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@@ -0,0 +1,606 @@
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+# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
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+#
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+# Licensed under the Apache License, Version 2.0 (the "License");
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+# you may not use this file except in compliance with the License.
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+# You may obtain a copy of the License at
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+#
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+# http://www.apache.org/licenses/LICENSE-2.0
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+#
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+# Unless required by applicable law or agreed to in writing, software
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+# distributed under the License is distributed on an "AS IS" BASIS,
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+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+# See the License for the specific language governing permissions and
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+# limitations under the License.
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+
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+
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+import json
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+import os
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+import shutil
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+import logging
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+import unicodedata
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+from functools import lru_cache
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+from typing import List, Optional
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+
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+from paddle.utils import try_import
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+
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+from .tokenizer_utils_base import AddedToken
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+from .tokenizer_utils import PretrainedTokenizer
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+from .tokenizer_utils import _is_control, _is_punctuation, _is_whitespace
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+
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+__all__ = ["CLIPTokenizer"]
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+
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+
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+@lru_cache()
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+def bytes_to_unicode():
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+ """
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+ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
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+ characters the bpe code barfs on.
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+
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+ The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
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+ if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
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+ decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
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+ tables between utf-8 bytes and unicode strings.
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+ """
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+ bs = (
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+ list(range(ord("!"), ord("~") + 1))
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+ + list(range(ord("¡"), ord("¬") + 1))
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+ + list(range(ord("®"), ord("ÿ") + 1))
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+ )
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+ cs = bs[:]
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+ n = 0
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+ for b in range(2**8):
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+ if b not in bs:
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+ bs.append(b)
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+ cs.append(2**8 + n)
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+ n += 1
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+ cs = [chr(n) for n in cs]
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+ return dict(zip(bs, cs))
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+
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+
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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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+
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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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+
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+
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+def whitespace_clean(text, re):
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+ text = re.sub(r"\s+", " ", text)
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+ text = text.strip()
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+ return text
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+
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+
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+def whitespace_tokenize(text):
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+ """Runs basic whitespace cleaning and splitting on a piece of text."""
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+ text = text.strip()
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+ if not text:
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+ return []
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+ tokens = text.split()
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+ return tokens
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+
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+
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+# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
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+class BasicTokenizer(object):
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+ """
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+ Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
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+
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+ Args:
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+ do_lower_case (`bool`, *optional*, defaults to `True`):
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+ Whether or not to lowercase the input when tokenizing.
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+ never_split (`Iterable`, *optional*):
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+ Collection of tokens which will never be split during tokenization. Only has an effect when
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+ `do_basic_tokenize=True`
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+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
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+ Whether or not to tokenize Chinese characters.
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+
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+ This should likely be deactivated for Japanese (see this
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+ [issue](https://github.com/huggingface/transformers/issues/328)).
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+ strip_accents (`bool`, *optional*):
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+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
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+ value for `lowercase` (as in the original BERT).
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+ do_split_on_punc (`bool`, *optional*, defaults to `True`):
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+ In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
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+ the full context of the words, such as contractions.
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+ """
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+
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+ def __init__(
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+ self,
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+ do_lower_case=True,
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+ never_split=None,
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+ tokenize_chinese_chars=True,
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+ strip_accents=None,
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+ do_split_on_punc=True,
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+ ):
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+ if never_split is None:
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+ never_split = []
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+ self.do_lower_case = do_lower_case
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+ self.never_split = set(never_split)
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+ self.tokenize_chinese_chars = tokenize_chinese_chars
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+ self.strip_accents = strip_accents
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+ self.do_split_on_punc = do_split_on_punc
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+
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+ def tokenize(self, text, never_split=None):
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+ """
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+ Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
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+
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+ Args:
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+ never_split (`List[str]`, *optional*)
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+ Kept for backward compatibility purposes. Now implemented directly at the base class level (see
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+ [`PreTrainedTokenizer.tokenize`]) List of token not to split.
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+ """
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+ # union() returns a new set by concatenating the two sets.
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+ never_split = (
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+ self.never_split.union(set(never_split))
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+ if never_split
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+ else self.never_split
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+ )
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+ text = self._clean_text(text)
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+
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+ # This was added on November 1st, 2018 for the multilingual and Chinese
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+ # models. This is also applied to the English models now, but it doesn't
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+ # matter since the English models were not trained on any Chinese data
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+ # and generally don't have any Chinese data in them (there are Chinese
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+ # characters in the vocabulary because Wikipedia does have some Chinese
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+ # words in the English Wikipedia.).
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+ if self.tokenize_chinese_chars:
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+ text = self._tokenize_chinese_chars(text)
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+ # prevents treating the same character with different unicode codepoints as different characters
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+ unicode_normalized_text = unicodedata.normalize("NFC", text)
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+ orig_tokens = whitespace_tokenize(unicode_normalized_text)
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+ split_tokens = []
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+ for token in orig_tokens:
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+ if token not in never_split:
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+ if self.do_lower_case:
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+ token = token.lower()
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+ if self.strip_accents is not False:
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+ token = self._run_strip_accents(token)
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+ elif self.strip_accents:
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+ token = self._run_strip_accents(token)
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+ split_tokens.extend(self._run_split_on_punc(token, never_split))
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+
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+ output_tokens = whitespace_tokenize(" ".join(split_tokens))
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+ return output_tokens
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+
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+ def _run_strip_accents(self, text):
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+ """Strips accents from a piece of text."""
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+ text = unicodedata.normalize("NFD", text)
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+ output = []
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+ for char in text:
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+ cat = unicodedata.category(char)
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+ if cat == "Mn":
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+ continue
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+ output.append(char)
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+ return "".join(output)
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+
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+ def _run_split_on_punc(self, text, never_split=None):
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+ """Splits punctuation on a piece of text."""
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+ if not self.do_split_on_punc or (
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+ never_split is not None and text in never_split
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+ ):
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+ return [text]
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+ chars = list(text)
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+ i = 0
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+ start_new_word = True
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+ output = []
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+ while i < len(chars):
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+ char = chars[i]
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+ if _is_punctuation(char):
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+ output.append([char])
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+ start_new_word = True
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+ else:
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+ if start_new_word:
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+ output.append([])
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+ start_new_word = False
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+ output[-1].append(char)
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+ i += 1
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+
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+ return ["".join(x) for x in output]
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+
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+ def _tokenize_chinese_chars(self, text):
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+ """Adds whitespace around any CJK character."""
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+ output = []
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+ for char in text:
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+ cp = ord(char)
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+ if self._is_chinese_char(cp):
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+ output.append(" ")
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+ output.append(char)
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+ output.append(" ")
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+ else:
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+ output.append(char)
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+ return "".join(output)
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+
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+ def _is_chinese_char(self, cp):
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+ """Checks whether CP is the codepoint of a CJK character."""
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+ # This defines a "chinese character" as anything in the CJK Unicode block:
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+ # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
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+ #
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+ # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
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+ # despite its name. The modern Korean Hangul alphabet is a different block,
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+ # as is Japanese Hiragana and Katakana. Those alphabets are used to write
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+ # space-separated words, so they are not treated specially and handled
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+ # like the all of the other languages.
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+ if (
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+ (cp >= 0x4E00 and cp <= 0x9FFF)
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+ or (cp >= 0x3400 and cp <= 0x4DBF) #
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+ or (cp >= 0x20000 and cp <= 0x2A6DF) #
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+ or (cp >= 0x2A700 and cp <= 0x2B73F) #
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+ or (cp >= 0x2B740 and cp <= 0x2B81F) #
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+ or (cp >= 0x2B820 and cp <= 0x2CEAF) #
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+ or (cp >= 0xF900 and cp <= 0xFAFF)
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+ or (cp >= 0x2F800 and cp <= 0x2FA1F) #
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+ ): #
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+ return True
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+
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+ return False
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+
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+ def _clean_text(self, text):
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+ """Performs invalid character removal and whitespace cleanup on text."""
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+ output = []
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+ for char in text:
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+ cp = ord(char)
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+ if cp == 0 or cp == 0xFFFD or _is_control(char):
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+ continue
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+ if _is_whitespace(char):
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+ output.append(" ")
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+ else:
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+ output.append(char)
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+ return "".join(output)
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+
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+
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+class CLIPTokenizer(PretrainedTokenizer):
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+ r"""
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+ Construct a CLIP tokenizer based on byte-level Byte-Pair-Encoding.
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+
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+ This tokenizer inherits from :class:`~paddlenlp.transformers.gpt.tokenizer.GPTTokenizer`.
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+ For more information regarding those methods, please refer to this superclass.
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+
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+ Args:
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+ vocab_file (str):
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+ Path to the vocabulary file.
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+ The vocab file contains a mapping from vocabulary strings to indices.
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+ merges_file (str):
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+ Path to the merge file.
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+ The merge file is used to split the input sentence into "subword" units.
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+ The vocab file is then used to encode those units as intices.
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+ errors (str):
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+ Paradigm to follow when decoding bytes to UTF-8.
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+ Defaults to `'replace'`.
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+ max_len (int, optional):
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+ The maximum value of the input sequence length.
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+ Defaults to `77`.
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+ bos_token (str, optional):
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+ The beginning of sequence token that was used during pretraining. Can be
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+ used a sequence classifier token.
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+ Defaults to `"<|startoftext|>"`.
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+ eos_token (str, optional):
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+ A special token representing the end of a sequence that was used during pretraining.
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+ Defaults to `"<|endoftext|>"`.
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+ unk_token (str, optional):
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+ A special token representing the *unknown (out-of-vocabulary)* token.
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+ An unknown token is set to be `unk_token` inorder to be converted to an ID.
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+ Defaults to `"<|endoftext|>"`.
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+ pad_token (str, optional):
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+ A special token used to make arrays of tokens the same size for batching purposes.
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+ Defaults to `"<|endoftext|>"`.
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+
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+ Examples:
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+ .. code-block::
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+
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+ from paddlenlp.transformers import AutoTokenizer
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+
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+ tokenizer = AutoTokenizer.from_pretrained('openai/clip-vit-base-patch32')
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+ print(tokenizer('He was a puppeteer'))
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+
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+ '''
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+ {'input_ids': [49406, 797, 739, 320, 7116, 38820, 528, 49407]}
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+ '''
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+
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+ """
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+
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+ # merges and vocab same as GPT2
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+ resource_files_names = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
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+ pretrained_resource_files_map = {"vocab_file": {}, "merges_file": {}}
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+ pretrained_init_configuration = {}
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+ model_input_names = [
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+ "input_ids",
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+ "attention_mask",
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+ ]
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+
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+ def __init__(
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+ self,
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+ vocab_file,
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+ merges_file,
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+ errors="replace",
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+ max_len=77,
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+ bos_token="<|startoftext|>",
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+ eos_token="<|endoftext|>",
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+ unk_token="<|endoftext|>",
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+ pad_token="<|endoftext|>",
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+ **kwargs
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+ ):
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+
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+ bos_token = (
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+ AddedToken(bos_token, lstrip=False, rstrip=False)
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+ if isinstance(bos_token, str)
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+ else bos_token
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+ )
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+ eos_token = (
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+ AddedToken(eos_token, lstrip=False, rstrip=False)
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+ if isinstance(eos_token, str)
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+ else eos_token
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+ )
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+ unk_token = (
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+ AddedToken(unk_token, lstrip=False, rstrip=False)
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+ if isinstance(unk_token, str)
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+ else unk_token
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+ )
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+ pad_token = (
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+ AddedToken(pad_token, lstrip=False, rstrip=False)
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+ if isinstance(pad_token, str)
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+ else pad_token
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+ )
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+
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+ self._build_special_tokens_map_extended(
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+ bos_token=bos_token,
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+ eos_token=eos_token,
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+ unk_token=unk_token,
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+ pad_token=pad_token,
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+ )
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+
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+ try:
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+ import ftfy
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+
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+ self.fix_text = ftfy.fix_text
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+ except ImportError:
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+ logging.info(
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+ "ftfy or spacy is not installed using custom BasicTokenizer instead of ftfy."
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+ )
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+ self.nlp = BasicTokenizer(
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+ strip_accents=False, do_split_on_punc=False, do_lower_case=True
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+ )
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+ self.fix_text = None
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+ self.re = try_import("regex")
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+
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+ self._vocab_file = vocab_file
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+ self._merges_file = merges_file
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+ self.max_len = max_len if max_len is not None else int(1e12)
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+
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+ with open(vocab_file, encoding="utf-8") as vocab_handle:
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+ self.encoder = json.load(vocab_handle)
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+ self.decoder = {v: k for k, v in self.encoder.items()}
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+ self.errors = errors # how to handle errors in decoding
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+ self.byte_encoder = bytes_to_unicode()
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+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
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+ with open(merges_file, encoding="utf-8") as merges_handle:
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+ bpe_merges = (
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+ merges_handle.read().strip().split("\n")[1 : 49152 - 256 - 2 + 1]
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+ )
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+ bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
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+ self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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+ self.cache = {
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+ "<|startoftext|>": "<|startoftext|>",
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+ "<|endoftext|>": "<|endoftext|>",
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+ }
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+
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+ self.pat = self.re.compile(
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+ r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
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+ self.re.IGNORECASE,
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+ )
|
|
|
+
|
|
|
+ @property
|
|
|
+ def vocab_size(self):
|
|
|
+ """
|
|
|
+ Returns the size of vocabulary.
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ int: The sum of size of vocabulary and the size of speical tokens.
|
|
|
+
|
|
|
+ """
|
|
|
+ return len(self.encoder)
|
|
|
+
|
|
|
+ def get_vocab(self):
|
|
|
+ return dict(self.encoder, **self.added_tokens_encoder)
|
|
|
+
|
|
|
+ def build_inputs_with_special_tokens(
|
|
|
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
|
|
+ ) -> List[int]:
|
|
|
+ """
|
|
|
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
|
|
+ adding special tokens. A CLIP sequence has the following format:
|
|
|
+
|
|
|
+ - single sequence: `<|startoftext|> X <|endoftext|>`
|
|
|
+
|
|
|
+ Pairs of sequences are not the expected use case, but they will be handled without a separator.
|
|
|
+
|
|
|
+ Args:
|
|
|
+ token_ids_0 (`List[int]`):
|
|
|
+ List of IDs to which the special tokens will be added.
|
|
|
+ token_ids_1 (`List[int]`, *optional*):
|
|
|
+ Optional second list of IDs for sequence pairs.
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ `List[int]`: List of input IDs with the appropriate special tokens.
|
|
|
+ """
|
|
|
+ bos_token = [self.bos_token_id]
|
|
|
+ eos_token = [self.eos_token_id]
|
|
|
+
|
|
|
+ if token_ids_1 is None:
|
|
|
+ return bos_token + token_ids_0 + eos_token
|
|
|
+ return bos_token + token_ids_0 + eos_token + eos_token + token_ids_1 + eos_token
|
|
|
+
|
|
|
+ def build_offset_mapping_with_special_tokens(
|
|
|
+ self, offset_mapping_0, offset_mapping_1=None
|
|
|
+ ):
|
|
|
+ """
|
|
|
+ Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
|
|
|
+
|
|
|
+ Should be overridden in a subclass if the model has a special way of building those.
|
|
|
+
|
|
|
+ Args:
|
|
|
+ offset_mapping_0 (List[tuple]):
|
|
|
+ List of char offsets to which the special tokens will be added.
|
|
|
+ offset_mapping_1 (List[tuple], optional):
|
|
|
+ Optional second list of char offsets for offset mapping pairs.
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ List[tuple]: List of char offsets with the appropriate offsets of special tokens.
|
|
|
+ """
|
|
|
+ if offset_mapping_1 is None:
|
|
|
+ return [(0, 0)] + offset_mapping_0 + [(0, 0)]
|
|
|
+
|
|
|
+ return (
|
|
|
+ [(0, 0)] + offset_mapping_0 + [(0, 0), (0, 0)] + offset_mapping_1 + [(0, 0)]
|
|
|
+ )
|
|
|
+
|
|
|
+ def get_special_tokens_mask(
|
|
|
+ self, token_ids_0, token_ids_1=None, already_has_special_tokens=False
|
|
|
+ ):
|
|
|
+ """
|
|
|
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
|
|
+ special tokens using the tokenizer `prepare_for_model` method.
|
|
|
+
|
|
|
+ Args:
|
|
|
+ token_ids_0 (`List[int]`):
|
|
|
+ List of IDs.
|
|
|
+ token_ids_1 (`List[int]`, *optional*):
|
|
|
+ Optional second list of IDs for sequence pairs.
|
|
|
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
|
|
+ Whether or not the token list is already formatted with special tokens for the model.
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
|
|
+ """
|
|
|
+
|
|
|
+ if already_has_special_tokens:
|
|
|
+ return super().get_special_tokens_mask(
|
|
|
+ token_ids_0=token_ids_0,
|
|
|
+ token_ids_1=token_ids_1,
|
|
|
+ already_has_special_tokens=True,
|
|
|
+ )
|
|
|
+
|
|
|
+ if token_ids_1 is None:
|
|
|
+ return [1] + ([0] * len(token_ids_0)) + [1]
|
|
|
+ return (
|
|
|
+ [1] + ([0] * len(token_ids_0)) + [1] + [1] + ([0] * len(token_ids_1)) + [1]
|
|
|
+ )
|
|
|
+
|
|
|
+ def create_token_type_ids_from_sequences(
|
|
|
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
|
|
+ ) -> List[int]:
|
|
|
+ """
|
|
|
+ Create a mask from the two sequences passed. CLIP does not make use of token type ids, therefore a list of
|
|
|
+ zeros is returned.
|
|
|
+
|
|
|
+ Args:
|
|
|
+ token_ids_0 (`List[int]`):
|
|
|
+ List of IDs.
|
|
|
+ token_ids_1 (`List[int]`, *optional*):
|
|
|
+ Optional second list of IDs for sequence pairs.
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ `List[int]`: List of zeros.
|
|
|
+ """
|
|
|
+ bos_token = [self.bos_token_id]
|
|
|
+ eos_token = [self.eos_token_id]
|
|
|
+
|
|
|
+ if token_ids_1 is None:
|
|
|
+ return len(bos_token + token_ids_0 + eos_token) * [0]
|
|
|
+ return len(
|
|
|
+ bos_token + token_ids_0 + eos_token + eos_token + token_ids_1 + eos_token
|
|
|
+ ) * [0]
|
|
|
+
|
|
|
+ def bpe(self, token):
|
|
|
+ if token in self.cache:
|
|
|
+ return self.cache[token]
|
|
|
+ word = tuple(token[:-1]) + (token[-1] + "</w>",)
|
|
|
+ pairs = get_pairs(word)
|
|
|
+
|
|
|
+ if not pairs:
|
|
|
+ return token + "</w>"
|
|
|
+
|
|
|
+ while True:
|
|
|
+ bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
|
|
|
+ if bigram not in self.bpe_ranks:
|
|
|
+ break
|
|
|
+ first, second = bigram
|
|
|
+ new_word = []
|
|
|
+ i = 0
|
|
|
+ while i < len(word):
|
|
|
+ try:
|
|
|
+ j = word.index(first, i)
|
|
|
+ except ValueError:
|
|
|
+ new_word.extend(word[i:])
|
|
|
+ break
|
|
|
+ else:
|
|
|
+ new_word.extend(word[i:j])
|
|
|
+ i = j
|
|
|
+
|
|
|
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
|
|
|
+ new_word.append(first + second)
|
|
|
+ i += 2
|
|
|
+ else:
|
|
|
+ new_word.append(word[i])
|
|
|
+ i += 1
|
|
|
+ new_word = tuple(new_word)
|
|
|
+ word = new_word
|
|
|
+ if len(word) == 1:
|
|
|
+ break
|
|
|
+ else:
|
|
|
+ pairs = get_pairs(word)
|
|
|
+ word = " ".join(word)
|
|
|
+ self.cache[token] = word
|
|
|
+ return word
|
|
|
+
|
|
|
+ def _tokenize(self, text):
|
|
|
+ """Tokenize a string."""
|
|
|
+ bpe_tokens = []
|
|
|
+ if self.fix_text is None:
|
|
|
+ text = " ".join(self.nlp.tokenize(text))
|
|
|
+ else:
|
|
|
+ text = whitespace_clean(self.fix_text(text), self.re).lower()
|
|
|
+
|
|
|
+ for token in self.re.findall(self.pat, text):
|
|
|
+ token = "".join(
|
|
|
+ self.byte_encoder[b] for b in token.encode("utf-8")
|
|
|
+ ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
|
|
|
+ bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
|
|
|
+ return bpe_tokens
|
|
|
+
|
|
|
+ def _convert_token_to_id(self, token):
|
|
|
+ """Converts a token (str) in an id using the vocab."""
|
|
|
+ return self.encoder.get(token, self.encoder.get(self.unk_token))
|
|
|
+
|
|
|
+ def _convert_id_to_token(self, index):
|
|
|
+ """Converts an index (integer) in a token (str) using the vocab."""
|
|
|
+ return self.decoder.get(index)
|
|
|
+
|
|
|
+ def convert_tokens_to_string(self, tokens):
|
|
|
+ """Converts a sequence of tokens (string) in a single string."""
|
|
|
+ text = "".join(tokens)
|
|
|
+ byte_array = bytearray([self.byte_decoder[c] for c in text])
|
|
|
+ text = (
|
|
|
+ byte_array.decode("utf-8", errors=self.errors).replace("</w>", " ").strip()
|
|
|
+ )
|
|
|
+ return text
|
|
|
+
|
|
|
+ def save_resources(self, save_directory):
|
|
|
+ """
|
|
|
+ Saves `SentencePiece <https://github.com/google/sentencepiece>`__ file
|
|
|
+ (ends with '.spm') under `save_directory`.
|
|
|
+
|
|
|
+ Args:
|
|
|
+ save_directory (str): Directory to save files into.
|
|
|
+ """
|
|
|
+ for name, file_name in self.resource_files_names.items():
|
|
|
+ source_path = getattr(self, "_%s" % name)
|
|
|
+
|
|
|
+ save_path = os.path.join(save_directory, file_name)
|
|
|
+ if os.path.abspath(source_path) != os.path.abspath(save_path):
|
|
|
+ shutil.copyfile(source_path, save_path)
|
Zhang Zelun