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import logging
import os
import json
from typing import Optional, Dict, List, Set, Tuple, Union, Literal, Type
from pydantic.dataclasses import dataclass
import numpy as np
from numpy.typing import NDArray
from transformers import PreTrainedTokenizerFast
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {
"tag_category": "tag_category.json",
}
PRETRAINED_VOCAB_FILES_MAP = {
"tag_category": {
"p1atdev/tokenizer_test_1": "https://huggingface.co/p1atdev/tokenizer_test_1/resolve/main/tag_category.json"
}
}
@dataclass
class Category:
name: str
max_count: Optional[int]
next_category: List[int]
can_end: bool
bos_token_id: int
eos_token_id: int
default_mask: int
@dataclass
class SpecialMapping:
allow: List[int]
disallow: List[int]
@dataclass
class TagCategoryConfig:
start_category: int
categories: Dict[str, Category]
special_mapping: Dict[
str, Dict[str, SpecialMapping]
] # {token_id: { category_id: SpecialMapping }}
category_tags_pairs: Dict[str, List[int]]
class OverrideMask:
allow: np.ndarray
disallow: np.ndarray
def __init__(self, allow: np.ndarray, disallow: np.ndarray) -> None:
self.allow = allow
self.disallow = disallow
def load_tag_category(config_json: str):
with open(config_json, "rb") as file:
config: TagCategoryConfig = TagCategoryConfig(**json.loads(file.read()))
return config
class DartTokenizer(PreTrainedTokenizerFast):
"""Dart tokenizer"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
def __init__(self, tag_category, **kwargs):
super().__init__(**kwargs)
self.tag_category_config = load_tag_category(tag_category)
self.category_bos_map = {
category.bos_token_id: category_id
for category_id, category in self.tag_category_config.categories.items()
}
self.category_eos_map = {
category.eos_token_id: category_id
for category_id, category in self.tag_category_config.categories.items()
}
self._id_to_category_map = np.zeros(self.vocab_size).astype("uint8")
for category_id, tokens in self.tag_category_config.category_tags_pairs.items():
self._id_to_category_map[tokens] = int(category_id)
self.category_mask = self.create_category_vocab_mask()
def create_vocab_mask(self, value: int = 1):
"""Create an array of vocab size filled with specified value"""
return np.full(self.vocab_size, value).astype("uint8")
def create_category_vocab_mask(self):
"""Create vocab masks for each category"""
return {
category_id: self.create_vocab_mask(
value=category.default_mask,
)
for category_id, category in self.tag_category_config.categories.items()
}
def get_token_ids_in_category(self, category_id: Union[int, str]):
"""Get token ids in the specified category"""
return self.tag_category_config.category_tags_pairs[str(category_id)]
def get_category(self, category_id: Union[int, str]):
"""Get the specified category config"""
return self.tag_category_config.categories[str(category_id)]
def get_special_mapping(self, token_id: Union[int, str]):
"""Get the special mapping of specified token id"""
return self.tag_category_config.special_mapping[str(token_id)]
def get_banned_tokens_mask(self, tokens: Union[str, List[str], int, List[int]]):
if isinstance(tokens, str):
tokens = [tokens]
elif isinstance(tokens, int):
tokens = [tokens]
elif isinstance(tokens, list):
tokens = [
self.convert_tokens_to_ids(token) if isinstance(token, str) else token
for token in tokens
]
assert isinstance(tokens, list) and all(
[isinstance(token, int) for token in tokens]
)
mask = self.create_vocab_mask(value=1)
mask[tokens] = 0
return mask
def convert_ids_to_category_ids(self, token_ids: Union[int, List[int]]):
return self._id_to_category_map[token_ids]
def get_next_tokens_mask(
self,
input_ids: List[int],
category_mask: Optional[Dict[str, np.ndarray]] = None,
) -> Tuple[np.ndarray, Dict[str, np.ndarray]]:
"""Get the next token's vocab mask and a category mask"""
if category_mask == None:
category_mask = self.category_mask
vocab_mask = self.create_vocab_mask(value=0)
if len(input_ids) == 0:
# only allow bos token
vocab_mask[self.bos_token_id] = 1
return vocab_mask, category_mask
# the last token's id in the input ids
last_token_id = input_ids[-1]
if last_token_id == self.unk_token_id:
# unknown token
logger.warning(
"The unk_token was provided! The vocab mask could not be created properly."
)
return self.create_vocab_mask(value=1), category_mask
# if the last token has a special mapping
if str(last_token_id) in self.tag_category_config.special_mapping.keys():
for category_id, mapping in self.get_special_mapping(last_token_id).items():
# update mask
category_mask[category_id][mapping.allow] = 1
category_mask[category_id][mapping.disallow] = 0
if last_token_id == self.bos_token_id:
# the first category
start_category_id = self.tag_category_config.start_category
start_category = self.get_category(start_category_id)
# only allow the next category's bos token
vocab_mask[start_category.bos_token_id] = 1
return vocab_mask, category_mask
elif last_token_id == self.eos_token_id:
# end of text. only allows pad token
vocab_mask[self.pad_token_id] = 1
return vocab_mask, category_mask
elif last_token_id in self.category_bos_map:
# begin of category
# only allow same category's tags
current_category_id = self.category_bos_map[last_token_id]
category = self.get_category(current_category_id)
tokens_in_category = self.get_token_ids_in_category(current_category_id)
vocab_mask[tokens_in_category] = 1
vocab_mask *= category_mask[str(current_category_id)]
vocab_mask[category.eos_token_id] = 1
return vocab_mask, category_mask # current category's mask
elif last_token_id in self.category_eos_map:
# boundary of categories
current_category_id = self.category_eos_map[last_token_id]
category = self.get_category(current_category_id)
if category.can_end:
# this category can finish generation
vocab_mask[self.eos_token_id] = 1
for next_category_id in category.next_category:
# allow the next category's bos token
vocab_mask[self.get_category(next_category_id).bos_token_id] = 1
return vocab_mask, category_mask
else:
# inside each category
current_category_id = self.convert_ids_to_category_ids(last_token_id).item()
tokens_in_category = self.get_token_ids_in_category(current_category_id)
vocab_mask[tokens_in_category] = 1
vocab_mask[self.get_category(current_category_id).eos_token_id] = 1
vocab_mask *= category_mask[str(current_category_id)]
vocab_mask[input_ids] = 0 # do not reuse used tokens
return vocab_mask, category_mask
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