From 09d823c8dc3db37d5a39ef1bd12d25869c3cf416 Mon Sep 17 00:00:00 2001 From: Yue Wang Date: Fri, 3 Sep 2021 22:19:22 +0800 Subject: [PATCH] remove duplicates --- CodeBLEU/bleu.py | 590 - CodeBLEU/calc_code_bleu.py | 80 - CodeBLEU/dataflow_match.py | 148 - CodeBLEU/keywords/c_sharp.txt | 107 - CodeBLEU/keywords/java.txt | 50 - CodeBLEU/parser/DFG.py | 1184 -- CodeBLEU/parser/__init__.py | 8 - CodeBLEU/parser/build.py | 21 - CodeBLEU/parser/build.sh | 8 - CodeBLEU/parser/my-languages.so | Bin 3000336 -> 0 bytes CodeBLEU/parser/utils.py | 108 - CodeBLEU/readme.txt | 1 - CodeBLEU/syntax_match.py | 77 - CodeBLEU/utils.py | 106 - CodeBLEU/weighted_ngram_match.py | 558 - apply_tokenizer.py | 17 - bleu.py | 134 - salesforce/codet5-merges.txt | 31740 ----------------------------- salesforce/codet5-vocab.json | 1 - smooth_bleu.py | 208 - train_tokenizer.py | 22 - 21 files changed, 35168 deletions(-) delete mode 100644 CodeBLEU/bleu.py delete mode 100644 CodeBLEU/calc_code_bleu.py delete mode 100644 CodeBLEU/dataflow_match.py delete mode 100644 CodeBLEU/keywords/c_sharp.txt delete mode 100644 CodeBLEU/keywords/java.txt delete mode 100644 CodeBLEU/parser/DFG.py delete mode 100644 CodeBLEU/parser/__init__.py delete mode 100644 CodeBLEU/parser/build.py delete mode 100644 CodeBLEU/parser/build.sh delete mode 100644 CodeBLEU/parser/my-languages.so delete mode 100644 CodeBLEU/parser/utils.py delete mode 100644 CodeBLEU/readme.txt delete mode 100644 CodeBLEU/syntax_match.py delete mode 100644 CodeBLEU/utils.py delete mode 100644 CodeBLEU/weighted_ngram_match.py delete mode 100644 apply_tokenizer.py delete mode 100644 bleu.py delete mode 100644 salesforce/codet5-merges.txt delete mode 100644 salesforce/codet5-vocab.json delete mode 100644 smooth_bleu.py delete mode 100644 train_tokenizer.py diff --git a/CodeBLEU/bleu.py b/CodeBLEU/bleu.py deleted file mode 100644 index 96f601f..0000000 --- a/CodeBLEU/bleu.py +++ /dev/null @@ -1,590 +0,0 @@ -# -*- coding: utf-8 -*- -# Natural Language Toolkit: BLEU Score -# -# Copyright (C) 2001-2020 NLTK Project -# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim -# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan -# URL: -# For license information, see LICENSE.TXT - -"""BLEU score implementation.""" - -import math -import sys -from fractions import Fraction -import warnings -from collections import Counter - -from evaluator.CodeBLEU.utils import ngrams -import pdb - - -def sentence_bleu( - references, - hypothesis, - weights=(0.25, 0.25, 0.25, 0.25), - smoothing_function=None, - auto_reweigh=False, -): - """ - Calculate BLEU score (Bilingual Evaluation Understudy) from - Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. - "BLEU: a method for automatic evaluation of machine translation." - In Proceedings of ACL. http://www.aclweb.org/anthology/P02-1040.pdf - >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', - ... 'ensures', 'that', 'the', 'military', 'always', - ... 'obeys', 'the', 'commands', 'of', 'the', 'party'] - >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops', - ... 'forever', 'hearing', 'the', 'activity', 'guidebook', - ... 'that', 'party', 'direct'] - >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', - ... 'ensures', 'that', 'the', 'military', 'will', 'forever', - ... 'heed', 'Party', 'commands'] - >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which', - ... 'guarantees', 'the', 'military', 'forces', 'always', - ... 'being', 'under', 'the', 'command', 'of', 'the', - ... 'Party'] - >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', - ... 'army', 'always', 'to', 'heed', 'the', 'directions', - ... 'of', 'the', 'party'] - >>> sentence_bleu([reference1, reference2, reference3], hypothesis1) # doctest: +ELLIPSIS - 0.5045... - If there is no ngrams overlap for any order of n-grams, BLEU returns the - value 0. This is because the precision for the order of n-grams without - overlap is 0, and the geometric mean in the final BLEU score computation - multiplies the 0 with the precision of other n-grams. This results in 0 - (independently of the precision of the othe n-gram orders). The following - example has zero 3-gram and 4-gram overlaps: - >>> round(sentence_bleu([reference1, reference2, reference3], hypothesis2),4) # doctest: +ELLIPSIS - 0.0 - To avoid this harsh behaviour when no ngram overlaps are found a smoothing - function can be used. - >>> chencherry = SmoothingFunction() - >>> sentence_bleu([reference1, reference2, reference3], hypothesis2, - ... smoothing_function=chencherry.method1) # doctest: +ELLIPSIS - 0.0370... - The default BLEU calculates a score for up to 4-grams using uniform - weights (this is called BLEU-4). To evaluate your translations with - higher/lower order ngrams, use customized weights. E.g. when accounting - for up to 5-grams with uniform weights (this is called BLEU-5) use: - >>> weights = (1./5., 1./5., 1./5., 1./5., 1./5.) - >>> sentence_bleu([reference1, reference2, reference3], hypothesis1, weights) # doctest: +ELLIPSIS - 0.3920... - :param references: reference sentences - :type references: list(list(str)) - :param hypothesis: a hypothesis sentence - :type hypothesis: list(str) - :param weights: weights for unigrams, bigrams, trigrams and so on - :type weights: list(float) - :param smoothing_function: - :type smoothing_function: SmoothingFunction - :param auto_reweigh: Option to re-normalize the weights uniformly. - :type auto_reweigh: bool - :return: The sentence-level BLEU score. - :rtype: float - """ - return corpus_bleu( - [references], [hypothesis], weights, smoothing_function, auto_reweigh - ) - - -def corpus_bleu( - list_of_references, - hypotheses, - weights=(0.25, 0.25, 0.25, 0.25), - smoothing_function=None, - auto_reweigh=False, -): - """ - Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all - the hypotheses and their respective references. - Instead of averaging the sentence level BLEU scores (i.e. marco-average - precision), the original BLEU metric (Papineni et al. 2002) accounts for - the micro-average precision (i.e. summing the numerators and denominators - for each hypothesis-reference(s) pairs before the division). - >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', - ... 'ensures', 'that', 'the', 'military', 'always', - ... 'obeys', 'the', 'commands', 'of', 'the', 'party'] - >>> ref1a = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', - ... 'ensures', 'that', 'the', 'military', 'will', 'forever', - ... 'heed', 'Party', 'commands'] - >>> ref1b = ['It', 'is', 'the', 'guiding', 'principle', 'which', - ... 'guarantees', 'the', 'military', 'forces', 'always', - ... 'being', 'under', 'the', 'command', 'of', 'the', 'Party'] - >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', - ... 'army', 'always', 'to', 'heed', 'the', 'directions', - ... 'of', 'the', 'party'] - >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was', - ... 'interested', 'in', 'world', 'history'] - >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history', - ... 'because', 'he', 'read', 'the', 'book'] - >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]] - >>> hypotheses = [hyp1, hyp2] - >>> corpus_bleu(list_of_references, hypotheses) # doctest: +ELLIPSIS - 0.5920... - The example below show that corpus_bleu() is different from averaging - sentence_bleu() for hypotheses - >>> score1 = sentence_bleu([ref1a, ref1b, ref1c], hyp1) - >>> score2 = sentence_bleu([ref2a], hyp2) - >>> (score1 + score2) / 2 # doctest: +ELLIPSIS - 0.6223... - :param list_of_references: a corpus of lists of reference sentences, w.r.t. hypotheses - :type list_of_references: list(list(list(str))) - :param hypotheses: a list of hypothesis sentences - :type hypotheses: list(list(str)) - :param weights: weights for unigrams, bigrams, trigrams and so on - :type weights: list(float) - :param smoothing_function: - :type smoothing_function: SmoothingFunction - :param auto_reweigh: Option to re-normalize the weights uniformly. - :type auto_reweigh: bool - :return: The corpus-level BLEU score. - :rtype: float - """ - # Before proceeding to compute BLEU, perform sanity checks. - - p_numerators = Counter() # Key = ngram order, and value = no. of ngram matches. - p_denominators = Counter() # Key = ngram order, and value = no. of ngram in ref. - hyp_lengths, ref_lengths = 0, 0 - - assert len(list_of_references) == len(hypotheses), ( - "The number of hypotheses and their reference(s) should be the " "same " - ) - - # Iterate through each hypothesis and their corresponding references. - for references, hypothesis in zip(list_of_references, hypotheses): - # For each order of ngram, calculate the numerator and - # denominator for the corpus-level modified precision. - for i, _ in enumerate(weights, start=1): - p_i = modified_precision(references, hypothesis, i) - p_numerators[i] += p_i.numerator - p_denominators[i] += p_i.denominator - - # Calculate the hypothesis length and the closest reference length. - # Adds them to the corpus-level hypothesis and reference counts. - hyp_len = len(hypothesis) - hyp_lengths += hyp_len - ref_lengths += closest_ref_length(references, hyp_len) - - # Calculate corpus-level brevity penalty. - bp = brevity_penalty(ref_lengths, hyp_lengths) - - # Uniformly re-weighting based on maximum hypothesis lengths if largest - # order of n-grams < 4 and weights is set at default. - if auto_reweigh: - if hyp_lengths < 4 and weights == (0.25, 0.25, 0.25, 0.25): - weights = (1 / hyp_lengths,) * hyp_lengths - - # Collects the various precision values for the different ngram orders. - p_n = [ - Fraction(p_numerators[i], p_denominators[i], _normalize=False) - for i, _ in enumerate(weights, start=1) - ] - - # Returns 0 if there's no matching n-grams - # We only need to check for p_numerators[1] == 0, since if there's - # no unigrams, there won't be any higher order ngrams. - if p_numerators[1] == 0: - return 0 - - # If there's no smoothing, set use method0 from SmoothinFunction class. - if not smoothing_function: - smoothing_function = SmoothingFunction().method1 - # Smoothen the modified precision. - # Note: smoothing_function() may convert values into floats; - # it tries to retain the Fraction object as much as the - # smoothing method allows. - p_n = smoothing_function( - p_n, references=references, hypothesis=hypothesis, hyp_len=hyp_lengths - ) - s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, p_n)) - s = bp * math.exp(math.fsum(s)) - return s - - -def modified_precision(references, hypothesis, n): - """ - Calculate modified ngram precision. - The normal precision method may lead to some wrong translations with - high-precision, e.g., the translation, in which a word of reference - repeats several times, has very high precision. - This function only returns the Fraction object that contains the numerator - and denominator necessary to calculate the corpus-level precision. - To calculate the modified precision for a single pair of hypothesis and - references, cast the Fraction object into a float. - The famous "the the the ... " example shows that you can get BLEU precision - by duplicating high frequency words. - >>> reference1 = 'the cat is on the mat'.split() - >>> reference2 = 'there is a cat on the mat'.split() - >>> hypothesis1 = 'the the the the the the the'.split() - >>> references = [reference1, reference2] - >>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS - 0.2857... - In the modified n-gram precision, a reference word will be considered - exhausted after a matching hypothesis word is identified, e.g. - >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', - ... 'ensures', 'that', 'the', 'military', 'will', - ... 'forever', 'heed', 'Party', 'commands'] - >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which', - ... 'guarantees', 'the', 'military', 'forces', 'always', - ... 'being', 'under', 'the', 'command', 'of', 'the', - ... 'Party'] - >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', - ... 'army', 'always', 'to', 'heed', 'the', 'directions', - ... 'of', 'the', 'party'] - >>> hypothesis = 'of the'.split() - >>> references = [reference1, reference2, reference3] - >>> float(modified_precision(references, hypothesis, n=1)) - 1.0 - >>> float(modified_precision(references, hypothesis, n=2)) - 1.0 - An example of a normal machine translation hypothesis: - >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', - ... 'ensures', 'that', 'the', 'military', 'always', - ... 'obeys', 'the', 'commands', 'of', 'the', 'party'] - >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops', - ... 'forever', 'hearing', 'the', 'activity', 'guidebook', - ... 'that', 'party', 'direct'] - >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', - ... 'ensures', 'that', 'the', 'military', 'will', - ... 'forever', 'heed', 'Party', 'commands'] - >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which', - ... 'guarantees', 'the', 'military', 'forces', 'always', - ... 'being', 'under', 'the', 'command', 'of', 'the', - ... 'Party'] - >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', - ... 'army', 'always', 'to', 'heed', 'the', 'directions', - ... 'of', 'the', 'party'] - >>> references = [reference1, reference2, reference3] - >>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS - 0.9444... - >>> float(modified_precision(references, hypothesis2, n=1)) # doctest: +ELLIPSIS - 0.5714... - >>> float(modified_precision(references, hypothesis1, n=2)) # doctest: +ELLIPSIS - 0.5882352941176471 - >>> float(modified_precision(references, hypothesis2, n=2)) # doctest: +ELLIPSIS - 0.07692... - :param references: A list of reference translations. - :type references: list(list(str)) - :param hypothesis: A hypothesis translation. - :type hypothesis: list(str) - :param n: The ngram order. - :type n: int - :return: BLEU's modified precision for the nth order ngram. - :rtype: Fraction - """ - # Extracts all ngrams in hypothesis - # Set an empty Counter if hypothesis is empty. - - counts = Counter(ngrams(hypothesis, n)) if len(hypothesis) >= n else Counter() - # Extract a union of references' counts. - # max_counts = reduce(or_, [Counter(ngrams(ref, n)) for ref in references]) - max_counts = {} - for reference in references: - reference_counts = ( - Counter(ngrams(reference, n)) if len(reference) >= n else Counter() - ) - for ngram in counts: - max_counts[ngram] = max(max_counts.get(ngram, 0), reference_counts[ngram]) - - # Assigns the intersection between hypothesis and references' counts. - clipped_counts = { - ngram: min(count, max_counts[ngram]) for ngram, count in counts.items() - } - - numerator = sum(clipped_counts.values()) - # Ensures that denominator is minimum 1 to avoid ZeroDivisionError. - # Usually this happens when the ngram order is > len(reference). - denominator = max(1, sum(counts.values())) - - return Fraction(numerator, denominator, _normalize=False) - - -def closest_ref_length(references, hyp_len): - """ - This function finds the reference that is the closest length to the - hypothesis. The closest reference length is referred to as *r* variable - from the brevity penalty formula in Papineni et. al. (2002) - :param references: A list of reference translations. - :type references: list(list(str)) - :param hyp_len: The length of the hypothesis. - :type hyp_len: int - :return: The length of the reference that's closest to the hypothesis. - :rtype: int - """ - ref_lens = (len(reference) for reference in references) - closest_ref_len = min( - ref_lens, key=lambda ref_len: (abs(ref_len - hyp_len), ref_len) - ) - return closest_ref_len - - -def brevity_penalty(closest_ref_len, hyp_len): - """ - Calculate brevity penalty. - As the modified n-gram precision still has the problem from the short - length sentence, brevity penalty is used to modify the overall BLEU - score according to length. - An example from the paper. There are three references with length 12, 15 - and 17. And a concise hypothesis of the length 12. The brevity penalty is 1. - >>> reference1 = list('aaaaaaaaaaaa') # i.e. ['a'] * 12 - >>> reference2 = list('aaaaaaaaaaaaaaa') # i.e. ['a'] * 15 - >>> reference3 = list('aaaaaaaaaaaaaaaaa') # i.e. ['a'] * 17 - >>> hypothesis = list('aaaaaaaaaaaa') # i.e. ['a'] * 12 - >>> references = [reference1, reference2, reference3] - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> brevity_penalty(closest_ref_len, hyp_len) - 1.0 - In case a hypothesis translation is shorter than the references, penalty is - applied. - >>> references = [['a'] * 28, ['a'] * 28] - >>> hypothesis = ['a'] * 12 - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> brevity_penalty(closest_ref_len, hyp_len) - 0.2635971381157267 - The length of the closest reference is used to compute the penalty. If the - length of a hypothesis is 12, and the reference lengths are 13 and 2, the - penalty is applied because the hypothesis length (12) is less then the - closest reference length (13). - >>> references = [['a'] * 13, ['a'] * 2] - >>> hypothesis = ['a'] * 12 - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS - 0.9200... - The brevity penalty doesn't depend on reference order. More importantly, - when two reference sentences are at the same distance, the shortest - reference sentence length is used. - >>> references = [['a'] * 13, ['a'] * 11] - >>> hypothesis = ['a'] * 12 - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> bp1 = brevity_penalty(closest_ref_len, hyp_len) - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(reversed(references), hyp_len) - >>> bp2 = brevity_penalty(closest_ref_len, hyp_len) - >>> bp1 == bp2 == 1 - True - A test example from mteval-v13a.pl (starting from the line 705): - >>> references = [['a'] * 11, ['a'] * 8] - >>> hypothesis = ['a'] * 7 - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS - 0.8668... - >>> references = [['a'] * 11, ['a'] * 8, ['a'] * 6, ['a'] * 7] - >>> hypothesis = ['a'] * 7 - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> brevity_penalty(closest_ref_len, hyp_len) - 1.0 - :param hyp_len: The length of the hypothesis for a single sentence OR the - sum of all the hypotheses' lengths for a corpus - :type hyp_len: int - :param closest_ref_len: The length of the closest reference for a single - hypothesis OR the sum of all the closest references for every hypotheses. - :type closest_ref_len: int - :return: BLEU's brevity penalty. - :rtype: float - """ - if hyp_len > closest_ref_len: - return 1 - # If hypothesis is empty, brevity penalty = 0 should result in BLEU = 0.0 - elif hyp_len == 0: - return 0 - else: - return math.exp(1 - closest_ref_len / hyp_len) - - -class SmoothingFunction: - """ - This is an implementation of the smoothing techniques - for segment-level BLEU scores that was presented in - Boxing Chen and Collin Cherry (2014) A Systematic Comparison of - Smoothing Techniques for Sentence-Level BLEU. In WMT14. - http://acl2014.org/acl2014/W14-33/pdf/W14-3346.pdf - """ - - def __init__(self, epsilon=0.1, alpha=5, k=5): - """ - This will initialize the parameters required for the various smoothing - techniques, the default values are set to the numbers used in the - experiments from Chen and Cherry (2014). - >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', 'ensures', - ... 'that', 'the', 'military', 'always', 'obeys', 'the', - ... 'commands', 'of', 'the', 'party'] - >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', 'ensures', - ... 'that', 'the', 'military', 'will', 'forever', 'heed', - ... 'Party', 'commands'] - >>> chencherry = SmoothingFunction() - >>> print(sentence_bleu([reference1], hypothesis1)) # doctest: +ELLIPSIS - 0.4118... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method0)) # doctest: +ELLIPSIS - 0.4118... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method1)) # doctest: +ELLIPSIS - 0.4118... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method2)) # doctest: +ELLIPSIS - 0.4489... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method3)) # doctest: +ELLIPSIS - 0.4118... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method4)) # doctest: +ELLIPSIS - 0.4118... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method5)) # doctest: +ELLIPSIS - 0.4905... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method6)) # doctest: +ELLIPSIS - 0.4135... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method7)) # doctest: +ELLIPSIS - 0.4905... - :param epsilon: the epsilon value use in method 1 - :type epsilon: float - :param alpha: the alpha value use in method 6 - :type alpha: int - :param k: the k value use in method 4 - :type k: int - """ - self.epsilon = epsilon - self.alpha = alpha - self.k = k - - def method0(self, p_n, *args, **kwargs): - """ - No smoothing. - """ - p_n_new = [] - for i, p_i in enumerate(p_n): - if p_i.numerator != 0: - p_n_new.append(p_i) - else: - _msg = str( - "\nThe hypothesis contains 0 counts of {}-gram overlaps.\n" - "Therefore the BLEU score evaluates to 0, independently of\n" - "how many N-gram overlaps of lower order it contains.\n" - "Consider using lower n-gram order or use " - "SmoothingFunction()" - ).format(i + 1) - warnings.warn(_msg) - # When numerator==0 where denonminator==0 or !=0, the result - # for the precision score should be equal to 0 or undefined. - # Due to BLEU geometric mean computation in logarithm space, - # we we need to take the return sys.float_info.min such that - # math.log(sys.float_info.min) returns a 0 precision score. - p_n_new.append(sys.float_info.min) - return p_n_new - - def method1(self, p_n, *args, **kwargs): - """ - Smoothing method 1: Add *epsilon* counts to precision with 0 counts. - """ - return [ - (p_i.numerator + self.epsilon) / p_i.denominator - if p_i.numerator == 0 - else p_i - for p_i in p_n - ] - - def method2(self, p_n, *args, **kwargs): - """ - Smoothing method 2: Add 1 to both numerator and denominator from - Chin-Yew Lin and Franz Josef Och (2004) Automatic evaluation of - machine translation quality using longest common subsequence and - skip-bigram statistics. In ACL04. - """ - return [ - Fraction(p_i.numerator + 1, p_i.denominator + 1, _normalize=False) - for p_i in p_n - ] - - def method3(self, p_n, *args, **kwargs): - """ - Smoothing method 3: NIST geometric sequence smoothing - The smoothing is computed by taking 1 / ( 2^k ), instead of 0, for each - precision score whose matching n-gram count is null. - k is 1 for the first 'n' value for which the n-gram match count is null/ - For example, if the text contains: - - one 2-gram match - - and (consequently) two 1-gram matches - the n-gram count for each individual precision score would be: - - n=1 => prec_count = 2 (two unigrams) - - n=2 => prec_count = 1 (one bigram) - - n=3 => prec_count = 1/2 (no trigram, taking 'smoothed' value of 1 / ( 2^k ), with k=1) - - n=4 => prec_count = 1/4 (no fourgram, taking 'smoothed' value of 1 / ( 2^k ), with k=2) - """ - incvnt = 1 # From the mteval-v13a.pl, it's referred to as k. - for i, p_i in enumerate(p_n): - if p_i.numerator == 0: - p_n[i] = 1 / (2 ** incvnt * p_i.denominator) - incvnt += 1 - return p_n - - def method4(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs): - """ - Smoothing method 4: - Shorter translations may have inflated precision values due to having - smaller denominators; therefore, we give them proportionally - smaller smoothed counts. Instead of scaling to 1/(2^k), Chen and Cherry - suggests dividing by 1/ln(len(T)), where T is the length of the translation. - """ - hyp_len = hyp_len if hyp_len else len(hypothesis) - for i, p_i in enumerate(p_n): - if p_i.numerator == 0 and hyp_len != 0: - incvnt = i + 1 * self.k / math.log( - hyp_len - ) # Note that this K is different from the K from NIST. - p_n[i] = incvnt / p_i.denominator - return p_n - - def method5(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs): - """ - Smoothing method 5: - The matched counts for similar values of n should be similar. To a - calculate the n-gram matched count, it averages the n−1, n and n+1 gram - matched counts. - """ - hyp_len = hyp_len if hyp_len else len(hypothesis) - m = {} - # Requires an precision value for an addition ngram order. - p_n_plus1 = p_n + [modified_precision(references, hypothesis, 5)] - m[-1] = p_n[0] + 1 - for i, p_i in enumerate(p_n): - p_n[i] = (m[i - 1] + p_i + p_n_plus1[i + 1]) / 3 - m[i] = p_n[i] - return p_n - - def method6(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs): - """ - Smoothing method 6: - Interpolates the maximum likelihood estimate of the precision *p_n* with - a prior estimate *pi0*. The prior is estimated by assuming that the ratio - between pn and pn−1 will be the same as that between pn−1 and pn−2; from - Gao and He (2013) Training MRF-Based Phrase Translation Models using - Gradient Ascent. In NAACL. - """ - hyp_len = hyp_len if hyp_len else len(hypothesis) - # This smoothing only works when p_1 and p_2 is non-zero. - # Raise an error with an appropriate message when the input is too short - # to use this smoothing technique. - assert p_n[2], "This smoothing method requires non-zero precision for bigrams." - for i, p_i in enumerate(p_n): - if i in [0, 1]: # Skips the first 2 orders of ngrams. - continue - else: - pi0 = 0 if p_n[i - 2] == 0 else p_n[i - 1] ** 2 / p_n[i - 2] - # No. of ngrams in translation that matches the reference. - m = p_i.numerator - # No. of ngrams in translation. - l = sum(1 for _ in ngrams(hypothesis, i + 1)) - # Calculates the interpolated precision. - p_n[i] = (m + self.alpha * pi0) / (l + self.alpha) - return p_n - - def method7(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs): - """ - Smoothing method 7: - Interpolates methods 4 and 5. - """ - hyp_len = hyp_len if hyp_len else len(hypothesis) - p_n = self.method4(p_n, references, hypothesis, hyp_len) - p_n = self.method5(p_n, references, hypothesis, hyp_len) - return p_n diff --git a/CodeBLEU/calc_code_bleu.py b/CodeBLEU/calc_code_bleu.py deleted file mode 100644 index 96131e7..0000000 --- a/CodeBLEU/calc_code_bleu.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -# -*- coding:utf-8 -*- -import argparse -from evaluator.CodeBLEU import bleu, weighted_ngram_match, syntax_match, dataflow_match -# import evaluator.CodeBLEU.weighted_ngram_match -# import evaluator.CodeBLEU.syntax_match -# import evaluator.CodeBLEU.dataflow_match - - -def get_codebleu(refs, hyp, lang, params='0.25,0.25,0.25,0.25'): - if not isinstance(refs, list): - refs = [refs] - alpha, beta, gamma, theta = [float(x) for x in params.split(',')] - - # preprocess inputs - pre_references = [[x.strip() for x in open(file, 'r', encoding='utf-8').readlines()] for file in refs] - hypothesis = [x.strip() for x in open(hyp, 'r', encoding='utf-8').readlines()] - - for i in range(len(pre_references)): - assert len(hypothesis) == len(pre_references[i]) - - references = [] - for i in range(len(hypothesis)): - ref_for_instance = [] - for j in range(len(pre_references)): - ref_for_instance.append(pre_references[j][i]) - references.append(ref_for_instance) - assert len(references) == len(pre_references) * len(hypothesis) - - # calculate ngram match (BLEU) - tokenized_hyps = [x.split() for x in hypothesis] - tokenized_refs = [[x.split() for x in reference] for reference in references] - - ngram_match_score = bleu.corpus_bleu(tokenized_refs, tokenized_hyps) - - # calculate weighted ngram match - keywords = [x.strip() for x in open('/export/share/wang.y/workspace/CodeT5Full/finetune/evaluator/CodeBLEU/keywords/' + lang + '.txt', 'r', encoding='utf-8').readlines()] - - def make_weights(reference_tokens, key_word_list): - return {token: 1 if token in key_word_list else 0.2 for token in reference_tokens} - - tokenized_refs_with_weights = [[[reference_tokens, make_weights(reference_tokens, keywords)] \ - for reference_tokens in reference] for reference in tokenized_refs] - - weighted_ngram_match_score = weighted_ngram_match.corpus_bleu(tokenized_refs_with_weights, tokenized_hyps) - - # calculate syntax match - syntax_match_score = syntax_match.corpus_syntax_match(references, hypothesis, lang) - - # calculate dataflow match - dataflow_match_score = dataflow_match.corpus_dataflow_match(references, hypothesis, lang) - - print('ngram match: {0}, weighted ngram match: {1}, syntax_match: {2}, dataflow_match: {3}'. \ - format(ngram_match_score, weighted_ngram_match_score, syntax_match_score, dataflow_match_score)) - - code_bleu_score = alpha * ngram_match_score \ - + beta * weighted_ngram_match_score \ - + gamma * syntax_match_score \ - + theta * dataflow_match_score - - return code_bleu_score - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--refs', type=str, nargs='+', required=True, - help='reference files') - parser.add_argument('--hyp', type=str, required=True, - help='hypothesis file') - parser.add_argument('--lang', type=str, required=True, - choices=['java', 'js', 'c_sharp', 'php', 'go', 'python', 'ruby'], - help='programming language') - parser.add_argument('--params', type=str, default='0.25,0.25,0.25,0.25', - help='alpha, beta and gamma') - - args = parser.parse_args() - code_bleu_score = get_codebleu(args.refs, args.hyp, args.lang, args.params) - print('CodeBLEU score: ', code_bleu_score) diff --git a/CodeBLEU/dataflow_match.py b/CodeBLEU/dataflow_match.py deleted file mode 100644 index 38676f5..0000000 --- a/CodeBLEU/dataflow_match.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -from evaluator.CodeBLEU.parser import DFG_python, DFG_java, DFG_ruby, DFG_go, DFG_php, DFG_javascript, DFG_csharp -from evaluator.CodeBLEU.parser import (remove_comments_and_docstrings, - tree_to_token_index, - index_to_code_token, - tree_to_variable_index) -from tree_sitter import Language, Parser -import pdb - -parser_path = '/export/share/wang.y/workspace/CodeT5Full/finetune/evaluator/CodeBLEU/parser' -dfg_function = { - 'python': DFG_python, - 'java': DFG_java, - 'ruby': DFG_ruby, - 'go': DFG_go, - 'php': DFG_php, - 'javascript': DFG_javascript, - 'c_sharp': DFG_csharp, -} - - -def calc_dataflow_match(references, candidate, lang): - return corpus_dataflow_match([references], [candidate], lang) - - -def corpus_dataflow_match(references, candidates, lang): - LANGUAGE = Language('{}/my-languages.so'.format(parser_path), lang) - parser = Parser() - parser.set_language(LANGUAGE) - parser = [parser, dfg_function[lang]] - match_count = 0 - total_count = 0 - - for i in range(len(candidates)): - references_sample = references[i] - candidate = candidates[i] - for reference in references_sample: - try: - candidate = remove_comments_and_docstrings(candidate, 'java') - except: - pass - try: - reference = remove_comments_and_docstrings(reference, 'java') - except: - pass - - cand_dfg = get_data_flow(candidate, parser) - ref_dfg = get_data_flow(reference, parser) - - normalized_cand_dfg = normalize_dataflow(cand_dfg) - normalized_ref_dfg = normalize_dataflow(ref_dfg) - - if len(normalized_ref_dfg) > 0: - total_count += len(normalized_ref_dfg) - for dataflow in normalized_ref_dfg: - if dataflow in normalized_cand_dfg: - match_count += 1 - normalized_cand_dfg.remove(dataflow) - if total_count == 0: - print( - "WARNING: There is no reference data-flows extracted from the whole corpus, and the data-flow match score degenerates to 0. Please consider ignoring this score.") - return 0 - score = match_count / total_count - return score - - -def get_data_flow(code, parser): - try: - tree = parser[0].parse(bytes(code, 'utf8')) - root_node = tree.root_node - tokens_index = tree_to_token_index(root_node) - code = code.split('\n') - code_tokens = [index_to_code_token(x, code) for x in tokens_index] - index_to_code = {} - for idx, (index, code) in enumerate(zip(tokens_index, code_tokens)): - index_to_code[index] = (idx, code) - try: - DFG, _ = parser[1](root_node, index_to_code, {}) - except: - DFG = [] - DFG = sorted(DFG, key=lambda x: x[1]) - indexs = set() - for d in DFG: - if len(d[-1]) != 0: - indexs.add(d[1]) - for x in d[-1]: - indexs.add(x) - new_DFG = [] - for d in DFG: - if d[1] in indexs: - new_DFG.append(d) - codes = code_tokens - dfg = new_DFG - except: - codes = code.split() - dfg = [] - # merge nodes - dic = {} - for d in dfg: - if d[1] not in dic: - dic[d[1]] = d - else: - dic[d[1]] = (d[0], d[1], d[2], list(set(dic[d[1]][3] + d[3])), list(set(dic[d[1]][4] + d[4]))) - DFG = [] - for d in dic: - DFG.append(dic[d]) - dfg = DFG - return dfg - - -def normalize_dataflow_item(dataflow_item): - var_name = dataflow_item[0] - var_pos = dataflow_item[1] - relationship = dataflow_item[2] - par_vars_name_list = dataflow_item[3] - par_vars_pos_list = dataflow_item[4] - - var_names = list(set(par_vars_name_list + [var_name])) - norm_names = {} - for i in range(len(var_names)): - norm_names[var_names[i]] = 'var_' + str(i) - - norm_var_name = norm_names[var_name] - relationship = dataflow_item[2] - norm_par_vars_name_list = [norm_names[x] for x in par_vars_name_list] - - return (norm_var_name, relationship, norm_par_vars_name_list) - - -def normalize_dataflow(dataflow): - var_dict = {} - i = 0 - normalized_dataflow = [] - for item in dataflow: - var_name = item[0] - relationship = item[2] - par_vars_name_list = item[3] - for name in par_vars_name_list: - if name not in var_dict: - var_dict[name] = 'var_' + str(i) - i += 1 - if var_name not in var_dict: - var_dict[var_name] = 'var_' + str(i) - i += 1 - normalized_dataflow.append((var_dict[var_name], relationship, [var_dict[x] for x in par_vars_name_list])) - return normalized_dataflow diff --git a/CodeBLEU/keywords/c_sharp.txt b/CodeBLEU/keywords/c_sharp.txt deleted file mode 100644 index c5bf351..0000000 --- a/CodeBLEU/keywords/c_sharp.txt +++ /dev/null @@ -1,107 +0,0 @@ -abstract -as -base -bool -break -byte -case -catch -char -checked -class -const -continue -decimal -default -delegate -do -double -else -enum -event -explicit -extern -false -finally -fixed -float -for -foreach -goto -if -implicit -in -int -interface -internal -is -lock -long -namespace -new -null -object -operator -out -override -params -private -protected -public -readonly -ref -return -sbyte -sealed -short -sizeof -stackalloc -static -string -struct -switch -this -throw -true -try -typeof -uint -ulong -unchecked -unsafe -ushort -using -virtual -void -volatile -while -add -alias -ascending -async -await -by -descending -dynamic -equals -from -get -global -group -into -join -let -nameof -notnull -on -orderby -partial -remove -select -set -unmanaged -value -var -when -where -yield diff --git a/CodeBLEU/keywords/java.txt b/CodeBLEU/keywords/java.txt deleted file mode 100644 index b009c1d..0000000 --- a/CodeBLEU/keywords/java.txt +++ /dev/null @@ -1,50 +0,0 @@ -abstract -assert -boolean -break -byte -case -catch -char -class -const -continue -default -do -double -else -enum -extends -final -finally -float -for -goto -if -implements -import -instanceof -int -interface -long -native -new -package -private -protected -public -return -short -static -strictfp -super -switch -synchronized -this -throw -throws -transient -try -void -volatile -while diff --git a/CodeBLEU/parser/DFG.py b/CodeBLEU/parser/DFG.py deleted file mode 100644 index bad674e..0000000 --- a/CodeBLEU/parser/DFG.py +++ /dev/null @@ -1,1184 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -from tree_sitter import Language, Parser -from .utils import (remove_comments_and_docstrings, - tree_to_token_index, - index_to_code_token, - tree_to_variable_index) - - -def DFG_python(root_node,index_to_code,states): - assignment=['assignment','augmented_assignment','for_in_clause'] - if_statement=['if_statement'] - for_statement=['for_statement'] - while_statement=['while_statement'] - do_first_statement=['for_in_clause'] - def_statement=['default_parameter'] - states=states.copy() - if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment': - idx,code=index_to_code[(root_node.start_point,root_node.end_point)] - if root_node.type==code: - return [],states - elif code in states: - return [(code,idx,'comesFrom',[code],states[code].copy())],states - else: - if root_node.type=='identifier': - states[code]=[idx] - return [(code,idx,'comesFrom',[],[])],states - elif root_node.type in def_statement: - name=root_node.child_by_field_name('name') - value=root_node.child_by_field_name('value') - DFG=[] - if value is None: - indexs=tree_to_variable_index(name,index_to_code) - for index in indexs: - idx,code=index_to_code[index] - DFG.append((code,idx,'comesFrom',[],[])) - states[code]=[idx] - return sorted(DFG,key=lambda x:x[1]),states - else: - name_indexs=tree_to_variable_index(name,index_to_code) - value_indexs=tree_to_variable_index(value,index_to_code) - temp,states=DFG_python(value,index_to_code,states) - DFG+=temp - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'comesFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in assignment: - if root_node.type=='for_in_clause': - right_nodes=[root_node.children[-1]] - left_nodes=[root_node.child_by_field_name('left')] - else: - if root_node.child_by_field_name('right') is None: - return [],states - left_nodes=[x for x in root_node.child_by_field_name('left').children if x.type!=','] - right_nodes=[x for x in root_node.child_by_field_name('right').children if x.type!=','] - if len(right_nodes)!=len(left_nodes): - left_nodes=[root_node.child_by_field_name('left')] - right_nodes=[root_node.child_by_field_name('right')] - if len(left_nodes)==0: - left_nodes=[root_node.child_by_field_name('left')] - if len(right_nodes)==0: - right_nodes=[root_node.child_by_field_name('right')] - DFG=[] - for node in right_nodes: - temp,states=DFG_python(node,index_to_code,states) - DFG+=temp - - for left_node,right_node in zip(left_nodes,right_nodes): - left_tokens_index=tree_to_variable_index(left_node,index_to_code) - right_tokens_index=tree_to_variable_index(right_node,index_to_code) - temp=[] - for token1_index in left_tokens_index: - idx1,code1=index_to_code[token1_index] - temp.append((code1,idx1,'computedFrom',[index_to_code[x][1] for x in right_tokens_index], - [index_to_code[x][0] for x in right_tokens_index])) - states[code1]=[idx1] - DFG+=temp - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in if_statement: - DFG=[] - current_states=states.copy() - others_states=[] - tag=False - if 'else' in root_node.type: - tag=True - for child in root_node.children: - if 'else' in child.type: - tag=True - if child.type not in ['elif_clause','else_clause']: - temp,current_states=DFG_python(child,index_to_code,current_states) - DFG+=temp - else: - temp,new_states=DFG_python(child,index_to_code,states) - DFG+=temp - others_states.append(new_states) - others_states.append(current_states) - if tag is False: - others_states.append(states) - new_states={} - for dic in others_states: - for key in dic: - if key not in new_states: - new_states[key]=dic[key].copy() - else: - new_states[key]+=dic[key] - for key in new_states: - new_states[key]=sorted(list(set(new_states[key]))) - return sorted(DFG,key=lambda x:x[1]),new_states - elif root_node.type in for_statement: - DFG=[] - for i in range(2): - right_nodes=[x for x in root_node.child_by_field_name('right').children if x.type!=','] - left_nodes=[x for x in root_node.child_by_field_name('left').children if x.type!=','] - if len(right_nodes)!=len(left_nodes): - left_nodes=[root_node.child_by_field_name('left')] - right_nodes=[root_node.child_by_field_name('right')] - if len(left_nodes)==0: - left_nodes=[root_node.child_by_field_name('left')] - if len(right_nodes)==0: - right_nodes=[root_node.child_by_field_name('right')] - for node in right_nodes: - temp,states=DFG_python(node,index_to_code,states) - DFG+=temp - for left_node,right_node in zip(left_nodes,right_nodes): - left_tokens_index=tree_to_variable_index(left_node,index_to_code) - right_tokens_index=tree_to_variable_index(right_node,index_to_code) - temp=[] - for token1_index in left_tokens_index: - idx1,code1=index_to_code[token1_index] - temp.append((code1,idx1,'computedFrom',[index_to_code[x][1] for x in right_tokens_index], - [index_to_code[x][0] for x in right_tokens_index])) - states[code1]=[idx1] - DFG+=temp - if root_node.children[-1].type=="block": - temp,states=DFG_python(root_node.children[-1],index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in while_statement: - DFG=[] - for i in range(2): - for child in root_node.children: - temp,states=DFG_python(child,index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - else: - DFG=[] - for child in root_node.children: - if child.type in do_first_statement: - temp,states=DFG_python(child,index_to_code,states) - DFG+=temp - for child in root_node.children: - if child.type not in do_first_statement: - temp,states=DFG_python(child,index_to_code,states) - DFG+=temp - - return sorted(DFG,key=lambda x:x[1]),states - - -def DFG_java(root_node,index_to_code,states): - assignment=['assignment_expression'] - def_statement=['variable_declarator'] - increment_statement=['update_expression'] - if_statement=['if_statement','else'] - for_statement=['for_statement'] - enhanced_for_statement=['enhanced_for_statement'] - while_statement=['while_statement'] - do_first_statement=[] - states=states.copy() - if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment': - idx,code=index_to_code[(root_node.start_point,root_node.end_point)] - if root_node.type==code: - return [],states - elif code in states: - return [(code,idx,'comesFrom',[code],states[code].copy())],states - else: - if root_node.type=='identifier': - states[code]=[idx] - return [(code,idx,'comesFrom',[],[])],states - elif root_node.type in def_statement: - name=root_node.child_by_field_name('name') - value=root_node.child_by_field_name('value') - DFG=[] - if value is None: - indexs=tree_to_variable_index(name,index_to_code) - for index in indexs: - idx,code=index_to_code[index] - DFG.append((code,idx,'comesFrom',[],[])) - states[code]=[idx] - return sorted(DFG,key=lambda x:x[1]),states - else: - name_indexs=tree_to_variable_index(name,index_to_code) - value_indexs=tree_to_variable_index(value,index_to_code) - temp,states=DFG_java(value,index_to_code,states) - DFG+=temp - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'comesFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in assignment: - left_nodes=root_node.child_by_field_name('left') - right_nodes=root_node.child_by_field_name('right') - DFG=[] - temp,states=DFG_java(right_nodes,index_to_code,states) - DFG+=temp - name_indexs=tree_to_variable_index(left_nodes,index_to_code) - value_indexs=tree_to_variable_index(right_nodes,index_to_code) - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in increment_statement: - DFG=[] - indexs=tree_to_variable_index(root_node,index_to_code) - for index1 in indexs: - idx1,code1=index_to_code[index1] - for index2 in indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in if_statement: - DFG=[] - current_states=states.copy() - others_states=[] - flag=False - tag=False - if 'else' in root_node.type: - tag=True - for child in root_node.children: - if 'else' in child.type: - tag=True - if child.type not in if_statement and flag is False: - temp,current_states=DFG_java(child,index_to_code,current_states) - DFG+=temp - else: - flag=True - temp,new_states=DFG_java(child,index_to_code,states) - DFG+=temp - others_states.append(new_states) - others_states.append(current_states) - if tag is False: - others_states.append(states) - new_states={} - for dic in others_states: - for key in dic: - if key not in new_states: - new_states[key]=dic[key].copy() - else: - new_states[key]+=dic[key] - for key in new_states: - new_states[key]=sorted(list(set(new_states[key]))) - return sorted(DFG,key=lambda x:x[1]),new_states - elif root_node.type in for_statement: - DFG=[] - for child in root_node.children: - temp,states=DFG_java(child,index_to_code,states) - DFG+=temp - flag=False - for child in root_node.children: - if flag: - temp,states=DFG_java(child,index_to_code,states) - DFG+=temp - elif child.type=="local_variable_declaration": - flag=True - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in enhanced_for_statement: - name=root_node.child_by_field_name('name') - value=root_node.child_by_field_name('value') - body=root_node.child_by_field_name('body') - DFG=[] - for i in range(2): - temp,states=DFG_java(value,index_to_code,states) - DFG+=temp - name_indexs=tree_to_variable_index(name,index_to_code) - value_indexs=tree_to_variable_index(value,index_to_code) - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - temp,states=DFG_java(body,index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in while_statement: - DFG=[] - for i in range(2): - for child in root_node.children: - temp,states=DFG_java(child,index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - else: - DFG=[] - for child in root_node.children: - if child.type in do_first_statement: - temp,states=DFG_java(child,index_to_code,states) - DFG+=temp - for child in root_node.children: - if child.type not in do_first_statement: - temp,states=DFG_java(child,index_to_code,states) - DFG+=temp - - return sorted(DFG,key=lambda x:x[1]),states - -def DFG_csharp(root_node,index_to_code,states): - assignment=['assignment_expression'] - def_statement=['variable_declarator'] - increment_statement=['postfix_unary_expression'] - if_statement=['if_statement','else'] - for_statement=['for_statement'] - enhanced_for_statement=['for_each_statement'] - while_statement=['while_statement'] - do_first_statement=[] - states=states.copy() - if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment': - idx,code=index_to_code[(root_node.start_point,root_node.end_point)] - if root_node.type==code: - return [],states - elif code in states: - return [(code,idx,'comesFrom',[code],states[code].copy())],states - else: - if root_node.type=='identifier': - states[code]=[idx] - return [(code,idx,'comesFrom',[],[])],states - elif root_node.type in def_statement: - if len(root_node.children)==2: - name=root_node.children[0] - value=root_node.children[1] - else: - name=root_node.children[0] - value=None - DFG=[] - if value is None: - indexs=tree_to_variable_index(name,index_to_code) - for index in indexs: - idx,code=index_to_code[index] - DFG.append((code,idx,'comesFrom',[],[])) - states[code]=[idx] - return sorted(DFG,key=lambda x:x[1]),states - else: - name_indexs=tree_to_variable_index(name,index_to_code) - value_indexs=tree_to_variable_index(value,index_to_code) - temp,states=DFG_csharp(value,index_to_code,states) - DFG+=temp - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'comesFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in assignment: - left_nodes=root_node.child_by_field_name('left') - right_nodes=root_node.child_by_field_name('right') - DFG=[] - temp,states=DFG_csharp(right_nodes,index_to_code,states) - DFG+=temp - name_indexs=tree_to_variable_index(left_nodes,index_to_code) - value_indexs=tree_to_variable_index(right_nodes,index_to_code) - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in increment_statement: - DFG=[] - indexs=tree_to_variable_index(root_node,index_to_code) - for index1 in indexs: - idx1,code1=index_to_code[index1] - for index2 in indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in if_statement: - DFG=[] - current_states=states.copy() - others_states=[] - flag=False - tag=False - if 'else' in root_node.type: - tag=True - for child in root_node.children: - if 'else' in child.type: - tag=True - if child.type not in if_statement and flag is False: - temp,current_states=DFG_csharp(child,index_to_code,current_states) - DFG+=temp - else: - flag=True - temp,new_states=DFG_csharp(child,index_to_code,states) - DFG+=temp - others_states.append(new_states) - others_states.append(current_states) - if tag is False: - others_states.append(states) - new_states={} - for dic in others_states: - for key in dic: - if key not in new_states: - new_states[key]=dic[key].copy() - else: - new_states[key]+=dic[key] - for key in new_states: - new_states[key]=sorted(list(set(new_states[key]))) - return sorted(DFG,key=lambda x:x[1]),new_states - elif root_node.type in for_statement: - DFG=[] - for child in root_node.children: - temp,states=DFG_csharp(child,index_to_code,states) - DFG+=temp - flag=False - for child in root_node.children: - if flag: - temp,states=DFG_csharp(child,index_to_code,states) - DFG+=temp - elif child.type=="local_variable_declaration": - flag=True - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in enhanced_for_statement: - name=root_node.child_by_field_name('left') - value=root_node.child_by_field_name('right') - body=root_node.child_by_field_name('body') - DFG=[] - for i in range(2): - temp,states=DFG_csharp(value,index_to_code,states) - DFG+=temp - name_indexs=tree_to_variable_index(name,index_to_code) - value_indexs=tree_to_variable_index(value,index_to_code) - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - temp,states=DFG_csharp(body,index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in while_statement: - DFG=[] - for i in range(2): - for child in root_node.children: - temp,states=DFG_csharp(child,index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - else: - DFG=[] - for child in root_node.children: - if child.type in do_first_statement: - temp,states=DFG_csharp(child,index_to_code,states) - DFG+=temp - for child in root_node.children: - if child.type not in do_first_statement: - temp,states=DFG_csharp(child,index_to_code,states) - DFG+=temp - - return sorted(DFG,key=lambda x:x[1]),states - - - - -def DFG_ruby(root_node,index_to_code,states): - assignment=['assignment','operator_assignment'] - if_statement=['if','elsif','else','unless','when'] - for_statement=['for'] - while_statement=['while_modifier','until'] - do_first_statement=[] - def_statement=['keyword_parameter'] - if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment': - states=states.copy() - idx,code=index_to_code[(root_node.start_point,root_node.end_point)] - if root_node.type==code: - return [],states - elif code in states: - return [(code,idx,'comesFrom',[code],states[code].copy())],states - else: - if root_node.type=='identifier': - states[code]=[idx] - return [(code,idx,'comesFrom',[],[])],states - elif root_node.type in def_statement: - name=root_node.child_by_field_name('name') - value=root_node.child_by_field_name('value') - DFG=[] - if value is None: - indexs=tree_to_variable_index(name,index_to_code) - for index in indexs: - idx,code=index_to_code[index] - DFG.append((code,idx,'comesFrom',[],[])) - states[code]=[idx] - return sorted(DFG,key=lambda x:x[1]),states - else: - name_indexs=tree_to_variable_index(name,index_to_code) - value_indexs=tree_to_variable_index(value,index_to_code) - temp,states=DFG_ruby(value,index_to_code,states) - DFG+=temp - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'comesFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in assignment: - left_nodes=[x for x in root_node.child_by_field_name('left').children if x.type!=','] - right_nodes=[x for x in root_node.child_by_field_name('right').children if x.type!=','] - if len(right_nodes)!=len(left_nodes): - left_nodes=[root_node.child_by_field_name('left')] - right_nodes=[root_node.child_by_field_name('right')] - if len(left_nodes)==0: - left_nodes=[root_node.child_by_field_name('left')] - if len(right_nodes)==0: - right_nodes=[root_node.child_by_field_name('right')] - if root_node.type=="operator_assignment": - left_nodes=[root_node.children[0]] - right_nodes=[root_node.children[-1]] - - DFG=[] - for node in right_nodes: - temp,states=DFG_ruby(node,index_to_code,states) - DFG+=temp - - for left_node,right_node in zip(left_nodes,right_nodes): - left_tokens_index=tree_to_variable_index(left_node,index_to_code) - right_tokens_index=tree_to_variable_index(right_node,index_to_code) - temp=[] - for token1_index in left_tokens_index: - idx1,code1=index_to_code[token1_index] - temp.append((code1,idx1,'computedFrom',[index_to_code[x][1] for x in right_tokens_index], - [index_to_code[x][0] for x in right_tokens_index])) - states[code1]=[idx1] - DFG+=temp - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in if_statement: - DFG=[] - current_states=states.copy() - others_states=[] - tag=False - if 'else' in root_node.type: - tag=True - for child in root_node.children: - if 'else' in child.type: - tag=True - if child.type not in if_statement: - temp,current_states=DFG_ruby(child,index_to_code,current_states) - DFG+=temp - else: - temp,new_states=DFG_ruby(child,index_to_code,states) - DFG+=temp - others_states.append(new_states) - others_states.append(current_states) - if tag is False: - others_states.append(states) - new_states={} - for dic in others_states: - for key in dic: - if key not in new_states: - new_states[key]=dic[key].copy() - else: - new_states[key]+=dic[key] - for key in new_states: - new_states[key]=sorted(list(set(new_states[key]))) - return sorted(DFG,key=lambda x:x[1]),new_states - elif root_node.type in for_statement: - DFG=[] - for i in range(2): - left_nodes=[root_node.child_by_field_name('pattern')] - right_nodes=[root_node.child_by_field_name('value')] - assert len(right_nodes)==len(left_nodes) - for node in right_nodes: - temp,states=DFG_ruby(node,index_to_code,states) - DFG+=temp - for left_node,right_node in zip(left_nodes,right_nodes): - left_tokens_index=tree_to_variable_index(left_node,index_to_code) - right_tokens_index=tree_to_variable_index(right_node,index_to_code) - temp=[] - for token1_index in left_tokens_index: - idx1,code1=index_to_code[token1_index] - temp.append((code1,idx1,'computedFrom',[index_to_code[x][1] for x in right_tokens_index], - [index_to_code[x][0] for x in right_tokens_index])) - states[code1]=[idx1] - DFG+=temp - temp,states=DFG_ruby(root_node.child_by_field_name('body'),index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in while_statement: - DFG=[] - for i in range(2): - for child in root_node.children: - temp,states=DFG_ruby(child,index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - else: - DFG=[] - for child in root_node.children: - if child.type in do_first_statement: - temp,states=DFG_ruby(child,index_to_code,states) - DFG+=temp - for child in root_node.children: - if child.type not in do_first_statement: - temp,states=DFG_ruby(child,index_to_code,states) - DFG+=temp - - return sorted(DFG,key=lambda x:x[1]),states - -def DFG_go(root_node,index_to_code,states): - assignment=['assignment_statement',] - def_statement=['var_spec'] - increment_statement=['inc_statement'] - if_statement=['if_statement','else'] - for_statement=['for_statement'] - enhanced_for_statement=[] - while_statement=[] - do_first_statement=[] - states=states.copy() - if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment': - idx,code=index_to_code[(root_node.start_point,root_node.end_point)] - if root_node.type==code: - return [],states - elif code in states: - return [(code,idx,'comesFrom',[code],states[code].copy())],states - else: - if root_node.type=='identifier': - states[code]=[idx] - return [(code,idx,'comesFrom',[],[])],states - elif root_node.type in def_statement: - name=root_node.child_by_field_name('name') - value=root_node.child_by_field_name('value') - DFG=[] - if value is None: - indexs=tree_to_variable_index(name,index_to_code) - for index in indexs: - idx,code=index_to_code[index] - DFG.append((code,idx,'comesFrom',[],[])) - states[code]=[idx] - return sorted(DFG,key=lambda x:x[1]),states - else: - name_indexs=tree_to_variable_index(name,index_to_code) - value_indexs=tree_to_variable_index(value,index_to_code) - temp,states=DFG_go(value,index_to_code,states) - DFG+=temp - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'comesFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in assignment: - left_nodes=root_node.child_by_field_name('left') - right_nodes=root_node.child_by_field_name('right') - DFG=[] - temp,states=DFG_go(right_nodes,index_to_code,states) - DFG+=temp - name_indexs=tree_to_variable_index(left_nodes,index_to_code) - value_indexs=tree_to_variable_index(right_nodes,index_to_code) - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in increment_statement: - DFG=[] - indexs=tree_to_variable_index(root_node,index_to_code) - for index1 in indexs: - idx1,code1=index_to_code[index1] - for index2 in indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in if_statement: - DFG=[] - current_states=states.copy() - others_states=[] - flag=False - tag=False - if 'else' in root_node.type: - tag=True - for child in root_node.children: - if 'else' in child.type: - tag=True - if child.type not in if_statement and flag is False: - temp,current_states=DFG_go(child,index_to_code,current_states) - DFG+=temp - else: - flag=True - temp,new_states=DFG_go(child,index_to_code,states) - DFG+=temp - others_states.append(new_states) - others_states.append(current_states) - if tag is False: - others_states.append(states) - new_states={} - for dic in others_states: - for key in dic: - if key not in new_states: - new_states[key]=dic[key].copy() - else: - new_states[key]+=dic[key] - for key in states: - if key not in new_states: - new_states[key]=states[key] - else: - new_states[key]+=states[key] - for key in new_states: - new_states[key]=sorted(list(set(new_states[key]))) - return sorted(DFG,key=lambda x:x[1]),new_states - elif root_node.type in for_statement: - DFG=[] - for child in root_node.children: - temp,states=DFG_go(child,index_to_code,states) - DFG+=temp - flag=False - for child in root_node.children: - if flag: - temp,states=DFG_go(child,index_to_code,states) - DFG+=temp - elif child.type=="for_clause": - if child.child_by_field_name('update') is not None: - temp,states=DFG_go(child.child_by_field_name('update'),index_to_code,states) - DFG+=temp - flag=True - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - else: - DFG=[] - for child in root_node.children: - if child.type in do_first_statement: - temp,states=DFG_go(child,index_to_code,states) - DFG+=temp - for child in root_node.children: - if child.type not in do_first_statement: - temp,states=DFG_go(child,index_to_code,states) - DFG+=temp - - return sorted(DFG,key=lambda x:x[1]),states - - - - -def DFG_php(root_node,index_to_code,states): - assignment=['assignment_expression','augmented_assignment_expression'] - def_statement=['simple_parameter'] - increment_statement=['update_expression'] - if_statement=['if_statement','else_clause'] - for_statement=['for_statement'] - enhanced_for_statement=['foreach_statement'] - while_statement=['while_statement'] - do_first_statement=[] - states=states.copy() - if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment': - idx,code=index_to_code[(root_node.start_point,root_node.end_point)] - if root_node.type==code: - return [],states - elif code in states: - return [(code,idx,'comesFrom',[code],states[code].copy())],states - else: - if root_node.type=='identifier': - states[code]=[idx] - return [(code,idx,'comesFrom',[],[])],states - elif root_node.type in def_statement: - name=root_node.child_by_field_name('name') - value=root_node.child_by_field_name('default_value') - DFG=[] - if value is None: - indexs=tree_to_variable_index(name,index_to_code) - for index in indexs: - idx,code=index_to_code[index] - DFG.append((code,idx,'comesFrom',[],[])) - states[code]=[idx] - return sorted(DFG,key=lambda x:x[1]),states - else: - name_indexs=tree_to_variable_index(name,index_to_code) - value_indexs=tree_to_variable_index(value,index_to_code) - temp,states=DFG_php(value,index_to_code,states) - DFG+=temp - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'comesFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in assignment: - left_nodes=root_node.child_by_field_name('left') - right_nodes=root_node.child_by_field_name('right') - DFG=[] - temp,states=DFG_php(right_nodes,index_to_code,states) - DFG+=temp - name_indexs=tree_to_variable_index(left_nodes,index_to_code) - value_indexs=tree_to_variable_index(right_nodes,index_to_code) - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in increment_statement: - DFG=[] - indexs=tree_to_variable_index(root_node,index_to_code) - for index1 in indexs: - idx1,code1=index_to_code[index1] - for index2 in indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in if_statement: - DFG=[] - current_states=states.copy() - others_states=[] - flag=False - tag=False - if 'else' in root_node.type: - tag=True - for child in root_node.children: - if 'else' in child.type: - tag=True - if child.type not in if_statement and flag is False: - temp,current_states=DFG_php(child,index_to_code,current_states) - DFG+=temp - else: - flag=True - temp,new_states=DFG_php(child,index_to_code,states) - DFG+=temp - others_states.append(new_states) - others_states.append(current_states) - new_states={} - for dic in others_states: - for key in dic: - if key not in new_states: - new_states[key]=dic[key].copy() - else: - new_states[key]+=dic[key] - for key in states: - if key not in new_states: - new_states[key]=states[key] - else: - new_states[key]+=states[key] - for key in new_states: - new_states[key]=sorted(list(set(new_states[key]))) - return sorted(DFG,key=lambda x:x[1]),new_states - elif root_node.type in for_statement: - DFG=[] - for child in root_node.children: - temp,states=DFG_php(child,index_to_code,states) - DFG+=temp - flag=False - for child in root_node.children: - if flag: - temp,states=DFG_php(child,index_to_code,states) - DFG+=temp - elif child.type=="assignment_expression": - flag=True - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in enhanced_for_statement: - name=None - value=None - for child in root_node.children: - if child.type=='variable_name' and value is None: - value=child - elif child.type=='variable_name' and name is None: - name=child - break - body=root_node.child_by_field_name('body') - DFG=[] - for i in range(2): - temp,states=DFG_php(value,index_to_code,states) - DFG+=temp - name_indexs=tree_to_variable_index(name,index_to_code) - value_indexs=tree_to_variable_index(value,index_to_code) - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - temp,states=DFG_php(body,index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in while_statement: - DFG=[] - for i in range(2): - for child in root_node.children: - temp,states=DFG_php(child,index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - else: - DFG=[] - for child in root_node.children: - if child.type in do_first_statement: - temp,states=DFG_php(child,index_to_code,states) - DFG+=temp - for child in root_node.children: - if child.type not in do_first_statement: - temp,states=DFG_php(child,index_to_code,states) - DFG+=temp - - return sorted(DFG,key=lambda x:x[1]),states - - -def DFG_javascript(root_node,index_to_code,states): - assignment=['assignment_pattern','augmented_assignment_expression'] - def_statement=['variable_declarator'] - increment_statement=['update_expression'] - if_statement=['if_statement','else'] - for_statement=['for_statement'] - enhanced_for_statement=[] - while_statement=['while_statement'] - do_first_statement=[] - states=states.copy() - if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment': - idx,code=index_to_code[(root_node.start_point,root_node.end_point)] - if root_node.type==code: - return [],states - elif code in states: - return [(code,idx,'comesFrom',[code],states[code].copy())],states - else: - if root_node.type=='identifier': - states[code]=[idx] - return [(code,idx,'comesFrom',[],[])],states - elif root_node.type in def_statement: - name=root_node.child_by_field_name('name') - value=root_node.child_by_field_name('value') - DFG=[] - if value is None: - indexs=tree_to_variable_index(name,index_to_code) - for index in indexs: - idx,code=index_to_code[index] - DFG.append((code,idx,'comesFrom',[],[])) - states[code]=[idx] - return sorted(DFG,key=lambda x:x[1]),states - else: - name_indexs=tree_to_variable_index(name,index_to_code) - value_indexs=tree_to_variable_index(value,index_to_code) - temp,states=DFG_javascript(value,index_to_code,states) - DFG+=temp - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'comesFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in assignment: - left_nodes=root_node.child_by_field_name('left') - right_nodes=root_node.child_by_field_name('right') - DFG=[] - temp,states=DFG_javascript(right_nodes,index_to_code,states) - DFG+=temp - name_indexs=tree_to_variable_index(left_nodes,index_to_code) - value_indexs=tree_to_variable_index(right_nodes,index_to_code) - for index1 in name_indexs: - idx1,code1=index_to_code[index1] - for index2 in value_indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in increment_statement: - DFG=[] - indexs=tree_to_variable_index(root_node,index_to_code) - for index1 in indexs: - idx1,code1=index_to_code[index1] - for index2 in indexs: - idx2,code2=index_to_code[index2] - DFG.append((code1,idx1,'computedFrom',[code2],[idx2])) - states[code1]=[idx1] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in if_statement: - DFG=[] - current_states=states.copy() - others_states=[] - flag=False - tag=False - if 'else' in root_node.type: - tag=True - for child in root_node.children: - if 'else' in child.type: - tag=True - if child.type not in if_statement and flag is False: - temp,current_states=DFG_javascript(child,index_to_code,current_states) - DFG+=temp - else: - flag=True - temp,new_states=DFG_javascript(child,index_to_code,states) - DFG+=temp - others_states.append(new_states) - others_states.append(current_states) - if tag is False: - others_states.append(states) - new_states={} - for dic in others_states: - for key in dic: - if key not in new_states: - new_states[key]=dic[key].copy() - else: - new_states[key]+=dic[key] - for key in states: - if key not in new_states: - new_states[key]=states[key] - else: - new_states[key]+=states[key] - for key in new_states: - new_states[key]=sorted(list(set(new_states[key]))) - return sorted(DFG,key=lambda x:x[1]),new_states - elif root_node.type in for_statement: - DFG=[] - for child in root_node.children: - temp,states=DFG_javascript(child,index_to_code,states) - DFG+=temp - flag=False - for child in root_node.children: - if flag: - temp,states=DFG_javascript(child,index_to_code,states) - DFG+=temp - elif child.type=="variable_declaration": - flag=True - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - elif root_node.type in while_statement: - DFG=[] - for i in range(2): - for child in root_node.children: - temp,states=DFG_javascript(child,index_to_code,states) - DFG+=temp - dic={} - for x in DFG: - if (x[0],x[1],x[2]) not in dic: - dic[(x[0],x[1],x[2])]=[x[3],x[4]] - else: - dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3])) - dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4]))) - DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])] - return sorted(DFG,key=lambda x:x[1]),states - else: - DFG=[] - for child in root_node.children: - if child.type in do_first_statement: - temp,states=DFG_javascript(child,index_to_code,states) - DFG+=temp - for child in root_node.children: - if child.type not in do_first_statement: - temp,states=DFG_javascript(child,index_to_code,states) - DFG+=temp - - return sorted(DFG,key=lambda x:x[1]),states - - - diff --git a/CodeBLEU/parser/__init__.py b/CodeBLEU/parser/__init__.py deleted file mode 100644 index d611572..0000000 --- a/CodeBLEU/parser/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -from .utils import (remove_comments_and_docstrings, - tree_to_token_index, - index_to_code_token, - tree_to_variable_index) -from .DFG import DFG_python,DFG_java,DFG_ruby,DFG_go,DFG_php,DFG_javascript,DFG_csharp \ No newline at end of file diff --git a/CodeBLEU/parser/build.py b/CodeBLEU/parser/build.py deleted file mode 100644 index cf7cabb..0000000 --- a/CodeBLEU/parser/build.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -from tree_sitter import Language, Parser - -Language.build_library( - # Store the library in the `build` directory - 'my-languages.so', - - # Include one or more languages - [ - 'tree-sitter-go', - 'tree-sitter-javascript', - 'tree-sitter-python', - 'tree-sitter-php', - 'tree-sitter-java', - 'tree-sitter-ruby', - 'tree-sitter-c-sharp', - ] -) - diff --git a/CodeBLEU/parser/build.sh b/CodeBLEU/parser/build.sh deleted file mode 100644 index 6051592..0000000 --- a/CodeBLEU/parser/build.sh +++ /dev/null @@ -1,8 +0,0 @@ -git clone https://github.com/tree-sitter/tree-sitter-go -git clone 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-import re -from io import StringIO -import tokenize - - -def remove_comments_and_docstrings(source, lang): - if lang in ['python']: - """ - Returns 'source' minus comments and docstrings. - """ - io_obj = StringIO(source) - out = "" - prev_toktype = tokenize.INDENT - last_lineno = -1 - last_col = 0 - for tok in tokenize.generate_tokens(io_obj.readline): - token_type = tok[0] - token_string = tok[1] - start_line, start_col = tok[2] - end_line, end_col = tok[3] - ltext = tok[4] - if start_line > last_lineno: - last_col = 0 - if start_col > last_col: - out += (" " * (start_col - last_col)) - # Remove comments: - if token_type == tokenize.COMMENT: - pass - # This series of conditionals removes docstrings: - elif token_type == tokenize.STRING: - if prev_toktype != tokenize.INDENT: - # This is likely a docstring; double-check we're not inside an operator: - if prev_toktype != tokenize.NEWLINE: - if start_col > 0: - out += token_string - else: - out += token_string - prev_toktype = token_type - last_col = end_col - last_lineno = end_line - temp = [] - for x in out.split('\n'): - if x.strip() != "": - temp.append(x) - return '\n'.join(temp) - elif lang in ['ruby']: - return source - else: - def replacer(match): - s = match.group(0) - if s.startswith('/'): - return " " # note: a space and not an empty string - else: - return s - - pattern = re.compile( - r'//.*?$|/\*.*?\*/|\'(?:\\.|[^\\\'])*\'|"(?:\\.|[^\\"])*"', - re.DOTALL | re.MULTILINE - ) - temp = [] - for x in re.sub(pattern, replacer, source).split('\n'): - if x.strip() != "": - temp.append(x) - return '\n'.join(temp) - - -def tree_to_token_index(root_node): - if (len(root_node.children) == 0 or root_node.type in ['string_literal', 'string', - 'character_literal']) and root_node.type != 'comment': - return [(root_node.start_point, root_node.end_point)] - else: - code_tokens = [] - for child in root_node.children: - code_tokens += tree_to_token_index(child) - return code_tokens - - -def tree_to_variable_index(root_node, index_to_code): - if (len(root_node.children) == 0 or root_node.type in ['string_literal', 'string', - 'character_literal']) and root_node.type != 'comment': - index = (root_node.start_point, root_node.end_point) - _, code = index_to_code[index] - if root_node.type != code: - return [(root_node.start_point, root_node.end_point)] - else: - return [] - else: - code_tokens = [] - for child in root_node.children: - code_tokens += tree_to_variable_index(child, index_to_code) - return code_tokens - - -def index_to_code_token(index, code): - start_point = index[0] - end_point = index[1] - if start_point[0] == end_point[0]: - s = code[start_point[0]][start_point[1]:end_point[1]] - else: - s = "" - s += code[start_point[0]][start_point[1]:] - for i in range(start_point[0] + 1, end_point[0]): - s += code[i] - s += code[end_point[0]][:end_point[1]] - return s diff --git a/CodeBLEU/readme.txt b/CodeBLEU/readme.txt deleted file mode 100644 index 833a5c1..0000000 --- a/CodeBLEU/readme.txt +++ /dev/null @@ -1 +0,0 @@ -python calc_code_bleu.py --refs reference_files --hyp candidate_file --language java ( or c_sharp) --params 0.25,0.25,0.25,0.25(default) \ No newline at end of file diff --git a/CodeBLEU/syntax_match.py b/CodeBLEU/syntax_match.py deleted file mode 100644 index bea76d0..0000000 --- a/CodeBLEU/syntax_match.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -from evaluator.CodeBLEU.parser import DFG_python, DFG_java, DFG_ruby, DFG_go, DFG_php, DFG_javascript, DFG_csharp -from evaluator.CodeBLEU.parser import (remove_comments_and_docstrings, - tree_to_token_index, - index_to_code_token, - tree_to_variable_index) -from tree_sitter import Language, Parser - -parser_path = '/export/share/wang.y/workspace/CodeT5Full/finetune/evaluator/CodeBLEU/parser' -dfg_function = { - 'python': DFG_python, - 'java': DFG_java, - 'ruby': DFG_ruby, - 'go': DFG_go, - 'php': DFG_php, - 'javascript': DFG_javascript, - 'c_sharp': DFG_csharp, -} - - -def calc_syntax_match(references, candidate, lang): - return corpus_syntax_match([references], [candidate], lang) - - -def corpus_syntax_match(references, candidates, lang): - JAVA_LANGUAGE = Language('{}/my-languages.so'.format(parser_path), lang) - parser = Parser() - parser.set_language(JAVA_LANGUAGE) - match_count = 0 - total_count = 0 - - for i in range(len(candidates)): - references_sample = references[i] - candidate = candidates[i] - for reference in references_sample: - try: - candidate = remove_comments_and_docstrings(candidate, 'java') - except: - pass - try: - reference = remove_comments_and_docstrings(reference, 'java') - except: - pass - - candidate_tree = parser.parse(bytes(candidate, 'utf8')).root_node - - reference_tree = parser.parse(bytes(reference, 'utf8')).root_node - - def get_all_sub_trees(root_node): - node_stack = [] - sub_tree_sexp_list = [] - depth = 1 - node_stack.append([root_node, depth]) - while len(node_stack) != 0: - cur_node, cur_depth = node_stack.pop() - sub_tree_sexp_list.append([cur_node.sexp(), cur_depth]) - for child_node in cur_node.children: - if len(child_node.children) != 0: - depth = cur_depth + 1 - node_stack.append([child_node, depth]) - return sub_tree_sexp_list - - cand_sexps = [x[0] for x in get_all_sub_trees(candidate_tree)] - ref_sexps = get_all_sub_trees(reference_tree) - - # print(cand_sexps) - # print(ref_sexps) - - for sub_tree, depth in ref_sexps: - if sub_tree in cand_sexps: - match_count += 1 - total_count += len(ref_sexps) - - score = match_count / total_count - return score diff --git a/CodeBLEU/utils.py b/CodeBLEU/utils.py deleted file mode 100644 index 9325999..0000000 --- a/CodeBLEU/utils.py +++ /dev/null @@ -1,106 +0,0 @@ -# Natural Language Toolkit: Utility functions -# -# Copyright (C) 2001-2020 NLTK Project -# Author: Steven Bird -# URL: -# For license information, see LICENSE.TXT - -from itertools import chain - -def pad_sequence( - sequence, - n, - pad_left=False, - pad_right=False, - left_pad_symbol=None, - right_pad_symbol=None, -): - """ - Returns a padded sequence of items before ngram extraction. - >>> list(pad_sequence([1,2,3,4,5], 2, pad_left=True, pad_right=True, left_pad_symbol='', right_pad_symbol='')) - ['', 1, 2, 3, 4, 5, ''] - >>> list(pad_sequence([1,2,3,4,5], 2, pad_left=True, left_pad_symbol='')) - ['', 1, 2, 3, 4, 5] - >>> list(pad_sequence([1,2,3,4,5], 2, pad_right=True, right_pad_symbol='')) - [1, 2, 3, 4, 5, ''] - :param sequence: the source data to be padded - :type sequence: sequence or iter - :param n: the degree of the ngrams - :type n: int - :param pad_left: whether the ngrams should be left-padded - :type pad_left: bool - :param pad_right: whether the ngrams should be right-padded - :type pad_right: bool - :param left_pad_symbol: the symbol to use for left padding (default is None) - :type left_pad_symbol: any - :param right_pad_symbol: the symbol to use for right padding (default is None) - :type right_pad_symbol: any - :rtype: sequence or iter - """ - sequence = iter(sequence) - if pad_left: - sequence = chain((left_pad_symbol,) * (n - 1), sequence) - if pad_right: - sequence = chain(sequence, (right_pad_symbol,) * (n - 1)) - return sequence - - -# add a flag to pad the sequence so we get peripheral ngrams? - - -def ngrams( - sequence, - n, - pad_left=False, - pad_right=False, - left_pad_symbol=None, - right_pad_symbol=None, -): - """ - Return the ngrams generated from a sequence of items, as an iterator. - For example: - >>> from nltk.util import ngrams - >>> list(ngrams([1,2,3,4,5], 3)) - [(1, 2, 3), (2, 3, 4), (3, 4, 5)] - Wrap with list for a list version of this function. Set pad_left - or pad_right to true in order to get additional ngrams: - >>> list(ngrams([1,2,3,4,5], 2, pad_right=True)) - [(1, 2), (2, 3), (3, 4), (4, 5), (5, None)] - >>> list(ngrams([1,2,3,4,5], 2, pad_right=True, right_pad_symbol='
    ')) - [(1, 2), (2, 3), (3, 4), (4, 5), (5, '
    ')] - >>> list(ngrams([1,2,3,4,5], 2, pad_left=True, left_pad_symbol='')) - [('', 1), (1, 2), (2, 3), (3, 4), (4, 5)] - >>> list(ngrams([1,2,3,4,5], 2, pad_left=True, pad_right=True, left_pad_symbol='', right_pad_symbol='')) - [('', 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, '')] - :param sequence: the source data to be converted into ngrams - :type sequence: sequence or iter - :param n: the degree of the ngrams - :type n: int - :param pad_left: whether the ngrams should be left-padded - :type pad_left: bool - :param pad_right: whether the ngrams should be right-padded - :type pad_right: bool - :param left_pad_symbol: the symbol to use for left padding (default is None) - :type left_pad_symbol: any - :param right_pad_symbol: the symbol to use for right padding (default is None) - :type right_pad_symbol: any - :rtype: sequence or iter - """ - sequence = pad_sequence( - sequence, n, pad_left, pad_right, left_pad_symbol, right_pad_symbol - ) - - history = [] - while n > 1: - # PEP 479, prevent RuntimeError from being raised when StopIteration bubbles out of generator - try: - next_item = next(sequence) - except StopIteration: - # no more data, terminate the generator - return - history.append(next_item) - n -= 1 - for item in sequence: - history.append(item) - yield tuple(history) - del history[0] \ No newline at end of file diff --git a/CodeBLEU/weighted_ngram_match.py b/CodeBLEU/weighted_ngram_match.py deleted file mode 100644 index f2d346d..0000000 --- a/CodeBLEU/weighted_ngram_match.py +++ /dev/null @@ -1,558 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -# Natural Language Toolkit: BLEU Score -# -# Copyright (C) 2001-2020 NLTK Project -# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim -# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan -# URL: -# For license information, see LICENSE.TXT - -"""BLEU score implementation.""" - -import math -import sys -from fractions import Fraction -import warnings -from collections import Counter - -from evaluator.CodeBLEU.utils import ngrams -import pdb - - -def sentence_bleu( - references, - hypothesis, - weights=(0.25, 0.25, 0.25, 0.25), - smoothing_function=None, - auto_reweigh=False, -): - """ - Calculate BLEU score (Bilingual Evaluation Understudy) from - Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. - "BLEU: a method for automatic evaluation of machine translation." - In Proceedings of ACL. http://www.aclweb.org/anthology/P02-1040.pdf - >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', - ... 'ensures', 'that', 'the', 'military', 'always', - ... 'obeys', 'the', 'commands', 'of', 'the', 'party'] - >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops', - ... 'forever', 'hearing', 'the', 'activity', 'guidebook', - ... 'that', 'party', 'direct'] - >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', - ... 'ensures', 'that', 'the', 'military', 'will', 'forever', - ... 'heed', 'Party', 'commands'] - >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which', - ... 'guarantees', 'the', 'military', 'forces', 'always', - ... 'being', 'under', 'the', 'command', 'of', 'the', - ... 'Party'] - >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', - ... 'army', 'always', 'to', 'heed', 'the', 'directions', - ... 'of', 'the', 'party'] - >>> sentence_bleu([reference1, reference2, reference3], hypothesis1) # doctest: +ELLIPSIS - 0.5045... - If there is no ngrams overlap for any order of n-grams, BLEU returns the - value 0. This is because the precision for the order of n-grams without - overlap is 0, and the geometric mean in the final BLEU score computation - multiplies the 0 with the precision of other n-grams. This results in 0 - (independently of the precision of the othe n-gram orders). The following - example has zero 3-gram and 4-gram overlaps: - >>> round(sentence_bleu([reference1, reference2, reference3], hypothesis2),4) # doctest: +ELLIPSIS - 0.0 - To avoid this harsh behaviour when no ngram overlaps are found a smoothing - function can be used. - >>> chencherry = SmoothingFunction() - >>> sentence_bleu([reference1, reference2, reference3], hypothesis2, - ... smoothing_function=chencherry.method1) # doctest: +ELLIPSIS - 0.0370... - The default BLEU calculates a score for up to 4-grams using uniform - weights (this is called BLEU-4). To evaluate your translations with - higher/lower order ngrams, use customized weights. E.g. when accounting - for up to 5-grams with uniform weights (this is called BLEU-5) use: - >>> weights = (1./5., 1./5., 1./5., 1./5., 1./5.) - >>> sentence_bleu([reference1, reference2, reference3], hypothesis1, weights) # doctest: +ELLIPSIS - 0.3920... - :param references: reference sentences - :type references: list(list(str)) - :param hypothesis: a hypothesis sentence - :type hypothesis: list(str) - :param weights: weights for unigrams, bigrams, trigrams and so on - :type weights: list(float) - :param smoothing_function: - :type smoothing_function: SmoothingFunction - :param auto_reweigh: Option to re-normalize the weights uniformly. - :type auto_reweigh: bool - :return: The sentence-level BLEU score. - :rtype: float - """ - return corpus_bleu( - [references], [hypothesis], weights, smoothing_function, auto_reweigh - ) - - -def corpus_bleu( - list_of_references, - hypotheses, - weights=(0.25, 0.25, 0.25, 0.25), - smoothing_function=None, - auto_reweigh=False, -): - """ - Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all - the hypotheses and their respective references. - Instead of averaging the sentence level BLEU scores (i.e. marco-average - precision), the original BLEU metric (Papineni et al. 2002) accounts for - the micro-average precision (i.e. summing the numerators and denominators - for each hypothesis-reference(s) pairs before the division). - >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', - ... 'ensures', 'that', 'the', 'military', 'always', - ... 'obeys', 'the', 'commands', 'of', 'the', 'party'] - >>> ref1a = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', - ... 'ensures', 'that', 'the', 'military', 'will', 'forever', - ... 'heed', 'Party', 'commands'] - >>> ref1b = ['It', 'is', 'the', 'guiding', 'principle', 'which', - ... 'guarantees', 'the', 'military', 'forces', 'always', - ... 'being', 'under', 'the', 'command', 'of', 'the', 'Party'] - >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', - ... 'army', 'always', 'to', 'heed', 'the', 'directions', - ... 'of', 'the', 'party'] - >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was', - ... 'interested', 'in', 'world', 'history'] - >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history', - ... 'because', 'he', 'read', 'the', 'book'] - >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]] - >>> hypotheses = [hyp1, hyp2] - >>> corpus_bleu(list_of_references, hypotheses) # doctest: +ELLIPSIS - 0.5920... - The example below show that corpus_bleu() is different from averaging - sentence_bleu() for hypotheses - >>> score1 = sentence_bleu([ref1a, ref1b, ref1c], hyp1) - >>> score2 = sentence_bleu([ref2a], hyp2) - >>> (score1 + score2) / 2 # doctest: +ELLIPSIS - 0.6223... - :param list_of_references: a corpus of lists of reference sentences, w.r.t. hypotheses - :type list_of_references: list(list(list(str))) - :param hypotheses: a list of hypothesis sentences - :type hypotheses: list(list(str)) - :param weights: weights for unigrams, bigrams, trigrams and so on - :type weights: list(float) - :param smoothing_function: - :type smoothing_function: SmoothingFunction - :param auto_reweigh: Option to re-normalize the weights uniformly. - :type auto_reweigh: bool - :return: The corpus-level BLEU score. - :rtype: float - """ - # Before proceeding to compute BLEU, perform sanity checks. - - p_numerators = Counter() # Key = ngram order, and value = no. of ngram matches. - p_denominators = Counter() # Key = ngram order, and value = no. of ngram in ref. - hyp_lengths, ref_lengths = 0, 0 - - assert len(list_of_references) == len(hypotheses), ( - "The number of hypotheses and their reference(s) should be the " "same " - ) - - # Iterate through each hypothesis and their corresponding references. - for references, hypothesis in zip(list_of_references, hypotheses): - # For each order of ngram, calculate the numerator and - # denominator for the corpus-level modified precision. - for i, _ in enumerate(weights, start=1): - p_i_numeraotr, p_i_denominator = modified_recall(references, hypothesis, i) - p_numerators[i] += p_i_numeraotr - p_denominators[i] += p_i_denominator - - # Calculate the hypothesis length and the closest reference length. - # Adds them to the corpus-level hypothesis and reference counts. - hyp_len = len(hypothesis) - hyp_lengths += hyp_len - ref_lengths += closest_ref_length(references, hyp_len) - - # Calculate corpus-level brevity penalty. - bp = brevity_penalty(ref_lengths, hyp_lengths) - - # Uniformly re-weighting based on maximum hypothesis lengths if largest - # order of n-grams < 4 and weights is set at default. - if auto_reweigh: - if hyp_lengths < 4 and weights == (0.25, 0.25, 0.25, 0.25): - weights = (1 / hyp_lengths,) * hyp_lengths - - # Collects the various recall values for the different ngram orders. - p_n = [ - (p_numerators[i], p_denominators[i]) - for i, _ in enumerate(weights, start=1) - ] - - # Returns 0 if there's no matching n-grams - # We only need to check for p_numerators[1] == 0, since if there's - # no unigrams, there won't be any higher order ngrams. - if p_numerators[1] == 0: - return 0 - - # If there's no smoothing, set use method0 from SmoothinFunction class. - if not smoothing_function: - smoothing_function = SmoothingFunction().method1 - # Smoothen the modified precision. - # Note: smoothing_function() may convert values into floats; - # it tries to retain the Fraction object as much as the - # smoothing method allows. - p_n = smoothing_function( - p_n, references=references, hypothesis=hypothesis, hyp_len=hyp_lengths - ) - # pdb.set_trace() - s = (w_i * math.log(p_i[0]/p_i[1]) for w_i, p_i in zip(weights, p_n)) - s = bp * math.exp(math.fsum(s)) - return s - - -def modified_recall(references, hypothesis, n): - """ - Calculate modified ngram recall. - :param references: A list of reference translations. - :type references: list(list(str)) - :param hypothesis: A hypothesis translation. - :type hypothesis: list(str) - :param n: The ngram order. - :type n: int - :return: BLEU's modified precision for the nth order ngram. - :rtype: Fraction - """ - # Extracts all ngrams in hypothesis - # Set an empty Counter if hypothesis is empty. - # pdb.set_trace() - numerator = 0 - denominator = 0 - - counts = Counter(ngrams(hypothesis, n)) if len(hypothesis) >= n else Counter() - # Extract a union of references' counts. - # max_counts = reduce(or_, [Counter(ngrams(ref, n)) for ref in references]) - max_counts = {} - for reference_and_weights in references: - reference = reference_and_weights[0] - weights = reference_and_weights[1] - reference_counts = ( - Counter(ngrams(reference, n)) if len(reference) >= n else Counter() - ) - # for ngram in reference_counts: - # max_counts[ngram] = max(max_counts.get(ngram, 0), counts[ngram]) - clipped_counts = { - ngram: min(count, counts[ngram]) for ngram, count in reference_counts.items() - } - # reweight - if n == 1 and len(weights) == len(reference_counts): - def weighted_sum(weights, counts): - sum_counts = 0 - for ngram, count in counts.items(): - sum_counts += count * (weights[ngram[0]] if ngram[0] in weights else 1) - return sum_counts - - numerator += weighted_sum(weights, clipped_counts) - denominator += max(1, weighted_sum(weights, reference_counts)) - - else: - numerator += sum(clipped_counts.values()) - denominator += max(1, sum(reference_counts.values())) - - # # Assigns the intersection between hypothesis and references' counts. - # clipped_counts = { - # ngram: min(count, max_counts[ngram]) for ngram, count in counts.items() - # } - - # numerator += sum(clipped_counts.values()) - # # Ensures that denominator is minimum 1 to avoid ZeroDivisionError. - # # Usually this happens when the ngram order is > len(reference). - # denominator += max(1, sum(counts.values())) - - #return Fraction(numerator, denominator, _normalize=False) - return numerator, denominator - - -def closest_ref_length(references, hyp_len): - """ - This function finds the reference that is the closest length to the - hypothesis. The closest reference length is referred to as *r* variable - from the brevity penalty formula in Papineni et. al. (2002) - :param references: A list of reference translations. - :type references: list(list(str)) - :param hyp_len: The length of the hypothesis. - :type hyp_len: int - :return: The length of the reference that's closest to the hypothesis. - :rtype: int - """ - ref_lens = (len(reference) for reference in references) - closest_ref_len = min( - ref_lens, key=lambda ref_len: (abs(ref_len - hyp_len), ref_len) - ) - return closest_ref_len - - -def brevity_penalty(closest_ref_len, hyp_len): - """ - Calculate brevity penalty. - As the modified n-gram precision still has the problem from the short - length sentence, brevity penalty is used to modify the overall BLEU - score according to length. - An example from the paper. There are three references with length 12, 15 - and 17. And a concise hypothesis of the length 12. The brevity penalty is 1. - >>> reference1 = list('aaaaaaaaaaaa') # i.e. ['a'] * 12 - >>> reference2 = list('aaaaaaaaaaaaaaa') # i.e. ['a'] * 15 - >>> reference3 = list('aaaaaaaaaaaaaaaaa') # i.e. ['a'] * 17 - >>> hypothesis = list('aaaaaaaaaaaa') # i.e. ['a'] * 12 - >>> references = [reference1, reference2, reference3] - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> brevity_penalty(closest_ref_len, hyp_len) - 1.0 - In case a hypothesis translation is shorter than the references, penalty is - applied. - >>> references = [['a'] * 28, ['a'] * 28] - >>> hypothesis = ['a'] * 12 - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> brevity_penalty(closest_ref_len, hyp_len) - 0.2635971381157267 - The length of the closest reference is used to compute the penalty. If the - length of a hypothesis is 12, and the reference lengths are 13 and 2, the - penalty is applied because the hypothesis length (12) is less then the - closest reference length (13). - >>> references = [['a'] * 13, ['a'] * 2] - >>> hypothesis = ['a'] * 12 - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS - 0.9200... - The brevity penalty doesn't depend on reference order. More importantly, - when two reference sentences are at the same distance, the shortest - reference sentence length is used. - >>> references = [['a'] * 13, ['a'] * 11] - >>> hypothesis = ['a'] * 12 - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> bp1 = brevity_penalty(closest_ref_len, hyp_len) - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(reversed(references), hyp_len) - >>> bp2 = brevity_penalty(closest_ref_len, hyp_len) - >>> bp1 == bp2 == 1 - True - A test example from mteval-v13a.pl (starting from the line 705): - >>> references = [['a'] * 11, ['a'] * 8] - >>> hypothesis = ['a'] * 7 - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS - 0.8668... - >>> references = [['a'] * 11, ['a'] * 8, ['a'] * 6, ['a'] * 7] - >>> hypothesis = ['a'] * 7 - >>> hyp_len = len(hypothesis) - >>> closest_ref_len = closest_ref_length(references, hyp_len) - >>> brevity_penalty(closest_ref_len, hyp_len) - 1.0 - :param hyp_len: The length of the hypothesis for a single sentence OR the - sum of all the hypotheses' lengths for a corpus - :type hyp_len: int - :param closest_ref_len: The length of the closest reference for a single - hypothesis OR the sum of all the closest references for every hypotheses. - :type closest_ref_len: int - :return: BLEU's brevity penalty. - :rtype: float - """ - if hyp_len > closest_ref_len: - return 1 - # If hypothesis is empty, brevity penalty = 0 should result in BLEU = 0.0 - elif hyp_len == 0: - return 0 - else: - return math.exp(1 - closest_ref_len / hyp_len) - - -class SmoothingFunction: - """ - This is an implementation of the smoothing techniques - for segment-level BLEU scores that was presented in - Boxing Chen and Collin Cherry (2014) A Systematic Comparison of - Smoothing Techniques for Sentence-Level BLEU. In WMT14. - http://acl2014.org/acl2014/W14-33/pdf/W14-3346.pdf - """ - - def __init__(self, epsilon=0.1, alpha=5, k=5): - """ - This will initialize the parameters required for the various smoothing - techniques, the default values are set to the numbers used in the - experiments from Chen and Cherry (2014). - >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', 'ensures', - ... 'that', 'the', 'military', 'always', 'obeys', 'the', - ... 'commands', 'of', 'the', 'party'] - >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', 'ensures', - ... 'that', 'the', 'military', 'will', 'forever', 'heed', - ... 'Party', 'commands'] - >>> chencherry = SmoothingFunction() - >>> print(sentence_bleu([reference1], hypothesis1)) # doctest: +ELLIPSIS - 0.4118... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method0)) # doctest: +ELLIPSIS - 0.4118... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method1)) # doctest: +ELLIPSIS - 0.4118... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method2)) # doctest: +ELLIPSIS - 0.4489... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method3)) # doctest: +ELLIPSIS - 0.4118... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method4)) # doctest: +ELLIPSIS - 0.4118... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method5)) # doctest: +ELLIPSIS - 0.4905... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method6)) # doctest: +ELLIPSIS - 0.4135... - >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method7)) # doctest: +ELLIPSIS - 0.4905... - :param epsilon: the epsilon value use in method 1 - :type epsilon: float - :param alpha: the alpha value use in method 6 - :type alpha: int - :param k: the k value use in method 4 - :type k: int - """ - self.epsilon = epsilon - self.alpha = alpha - self.k = k - - def method0(self, p_n, *args, **kwargs): - """ - No smoothing. - """ - p_n_new = [] - for i, p_i in enumerate(p_n): - if p_i[0] != 0: - p_n_new.append(p_i) - else: - _msg = str( - "\nThe hypothesis contains 0 counts of {}-gram overlaps.\n" - "Therefore the BLEU score evaluates to 0, independently of\n" - "how many N-gram overlaps of lower order it contains.\n" - "Consider using lower n-gram order or use " - "SmoothingFunction()" - ).format(i + 1) - warnings.warn(_msg) - # When numerator==0 where denonminator==0 or !=0, the result - # for the precision score should be equal to 0 or undefined. - # Due to BLEU geometric mean computation in logarithm space, - # we we need to take the return sys.float_info.min such that - # math.log(sys.float_info.min) returns a 0 precision score. - p_n_new.append(sys.float_info.min) - return p_n_new - - def method1(self, p_n, *args, **kwargs): - """ - Smoothing method 1: Add *epsilon* counts to precision with 0 counts. - """ - return [ - ((p_i[0] + self.epsilon), p_i[1]) - if p_i[0] == 0 - else p_i - for p_i in p_n - ] - - def method2(self, p_n, *args, **kwargs): - """ - Smoothing method 2: Add 1 to both numerator and denominator from - Chin-Yew Lin and Franz Josef Och (2004) Automatic evaluation of - machine translation quality using longest common subsequence and - skip-bigram statistics. In ACL04. - """ - return [ - (p_i[0] + 1, p_i[1] + 1) - for p_i in p_n - ] - - def method3(self, p_n, *args, **kwargs): - """ - Smoothing method 3: NIST geometric sequence smoothing - The smoothing is computed by taking 1 / ( 2^k ), instead of 0, for each - precision score whose matching n-gram count is null. - k is 1 for the first 'n' value for which the n-gram match count is null/ - For example, if the text contains: - - one 2-gram match - - and (consequently) two 1-gram matches - the n-gram count for each individual precision score would be: - - n=1 => prec_count = 2 (two unigrams) - - n=2 => prec_count = 1 (one bigram) - - n=3 => prec_count = 1/2 (no trigram, taking 'smoothed' value of 1 / ( 2^k ), with k=1) - - n=4 => prec_count = 1/4 (no fourgram, taking 'smoothed' value of 1 / ( 2^k ), with k=2) - """ - incvnt = 1 # From the mteval-v13a.pl, it's referred to as k. - for i, p_i in enumerate(p_n): - if p_i.numerator == 0: - p_n[i] = 1 / (2 ** incvnt * p_i.denominator) - incvnt += 1 - return p_n - - def method4(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs): - """ - Smoothing method 4: - Shorter translations may have inflated precision values due to having - smaller denominators; therefore, we give them proportionally - smaller smoothed counts. Instead of scaling to 1/(2^k), Chen and Cherry - suggests dividing by 1/ln(len(T)), where T is the length of the translation. - """ - hyp_len = hyp_len if hyp_len else len(hypothesis) - for i, p_i in enumerate(p_n): - if p_i.numerator == 0 and hyp_len != 0: - incvnt = i + 1 * self.k / math.log( - hyp_len - ) # Note that this K is different from the K from NIST. - p_n[i] = incvnt / p_i.denominator - return p_n - - def method5(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs): - """ - Smoothing method 5: - The matched counts for similar values of n should be similar. To a - calculate the n-gram matched count, it averages the n−1, n and n+1 gram - matched counts. - """ - hyp_len = hyp_len if hyp_len else len(hypothesis) - m = {} - # Requires an precision value for an addition ngram order. - p_n_plus1 = p_n + [modified_precision(references, hypothesis, 5)] - m[-1] = p_n[0] + 1 - for i, p_i in enumerate(p_n): - p_n[i] = (m[i - 1] + p_i + p_n_plus1[i + 1]) / 3 - m[i] = p_n[i] - return p_n - - def method6(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs): - """ - Smoothing method 6: - Interpolates the maximum likelihood estimate of the precision *p_n* with - a prior estimate *pi0*. The prior is estimated by assuming that the ratio - between pn and pn−1 will be the same as that between pn−1 and pn−2; from - Gao and He (2013) Training MRF-Based Phrase Translation Models using - Gradient Ascent. In NAACL. - """ - hyp_len = hyp_len if hyp_len else len(hypothesis) - # This smoothing only works when p_1 and p_2 is non-zero. - # Raise an error with an appropriate message when the input is too short - # to use this smoothing technique. - assert p_n[2], "This smoothing method requires non-zero precision for bigrams." - for i, p_i in enumerate(p_n): - if i in [0, 1]: # Skips the first 2 orders of ngrams. - continue - else: - pi0 = 0 if p_n[i - 2] == 0 else p_n[i - 1] ** 2 / p_n[i - 2] - # No. of ngrams in translation that matches the reference. - m = p_i.numerator - # No. of ngrams in translation. - l = sum(1 for _ in ngrams(hypothesis, i + 1)) - # Calculates the interpolated precision. - p_n[i] = (m + self.alpha * pi0) / (l + self.alpha) - return p_n - - def method7(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs): - """ - Smoothing method 7: - Interpolates methods 4 and 5. - """ - hyp_len = hyp_len if hyp_len else len(hypothesis) - p_n = self.method4(p_n, references, hypothesis, hyp_len) - p_n = self.method5(p_n, references, hypothesis, hyp_len) - return p_n diff --git a/apply_tokenizer.py b/apply_tokenizer.py deleted file mode 100644 index a95b154..0000000 --- a/apply_tokenizer.py +++ /dev/null @@ -1,17 +0,0 @@ -from tokenizers import ByteLevelBPETokenizer - -tokenizer = ByteLevelBPETokenizer.from_file( - "./salesforce/codet5-vocab.json", - "./salesforce/codet5-merges.txt" -) -tokenizer.add_special_tokens([ - "", - "", - "", - "", - "" -]) - -print( - tokenizer.encode(" hello Don't you love 🤗 Transformers yes . ").tokens -) \ No newline at end of file diff --git a/bleu.py b/bleu.py deleted file mode 100644 index 47e1335..0000000 --- a/bleu.py +++ /dev/null @@ -1,134 +0,0 @@ -# Copyright 2017 Google Inc. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -"""Python implementation of BLEU and smooth-BLEU. - -This module provides a Python implementation of BLEU and smooth-BLEU. -Smooth BLEU is computed following the method outlined in the paper: -Chin-Yew Lin, Franz Josef Och. ORANGE: a method for evaluating automatic -evaluation metrics for machine translation. COLING 2004. -""" - -import collections -import math - - -def _get_ngrams(segment, max_order): - """Extracts all n-grams upto a given maximum order from an input segment. - - Args: - segment: text segment from which n-grams will be extracted. - max_order: maximum length in tokens of the n-grams returned by this - methods. - - Returns: - The Counter containing all n-grams upto max_order in segment - with a count of how many times each n-gram occurred. - """ - ngram_counts = collections.Counter() - for order in range(1, max_order + 1): - for i in range(0, len(segment) - order + 1): - ngram = tuple(segment[i:i+order]) - ngram_counts[ngram] += 1 - return ngram_counts - - -def compute_bleu(reference_corpus, translation_corpus, max_order=4, - smooth=False): - """Computes BLEU score of translated segments against one or more references. - - Args: - reference_corpus: list of lists of references for each translation. Each - reference should be tokenized into a list of tokens. - translation_corpus: list of translations to score. Each translation - should be tokenized into a list of tokens. - max_order: Maximum n-gram order to use when computing BLEU score. - smooth: Whether or not to apply Lin et al. 2004 smoothing. - - Returns: - 3-Tuple with the BLEU score, n-gram precisions, geometric mean of n-gram - precisions and brevity penalty. - """ - matches_by_order = [0] * max_order - possible_matches_by_order = [0] * max_order - reference_length = 0 - translation_length = 0 - for (references, translation) in zip(reference_corpus, - translation_corpus): - reference_length += min(len(r) for r in references) - translation_length += len(translation) - - merged_ref_ngram_counts = collections.Counter() - for reference in references: - merged_ref_ngram_counts |= _get_ngrams(reference, max_order) - translation_ngram_counts = _get_ngrams(translation, max_order) - overlap = translation_ngram_counts & merged_ref_ngram_counts - for ngram in overlap: - matches_by_order[len(ngram)-1] += overlap[ngram] - for order in range(1, max_order+1): - possible_matches = len(translation) - order + 1 - if possible_matches > 0: - possible_matches_by_order[order-1] += possible_matches - - precisions = [0] * max_order - for i in range(0, max_order): - if smooth: - precisions[i] = ((matches_by_order[i] + 1.) / - (possible_matches_by_order[i] + 1.)) - else: - if possible_matches_by_order[i] > 0: - precisions[i] = (float(matches_by_order[i]) / - possible_matches_by_order[i]) - else: - precisions[i] = 0.0 - - if min(precisions) > 0: - p_log_sum = sum((1. / max_order) * math.log(p) for p in precisions) - geo_mean = math.exp(p_log_sum) - else: - geo_mean = 0 - - ratio = float(translation_length) / reference_length - - if ratio > 1.0: - bp = 1. - else: - bp = math.exp(1 - 1. / ratio) - - bleu = geo_mean * bp - - return (bleu, precisions, bp, ratio, translation_length, reference_length) - - -def _bleu(ref_file, trans_file, subword_option=None): - max_order = 4 - smooth = True - ref_files = [ref_file] - reference_text = [] - for reference_filename in ref_files: - with open(reference_filename) as fh: - reference_text.append(fh.readlines()) - per_segment_references = [] - for references in zip(*reference_text): - reference_list = [] - for reference in references: - reference_list.append(reference.strip().split()) - per_segment_references.append(reference_list) - translations = [] - with open(trans_file) as fh: - for line in fh: - translations.append(line.strip().split()) - bleu_score, _, _, _, _, _ = compute_bleu(per_segment_references, translations, max_order, smooth) - return round(100 * bleu_score,2) \ No newline at end of file diff --git a/salesforce/codet5-merges.txt b/salesforce/codet5-merges.txt deleted file mode 100644 index 1534b73..0000000 --- a/salesforce/codet5-merges.txt +++ /dev/null @@ -1,31740 +0,0 @@ -#version: 0.2 - Trained by `huggingface/tokenizers` -Ġ ( -Ġ ) -Ġ . -e r -o n -r e -i n -Ġ t -Ġ , -a t -Ġ $ -Ġ s -Ġ = -Ġ ; -e n -Ġ c -Ġ i -e t -Ġ a -o r -e s -Ġ Ġ -Ġ re -Ġ f -i on -Ġt h -a l -Ġ { -Ġ } -Ġ n -i s -e l -Ġ p -Ġ : -u r -Ġ ' -a r -l e -c t -Ġ - -a m -Ġ d -r o -a n -i t -Ġ [ -s e -Ġ ] -Ġi f -in g -c e -Ġ m -t r -Ġ g -Ġ " -Ġ in -Ġ- > -u n -en t -Ġ o -ur n -u t -d e -Ġ b -Ġre t -Ġth e -Ġret urn -Ġ l -e d -i l -Ġ v -u l -Ġth is -s t -i c -Ġg et -p t -e x -am e -at e -Ġ w -Ġ / -c h -u e -a s -a g -p e -Ġ S -ct ion -i d -e m -o t -Ġi s -r a -o l -Ġc on -Ġs el -Ġt o -e w -i g -a d -o m -c k -Ġf or -Ġsel f -Ġ h -at ion -Ġ/ / -o d -er r -tr ing -q u -u b -Ġ 0 -pt ion -is t -ĠĠ ĠĠ -a b -u m -Ġ * -i m -I n -l o -Ġs t -Ġ C -e ct -t er -y pe -Ġ _ -o de -al ue -Ġa n -Ġ err -Ġn ew -es t -at a -Ġ + -i le -Ġ T -Ġre s -Ġ ! 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are -Ġcol lection -ĠĠĠĠĠĠĠĠ Ġ -M arshall -De finition -Ġt mp -N umber -posit ory -P T -q ue -Ġc or -ay load -ĠIO Exception -ock et -A ccess -G ener -Head er -Ġbe fore -Ġcon st -Ġd irectory -De fault -cal e -> ' -A d -O M -id d -Ġpro vi -Ġ ĉĉ -An d -S el -Argument Exception -Ġo ver -A D -ateg ory -oc i -c ount -f or -Ġmatch es -de fault -Add ress -w ay -il ity -qu ence -Up date -B uffer -ch eck -Ġw arn -O ption -m ax -l en -Ġm ore -ra w -y s -Ġd if -rib e -ĠCon fig -Ġch annel -Ex ec -Ġtem p -p en -N ew -Ġpl ugin -ĠD ata -u id -g in -ir on -Ġcontain s -Ġth ere -m atch -t on -w ith -B e -in ed -Em pty -ĠA n -Ġs ave -Ġv er -l abel -ĠE rr -Ġget C -und le -Ġ Key -Ġim port -Ġre quired -ri x -R em -c ul -le g -Ġp attern -st art -m it -ĠR un -r am -In valid -at rix -E ntity -a uth -vi ew -Ġro ute -Ġset tings -ĠU p -r on -Ġre c -is ion -Con nection -Ġt ypes -Ġvalid ate -res h -ĠSer vice -Re f -Ġstr uct -Ġcon s -Ġg o -it ies -ĠIn valid -Ġs chema -P age -Ġm ode -d b -Ġs ame -ar gs -b ase -## ## -R el -V alues -n ames -O T -ĠError f -ĠUR L -j son -he ad -i ent -ed ia -Ġnames pace -Ġm etadata -Ġf ull -Ġpro ject -N AME -T he -etail s -P art -Util s -S pec -c all -el p -Ġb ind -Re ad -Ġ' % -C re -R un -g ing -R ow -ĠR eturn -ent ial -Ġle ft -Ġ uri -ach ed -Ġs printf -Ġa ccess -tr ies -li ce -C l -param s -n ull -ay out -leg al -cod ing -ry pt -T ypes -Ġen code -ĠLO G -st ate -Ġs l -Ġtime out -ht t -Ġ'/ ' -Q L -Ġx ml -Ġm ay -Ġsel ect -Ġcl s -ĠN ode -ĠD E -ig h -str aint -tr a -lo de -l legal -Ġcol or -Ġn ow -b s -m ethod -Im age -N ull -Loc al -F ound -Ġst d -at er -F A -I f -s o -ch ed -P r -w er -im ple -L I -ang es -s ure -E D -Ġin valid -Marshall er -Ġa ut -M atch -lo se -Ġexist ing -Ġdat abase -Ġstring s -Ġd ict -um e -Ġid x -ĠValue Error -s l -Ġ' _ -Ġre p -Ġh eight -ĠC heck -p y -enc y -Ġw ord -Ġt itle -Ġt otal -re place -f rom -Ser ver -Ġpar ser -F unction -vi ous -ow er -escri pt -stant s -res ult -Ġb uilder -A B -e ar -i ed -n ing -v ices -n own -oun ter -Ġit s -L ink -Ġelse if -r iver -in ary -ic s -Ġgener ate -en u -//// //// -Ġreturn ed -n um -Ġget M -Ġde c -Ġst ack -and om -Ġprovi ded -Ġe v -Ġre ference -Re cord -o ptions -Ġloc ation -Ġbe en -Ġ' " -ĠI N -t able -- > -S ER -U N -Ġa m -at ic -ug h -is try -mb ol -Ġ1 00 -c le -Ġst op -Ġext end -N ext -n o -if t -f g -1 2 -form at -Ġpart s -ĠI llegal -Ġs um -c s -ĠIn teger -Ġr ight -Ġsc ope -bo x -P oint -ĠC ms -m essage -Ġt ree -on th -Ġo pt -Ġst yle -v ed -Ch ar -S ession -Ġf etch -n ode -By tes -i que -Ġtrans l -1 0 -ĠS ee -it er -Ġup d -ĠD ate -Ġcolumn s -iss ing -Cont ainer -B ase -Ġinit ial -P er -T ask -R ule -Ġlo ck -ĠU ser -def ined -Ġsh ape -d f -Ġp ackage -Ġthe m -c el -Ġobject s -as sed -Ġelement s -' , -f ilter -Com mand -Ġvari able -Ġm eta -A M -Ġw rap -S ec -Ġcon f -b e -ĠT ra -Ġin v -Ġn odes -ĠCol lection -Ġpass word -Ġs ingle -A P -V ER -Ġsec ond -Ġ" / -: // -id get -Ġtr im -b ers -th is -Ġn ormal -Ġ" % -i able -esc ribe -Ġsu ccess -Ġde fin -= ' -a w -Ġde fer -ĠI m -EN T -List ener -L abel -) " -A C -Pro cess -i ction -Ġt x -Ġap ply -Ġl et -id s -st ore -ass word -Ġst ep -Ġa ccount -arsh al -Ġ Q -ĠH TTP -Ġsup er -ut ure -f l -Ġin sert -Pre fix -Ġresol ve -F F -E L -C ON -Pro vider -B ody -d is -Ġj ava -t en -Ġu int -Ġo per -ĠD o -St ore -are d -R I -ro und -P ackage -ĠA l -escript or -iron ment -Ġ' , -b ser -Ġm ark -up le -Ġcon dition -Ġpro p -qu ery -p atch -an y -UL T -Ġp assed -H elper -M etadata -J ob -N o -ĠP ar -Ġv is -Ġorig inal -Tem plate -m odel -Ġ Z -ĠF ield -Ġaw s -ĠN ot -W riter -D B -Ġse g -Ġ' { -re quest -E X -Ġf n -Ġ< < -Pro perties -Ġp ack -Ġpre g -Ex pression -ĠP r -Ġcom plet -ert ific -an k -Ġi gnore -ĠRes ponse -a ces -Ġal ias -M ode -H ash -OD O -S C -1 6 -Field s -ĠUp date -t oken -ol ume -ig ger -Ġav ailable -S I -Ġf ail -n ap -at s -Ġlo ok -Ġchild ren -T arget -st atus -ent er -g ress -ang ed -red ential -ĠS ystem -Ġhttp s -ens ions -Of fset -ur ity -Ġ' \ -tr ans -Ġt w -F l -Ġin clude -Ġtag s -Ġget D -t ed -end er -Ġde vice -ĠS T -Ġimp lement -h and -ĠM essage -t a -t ypes -Ġth an -f iles -Le vel -IN D -el y -Ġor g -Ġi p -I ter -ĠM ath -Ġl ines -Ġg ot -ĠV alid -Ġp k -Ġget Value -Ġchar act -Ġ' < -en ces -l n -Ġ 7 -v alues -Ġsub str -et ime -R oot -Ġbe gin -d s -d own -Ġde finition -ĠA t -Ġget Message -Ġprotocol Marshaller -ĠD B -W idth -Ġp ayload -i od -n s -g ine -Ġq ueue -al t -im ary -ol low -Ġs b -ubl ish -Ġp y -Key s -pt h -ĠĠĠĠĠĠĠĠ ĠĠĠĠ -T YPE -Ġ' - -or ies -Param eters -Ġat t -Re ference -Ġsh ow -Ġrep resent -ĠArray List -C ur -o x -in ce -Ġd ist -\ " -et s -d a -sh ot -Ġpre vious -Ġab s -Ġ' . -P O -s es -W ork -im um -g ed -Ġcle ar -ap i -f low -Ġex it -Call back -======== ======== -Ġtok ens -c y -ri or -p age -d oc -a ded -p c -eth er -ĠR E -er o -ast er -Ġch unk -Ġse ction ----------------- ---------------- -ip le -ĠC lient -Ġen c -Ġde pend -Or der -C ase -r an -at abase -key s -i er -Ġar r -T H -l ing -ĠF or -r ag -A ct -w ise -Ġd omain -ers ist -ret urn -Ġd im -In ter -lo y -s or -Ġ 9 -l ength -s ing -T ree -con text -/ ' -ĠH ash -am pl -ver se -Ġd escription -Ġro le -ĠS end -oin ter -Ġt erm -Ex t -Ġn etwork -Ġc ell -head ers -a de -apt er -Ġs kip -Ġb ound -ul ar -Ġ" ' -Ġtrans action -c ache -Ġend point -Ġde code -v ers -: ' -At tributes -1 1 -H P -A X -Ġst orage -Th is -ĠD elete -htt p -) ' -put e -le ase -ĠRun time -Ġtrans form -Ġbase d -Ġb ot -af e -Re ader -yn ch -Ġl at -n ect -Ġw rit -Doc ument -I s -Ġap plication -Ġcre ated -Ġo w -E rr -ĠB y -ĠRe g -Ġi o -par ator -Ġw ait -riter ia -L ock -Col lection -Ġrel ation -L A -Ġf ailed -am b -Ġj ust -ook ie -5 5 -m a -ĠH ttp -Ġstart s -ertific ate -d u -Ġw ant -Ġal so -ik e -Ġd ay -Ġch ange -il ename -nap shot -Ġg lobal -Ġdefin ed -az on -Pos ition -f s -A PI -em ber -et ry -T ER -Ġl ib -Ġdis play -Lo ad -h ost -D I -Ġcal led -pro cess -C an -Ġstr ip -In it -o ute -Ġfe ature -Ġlocal e -c at -e vent -Ġass oci -A g -N um -I P -Ġb as -Ġb atch -P olicy -Ġre quire -o se -ĠI s -m on -ĠE vent -Ġd own -Ġsy s -Ġmethod s -ĠRes ource -ĠS ub -Ġ1 2 -H ost 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-we en -Ġuser name -Ġadd ition -ĠRe ad -Ġcle an -l ast -Con vert -Ġbe cause -up date -w are -Ġd on -Ġsc ript -Cre ates -im age -Ġd one -struct or -9 9 -Ġser ial -Loc ation -s ible -amb da -in it -BIND ING -res ponse -ĠIn put -Ġw indow -Ġm essages -Ġr andom -Ġco ord -{ $ -M ax -ĠF orm -ex p -c er -******** ******** -g ra -as on -ĠIllegal ArgumentException -ĠO n -Ġid entifier -ig r -n et -as et -lo ud -in ter -ph a -Ġin s -re m -er ce -S ign -p ost -Not Found -ire ction -Ġ Query -As ync -li as -: " -Ġm ain -Ġiter ator -Ġc fg -.. . -P AR -i an -Ġcan not -M eta -AB LE -Ġb ucket -R OR -Set s -ĠD escribe -00 00 -Ġs lice -cur s -at ures -Ġac cept -Ġen um -s pec -idd en -Ġtw o -im estamp -Ġrem ov -Ġlo op -U M -Ġp an -um p -Un it -cc ur -Ġp e -Ġf ollow -o ice -Ġpre v -am ed -Ġh elp -g reg -Ġb l -Ġget F -Ġ< > -i res -le vel -h ash -p loy -iv ity -w idth -v ision -field s -c ing -Ġin cl -Ġy ield -u ccess -Ġ* * -col umn -Im pl -De bug -e ded -Ġb it -Ġd es -AG E -Ġs ite -i i -Ġun it -D el -T ML -ĠS printf -im al -ĠH and -re en -end ar -out put -Ġp ool -end ing -ched ule -b r -R A -ĠS c -ĠL oc -g es -D irectory -f ull -Ġcl uster -Ġwh ether -In ner -Ġtim estamp -s up -ĠReturn s -at form -ure d -Ġre al -P ool -Ġsup port -: : -add ress -ĠĠĠĠĠĠĠĠ ĠĠĠĠĠ -Ġauth or -Ġex tra -I C -Ġ1 6 -Ġc s -Ġmap ping -Ġc b -str act -ist ory -re l -Ġcom ment -e om -d ay -Ġget B -d k -ĠPar se -St yle -Lo ader -Ġwith out -W h -Ġpro xy -sel f -L en -d iv -Ġpro vider -c o -il er -d o -Ġgot o -Ġthe y -ĠP HP -Ġget Class -Ġn et -x ml -Res ol -Ġ esc -chem e -l l -Ġlog ging -N D -Ch annel -ap p -Ġpre pare -sum er -war d -irt ual -g round -{ } -Ġf ix -Con stants -com p -p ack -ire d -Ġc ore -Ġspec ific -j s -F ailed -redential s -ith er -ĠL A -Ġdest ination -M in -Ġr ules -( " -Cont roller -) . -Ġallow ed -A lias -m ed -p arent -Ġcontent s -H ttp -l ib -y m -Ġfl ags -Ġfor ce -Com p -Ġ' # -2 5 -ult i -Ġread er -iv ed -C opy -Ġp ol -Ġpath s -F e -ay ment -el ement -Col or -and ard -rior ity -al k -An y -ĠP re -he et 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-Ġun ique -gor ith -Ġadd r -Var iable -ach ine -ĠIn t -Ġfin ally -Ġad ded -Ġd ig -UR I -Ġrun time -scri ption -G ER -Ġse quence -0 2 -Ġ" , -m iss -CT ION -w w -m arshal -Ġun defined -pl ied -pon d -Ġb inary -" , -I ME -Ġsort ed -B uild -our se -S P -Ġwe ight -Mod ule -Head ers -Ġdif f -ĠA uth -ĠI d -F ind -Item s -Ġre st -iz es -Ġoption al -Ġget Type -ateg y -idd le -Ġindex Of -Ġo ur -l ers -Ġred irect -Ġpro b -Ġm onth -en ers -Ġmem ber -O K -Ġs im -Ġpoint s -Ġz ip -at al -res ource -bser v -Ġm atrix -est ed -Ġc ategory -i pe -Ġvari ables -Ġn s -eter mine -ĠT oken -N S -Ġg en -ut f -Ġs cale -W arn -Ġm an -Ġimp lode -S ystem -ĠM odel -c over -Ġpl ace -ĠX ML -s ource -li p -Ġsel ected -ermiss ions -Ġexp lode -Ġv ert -ĠS E -ar g -ĠL o -Ġstat s -Add r -Ġat om -Ġun til -Ġget T -Ġd type -Q ueue -s ession -J son -ĠIn ternal -D escriptor -m ode -Ġd etails -op en -D O -iction ary -Ġt uple -P ER -Ġl ambda -Ġ ĉ -g r -Iter ator -v let -ate way -Par se -tem plate -Ġstr len -Ġan notation -N odes -ĠN 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able -Ġn amed -EX T -Ġs imple -Ġ" { -ĠLOG GER -t ask -ĠW ork -de bug -le x -po se -sum e -Ġan s -Ġvar s -P air -c ast -t ags -Ġz one -Ġb in -Ġf all -St ep -auth or -al th -ĠF ind -D own -m etadata -Ġser ies -ain s -Ġro t -c an -Ġget R -Ġm aster -c md -quest ion -in ates -con dition -hand ler -ex ec -Ġg p -D ER -Ġstr tolower -m anager -Ġse parator -Ġs afe -anag ed -ĠS pec -Ġfunction s -ut down -T x -ĠO peration -Ġal pha -Ġcontain ing -Ġ" _ -fer red -ĠH E -St ats -Ġm igr -at io -og n -Ġd etermine -Ġ2 0 -X ML -ĠĠĠĠ ĠĠ -Ġlo aded -Ġtransl ate -Var i -ĠThrow able -Ġmax imum -C OM -Ġaction s -he ight -P ATH -Child ren -Ġvalid ator -ĠJ oin -Ġid entity -Ad apter -D esc -istr ation -Ġre verse -ĠG o -Ġback end -Ġh elper -Id x -Ġdown load -Ġc andid -Z one -v c -ĠisDebug Enabled -Error s -lo aded -Ġp id -ĠE R -Ġm ath -Ġdis k -ĠT R -Dis play -Ġen able -ĠM ax -E CT -in ator -Ġenumer ate -ĠLog ger -Reg istry -Ġstarts with -D ay -str ap -T itle -Ġ2 4 -C o -Ġp s -Ġne g -o om -Ġf p -D atabase -PAR ATOR -s ite -ĠAp plication -C P -Ġth ese -Ġneed s -Ġpe er -Ġp ag -sc ript -Ġn b -le ep -ĠW rap -Ġw atch -Ġv m -ĠN OT -Valid ate -Ġcheck s -set tings -Ġaw ait -th ing -ĠR el -ĠPro perty -Ġsign al -' : -] " -En c -Ġl on -Ġd eleted -Ġdir name -ĠF unction -n ext -Ġs alt -ist ics -par ser -Ġfield Name -Ġ' : -Ġf ig -ched ul -f n -I O -Ġind ices -T O -ĠB ack -HER E -s ystem -Row s -Resol ver -ar ation -Ġget Data -ĠHead er -Part s -Ġcur r -AN D -d omain -Ġm etrics -ĠO bservable -LE TE -Warn ings -5 6 -Ġd i -c d -al an -ĠHash Map -Ġm ut -ĠDoc ument -Sel ector -ĠS ib -Ġwork er -Ġvalue Of -err ors -Ġcorres pond -Ġas ync -ch annel -Ġget Request -Ġ// $ -p lement -D b -Ġre ply -k wargs -Id entity -Ġrepresent ation -Ġ100 0 -ip her -y g -Ġget Path -imit er -ang le -ĠI tem -Ġs k -ĠM ake -er os -ry ption -ĠOr der -Input Stream -T R -at tributes -Ġconfig ured -ĠS el -H tml -Valid ation -Ġs cheme -Ġinv oke -Ġ' < -Ġs napshot -Ġcall ing -ĠV iew -! 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-ĠAp p -Fl ag -Ġlist eners -AL SE -Ġday s -Ġcl ick -res ources -T IME -AL L -ĠP oint -Ġex cl -j ob -Ġp ages -n odes -Ġtra in -Ġus es -Ġf low -Ġsl ot -> \ -Ġins pect -S ymbol -label s -Ġbas ic -Ġpro per -av ing -2 1 -Ġflat ten -Ġ1 9 -Ġserial ized -Ġm ount -r ule -Ġn d -Ġcol s -Ġth ose -al e -od er -Ġre ferences -Ġarray s -c a -I VE -ĠA S -Ġh ist -Us age -Log in -F ilename -Ġallow s -In stances -Ġl v -w rit -Ġe Z -Pl an -Ġ$ $ -Ġs in -D irect -In formation -U I -ĠEx pression -F n -Ġmark er -Sign ature -ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ -Ġweight s -ĠL ine -Ġ[ ] -trans l -ĠL E -Ġcon stant -Ġsy m -ĠTR UE -Ġmer ged -ĠS plit -Ġdat as -in valid -Ġcont act -ook ies -ol ve -b utton -y ntax -ĠRe ader -H ook -Ġparse Int -M etric -ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ -IT E -ar r -En tries -Co ord -Ġpro gram -Ġdig est -rid ge -Ġp ur -amp a -N E -al es -ong o -Ġf act -ĠĠĠĠĠĠĠĠ ĠĠĠ -Ġback ground -d iff -m enu -ampa ign -Ġm ock -dis play -Ġpro c -M edia -ĠPro ject -m ult -Filter s -ĠC OM -r ong -in stall -Ġe ffect -ĠCom p -Ġadd All 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Content -s napshot -Ġdat atype -m is -Ġm g -ĠF ailed -Ġi tr -Cont ainers -ĠNew Reader -mag ic -9 3 -o sed -le e -Ġto Blocking -ĠC raft -sub scription -SPON SE -Ġget Num -ĠgetD b -, ' -ĠIn stant -Ġvari ous -NO RE -Ġindic ating -B roker -Ġset Time -Ġcover age -ĠRest ore -oins pection -Ġperform s -ĠC ap -Ġ2 2 -Ġun ders -Start s -Ġtop ics -ĠFiles ystem -ĠNotImplemented Error -Config ure -Sign al -Ġpopul ation -MS G -Ġduplic ates -7 1 -b rowser -et ter -Ġj Query -Ġevent Type -ĠPer form -Ġb ank -P AGE -ent ially -Ġget Form -ir med -bl ack -Out Of -Ġfull Path -RO W -P e -ĠCms Exception -am era -Ġ[ {} -ass ert -D um -Ġis Function -Ġus ually -iter als -E DEFAULT -Ġget Label -Complet ion -Ġcrit ical -Ġassert ion -std err -j avascript -Ġpro g -Request ed -OR TED -t w -Ġpro ceed -h ide -F ROM -it o -Ġget Module -Ġsh ipping -e q -s r -def ine -ir ing -Par sed -Marshal er -ĠValue Of -SI GN -G L -Ġs z -ĠgetD atabase -Pk g -index es -Act ual -Ġtimestamp s -Pattern s -Conf irm -Ġfac ade -OFF SET -Ġ) ; 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-ĠisLog gable -Ġbind Value -ĠG ateway -IL D -Ġdetermin ed -Ġasc ii -ĠA CTION -LE S -Ġyear s -nost ic -PA Y -por ation -ĠINT ER -Ġsever al -Ġcach ing -el low -RE SOURCE -Ġop code -help ers -ĠgetPr imary -Ġg id -Ġb am -Ġun marshal -Ġprodu ce -B LOCK -Ġg v -ĠS P -Ph ase -u ation -Ġb undles -TT L -s q -ap ps -D ao -Ġreplace ments -in x -M on -Exception s -ĠCon structor -Ġr ng -Ġz ap -request s -\' ' -act iv -Ġgen es -Ġselector s -Pack ages -Ġsever ity -On Error -Ġtext ure -Rem aining -Ġ" ;" -Ġne arest -ĠKey s -Mut ex -Ġcompilation Context -ĠTo Lower -ĠIter ate -OP EN -% % -b log -Ġset C -ar p -ext methods -ĠP UT -Ġext methods -Ġbuffer ed -Dyn Class -cp u -Ġeffect ive -N eeded -B racket -sv g -Call able -et o -Check ed -ang er -Ex tended -ĠD ATE -Ġcomp ose -Initial ized -Ġattrib s -attach ment -al ive -Ġend ing -ĠST ACK -IL LI -Ġparam Name -O Auth -ĠWrap f -complet ion -Ġspec ifies -UT C -ĠN S -ĠD r -ĠS ample -arg est -In tegr -b am -Ġget Task -Ġe DataType -art icle -Ġapi Client -pack et 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-Ġnot ice -ĠContent Type -ĠF ull -li ps -ANG UL -ĠL if -ĠisN aN -m ouse -To Array -ov y -Prefix es -FO RE -TERN AL -ĠSel ector -M ASK -ĠH O -0000 0 -UST OM -ĠStd err -Ġblack list -T wo -exist ing -M AND -PA RE -ĠR ol -Start ing -ĠPI PE -Rule Call -t ar -Al tern -Ġfail ures -vari ance -Ġp ause -ib ration -Com merce -for um -ĠS U -ft p -D rag -M is -as ing -Type Id -Ġim mutable -Ġtw ig -v t -Ġsl ave -ĠBack end -er es -ĠA D -Sort ed -Ġget env -ĠM ODE -Ġadd Field -p ixel -ĠLog in -ĠHe alth -4 04 -Ġ" | -n ed -Optional Parameter -C nt -Ġget Group -Ġtr iggers -OL DER -Ġmark ers -Ġaddr s -ĠComplet ableFuture -m ime -ĠConfig ure -Ġang ular -} ` -ĠContainer Builder -ĠA zure -op le -attem pt -Pl ay -pl orer -tick s -ĠASC II -Ġh ack -ĠP R -key words -Ġfl ux -Ġstr ong -I CE -ul ator -pro du -cal led -L U -Ġp ress -ic ated -AT IC -ĠCal led -Ġin place -Ġcre ator -File System -NE CTION -Pol ygon -Ġpro files -ĠTop ic -M aterial -Ġm andatory -resol ved -M ATCH -ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ -Ġdel iver 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-S ym -Ġget Block -qu ared -read s -ĠJava Script -P ersistent -ĠFile InputStream -cre ase -AA AA -C andidate -Con sum -Ġsub scriptions -Ser vers -s izes -Ġto oltip -ĠL DAP -Ġpl aintext -Dis covery -ĠOP EN -Ġc type -ĠC ert -ĠN ODE -S izes -Set Name -K nown -m ust -Ġlocal Name -CR Y -" ] -j ar -A ck -Ġen sures -Ġext ent -Ġmut ation -CLI ENT -N arrow -cho ices -p u -z a -ĠgetT imestamp -Bound ary -Ġhand shake -Ġcoe ff -SE C -Ab breviated -vi ct -Ġf write -Mod ifiers -Runtime Exception -Ġget Argument -ĠPublic Key -ht a -Prop ag -Ġv lan -Ġclaim s -f ragment -Ġsm ooth -ĠS uccess -ĠgetDecl ared -Ġget Active -q q -format s -ĠSI ZE -Ġvalid ates -FA ILED -f w -con straints -Ġtab let -v lan -IT LE -ĠgetS c -Ġ" ` -Ġget Order -St ub -Ġsplit ext -TT ER -Ġalloc ated -om ation -ĠgetS e -ĠRE G -Ġlow est -s yn -In struction -99 9 -T yped -EN V -m ing -Ex pect -Ġal g -get ter -Ġsc r -Ġbl k -ĠDepend ency -U id -File Info -ĠgetTable Name -Ġexec utes -LL ABLE -w ik -se m -Ġse ems -oun ded -V ault -ĠResponse Interface -Ġb ias -ĠgetC an -Del imiter -Ġap plicable -ins pect -pod s -Ġ] ] -Error Code -" .' -ĠS M -ep och -content object -check box -Ġindent ation -sm all -Bounds Exception -S ender -ĠP ayment -Ġread line -Mult ipart -L ambda -ĠM achine -QU O -Exp iration -os a -RE ST -Ġet ree -ĠSign al -sc roll -Ġassum ed -Qu antity -por al -Ġp andas -ĠS K -In jection -Ġinteg ration -D er -ĠP atch -AT TR -Ġshould n -Ġrecip ients -ĠOrg anization -Ġis Enabled -ĠA C -Ġr sp -L T -Ġapi Version -Ġmodel Name -Ap pro -Match Set -an ent -ĠO pt -ĠSIG N -Ġ& = -Ġdat um -Ġexcl usive -Writ able -M y -Ġf aster -Ġarray Node -Ġuser Name -Read Only -er as -ĠAssertion Error -L D -le af -filter ed -ĠOrder By -Ins pect -H ierarchy -Ġget Collection -ynam o -Sp aces -Ġinherit ed -Ġerror Code -ĠChar set -Det ect -ĠSY LLABLE -lib rary -Ġ' // -Ġpublic Key -auss ian -Ġadd Listener -Ġid le -Aggreg ation -ĠCH AR -h our -Ġs ib -ĠAss ign -Ġra di -level s -IF Y -im s -Ġsuper class -Qu art -ĠNumber FormatException -Validation 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-PI X -64 8 -Ġlocal Path -Task Request -be am -ĠParse Float -Transaction Id -PRE SSED -Ġconstruct ing -ĠRecord s -ĠPer forms -DELETE D -sent ences -ĠVert ical -CHED ULE -HEL P -Ġarct an -B anner -G GER -p aper -q r -Ġ{ *} -Ġin consistent -Ġform Name -AT ILE -Ġsource Length -ĠNew Int -col our -EX PECTED -last name -Ġtrigger ing -ĠChar m -Network Interface -Ġmut ated -Xml Content -ĠReflection Utils -ĠCH ANNEL -Ġarch itecture -ĠGrid Field -ĠDial ect -Ġxp do -ĠgetTop ic -integ r -STRI B -C tor -s ensor -t re -Ġt err -Ġt body -ur m -Ġm al -ent ive -ĠC rawler -Ġerr Invalid -ĠT M -Ġtype Element -ĠIn direct -ĠH ardware -ms gs -Ġchild Name -Service Account -With Options -ĠAr ithmetic -Ġimage copy -eg ot -Content Types -Ġiter ables -ĠZ MQ -ĠST YLE -Ġdist ro -ĠAd vanced -Pointer s -Ġes i -Virtual Machine -ĠSkip ping -Ġdc m -ĠLast Index -comb ined -osa ic -ĠAT OM -H op -Ġ) + -Ġp Class -Ġb at -Ġan alyzed -Ġset Values -Ġset Public -rom atic -Ġadd Route -Ġform ated -Ġcurrent Item -Ġtrans parency -Ġz b -sc oped -Ġjoin Type -ĠUn safe -Ġserver Config -az el -DE PTH -ĠgetP ublish -inter cept -(.* ?)\ -Alloc ate -Ġelect ron -ĠJsp Exception -T aken -p wd -Ġ$ ( -Ġf seek -un es -ĠT IFF -Ġde vi -Ġdata Item -Ġclass Definition -Ġy c -Ġhas Many -av ings -By Identifier -Ġ3 04 -Ġdoc block -eg ments -ĠDe vices -Ġpackage Path -Ġsim ultaneous -Ġsample Rate -Term in -ĠConfiguration Error -Writ ers -ĠgetString Value -Icon s -REG ISTER -ĠMeta Data -Ġmo zilla -ĠAN ALY -det ection -Ġencounter s -ĠCHAR ACTER -Provisioning Artifact -ĠSep arator -mploy ee -' $ -V elocity -m st -p reset -Ġc ats -Ġa Node -Ġl just -Ġget Reflection -em itter -Ġh ps -In complete -ĠM aintenance -Ġj unction -Ġcom pl -ĠR oom -List Response -pro cs -Set Request -Ġsub menu -ĠV ote -Ġempty Map -Ġ4 29 -SE TT -Ġimage Url -pre process -Ġarg min -group Id -Le arning -Ġaut oloader -ĠErrCode InternalServerError -ĠBlock ing -Ġshift s -Changed Event -Ġte aser -ĠNamed TemporaryFile -Ġfoot print -ĠEQUAL S -V alued -s park -t len -ro unded 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-Ġoffset Y -Ġlink ing -default Value -ĠgetC ertificate -ĠErrCode BadRequestException -15 2 -Ġpid file -ĠCache Entry -ĠView Group -Ġclick s -Ġcast ing -std dev -Ġneighb ours -pool s -WR AP -monitor ing -Ġconstr ained -integ ration -pear ance -T iles -d ll -m ongodb -pt ype -In String -Ġset Next -Ġuser names -com posite -ant a -Ġhttp Post -Ġsl ab -Provider Interface -Ġignore Case -Us able -Ġperm alink -Ġfeed s -Backup Plan -ĠExt end -ĠDel egate -ĠREST Client -SCRIPT OR -ĠGray U -SUM ER -Privile ges -ivar iate -Ġi OS -Ġd P -Ġget HttpClient -Ġw est -ve cs -ĠĠĠ Ċ -Ġfile Store -Ġun m -Ġfield Label -Map Uint -Ġcontent type -ĠUn iversal -Cache Manager -ĠCh anged -ATE GY -Ġchannel Id -Ġcons ult -ĠRE PORT -Ġsite Name -ĠSE CURITY -77 5 -ĠInstance Group -Go al -upload s -atom ic -rele vant -D RIVER -S olver -V A -Ġd store -Ġset Min -Ġdata points -ver ification -ĠL C -Ġon Update -Ġtarget Id -Ġcache able -From Map -ED GE -CON N -Ġtransform ing -ĠWeb ACL -clo ser -Ġchron ology -AU GE -micro soft 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Name -Ġpre amble -ĠCon cept -Ġpr uned -ock opt -ĠUn its -ĠgetP ag -sub scribed -Ġexecute Delete -Ġinv ocations -Ġ"/ $ -Ġrepresent ations -ĠHttp Header -Ġ201 1 -olog ies -ĠProperty Key -ĠCOL ON -ĠVER BOSE -CHAR ACTER -Ġwhite List -M ul -e Life -n ear -le ader -pon ly -ĠL in -stan c -Ġid Field -Ġun handled -Ġun escaped -Ġcreate Connection -Ġsource Method -Ġfind iter -Ap plicable -ĠFile Mode -max length -ĠRun e -ĠDis tributed -Sup ports -14 6 -Ġrefresh ed -Ġrc Params -AV ING -ĠfindBy Uuid -Ġrad ial -Dr ift -Ġfew er -Ġfasta file -Ġparenthes es -Ġt ween -Ġd ies -an ies -pt entive -est art -Ġ1 17 -Ġde ck -Ġkey Bytes -ĠL i -ĠG son -List Entry -Ġstart Value -Add on -IT U -Ġentity Cache -Sub tract -Ġproject Root -Ġdec ryption -Rule Name -SC REEN -Ġremote Address -ĠDis cover -conf idence -ĠEnv iron -Ġdelet eres -ĠgetPr ivate -ĠVAR CHAR -bel ow -ĠEV T -Provisioned Product -Ġbol eto -Ġrob ust -) + -; \ -ek lif -ĠJob Execution -Ġbin ascii -Div ision -Ġna ive -REGEX P -ĠINIT IAL -ĠCompiler Exception 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ferences -Ġcategory Id -Ġpartic les -desc r -REG ION -ĠCommerce Order -Sur rogate -ĠgetSh op -Ġkb f -Ġdu k -archi Id -Ġkundera Metadata -oupl ing -ĠFindString Submatch -F V -Ġt u -Ġm Current -Ġo vers -Ġo Visitor -Ġw elcome -ĠS ink -Ġset Container -ĠR ENDER -Ġun recognized -Ġend if -Ġhas Role -path name -Ġsource Name -ĠPro filer -log ic -Mod ifications -No Such -Ġtab ular -CO ST -Float ing -Ġadapt ed -member of -ĠEnum Set -ĠgetArray Copy -ĠisInstance Of -Ġhyp oth -hed ral -: :' -h c -y te -Ġc udnn -Ġf y -qu et -Ġst m -ĠC OR -ĠP rompt -Ġor i -En ded -Ġappend Skipped -Ġcode Printer -Return ing -til es -Or Array -Ġ'/ [\ -Ġequ ivalence -Ġmem db -Connection Error -sl ider -ĠgetB l -Ġprom ote -ĠVersion s -ĠgetPath Info -Are as -ĠGP G -ĠSp in -Ġinstant iation -Ġaff ine -ĠMy Sql -AUTHORIZ ED -jav ax -" % -P Q -n k -s For -Ġre actions -Ġn sp -Ġp Chart -qu bits -Ġlo ose -ĠM ATH -fa ke -og onal -Ġinstance Of -yp y -data center -Stream Name -Ġvari ations -RO WS -Ġsrc s -Pr une -ĠSend Message -17 2 -ĠEX IT -ĠVER T -cb c -Mut ator -STATE MENT -TRAN SIENT -SR C -Destroy ed -Ġconcept s -rele ases -Accel erator -ĠappendSkipped Tokens -Ġi on -Ġf ol -Ġn ib -Ġg ames -Ġset Logger -Ġset Scale -ĠD rag -Ġadd Last -ĠB illing -ĠG CS -En g -Ġcreate Directory -For Update -Ġed ition -gin x -Server Name -ĠQuery Param -Channel Response -PER IOD -ĠStream s -El m -Replace ments -associ ated -Ġfq cn -Ġsmooth ed -Ġattack s -tlen eck -D RA -L ID -l am -s nap -w it -Ġc ad -Ġis Node -Re pos -ĠP ENDING -Name To -ĠD ns -Ġnot ifiable -Ġstr URI -pr ivile -Parameter Type -Connection Exception -12 34 -ero ute -Init iate -ĠIs Empty -ĠPre pend -ĠState fulSet -ĠText Type -Associ ates -Rec v -Super class -ĠCallback s -Ġconnector s -Ġsit uations -CLOSE D -Ġreplic ate -s ip -Ġf ct -Ġn avigate -ur ated -Ġis Started -Ġnew Block -Ġlog Exception -Ġcheck Access -LE MEN -Time Series -Event Details -From Url -VER SE -ĠMethod Spec -Ġweb kit -Profile Def -17 6 -times eries -Align ed -ĠMag ic -INIT IAL -ĠPlace ment -W ARE -g w -Ġi etf -un less -Ġb ower -Ġget Xml -Ġ/ >" -ĠC op -Ġe cs -Con sent -ĠP ow -ĠD est -ht ein -Time Millis -instance id -Ġauth Type -ie f -ĠRequest Mapping -Str s -Ġpop s -Access DeniedException -SC ORE -ĠTrans ient -Ġregex es -VI RON -ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ -201 6 -ĠUrl Formatter -ĠCONFIG URATION -Margin s -ĠDes erialize -epoch s -Effect ive -ĠAW SEC -' >" -C reds -F lip -J wt -M elis -c um -w sgi -Ġm as -Ġo d -ig id -In ference -ĠO racle -pr une -ens htein -String Array -Ch rome -ST AT -Ġpos sibilities -Response Error -Manager Services -Ġarg in -ĠState ments -Ġalign ments -ĠgetTime Zone -ĠIGNORE CASE -Ġindivid ually -Ġs arl -Ġp att -Ġv or -Ġ== ================ -Ġapp Path -00 5 -String Literal -ph ased -Ġtrans ports -Service Ids -Ġ\" {$ -From Node -Version Info -Par ms -Ġdocument Id -Le ap -ĠgetP ool -Image Size -dis ks -Ġsome how -Ġns ide -Ġrout ines -uff man -ĠText ure -ĠProperty Accessor -Region al -84 0 -Ġreflection Property -ĠgetNode Value 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-sc ss -pre vent -Ġopen Session -Ġconst it -ĠCheck out -ND AR -opt imize -Ġmut ator -23 0 -Ġspecial ized -ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ -Ġvirtual Network -ĠInstance Type -ĠcaseIfc Root -ĠLimit Token -ĠNe eds -Ġdrag gable -Ġrol lout -ĠstringTo CharP -Ġvect y -+ )' -t ele -Ġp Type -ĠS olution -ter ior -Ġid List -Ġy data -Ġdis allow -Service Id -Ġremove Item -Ġprocess Configuration -Util ization -Ġimage Path -Call Exception -Ġsl im -Ġ'. $ -ĠRead s -DO MElement -copy right -ĠgetPro totype -Ġcomb ining -Ġsym fony -coord inate -Ġparsing Css -Pop ulates -associ ations -FE ED -Rev oke -E urope -P o -Ġ" = -Ġis Final -th est -ĠI Entity -Ġbe acon -ĠL S -Ġout Real -pro vided -Ġsc m -Ġline Start -SE CURITY -Not Exists -Ġsrc Offset -Ġav ro -ĠCheck box -Ġfetch one -Cluster Name -Ġvisit Insn -Ġdecor ation -Ġrecover able -Optional Attribute -cons ensus -EVENT S -Oneof Marshaler -Oneof Unmarshaler -ĠgetSite Path -Dum ps -ĠBACK GROUND -MEN U -E r -I ED -Ġre ferred -Ġp eg -Ġis Full -est ream -Ġset Meta -Ġun iverse -Ġpl anner -Ġsc raper -Ġtag Id -Or Name -Instance Profile -ĠClient ID -18 4 -root Scope -Ġselected Index -cor poration -prec ate -Ġtranslate FieldName -Ġsn r -LOG ICAL -Tool Tip -ĠMESSAGE S -Ġresid uals -ĠAB C -Oneof Sizer -Ġrobot s -' > -, . -E B -X Expression -c reds -m aker -x si -de precation -Ġget Debug -Ġh cl -im ported -Ġnew node -ĠL V -con cern -Ġcur ly -ĠW y -Ġhas Header -Data Key -Ġcreate Item -back ward -Index Name -Ġop c -For Testing -Ġbuild Query -LI CT -Inter est -Sup press -right Operand -Ġest imation -Report ing -29 5 -Conversion s -Ġask ing -Bus y -ĠUP PER -ĠAv ro -break s -sy lius -Ġprep ares -WAY S -ĠConsum es -X type -u z -al location -Ġo Obj -Ġget Timeout -Ġis User -Ġh um -ation Exception -ĠP ip -res tricted -Ġpath String -Ġse es -By Tag -Ġcopy file -Pl ots -ĠConfig urable -ĠUser Name -ĠgetB ranch -13 1 -ĠCON STRAINT -Ġ32 76 -ĠMAX RESULTS -web kit -Region Code -Wait For -Ġvec s -ĠEm ber -ĠCHAR SET -Prepared Statement -Ġcook book -Logged In -C our -M essenger -at ables -Ġp data -un time -Ġget Dependencies -In Context -ĠC Y -ull an -Ġadd Type -Ġ2 54 -ĠU uid -ER E -Ġformat Date -cord ance -Ġmin Size -Ġimage Size -Ġcontroller Class -Max Length -ĠSc ene -Ġpkg name -ĠMatch ing -/{ }" -lan e -Cor porationId -Ġrespon sibility -Screen shot -ĠsetMax Results -ĠSpl ash -Ġdic om -RUN NING -Ġcontin ues -M otion -s as -re curse -pe p -Ġis Ajax -Ġst ype -ir ki -nt ime -Ġbe zier -Ġadd Violation -ĠB OTH -Res trict -Ġun likely -Ġoutput Directory -Ġurl String -Ġtrans fers -Ġclient Y -EN ABLE -ick et -Ġput ting -Ġcustom ers -ĠLog out -ĠData InputStream -Ġsl c -ĠgetT est -Ġcharacter istics -ĠStart ed -DS L -Tx Id -Ġaggreg ates -ĠInstance ID -year s -Alloc ated -Obser vers -Zip File -ĠgetDeclared Field -Ġmiss ion -Elect ric -L and -T XT -b ear -i am -Ġn k -Ġget Association -Ġh or -qu ares -ĠT ITLE -ĠP IL -ĠM ux -ĠR s -ĠG PC -Ġun successful -Ġser f -ID ENTITY -Data Size -ĠJ uju -RE A -ĠSt orable -vel oc -til t -Tr ash -Stream Writer -Ġload Data -User Input -Ġ8 1 -Job Name -18 9 -co hort -fe ats -bl ade -Endpoint Request -Ġedit ors -ĠAL PH -ĠVM ware -pa id -screen shot -Big Decimal -ĠAC COUNT -ĠExecutable Element -VOL ATILE -Ġespec ially -Ġt arball -ĠE asy -Ġcom pliance -Ġlen ient -Ġcurrent Char -Ġevent data -Ġz order -Ġcolumn Info -ĠFile Type -LO C -DE STINATION -ĠgetD istance -Ġclear All -Ġlower Case -Ġrepository Name -Ġowner Id -ĠErrCode LimitExceededException -ĠFl ickr -Graph ic -Ġ24 7 ----------------------------------------------------------------- -------- -Ġunc ertainty -Tax FixedRate -]+) / -Ġnx t -olec ules -mut ations -Ġrequ iring -ĠSK U -ĠDiag nostics -ĠgetBind ing -ĠAnt lr -Friendly URL -ĠDif ference -ĠCensus Column -h ale -k alem -Ġm aj -Ġl cd -Ġsel enium -ĠI Guest -ĠO ct -ĠSt yles -From Object -Ġdelete File -Ġassign able -Vis its -14 7 -'] . -30 6 -ĠJava Class -Ġcn x -Generic ApplicationPropertyOf -Ġtensor flow -ĠRol ling -Sym metric -Plural Rule -Ent itlement -ĠpChart Object -G REEN -H ole -Ġc asc -Ġg ist -Ġl State -Ġget Encoding -Ġset Read -ort ion -ĠW IM -ix ionary -ole ans -ĠGet Type -Ġcode d -Ġrange Start -ĠUn checked -Column Value -TER MIN -ĠIt s -NotFound Fault -lip ay -Descriptor Proto -Box es -ops is -Ġexcl usions -CR L -ĠCre ator -Ġverb atim -Delay ed -frag ments -Ġelim inate -ĠREMO TE -, } -J P -S MS -f A -Ġd rops -Ġo Input -ĠS V -ĠC E -ĠA X -th ers -ĠP at -Set MaxResults -Class Metadata -Ġcl nt -Get Int -ĠSet MaxResults -Ġgroup dict -Ġservice Type -ĠField Definition -US AGE -ĠCertificate Exception -Attachment FileEntry -Ġintern ational -decl are -Ġinstrument s -ĠSUP PORTED -ĠMED IUM -Ġstere o -B roken -M agento -n or -t ap -Ġa Source -Ġf ires -ent o -Ġget Export -Ġget Computed -ĠS anitize -ĠN X -Ġ# ################ -oc o -Ġon Next -Ġun ordered -Ġ? >' -Ġitem getter -Ġcl f -ĠSt ates -ĠSet Tags -Ġwrite Bytes -Resource Quota -Ġapi Url -Cache Dir -Local Name -ĠNot es -ĠgetB ond -Parent ID -ĠNumber Format -VI SIBLE -ĠRO W -Construct ors -Throw n -Ġhistogram s -ĠgetAtom Count -optim 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-ĠHTTP Response -Ġbl ind -Ġdelta Y -ĠAbstract Expression -ĠLoad Int -Ġmanifest s -ĠDirect or -Ġcontinu ing -ĠDes ign -ĠImplement s -nu ke -OptionValue Rel -ĠWH EN -ĠOff line -Ġlef tover -. : -= - -R NA -S ense -b ands -m aintenance -r ms -Ġget Entries -od ium -Ġst ations -ĠC AP -Ġerr Chan -ath ers -Ġr sc -ĠM s -ĠE CS -Ġj peg -ĠB LANK -Ġclass File -ler ts -Or More -Ġinit E -From Name -Ġtree Node -Ġregion al -fe at -Ġdst Index -dr upal -Groups Input -Insert ion -ĠSearch Result -Ġbig Decimal -ĠSecurity Group -Changes et -VAR I -ĠFin ished -Creation Time -ĠOver write -Ġded ent -ĠAC CEPT -band width -3 60 -A ir -F W -F illed -M enus -N ick -m anagers -Ġg g -Ġb orrow -Ġv card -em plate -Ġto ast -ĠT D -Ex tern -Ġend Tag -Ġcheck RepeatedField -ĠTh elia -ĠGet Response -ĠX text -ĠEx ponent -Ġdb Type -Ġtask ID -Tra ces -Ġpoint cut -ĠAn onymous -Host Exception -ĠDescribe Reserved -other wise -Ġrest Client -Required Exception -Identity Pool -Initial izing -ĠGP S -Ġhist oric -ĠUs ually -Ġpropag ated -ĠRece ipt -CONNECT OR -Optim izer -Ġparameterized Host -CallWith MethodType -ĠENabu CoreException -FriendlyURL Entry -X HR -p aste -Ġm ak -Ġo h -Ġo View -Ġb Display -Ġget Mapper -Ġget Transport -Ġget Qualified -ĠS py -Ġsel ler -Ġ1 32 -ĠP ress -and oned -ĠI face -li ased -ĠU Int -ost er -pro blems -String Size -ĠRes tricted -point ment -ĠNew File -Ġcopy To -FI LL -ĠLO WER -Ġclear ing -Ġins n -Sign ificant -(" ' -DO CTYPE -ĠRecord CallWithMethodType -OB ILE -ĠCurrent Session -ĠVolume Attachment -selector s -ĠLat est -Ġeigen values -ĠtoURL Values -Ġget Interfaces -In dividual -Ġclass Map -IN CREMENT -File Filter -Ġpl at -Ġ3 000 -Ġ'/ . -Ap ns -Ġimage Name -Ġorder er -ĠUp per -Ġdst Path -ĠVer bose -Tri ed -ĠEN ABLED -Ġiterate e -Ġdl p -ĠPop bill -ĠPopbill Exception -Q R -c ook -o Script -p mag -x MessageHeader -ĠS ocial -ĠC LA -ĠT ell -Ġk el -th roat -ĠL L -ĠO tp -Ġen hanced -Ġparam et -Ġout comes -ĠB SON -Ġsub directory -ep i -Context Factory -ĠEx pressions -Ġiter ated -Format Error -ĠgetP eer -Ġinv ol -Ġjava Type -ĠZ K -api key -Ġcss Class -Ġbean Type -17 7 -ĠMax Int -ffic he -\/ \ -ĠisN egative -ITEM S -Adapt or -E ps -F OLLOW -F ATAL -re visions -Ġ} }' -Ġget Upload -Ġis Match -qu es -In ode -ĠC e -ĠC ategories -ath on -ir it -Ġerror List -Data Item -Ġinput Value -ĠUn subscribe -Resource Path -Base Name -ta u -(' % -Servlet Response -Ġquot ing -Ġdimension ality -Selection s -ĠProvider CallContext -TAG S -calcul ation -Ġmarket place -ĠSEL F -Ġm gmt -Ġg si -Ġis Boolean -ĠC amel -ult ure -Ex c -get File -Ġdis miss -par ms -Ġlast Key -Ġad vice -From Bytes -Manager Interface -Property Names -Ġstream Name -Ġview Path -ĠArray Type -ĠgetP erson -Ġtotal Bytes -ĠToken Interface -Ġzone Id -ĠNOT ICE -23 6 -Ġdum ped -Modified Since -Ġtrain able -Sk us -mut ex -ĠBU TTON -Typed Link -Tier PriceEntry -Ġbirth day -Ġcirc um -ĠgetBound ingClientRect -Ġprece ded -H aving -] ( -c ubes -i ert -m ousedown -v ect -et as -it rus -Ġis P -Ġerr Ch -Ġde queue -Ġdata Value -Ġ2 70 -get text -Path Segment -Ġcheck Valid -Ġsize Of -Ġuse Objects -ĠRes ponses -ĠType String -Model Impl -Ġarg Name -Ġcomponent Id -Ġexecute Create -Ġbind Param -=' {$ -last i -Window Id -HER IT -Short Name -ĠUN ION -ĠgetCurrent Request -ĠgetFile System -Fin ally -WE IGHT -ĠOper a -Ġanim ated -person al -coll ation -hib ited -H I -H LC -H IGH -Ġw fe -Ġto m -ĠC QL -Ġset Service -ĠP aint -Ġon Message -Ġappend OptionalAttribute -To Process -Ġq m -Ġlast Child -Th rift -Call Back -ĠIN DENT -Inter preter -Ġ12 34 -irt y -Ġdraw s -END OR -ĠCore Exception -graph s -termin ate -ĠSym phony -Visit ed -Extr as -Kub elet -A round -D ue -a ht -c ortex -Ġp Name -Ġget ByName -Ġto XML -ĠD J -Ġerror f -Ġx pos -By Class -oin crement -Ġjson Response -ĠgetS heet -ĠK nown -End Position -Line Item -ĠLog Entry -ĠgetM er -AC ITY -16 2 -Cur ly -ian ces -Ġweb hooks -Ġgp Uniform -Ġele v -Ġ"[ % -Connect ing -IF EST -ĠEOF Exception -Ġleader board -ĠWrit es -ĠBU ILD -Ġdeleg ated -Ġvisual ization -Ġaria Utils -Ġaffect s -Ġuniqu ely -Repl acer -E phemeral -M IS -i u -t oday -re plica -Ġs pectral -Ġre compute -Ġn odelist -Ġd end -Ġget On -Ġget Year -ĠD M -ĠR ack -Ġus leep -og ene -Un register -AL GORITHM -Ġobj Type -Form Data -Sh apes -New Client -Ġ9 999 -Ġsystem Id -Ġretry Count -ĠNO I -DER IVED -ĠBig Query -Snapshot Request -Ġlex ic -MIN UTE -cu its -Esc apes -Ġreach es -PAY PAL -ĠAlias es -Occur red -Ġyi elded -B G -P LE -p iece -ro cal -ĠF ingerprint -Ġ\ ( -ser ve -Ġrequest Info -Ġcreate TempFile -Ġitem Data -Ġdefault Locale -Ġnum Columns -By Value -ĠRe positories -Ġmin max -Ġfl ds -Text s -Filter ing -Ġ10 9 -Ġcmd args -AD ING -Ġsl ides -status Code -Ġflush ing -Ġacc ent -Bl ueprint -BO OT -ĠParameter ized -Off ice -ĠErrCodeInvalid ParameterException -simple Request -visit ed -diag onal -ĠContract s -Ġchann eldb -H mac -K W -M orph -N rm -R DF -r h -at ility -Ġs chem -Ġf set -Ġg ear -Ġb uy -ol ate -Ġan gege -Ġset Table -Ġkey frame -ord ova -Ġ2 14 -Ġlist Iterator -Config Rule -Ġclo bber -IT OR -And Value -ĠST DERR -Ġ"' ." -ĠQuery Options -ĠDescribe Cluster -Ġpe ptide -Ġcollect ing -ĠPAR SE -Ġadmin istr -Ġstyles heets -arante ed -Ġintros pect -F leets -Ġm ps -st o -Ġget Args -Ġget Chain -Ġto ml -ĠC DN -Ġerr Code -ĠF ee -gs ql -ĠG D -Ġun bound -Ġsc o -Ġbyte Count -Or Throw -Ġaut of -Task Id -Ġ"' { -Ġremote Addr -ĠQuery Exception -IC Ag -Api Gateway -Ġmigr ator -ĠQu eries -Ġhy brid -ĠKEY WORD -Ġff dc -ĠPRI ORITY -Play list -ĠTyp ically -Synchron ization -ĠGPPROGRAM UNIFORM -B IDDEN -D V -E ra -P V -] ?[ -_ #{ -d ag -s olid -ct or -Ġo Object -Ġget size -err u -ĠC AT -ĠC SI -Ex cept -Ġset All -ĠF LOW -ĠM ON -Ġdata Size -os pf -ID I -ler i -To Long -Ġmodel UUID -Ġmax Retries -Ġwrite able -Th emes -Ġimage Id -ĠgetM ult -Ġfetch Mode -ĠQ gs -SI LON -ĠIs Null -inter section -Ġmultiple x -ĠWh ite -19 8 -Ġmer kle -ĠDocument BuilderFactory -Sort able -Calcul ating -Mail box -IR C -ĠHe ap -ĠGray S -Ġgate ways -ĠserialVersion UID -B UNDLE -F P -L t -L ik -d sl -h ar -p expect -Ġs j -Ġf iring -Ġn tp -Ġd as -Ġg lossary -Ġget JS -ab it -Ġst ellar -Re quester -Ġbe aring -Ġpath Prefix -ĠG ossip -Ġun wanted -ĠH Base -ĠH ISTORY -Set Key -ĠV CF -RE FER -Info Inner -Ġtext wrap -ĠSet Description -Ġop name -und ance -Event Listeners -ĠNew Value -AB ET -patch es -mag nitude -Ġsq s -26 5 -ĠGu ess -ĠPER MISSION -Ġprot os -ĠBot tom -C ritical -S ID -d escriptions -s omething -en velope -Ġ* ' -Ġj wk -ĠW AL -ĠJ ulian -ĠJ awr -Add ition -Ġcolumn Value -ĠError Message -Ġproperty Key -Ġwhere Clause -Read ers -Template Instance -inter action -25 3 -15 3 -Ġĉĉĉ Ġ -ĠBatch Delete -IAL S -ĠErrCodeInvalid RequestException -SIB ILITY -Ġ'! =' -CALL BACK -Ġrounding Mode -ĠbDisplay Option -C oding -F RACTION -Y G -e ast -s x -s lope -se ason -Ġget Translator -Ġres cale -up ons -Ġe fficiency -ĠP rior -ĠR AM -Ġpr ere -to P -Al bum -Ġsw ift -ĠType Code -Ġso il -RO UTE -ont ab -Ġimport Path -ĠQ Text -Ġ7 4 -env s -Ġlang code -Ins ights -ĠNetwork s -final ize -Ġ'/^ ' -Ġprox ier -Ġreplic ated -artifact s -PU SH -ĠRek ognition -L int -\ ( -Ġo Control -Ġb ul -ĠC redit -ĠC amera -Ġj e -Ġx proto -De partment -ĠV K -Ġcontext level -Ġsc p -ĠGet ID -Ġparent Element -Ġexist ent -Ġgroup ID -Ġwrite With -Ġdat atable -Ġjson Generator -Ġequ ations -Tra its -ĠUp sert -Ġinitial ised -Ġmeta Key -PO P -PO CH -inter op -ĠIter ation -Ġĉ ĠĠ -14 5 -19 1 -Ġreason Phrase -ugg able -Ġcy clic -Pay Pal -Descend ants -hydr ate -D UP -M illi -w ell -w itch -ing e -Ġget Attr -Ġr ds -Ġarray To -Ġconfig Dir -Ġcreate Search -Ġsw ipe -Ġpage Name -ĠAdd On -Ġhost Port -ĠgetP ack -Ġover load -Ġsl aves -TH REW -ĠCloud HSM -FIX ED -ĠPRO PERTIES -reat Intel -ĠgetNext Location -Ġpretty Print -ĠFlow able -Ġrepeated ly -ĠTablet Type -Ġ'= >' -ĠFollow Sets -Timed Out -VIRON MENT -A udience -D up -G rad -T ape -a ViewData -i ences -z s -in ators -Ġs ane -an el -Ġget Charset -em aker -Ġis Name -ĠF d -ĠO LD -ec om -Ġout liers -ĠG AUGE -Ġcur ses -ĠW CS -pr im -pr inter -ĠJ WK -Ġevent Handler -Ġtrans itive -Ġmax val -ĠX Expression -EN C -Ġref 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''), # 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] - - -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() - if type(s) is not str: - s = " ".join(s) - # language-independent part: - for (pattern, replace) in normalize1: - s = re.sub(pattern, replace, s) - s = xml.sax.saxutils.unescape(s, {'"': '"'}) - # language-dependent part (assuming Western languages): - s = " %s " % s - if not preserve_case: - s = s.lower() # this might not be identical to the original - for (pattern, replace) in normalize2: - s = re.sub(pattern, replace, s) - return s.split() - - -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 counts.items(): - maxcounts[ngram] = max(maxcounts.get(ngram, 0), count) - return ([len(ref) for ref in refs], maxcounts) - - -def cook_test(test, item, n=4): - '''Takes a test sentence and returns an object that - encapsulates everything that BLEU needs to know about it.''' - (reflens, refmaxcounts) = item - 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 counts.items(): - result["correct"][len(ngram) - 1] += min(refmaxcounts.get(ngram, 0), count) - - return result - - -def score_cooked(allcomps, n=4, ground=0, smooth=1): - 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 - all_bleus = [] - for k in range(n): - correct = totalcomps['correct'][k] - guess = totalcomps['guess'][k] - addsmooth = 0 - if smooth == 1 and k > 0: - addsmooth = 1 - logbleu += math.log(correct + addsmooth + sys.float_info.min) - math.log(guess + addsmooth + sys.float_info.min) - if guess == 0: - all_bleus.append(-10000000) - else: - all_bleus.append(math.log(correct + sys.float_info.min) - math.log(guess)) - - logbleu /= float(n) - all_bleus.insert(0, logbleu) - - brevPenalty = min(0, 1 - float(totalcomps['reflen'] + 1) / (totalcomps['testlen'] + 1)) - for i in range(len(all_bleus)): - if i == 0: - all_bleus[i] += brevPenalty - all_bleus[i] = math.exp(all_bleus[i]) - return all_bleus - - -def bleu(refs, candidate, ground=0, smooth=1): - refs = cook_refs(refs) - test = cook_test(candidate, refs) - return score_cooked([test], ground=ground, smooth=smooth) - - -def splitPuncts(line): - return ' '.join(re.findall(r"[\w]+|[^\s\w]", line)) - - -def computeMaps(predictions, goldfile): - predictionMap = {} - goldMap = {} - gf = open(goldfile, 'r') - - for row in predictions: - cols = row.strip().split('\t') - if len(cols) == 1: - (rid, pred) = (cols[0], '') - else: - (rid, pred) = (cols[0], cols[1]) - predictionMap[rid] = [splitPuncts(pred.strip().lower())] - - for row in gf: - (rid, pred) = row.split('\t') - if rid in predictionMap: # Only insert if the id exists for the method - if rid not in goldMap: - goldMap[rid] = [] - goldMap[rid].append(splitPuncts(pred.strip().lower())) - - sys.stderr.write('Total: ' + str(len(goldMap)) + '\n') - return (goldMap, predictionMap) - - -# m1 is the reference map -# m2 is the prediction map -def bleuFromMaps(m1, m2): - score = [0] * 5 - num = 0.0 - - for key in m1: - if key in m2: - bl = bleu(m1[key], m2[key][0]) - score = [score[i] + bl[i] for i in range(0, len(bl))] - num += 1 - return [s * 100.0 / num for s in score] - - -if __name__ == '__main__': - reference_file = sys.argv[1] - predictions = [] - for row in sys.stdin: - predictions.append(row) - (goldMap, predictionMap) = computeMaps(predictions, reference_file) - print(bleuFromMaps(goldMap, predictionMap)[0]) diff --git a/train_tokenizer.py b/train_tokenizer.py deleted file mode 100644 index 4ab80ca..0000000 --- a/train_tokenizer.py +++ /dev/null @@ -1,22 +0,0 @@ -from tokenizers import ByteLevelBPETokenizer - -paths = ['train_code.txt', 'train_doc.txt'] - -# Initialize a tokenizer -tokenizer = ByteLevelBPETokenizer() - -# Customize training -tokenizer.train(files=paths, vocab_size=32000, min_frequency=3, special_tokens=[ - "", - "", - "", - "", - "" -]) - -# Save files to disk -tokenizer.save_model("./salesforce", "codet5") - -print( - tokenizer.encode(" hello Don't you love 🤗 Transformers yes . ").tokens -)