Calculate effective reference sentence length.
    
    if eff_ref_len == "shortest":
        result["reflen"] = min(reflens)
    elif eff_ref_len == "average":
        result["reflen"] = float(sum(reflens))/len(reflens)
    elif eff_ref_len == "closest":
        min_diff = None
        for reflen in reflens:
            if min_diff is None or abs(reflen-len(test)) < min_diff:
                min_diff = abs(reflen-len(test))
                result['reflen'] = reflen

    result["guess"] = [max(len(test)-k+1,0) for k in range(1,n+1)]

    result['correct'] = [0]*n
    counts = count_ngrams(test, n)
    for (ngram, count) in list(counts.items()):
        result["correct"][len(ngram)-1] += min(refmaxcounts.get(ngram,0), count)

    return result

def score_cooked(allcomps, n=4):
    totalcomps = {'testlen':0, 'reflen':0, 'guess':[0]*n, 'correct':[0]*n}
    for comps in allcomps:
        for key in ['testlen','reflen']:
            totalcomps[key] += comps[key]
        for key in ['guess','correct']:
            for k in range(n):
                totalcomps[key][k] += comps[key][k]
    logbleu = 0.0
    for k in range(n):
        if totalcomps['correct'][k] == 0:
            return 0.0
        #log.write("%d-grams: %f\n" % (k,float(totalcomps['correct'][k])/totalcomps['guess'][k]))
        logbleu += math.log(totalcomps['correct'][k])-math.log(totalcomps['guess'][k])
    logbleu /= float(n)
    #log.write("Effective reference length: %d test length: %d\n" % (totalcomps['reflen'], totalcomps['testlen']))
    logbleu += min(0,1-float(totalcomps['reflen'])/totalcomps['testlen'])
    return math.exp(logbleu)
