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"""Tokenization classes for Salesforce CTRL.""" |
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import json |
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import os |
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from typing import Optional, Tuple |
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import regex as re |
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from ...tokenization_utils import PreTrainedTokenizer |
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from ...utils import logging |
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logger = logging.get_logger(__name__) |
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VOCAB_FILES_NAMES = { |
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"vocab_file": "vocab.json", |
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"merges_file": "merges.txt", |
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} |
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PRETRAINED_VOCAB_FILES_MAP = { |
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"vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"}, |
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"merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"}, |
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} |
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { |
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"ctrl": 256, |
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} |
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CONTROL_CODES = { |
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"Pregnancy": 168629, |
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"Christianity": 7675, |
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"Explain": 106423, |
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"Fitness": 63440, |
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"Saving": 63163, |
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"Ask": 27171, |
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"Ass": 95985, |
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"Joke": 163509, |
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"Questions": 45622, |
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"Thoughts": 49605, |
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"Retail": 52342, |
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"Feminism": 164338, |
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"Writing": 11992, |
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"Atheism": 192263, |
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"Netflix": 48616, |
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"Computing": 39639, |
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"Opinion": 43213, |
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"Alone": 44967, |
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"Funny": 58917, |
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"Gaming": 40358, |
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"Human": 4088, |
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"India": 1331, |
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"Joker": 77138, |
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"Diet": 36206, |
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"Legal": 11859, |
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"Norman": 4939, |
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"Tip": 72689, |
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"Weight": 52343, |
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"Movies": 46273, |
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"Running": 23425, |
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"Science": 2090, |
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"Horror": 37793, |
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"Confession": 60572, |
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"Finance": 12250, |
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"Politics": 16360, |
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"Scary": 191985, |
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"Support": 12654, |
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"Technologies": 32516, |
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"Teenage": 66160, |
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"Event": 32769, |
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"Learned": 67460, |
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"Notion": 182770, |
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"Wikipedia": 37583, |
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"Books": 6665, |
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"Extract": 76050, |
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"Confessions": 102701, |
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"Conspiracy": 75932, |
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"Links": 63674, |
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"Narcissus": 150425, |
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"Relationship": 54766, |
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"Relationships": 134796, |
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"Reviews": 41671, |
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"News": 4256, |
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"Translation": 26820, |
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"multilingual": 128406, |
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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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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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pairs = set(pairs) |
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return pairs |
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class CTRLTokenizer(PreTrainedTokenizer): |
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""" |
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Construct a CTRL tokenizer. Based on Byte-Pair-Encoding. |
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to |
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this superclass for more information regarding those methods. |
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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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merges_file (`str`): |
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Path to the merges file. |
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unk_token (`str`, *optional*, defaults to `"<unk>"`): |
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this |
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token instead. |
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""" |
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vocab_files_names = VOCAB_FILES_NAMES |
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP |
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES |
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control_codes = CONTROL_CODES |
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def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs): |
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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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with open(merges_file, encoding="utf-8") as merges_handle: |
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merges = merges_handle.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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super().__init__(unk_token=unk_token, **kwargs) |
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@property |
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def vocab_size(self): |
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return len(self.encoder) |
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def get_vocab(self): |
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return dict(self.encoder, **self.added_tokens_encoder) |
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def bpe(self, token): |
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if token in self.cache: |
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return self.cache[token] |
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word = tuple(token) |
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word = tuple(list(word[:-1]) + [word[-1] + "</w>"]) |
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pairs = get_pairs(word) |
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if not pairs: |
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return token |
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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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except ValueError: |
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new_word.extend(word[i:]) |
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break |
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else: |
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new_word.extend(word[i:j]) |
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i = j |
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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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word = word[:-4] |
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self.cache[token] = word |
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return word |
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def _tokenize(self, text): |
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"""Tokenize a string.""" |
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split_tokens = [] |
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words = re.findall(r"\S+\n?", text) |
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for token in words: |
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split_tokens.extend(list(self.bpe(token).split(" "))) |
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return split_tokens |
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def _convert_token_to_id(self, token): |
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"""Converts a token (str) in an id using the vocab.""" |
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return self.encoder.get(token, self.encoder.get(self.unk_token)) |
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def _convert_id_to_token(self, index): |
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"""Converts an index (integer) in a token (str) using the vocab.""" |
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return self.decoder.get(index, self.unk_token) |
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def convert_tokens_to_string(self, tokens): |
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"""Converts a sequence of tokens (string) in a single string.""" |
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out_string = " ".join(tokens).replace("@@ ", "").strip() |
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return out_string |
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: |
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if not os.path.isdir(save_directory): |
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logger.error(f"Vocabulary path ({save_directory}) should be a directory") |
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return |
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vocab_file = os.path.join( |
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] |
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) |
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merge_file = os.path.join( |
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] |
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) |
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with open(vocab_file, "w", encoding="utf-8") as f: |
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f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") |
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index = 0 |
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with open(merge_file, "w", encoding="utf-8") as writer: |
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writer.write("#version: 0.2\n") |
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for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): |
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if index != token_index: |
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logger.warning( |
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f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." |
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" Please check that the tokenizer is not corrupted!" |
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) |
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index = token_index |
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writer.write(" ".join(bpe_tokens) + "\n") |
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index += 1 |
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return vocab_file, merge_file |
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