update

README.md

@@ -27,7 +27,7 @@ Assistant:"""
 
 
 model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-5-world-1b5", trust_remote_code=True).to(torch.float32)
-tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-5-world-1b5", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-5-world-1b5", trust_remote_code=True, padding_side='left')
 
 text = "请介绍北京的旅游景点"
 prompt = generate_prompt(text)
@@ -83,7 +83,7 @@ Assistant:"""
 
 
 model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-5-world-1b5", trust_remote_code=True, torch_dtype=torch.float16).to(0)
-tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-5-world-1b5", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-5-world-1b5", trust_remote_code=True, padding_side='left')
 
 text = "介绍一下大熊猫"
 prompt = generate_prompt(text)
@@ -130,7 +130,7 @@ User: {instruction}
 Assistant:"""
 
 model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-5-world-1b5", trust_remote_code=True).to(torch.float32)
-tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-5-world-1b5", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-5-world-1b5", trust_remote_code=True, padding_side='left')
 
 texts = ["请介绍北京的旅游景点", "介绍一下大熊猫", "乌兰察布"]
 prompts = [generate_prompt(text) for text in texts]

tokenization_rwkv5.py

@@ -0,0 +1,230 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# 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 classes for RWKV5."""
+
+import os
+from typing import TYPE_CHECKING, List, Optional, Tuple
+import re
+
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+from transformers.utils import logging
+
+
+if TYPE_CHECKING:
+    pass
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.txt",
+}
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "ArthurZ/rwkv-5-utf": "https://huggingface.co/ArthurZ/rwkv-5-utf/blob/main/vocab.txt",
+    },
+}
+
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a piece of text.
+    The separators are kept
+    """
+    text = text.strip()
+    if not text:
+        return []
+    tokens = re.split(b"(?= )", text)
+    return tokens
+
+
+class WordpieceTokenizer(object):
+    """Runs WordPiece tokenization."""
+
+    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+        self.vocab = vocab
+        self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def tokenize(self, text):
+        """
+        Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+        tokenization using the given vocabulary.
+
+        For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
+
+        Args:
+            text: A single token or whitespace separated tokens. This should have
+                already been passed through *BasicTokenizer*.
+
+        Returns:
+            A list of wordpiece tokens.
+        """
+
+        output_tokens = []
+        for token in whitespace_tokenize(text):
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = bytes(chars[start:end])
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr.decode())
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens
+
+
+class Rwkv5Tokenizer(PreTrainedTokenizer):
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = {"ArthurZ/rwkv-5-utf": 2048}
+
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(self, vocab_file, bos_token="<s>", eos_token="<s>", unk_token="<s>", pad_token="<s>",**kwargs):
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
+                " model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+            )
+
+        with open(vocab_file, "r") as reader:
+            tokens = reader.readlines()
+        vocab = {}
+        for index, token in enumerate(tokens):
+            token = eval(token.rstrip("\n"))
+            vocab[token] = index
+
+        self.add_bos_token = True
+        self.encoder = vocab
+        self.decoder = {v: k for k, v in vocab.items()}
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.encoder, unk_token=str(unk_token))
+        self._added_tokens_decoder = {0: AddedToken(str(bos_token))}
+        super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, **kwargs)
+
+    @property
+    def vocab_size(self):
+        return len(self.encoder)
+
+    def get_vocab(self):
+        vocab = {str(self.convert_ids_to_tokens(i)): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text, split_special_tokens=False):
+        return self.wordpiece_tokenizer.tokenize(text.encode("utf-8"))
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (byte) to an id using the vocab."""
+        if not isinstance(token, bytes):
+            token = token.encode("utf-8", errors="replace")
+        return self.encoder.get(token, self.unk_token_id)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (byte) using the vocab."""
+        token = self.decoder.get(index, self.unk_token)
+        if isinstance(token, (bytes)):
+            token = token.decode("utf-8", errors="replace")
+        return token
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (bytes) in a single string. Additional tokens are encoded to bytes"""
+        out_string = b"".join([k.encode(errors="replace") if isinstance(k, str) else k for k in tokens]).decode(
+            "utf-8"
+        )
+        return out_string
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        index = 0
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+            )
+        else:
+            vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+        with open(vocab_file, "w") as writer:
+            for token, token_index in sorted(self.encoder.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+                        " Please check that the vocabulary is not corrupted!"
+                    )
+                    index = token_index
+                writer.write(str(token) + "\n")
+                index += 1
+        return (vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is None:
+            return output
+
+        return output + bos_token_ids + token_ids_1
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if not self.add_bos_token:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0))
+        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))

tokenization_rwkv_world.py

@@ -1,549 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
-#
-# 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 classes for RWKV5."""
-
-import json
-import os
-from typing import TYPE_CHECKING, List, Optional, Tuple, Union
-
-from transformers.tokenization_utils import PreTrainedTokenizer
-from transformers.tokenization_utils_base import (
-    BatchEncoding,
-    EncodedInput,
-    TextInput,
-    TruncationStrategy,
-)
-from transformers.utils import PaddingStrategy, TensorType, logging, to_py_obj
-
-
-if TYPE_CHECKING:
-    from transformers.pipelines.conversational import Conversation
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {
-    "vocab_file": "rwkv_vocab_v20230424.txt",
-}
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "RWKV/rwkv-5-world-169m": "https://huggingface.co/RWKV/rwkv-5-world-169m/blob/main/rwkv_vocab_v20230424.txt",
-    },
-}
-
-
-class TRIE:
-    __slots__ = tuple("ch,to,values,front".split(","))
-    to: list
-    values: set
-
-    def __init__(self, front=None, ch=None):
-        self.ch = ch
-        self.to = [None for ch in range(256)]
-        self.values = set()
-        self.front = front
-
-    def __repr__(self):
-        fr = self
-        ret = []
-        while fr is not None:
-            if fr.ch is not None:
-                ret.append(fr.ch)
-            fr = fr.front
-        return "<TRIE %s %s>" % (ret[::-1], self.values)
-
-    def add(self, key: bytes, idx: int = 0, val=None):
-        if idx == len(key):
-            if val is None:
-                val = key
-            self.values.add(val)
-            return self
-        ch = key[idx]
-        if self.to[ch] is None:
-            self.to[ch] = TRIE(front=self, ch=ch)
-        return self.to[ch].add(key, idx=idx + 1, val=val)
-
-    def find_longest(self, key: bytes, idx: int = 0):
-        u: TRIE = self
-        ch: int = key[idx]
-
-        while u.to[ch] is not None:
-            u = u.to[ch]
-            idx += 1
-            if u.values:
-                ret = idx, u, u.values
-            if idx == len(key):
-                break
-            ch = key[idx]
-        return ret
-
-
-class RWKVWorldTokenizer(PreTrainedTokenizer):
-    vocab_files_names = VOCAB_FILES_NAMES
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(self, vocab_file, errors="replace", pad_token="0", **kwargs):
-        self.add_bos_token = False
-        self.encoder = {}
-        sorted = []  # must be already sorted
-        with open(vocab_file, "r", encoding="utf-8") as f:
-            lines = f.readlines()
-        for l in lines:
-            idx = int(l[: l.index(" ")])
-            x = eval(l[l.index(" ") : l.rindex(" ")])
-            x = x.encode("utf-8") if isinstance(x, str) else x
-            assert isinstance(x, bytes)
-            assert len(x) == int(l[l.rindex(" ") :])
-            sorted += [x]
-            self.encoder[idx] = x
-
-        self.decoder = {}
-        for k, v in self.encoder.items():
-            self.decoder[v] = int(k)
-
-        self.trie = TRIE()
-        for t, i in self.decoder.items():
-            _ = self.trie.add(t, val=(t, i))
-        self.errors = errors  # how to handle errors in decoding
-        self.cache = {}
-        self.first_max_length = 0
-        super().__init__(
-            errors=errors,
-            **kwargs,
-        )
-
-    @property
-    def eos_token_id(self) -> Optional[int]:
-        return 0
-
-    @property
-    def eot_token_id(self) -> Optional[int]:
-        return 0
-
-    @property
-    def pad_token_id(self) -> Optional[int]:
-        return 0
-
-    @property
-    def vocab_size(self):
-        return len(self.encoder)
-
-    def get_vocab(self):
-        return dict(self.encoder, **self.added_tokens_encoder)
-
-    def add_tokens(self, new_tokens, special_tokens: bool = False):
-        for token in new_tokens:
-            token_id = self.convert_tokens_to_ids(token)
-            self.added_tokens_decoder[token_id] = token
-
-    def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
-        if isinstance(ids, int):
-            ids = [ids]
-        tokens = []
-        for id_ in ids:
-            if id_ in self.added_tokens_decoder:
-                tokens.append(self.added_tokens_decoder[id_])
-            else:
-                tokens.append(self._convert_id_to_token(id_))
-        return tokens
-
-    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        if self.add_bos_token:
-            bos_token_ids = [self.bos_token_id]
-        else:
-            bos_token_ids = []
-
-        output = bos_token_ids + token_ids_0
-
-        if token_ids_1 is None:
-            return output
-
-        return output + bos_token_ids + token_ids_1
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        if not self.add_bos_token:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False
-            )
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0))
-        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
-
-    def encodeBytes(self, src: bytes):
-        idx: int = 0
-        tokens = []
-        while idx < len(src):
-            _idx: int = idx
-            idx, _, values = self.trie.find_longest(src, idx)
-            assert idx != _idx
-            _, token = next(iter(values))
-            tokens.append(token)
-        return tokens
-
-    def decodeBytes(self, tokens):
-        return b"".join(map(lambda i: self.encoder[i], tokens))  # noqa
-
-    def _tokenize(self, text, **kwargs):
-        """Tokenize a string."""
-        return self.encodeBytes(text.encode("utf-8"))
-
-    def _decode_tokens(self, tokens):
-        try:
-            return self.decodeBytes(tokens).decode("utf-8")
-        except Exception:
-            return "\ufffd"  # bad utf-8
-
-    def _decode(
-        self,
-        token_ids: Union[int, List[int]],
-        skip_special_tokens: bool = False,
-        **kwargs,
-    ) -> str:
-        def remove_zeros_from_first_segment(token_ids, first_max_length):
-            first_segment = token_ids[:first_max_length]
-            first_segment_cleaned = [token for token in first_segment if token != 0]
-            return first_segment_cleaned + token_ids[first_max_length:]
-
-        # Convert inputs to python lists
-        token_ids = to_py_obj(token_ids)
-        token_ids = remove_zeros_from_first_segment(token_ids, self.first_max_length)
-        if isinstance(token_ids, int):
-            if token_ids in self.all_special_ids and skip_special_tokens:
-                return ""
-            return self.encoder.get(token_ids, self.unk_token)
-        elif isinstance(token_ids, list):
-            self.first_max_length
-            out_str = ""
-            out_last = 0
-            out_tokens = []
-            for i, token in enumerate(token_ids):
-                if token == 0:
-                    break
-                out_tokens += [token]
-                tmp = self._decode_tokens(out_tokens[out_last:])
-                if "\ufffd" not in tmp:
-                    out_str += tmp
-                    out_last = i + 1
-            return out_str
-        else:
-            return token_ids
-
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.encoder.get(token, self.encoder.get(self.unk_token))
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.decoder.get(index)
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.exists(save_directory):
-            os.mkdir(save_directory)
-        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"]
-        )
-
-        with open(vocab_file, "w", encoding="utf-8") as f:
-            for idx, x in self.encoder.items():
-                if isinstance(x, str):
-                    x = x.decode("utf-8")
-                line = f"{idx} {repr(x)} {len(x)}\n"
-                f.write(line)
-
-        return (vocab_file,)
-
-    def prepare_for_tokenization(self, text, **kwargs):
-        return (text, kwargs)
-
-    def _get_padding_truncation_strategies(
-        self, padding=False, truncation=None, max_length=None, pad_to_multiple_of=None, verbose=True, **kwargs
-    ):
-        return PaddingStrategy.LONGEST, TruncationStrategy.DO_NOT_TRUNCATE, -1, kwargs
-
-    def _encode_plus(
-        self,
-        text: Union[TextInput, EncodedInput],
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        def get_input_ids(text, max_length=None, pad_token_id=0):
-            def pad_sequence(seq, max_len, pad_tok):
-                return [pad_tok] * (max_len - len(seq)) + seq
-
-            if isinstance(text, str):
-                tokens = self._tokenize(text)
-                if max_length is not None:
-                    tokens = pad_sequence(tokens, max_length, pad_token_id)
-                return tokens
-
-            elif isinstance(text, list) and len(text) > 0 and isinstance(text[0], str):
-                tokenized_texts = [self._tokenize(t) for t in text]
-                if max_length is None:
-                    max_length = max(len(t) for t in tokenized_texts)
-                return [pad_sequence(t, max_length, pad_token_id) for t in tokenized_texts]
-
-            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
-                if max_length is not None and len(text) < max_length:
-                    return pad_sequence(text, max_length, pad_token_id)
-                return text
-
-            else:
-                raise ValueError(
-                    "Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
-                )
-
-        if return_offsets_mapping:
-            raise NotImplementedError(
-                "return_offset_mapping is not available when using Python tokenizers. "
-                "To use this feature, change your tokenizer to one deriving from "
-                "transformers.PreTrainedTokenizerFast. "
-                "More information on available tokenizers at "
-                "https://github.com/huggingface/transformers/pull/2674"
-            )
-
-        first_ids = get_input_ids(text)
-
-        return self.prepare_for_model(
-            first_ids,
-            pair_ids=None,
-            add_special_tokens=add_special_tokens,
-            padding=padding_strategy.value,
-            truncation=truncation_strategy.value,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            prepend_batch_axis=True,
-            return_attention_mask=return_attention_mask,
-            return_token_type_ids=return_token_type_ids,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_length=return_length,
-            verbose=verbose,
-        )
-
-    def _batch_encode_plus(
-        self,
-        batch_text_or_text_pairs: Union[
-            List[TextInput],
-            List[EncodedInput],
-        ],
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        def get_input_ids(text, max_length=None, pad_token_id=0):
-            def pad_sequence(seq, max_len, pad_tok):
-                return [pad_tok] * (max_len - len(seq)) + seq
-
-            if isinstance(text, str):
-                tokens = self._tokenize(text)
-                if max_length is not None:
-                    tokens = pad_sequence(tokens, max_length, pad_token_id)
-                return tokens
-
-            elif isinstance(text, list) and len(text) > 0 and isinstance(text[0], str):
-                tokenized_texts = [self._tokenize(t) for t in text]
-                if max_length is None:
-                    max_length = max(len(t) for t in tokenized_texts)
-                return [pad_sequence(t, max_length, pad_token_id) for t in tokenized_texts]
-
-            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
-                if max_length is not None and len(text) < max_length:
-                    return pad_sequence(text, max_length, pad_token_id)
-                return text
-
-            else:
-                raise ValueError(
-                    "Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
-                )
-
-        if return_offsets_mapping:
-            raise NotImplementedError(
-                "return_offset_mapping is not available when using Python tokenizers. "
-                "To use this feature, change your tokenizer to one deriving from "
-                "transformers.PreTrainedTokenizerFast."
-            )
-
-        first_max_length = 0
-        second_max_length = 0
-        for ids_or_pair_ids in batch_text_or_text_pairs:
-            if not isinstance(ids_or_pair_ids, (list, tuple)):
-                ids, pair_ids = ids_or_pair_ids, None
-            else:
-                ids, pair_ids = ids_or_pair_ids
-            first_ids = get_input_ids(ids)
-            second_ids = get_input_ids(pair_ids) if pair_ids is not None else None
-            first_max_length = max(first_max_length, len(first_ids))
-            if second_ids is not None:
-                second_max_length = max(second_max_length, len(second_ids))
-
-        self.first_max_length = first_max_length
-        input_ids = []
-        for ids_or_pair_ids in batch_text_or_text_pairs:
-            if not isinstance(ids_or_pair_ids, (list, tuple)):
-                ids, pair_ids = ids_or_pair_ids, None
-            else:
-                ids, pair_ids = ids_or_pair_ids
-
-            first_ids = get_input_ids(ids, max_length=first_max_length)
-            second_ids = get_input_ids(pair_ids, max_length=second_max_length) if pair_ids is not None else None
-            input_ids.append((first_ids, second_ids))
-
-        batch_outputs = self._batch_prepare_for_model(
-            input_ids,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_attention_mask=return_attention_mask,
-            return_token_type_ids=return_token_type_ids,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_length=return_length,
-            return_tensors=return_tensors,
-            verbose=verbose,
-        )
-
-        return BatchEncoding(batch_outputs)
-
-    def decode(
-        self,
-        token_ids: Union[int, List[int]],
-        skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = None,
-        **kwargs,
-    ) -> str:
-        """
-        Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
-        tokens and clean up tokenization spaces.
-
-        Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
-
-        Args:
-            token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
-                List of tokenized input ids. Can be obtained using the `__call__` method.
-            skip_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*):
-                Whether or not to clean up the tokenization spaces. If `None`, will default to
-                `self.clean_up_tokenization_spaces`.
-            kwargs (additional keyword arguments, *optional*):
-                Will be passed to the underlying model specific decode method.
-
-        Returns:
-            `str`: The decoded sentence.
-        """
-        # Convert inputs to python lists
-        return self._decode(
-            token_ids=token_ids,
-            skip_special_tokens=skip_special_tokens,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            **kwargs,
-        )
-
-    def batch_decode(
-        self,
-        sequences: Union[List[int], List[List[int]]],
-        skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = None,
-        **kwargs,
-    ) -> List[str]:
-        """
-        Convert a list of lists of token ids into a list of strings by calling decode.
-
-        Args:
-            sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
-                List of tokenized input ids. Can be obtained using the `__call__` method.
-            skip_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not to remove special tokens in the decoding.
-            clean_up_tokenization_spaces (`bool`, *optional*):
-                Whether or not to clean up the tokenization spaces. If `None`, will default to
-                `self.clean_up_tokenization_spaces`.
-            kwargs (additional keyword arguments, *optional*):
-                Will be passed to the underlying model specific decode method.
-
-        Returns:
-            `List[str]`: The list of decoded sentences.
-        """
-        return [
-            self.decode(
-                seq,
-                skip_special_tokens=skip_special_tokens,
-                clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-                **kwargs,
-            )
-            for seq in sequences
-        ]
-
-    def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
-        input_ids = []
-        for is_user, text in conversation.iter_texts():
-            input_ids.extend(self.encode(text, add_special_tokens=False) + [self.eos_token_id])
-        if len(input_ids) > self.model_max_length:
-            input_ids = input_ids[-self.model_max_length :]
-        return input_ids

tokenizer_config.json

@@ -1,12 +1,23 @@
 {
-  "name_or_path": "rwkv-world",
-  "add_prefix_space": false,
-  "tokenizer_class": "RWKVWorldTokenizer",
-  "use_fast": false,
   "auto_map": {
     "AutoTokenizer": [
-      "tokenization_rwkv_world.RWKVWorldTokenizer",
+      "tokenization_rwkv5.Rwkv5Tokenizer",
       null
       ]
-  }
+  },
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<s>",
+  "model_max_length": 1000000000000000019884624838656,
+  "unk_token": "<s>"
 }