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import re
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import ftfy
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import json
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import spacy
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from tqdm import tqdm
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word.
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word is represented as tuple of symbols (symbols being variable-length strings)
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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return pairs
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def text_standardize(text):
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"""
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fixes some issues the spacy tokenizer had on books corpus
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also does some whitespace standardization
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"""
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text = text.replace('—', '-')
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text = text.replace('–', '-')
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text = text.replace('―', '-')
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text = text.replace('…', '...')
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text = text.replace('´', "'")
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text = re.sub('''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text)
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text = re.sub('\s*\n\s*', ' \n ', text)
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text = re.sub('[^\S\n]+', ' ', text)
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return text.strip()
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class TextEncoder(object):
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"""
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mostly a wrapper for a public python bpe tokenizer
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"""
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def __init__(self, encoder_path, bpe_path):
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self.nlp = spacy.load("en_core_web_sm", disable=['parser', 'tagger', 'ner', 'textcat'])
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self.encoder = json.load(open(encoder_path))
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self.decoder = {v:k for k,v in self.encoder.items()}
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merges = open(bpe_path,encoding='utf-8').read().split('\n')[1:-1]
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merges = [tuple(merge.split()) for merge in merges]
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self.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {}
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def bpe(self, token):
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word = tuple(token[:-1]) + ( token[-1] + '</w>',)
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if token in self.cache:
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return self.cache[token]
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pairs = get_pairs(word)
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if not pairs:
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return token+'</w>'
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while True:
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bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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new_word.extend(word[i:j])
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i = j
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except:
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new_word.extend(word[i:])
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break
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if word[i] == first and i < len(word)-1 and word[i+1] == second:
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new_word.append(first+second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = ' '.join(word)
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if word == '\n </w>':
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word = '\n</w>'
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self.cache[token] = word
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return word
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def encode(self, texts, verbose=True):
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texts_tokens = []
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if verbose:
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for text in tqdm(texts, ncols=80, leave=False):
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text = self.nlp(text_standardize(ftfy.fix_text(text)))
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text_tokens = []
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for token in text:
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text_tokens.extend([self.encoder.get(t, 0) for t in self.bpe(token.text.lower()).split(' ')])
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texts_tokens.append(text_tokens)
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else:
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for text in texts:
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text = self.nlp(text_standardize(ftfy.fix_text(text)))
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text_tokens = []
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for token in text:
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text_tokens.extend([self.encoder.get(t, 0) for t in self.bpe(token.text.lower()).split(' ')])
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texts_tokens.append(text_tokens)
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return texts_tokens
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