118 lines
4.4 KiB
Python
118 lines
4.4 KiB
Python
# coding=utf-8
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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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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"""Tokenization class for Dia."""
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from typing import Optional
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class DiaTokenizer(PreTrainedTokenizer):
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"""
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Construct a Dia tokenizer. Dia simply uses raw bytes utf-8 encoding except for special tokens `[S1]` and `[S2]`.
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This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
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refer to this superclass for more information regarding those methods.
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Args:
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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unk_token (`str`, *optional*, defaults to `"<pad>"`):
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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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max_length (`int`, *optional*, defaults to 1024):
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The maximum length of the sequences when encoding. Sequences longer than this will be truncated.
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offset (`int`, *optional*, defaults to 0):
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The offset of the tokenizer.
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"""
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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self,
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pad_token: Optional[str] = "<pad>",
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unk_token: Optional[str] = "<pad>",
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max_length: Optional[int] = 1024,
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offset: int = 0,
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**kwargs,
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):
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# We have no eos/bos tokens but allow padding -- no l/r strip as we treat them as tokens as well
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pad_token = AddedToken(pad_token) if isinstance(pad_token, str) else pad_token
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unk_token = AddedToken(unk_token) if isinstance(unk_token, str) else unk_token
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self._utf_vocab_size = 2**8 # utf is 8 bits
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self._added_tokens_decoder = {0: pad_token, 1: AddedToken("[S1]"), 2: AddedToken("[S2]")}
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self.offset = offset
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super().__init__(
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unk_token=unk_token,
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pad_token=pad_token,
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max_length=max_length,
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**kwargs,
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)
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@property
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def vocab_size(self):
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return self._utf_vocab_size
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def get_vocab(self):
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.offset)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def _tokenize(self, text: str) -> list[str]:
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"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
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tokens = [chr(i) for i in text.encode("utf-8")]
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return 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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if len(token) != 1:
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token_id = None
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else:
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token_id = ord(token) + self.offset
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return token_id
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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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token = chr(index - self.offset)
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return token
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def convert_tokens_to_string(self, tokens: list[str]) -> str:
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"""Converts a sequence of tokens (string) in a single string."""
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bstring = b""
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for token in tokens:
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if token in self.added_tokens_decoder:
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added_token_obj = self.added_tokens_decoder[token]
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tok_string = str(added_token_obj).encode("utf-8")
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elif token in self.added_tokens_encoder:
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tok_string = token.encode("utf-8")
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else:
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tok_string = token.encode("utf-8") # Assume general string token
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bstring += tok_string
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string = bstring.decode("utf-8", errors="ignore")
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return string
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# No vocab file
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
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return ()
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__all__ = ["DiaTokenizer"]
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