Grounded-Segment-Anything
/
transformers_4_35_0
/models
/blenderbot_small
/tokenization_blenderbot_small.py
| # coding=utf-8 | |
| # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. 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. | |
| """Tokenization class for BlenderbotSmall.""" | |
| import json | |
| import os | |
| from typing import Dict, List, Optional, Tuple | |
| import regex as re | |
| from ...tokenization_utils import PreTrainedTokenizer | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| VOCAB_FILES_NAMES = { | |
| "vocab_file": "vocab.json", | |
| "merges_file": "merges.txt", | |
| "tokenizer_config_file": "tokenizer_config.json", | |
| } | |
| PRETRAINED_VOCAB_FILES_MAP = { | |
| "vocab_file": { | |
| "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json" | |
| }, | |
| "merges_file": { | |
| "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt" | |
| }, | |
| "tokenizer_config_file": { | |
| "facebook/blenderbot_small-90M": ( | |
| "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json" | |
| ) | |
| }, | |
| } | |
| PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot_small-90M": 512} | |
| 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 = set() | |
| prev_char = word[0] | |
| for char in word[1:]: | |
| pairs.add((prev_char, char)) | |
| prev_char = char | |
| pairs = set(pairs) | |
| return pairs | |
| class BlenderbotSmallTokenizer(PreTrainedTokenizer): | |
| """ | |
| Constructs a Blenderbot-90M tokenizer based on BPE (Byte-Pair-Encoding) | |
| This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to | |
| the superclass for more information regarding methods. | |
| Args: | |
| vocab_file (`str`): | |
| File containing the vocabulary. | |
| merges_file (`str`): | |
| Path to the merges file. | |
| bos_token (`str`, *optional*, defaults to `"__start__"`): | |
| The beginning of sentence token. | |
| eos_token (`str`, *optional*, defaults to `"__end__"`): | |
| The end of sentence token. | |
| unk_token (`str`, *optional*, defaults to `"__unk__"`): | |
| The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this | |
| token instead. | |
| pad_token (`str`, *optional*, defaults to `"__null__"`): | |
| The token used for padding, for example when batching sequences of different lengths. | |
| kwargs (*optional*): | |
| Additional keyword arguments passed along to [`PreTrainedTokenizer`] | |
| """ | |
| vocab_files_names = VOCAB_FILES_NAMES | |
| pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP | |
| max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES | |
| model_input_names = ["input_ids", "attention_mask"] | |
| def __init__( | |
| self, | |
| vocab_file, | |
| merges_file, | |
| bos_token="__start__", | |
| eos_token="__end__", | |
| unk_token="__unk__", | |
| pad_token="__null__", | |
| **kwargs, | |
| ): | |
| with open(vocab_file, encoding="utf-8") as vocab_handle: | |
| self.encoder = json.load(vocab_handle) | |
| self.decoder = {v: k for k, v in self.encoder.items()} | |
| with open(merges_file, encoding="utf-8") as merges_handle: | |
| merges = merges_handle.read().split("\n")[1:-1] | |
| merges = [tuple(merge.split()) for merge in merges] | |
| self.bpe_ranks = dict(zip(merges, range(len(merges)))) | |
| self.cache = {} | |
| super().__init__(unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, **kwargs) | |
| def vocab_size(self) -> int: | |
| return len(self.encoder) | |
| def get_vocab(self) -> Dict: | |
| return dict(self.encoder, **self.added_tokens_encoder) | |
| def bpe(self, token: str) -> str: | |
| if token in self.cache: | |
| return self.cache[token] | |
| token = re.sub("([.,!?()])", r" \1", token) | |
| token = re.sub("(')", r" \1 ", token) | |
| token = re.sub(r"\s{2,}", " ", token) | |
| if "\n" in token: | |
| token = token.replace("\n", " __newln__") | |
| tokens = token.split(" ") | |
| words = [] | |
| for token in tokens: | |
| if not len(token): | |
| continue | |
| token = token.lower() | |
| word = tuple(token) | |
| word = tuple(list(word[:-1]) + [word[-1] + "</w>"]) | |
| pairs = get_pairs(word) | |
| if not pairs: | |
| words.append(token) | |
| continue | |
| 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) | |
| new_word.extend(word[i:j]) | |
| i = j | |
| except ValueError: | |
| new_word.extend(word[i:]) | |
| break | |
| 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) | |
| word = word[:-4] | |
| self.cache[token] = word | |
| words.append(word) | |
| return " ".join(words) | |
| def _tokenize(self, text: str) -> List[str]: | |
| """Split a string into tokens using BPE.""" | |
| split_tokens = [] | |
| words = re.findall(r"\S+\n?", text) | |
| for token in words: | |
| split_tokens.extend(list(self.bpe(token).split(" "))) | |
| return split_tokens | |
| def _convert_token_to_id(self, token: str) -> int: | |
| """Converts a token to an id using the vocab.""" | |
| token = token.lower() | |
| return self.encoder.get(token, self.encoder.get(self.unk_token)) | |
| def _convert_id_to_token(self, index: int) -> str: | |
| """Converts an index (integer) in a token (str) using the vocab.""" | |
| return self.decoder.get(index, self.unk_token) | |
| def convert_tokens_to_string(self, tokens: List[str]) -> str: | |
| """Converts a sequence of tokens in a single string.""" | |
| out_string = " ".join(tokens).replace("@@ ", "").strip() | |
| return out_string | |
| def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: | |
| if not os.path.isdir(save_directory): | |
| logger.error(f"Vocabulary path ({save_directory}) should be a directory") | |
| return | |
| vocab_file = os.path.join( | |
| save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] | |
| ) | |
| merge_file = os.path.join( | |
| save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] | |
| ) | |
| with open(vocab_file, "w", encoding="utf-8") as f: | |
| f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") | |
| index = 0 | |
| with open(merge_file, "w", encoding="utf-8") as writer: | |
| writer.write("#version: 0.2\n") | |
| for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): | |
| if index != token_index: | |
| logger.warning( | |
| f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." | |
| " Please check that the tokenizer is not corrupted!" | |
| ) | |
| index = token_index | |
| writer.write(" ".join(bpe_tokens) + "\n") | |
| index += 1 | |
| return vocab_file, merge_file | |
| # Copied from transformers.models.blenderbot.tokenization_blenderbot.BlenderbotTokenizer.default_chat_template | |
| def default_chat_template(self): | |
| """ | |
| A very simple chat template that just adds whitespace between messages. | |
| """ | |
| return ( | |
| "{% for message in messages %}" | |
| "{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}" | |
| "{{ message['content'] }}" | |
| "{% if not loop.last %}{{ ' ' }}{% endif %}" | |
| "{% endfor %}" | |
| "{{ eos_token }}" | |
| ) | |