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Parent(s):
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Browse files- rwkv_vocab_v20230424.txt +0 -0
- tokenization_rwkv_world.py +82 -193
rwkv_vocab_v20230424.txt
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tokenization_rwkv_world.py
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@@ -52,186 +52,52 @@ if TYPE_CHECKING:
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "rwkv_vocab_v20230424.
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}
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def __init__(self, special_ids):
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self.root = self.Node()
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self.data = {}
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self.r_data = {}
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self.special_ids = special_ids
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def insert(self, word, data):
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self.data[word] = data
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self.r_data[data] = word
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idx = 0
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node = self.root
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while idx < len(word):
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w = word[idx]
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is_leaf = (idx == (len(word) - 1))
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leaf_data = (data if is_leaf else None)
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# 不存在则插入
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if not node.has_next(w):
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node.add_node(w, self.Node(is_leaf=is_leaf, leaf_data=leaf_data))
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# last word
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node = node.get_node(w)
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idx += 1
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if not node.is_leaf():
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node.set_leaf()
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node.set_data(data)
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def findStrict(self, word):
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idx = 0
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node = self.root
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while node is not None and idx < len(word):
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w = word[idx]
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if not node.has_next(w):
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return None
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# last word
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node = node.get_node(w)
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idx += 1
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if node.is_leaf():
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return node.get_data()
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return None
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def prefix(self, word):
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idx = 0
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node = self.root
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result = []
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while node is not None and idx < len(word):
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w = word[idx]
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if not node.has_next(w):
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return result
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# last word
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node = node.get_node(w)
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if node.is_leaf():
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result.append([word[:idx + 1], node.get_data()])
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idx += 1
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return result
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def max_prefix(self, content, start_idx):
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idx = start_idx
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node = self.root
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l = len(content)
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result = [["", ], ]
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while node is not None and idx < l:
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w = content[idx]
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if not node.has_next(w):
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return result[-1]
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# last word
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node = node.get_node(w)
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if node.is_leaf():
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result.append([content[start_idx:idx + 1], node.get_data()])
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idx += 1
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idx = start_idx
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node = self.root
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l = len(content)
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result = [["", (3, 0)], ]
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while node is not None and idx < l:
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w = content[idx]
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if not node.has_next(w):
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break
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# last word
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node = node.get_node(w)
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if node.is_leaf():
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result.append([content[start_idx:idx + 1], node.get_data()])
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idx += 1
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if len(result) > 1:
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result = sorted(result, key=lambda x: x[1][1])
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return result[-1]
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def match(self, content, add_unk=True, unk_id=-1, **kwargs):
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# length
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l = len(content)
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i = 0
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result_list = []
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while i < l:
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match_word = self.max_prefix(content=content, start_idx=i)
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# print(match_word)
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w = match_word[0]
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if len(w) > 0:
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result_list.append(match_word[1])
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i += len(w)
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else:
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if add_unk:
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result_list.append(unk_id)
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i += 1
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return result_list
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def id2str(self, ids, escape_special_ids=True, end_ids=[], **kwargs):
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res_str = ""
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for rid in ids:
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if rid in self.r_data:
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if rid in end_ids:
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break
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if escape_special_ids and rid in self.special_ids:
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continue
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rstr = self.r_data[rid]
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res_str += rstr
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elif rid == 0:
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break
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res_str += "UNK"
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return res_str
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def id2str_v2(self, ids, escape_special_ids=True, end_ids=[], **kwargs):
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res_str = ""
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for rid in ids:
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if rid in self.r_data:
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if rid in end_ids:
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break
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rstr = self.r_data[rid]
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if escape_special_ids and rid in self.special_ids:
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continue
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res_str += rstr
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elif rid == 0:
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break
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else:
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print("ERROR unknown id %d" % rid)
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res_str += "UNK"
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return res_str
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class RWKVWorldTokenizer(PreTrainedTokenizer):
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vocab_files_names = VOCAB_FILES_NAMES
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@@ -244,17 +110,30 @@ class RWKVWorldTokenizer(PreTrainedTokenizer):
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**kwargs
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):
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self.add_bos_token = False
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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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super().__init__(
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errors=errors,
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**kwargs,
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)
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self.decoder = {
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self.errors = errors # how to handle errors in decoding
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self.cache = {}
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return [1] + ([0] * len(token_ids_0))
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
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def _tokenize(self, text, **kwargs):
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"""Tokenize a string."""
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return self.
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def _decode(self,
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token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
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if isinstance(token_ids, int):
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if token_ids in self.all_special_ids and skip_special_tokens:
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return ""
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return self.
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elif isinstance(token_ids, list):
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return self.
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token_ids,
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escape_special_ids=skip_special_tokens,
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**kwargs
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)
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else:
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return token_ids
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) -> BatchEncoding:
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def get_input_ids(text):
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if isinstance(text, str):
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text_id = self.
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return text_id
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elif isinstance(text, list) and len(text) > 0 and isinstance(text[0], str):
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return [self.
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elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
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return text
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else:
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) -> BatchEncoding:
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def get_input_ids(text):
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if isinstance(text, str):
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text_id = self.
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return text_id
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elif isinstance(text, list) and len(text) > 0 and isinstance(text[0], str):
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return [self.
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elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
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return text
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else:
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "rwkv_vocab_v20230424.txt",
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}
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class TRIE:
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__slots__ = tuple("ch,to,values,front".split(","))
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to:list
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values:set
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def __init__(self, front=None, ch=None):
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self.ch = ch
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self.to = [None for ch in range(256)]
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self.values = set()
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self.front = front
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def __repr__(self):
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fr = self
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ret = []
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while(fr!=None):
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if(fr.ch!=None):
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ret.append(fr.ch)
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fr = fr.front
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return "<TRIE %s %s>"%(ret[::-1], self.values)
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def add(self, key:bytes, idx:int=0, val=None):
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if(idx == len(key)):
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if(val is None):
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val = key
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self.values.add(val)
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return self
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ch = key[idx]
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if(self.to[ch] is None):
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self.to[ch] = TRIE(front=self, ch=ch)
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return self.to[ch].add(key, idx=idx+1, val=val)
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def find_longest(self, key:bytes, idx:int=0):
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u:TRIE = self
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ch:int = key[idx]
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while(u.to[ch] is not None):
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u = u.to[ch]
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idx += 1
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if(u.values):
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ret = idx, u, u.values
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if(idx==len(key)):
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break
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ch = key[idx]
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return ret
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class RWKVWorldTokenizer(PreTrainedTokenizer):
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vocab_files_names = VOCAB_FILES_NAMES
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**kwargs
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):
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self.add_bos_token = False
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+
self.encoder = {}
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sorted = [] # must be already sorted
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| 115 |
+
with open(vocab_file, "r", encoding="utf-8") as f:
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lines = f.readlines()
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| 117 |
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for l in lines:
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idx = int(l[:l.index(' ')])
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x = eval(l[l.index(' '):l.rindex(' ')])
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| 120 |
+
x = x.encode("utf-8") if isinstance(x, str) else x
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| 121 |
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assert isinstance(x, bytes)
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assert len(x) == int(l[l.rindex(' '):])
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sorted += [x]
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self.encoder[idx] = x
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super().__init__(
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errors=errors,
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**kwargs,
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)
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+
self.decoder = {}
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| 131 |
+
for k,v in self.encoder.items():
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self.decoder[v] = int(k)
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+
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self.trie = TRIE()
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| 135 |
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for t, i in self.decoder.items():
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_ = self.trie.add(t, val=(t, i))
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| 137 |
self.errors = errors # how to handle errors in decoding
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| 138 |
self.cache = {}
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| 139 |
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| 190 |
return [1] + ([0] * len(token_ids_0))
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| 191 |
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
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| 192 |
|
| 193 |
+
def encodeBytes(self, src:bytes):
|
| 194 |
+
idx:int = 0
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| 195 |
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tokens = []
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| 196 |
+
while (idx < len(src)):
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| 197 |
+
_idx:int = idx
|
| 198 |
+
idx, _, values = self.trie.find_longest(src, idx)
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| 199 |
+
assert(idx != _idx)
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| 200 |
+
_, token = next(iter(values))
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tokens.append(token)
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+
return tokens
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+
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+
def decodeBytes(self, tokens):
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| 205 |
+
return b''.join(map(lambda i: self.encoder[i], tokens))
|
| 206 |
+
|
| 207 |
def _tokenize(self, text, **kwargs):
|
| 208 |
"""Tokenize a string."""
|
| 209 |
+
return self.encodeBytes(text.encode("utf-8"))
|
| 210 |
|
| 211 |
def _decode(self,
|
| 212 |
token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
|
|
|
|
| 219 |
if isinstance(token_ids, int):
|
| 220 |
if token_ids in self.all_special_ids and skip_special_tokens:
|
| 221 |
return ""
|
| 222 |
+
return self.encoder.get(token_ids, self.unk_token)
|
| 223 |
elif isinstance(token_ids, list):
|
| 224 |
+
return self.decodeBytes(token_ids).decode('utf-8')
|
|
|
|
|
|
|
|
|
|
|
|
|
| 225 |
else:
|
| 226 |
return token_ids
|
| 227 |
|
|
|
|
| 272 |
) -> BatchEncoding:
|
| 273 |
def get_input_ids(text):
|
| 274 |
if isinstance(text, str):
|
| 275 |
+
text_id = self._tokenize(text)
|
| 276 |
return text_id
|
| 277 |
elif isinstance(text, list) and len(text) > 0 and isinstance(text[0], str):
|
| 278 |
+
return [self._tokenize(t) for t in text]
|
| 279 |
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
|
| 280 |
return text
|
| 281 |
else:
|
|
|
|
| 337 |
) -> BatchEncoding:
|
| 338 |
def get_input_ids(text):
|
| 339 |
if isinstance(text, str):
|
| 340 |
+
text_id = self._tokenize(text)
|
| 341 |
return text_id
|
| 342 |
elif isinstance(text, list) and len(text) > 0 and isinstance(text[0], str):
|
| 343 |
+
return [self._tokenize(t) for t in text]
|
| 344 |
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
|
| 345 |
return text
|
| 346 |
else:
|