update tokenizer

transformers/tokenization_ctrl.py

@@ -16,21 +16,13 @@
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
-import sys
 import json
 import logging
 import os
 import regex as re
 from io import open
-import pdb
 
-try:
+from .tokenization_bert import BasicTokenizer
-    from functools import lru_cache
-except ImportError:
-    # Just a dummy decorator to get the checks to run on python2
-    # because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now.
-    def lru_cache():
-        return lambda func: func
 
 from .tokenization_utils import PreTrainedTokenizer
 
@@ -53,49 +45,47 @@ PRETRAINED_VOCAB_FILES_MAP = {
 }
 
 PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    'ctrl': 1280,
+    'ctrl': 256,
 }
 
-@lru_cache()
+def text_standardize(text):
-def bytes_to_unicode():
     """
-    Returns list of utf-8 byte and a mapping to unicode strings.
+    fixes some issues the spacy tokenizer had on books corpus
-    We specifically avoids mapping to whitespace/control characters the bpe code barfs on.
+    also does some whitespace standardization
+    """
+    text = text.replace('—', '-')
+    text = text.replace('–', '-')
+    text = text.replace('―', '-')
+    text = text.replace('…', '...')
+    text = text.replace('´', "'")
+    text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text)
+    text = re.sub(r'\s*\n\s*', ' \n ', text)
+    text = re.sub(r'[^\S\n]+', ' ', text)
+    return text.strip()
 
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    """
-    _chr = unichr if sys.version_info[0] == 2 else chr
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [_chr(n) for n in cs]
-    return dict(zip(bs, cs))
 
 def get_pairs(word):
     """Return set of symbol pairs in a word.
 
     Word is represented as tuple of symbols (symbols being variable-length strings).
     """
-    pairs= []
+    # pairs = []
+    # prev_char = word[0]
+    # for i, char in enumerate(word[1:]):
+    #     #_i = i + 1
+    #     #if word[_i+1:] == tuple('</w>'):
+    #     #    pairs.append((prev_char, char+'</w>'))
+    #     #    break
+    #     #else:
+    #     if True:
+    #         pairs.append((prev_char, char))
+    #         prev_char = char
+
+    pairs = set()
     prev_char = word[0]
-    for i, char in enumerate(word[1:]):
+    for char in word[1:]:
-        #_i = i + 1
+        pairs.add((prev_char, char))
-        #if word[_i+1:] == tuple('</w>'):
+        prev_char = char
-        #    pairs.append((prev_char, char+'</w>'))
-        #    break
-        #else:
-        if True:
-            pairs.append((prev_char, char))
-            prev_char = char
 
     pairs = set(pairs)
     return pairs
@@ -113,24 +103,28 @@ class CTRLTokenizer(PreTrainedTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 
-    def __init__(self, vocab_file, merges_file, errors='replace', unk_token="<unk>",
+    def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
-                 bos_token="<|endoftext|>", eos_token="<|endoftext|>", **kwargs):
+        super(CTRLTokenizer, self).__init__(unk_token=unk_token, **kwargs)
-        super(CTRLTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
         self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
         self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
 
-        self.encoder = json.load(open(vocab_file, encoding="utf-8"))
+        try:
-        self.decoder = {v: k for k, v in self.encoder.items()}
+            import ftfy
-        self.errors = errors  # how to handle errors in decoding
+            from spacy.lang.en import English
-        self.byte_encoder = bytes_to_unicode()
+            _nlp = English()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+            self.nlp = _nlp.Defaults.create_tokenizer(_nlp)
-        bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
+            self.fix_text = ftfy.fix_text
-        bpe_merges = [tuple(merge.split()) for merge in bpe_data]
+        except ImportError:
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+            logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
-        self.cache = {}
+            self.nlp = BasicTokenizer(do_lower_case=True)
+            self.fix_text = None
 
-        # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
+        self.encoder = json.load(open(vocab_file, encoding="utf-8"))
-        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+        self.decoder = {v:k for k,v in self.encoder.items()}
+        merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
+        merges = [tuple(merge.split()) for merge in merges]
+        self.bpe_ranks = dict(zip(merges, range(len(merges))))
+        self.cache = {}
 
     @property
     def vocab_size(self):
@@ -179,23 +173,27 @@ class CTRLTokenizer(PreTrainedTokenizer):
         self.cache[token] = word
         return word
 
-    def _tokenize(self, text, add_prefix_space=False):
+    def _tokenize(self, text):
         """ Tokenize a string.
-            Args:
-                - add_prefix_space (boolean, default False):
-                    Begin the sentence with at least one space toto get invariance to word order in CTRL (and RoBERTa) tokenizers.
         """
-        if add_prefix_space:
+        split_tokens = []
-            text = ' ' + text
+        if self.fix_text is None:
-
+            # Using BERT's BasicTokenizer
-        bpe_tokens = []
+            text = self.nlp.tokenize(text)
-        for token in text.split():
+            for token in text:
-            if sys.version_info[0] == 2:
+                split_tokens.extend([t for t in self.bpe(token).split(' ')])
-                token = ''.join(self.byte_encoder[ord(b)] for b in token) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
+        else:
-            else:
+            # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
-                token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
+            text = self.nlp(text_standardize(self.fix_text(text)))
-            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
+            for token in text:
-        return bpe_tokens
+                split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')])
+        # for token in text.split():
+        #     if sys.version_info[0] == 2:
+        #         token = ''.join(self.byte_encoder[ord(b)] for b in token) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
+        #     else:
+        #         token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
+        #     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
+        return split_tokens
 
     def _convert_token_to_id(self, token):
         """ Converts a token (str/unicode) in an id using the vocab. """
@@ -203,13 +201,12 @@ class CTRLTokenizer(PreTrainedTokenizer):
 
     def _convert_id_to_token(self, index):
         """Converts an index (integer) in a token (string/unicode) using the vocab."""
-        return self.decoder.get(index)
+        return self.decoder.get(index, self.unk_token)
 
     def convert_tokens_to_string(self, tokens):
         """ Converts a sequence of tokens (string) in a single string. """
-        text = ''.join(tokens)
+        out_string = ''.join(tokens).replace('@@', ' ').strip()
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
+        return out_string
-        return text
 
     def save_vocabulary(self, save_directory):
         """Save the tokenizer vocabulary and merge files to a directory."""
@@ -235,10 +232,8 @@ class CTRLTokenizer(PreTrainedTokenizer):
 
         return vocab_file, merge_file
 
-    def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
+    # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
-        filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
+    #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
-        tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
+    #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
-        tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
+    #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
-        return ''.join(tokens_generated_so_far)
+    #     return ''.join(tokens_generated_so_far)
-    
-