Update tokenization_rwkv5.py

tokenization_rwkv5.py

@@ -15,238 +15,8 @@
 """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))
-# 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
@@ -267,7 +37,6 @@ PRETRAINED_VOCAB_FILES_MAP = {
 }
 
 
-
 def whitespace_tokenize(text):
     """Runs basic whitespace cleaning and splitting on a piece of text.
     The separators are kept
@@ -282,10 +51,9 @@ def whitespace_tokenize(text):
 class WordpieceTokenizer(object):
     """Runs WordPiece tokenization."""
 
-    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+    def __init__(self, vocab, unk_token):
         self.vocab = vocab
         self.unk_token = unk_token
-        self.max_input_chars_per_word = max_input_chars_per_word
 
     def tokenize(self, text):
         """
@@ -305,10 +73,6 @@ class WordpieceTokenizer(object):
         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 = []
@@ -324,9 +88,12 @@ class WordpieceTokenizer(object):
                 if cur_substr is None:
                     is_bad = True
                     break
-                sub_tokens.append(cur_substr.decode())
+                try:
+                    cur_substr = cur_substr.decode()
+                except UnicodeDecodeError:
+                    cur_substr = str(cur_substr)
+                sub_tokens.append(cur_substr)
                 start = end
-
             if is_bad:
                 output_tokens.append(self.unk_token)
             else:
@@ -341,7 +108,7 @@ class Rwkv5Tokenizer(PreTrainedTokenizer):
 
     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):
+    def __init__(self, vocab_file, bos_token="<s>", eos_token="<s>", unk_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"
@@ -360,7 +127,7 @@ class Rwkv5Tokenizer(PreTrainedTokenizer):
         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)
+        super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
 
     @property
     def vocab_size(self):
@@ -376,7 +143,9 @@ class Rwkv5Tokenizer(PreTrainedTokenizer):
 
     def _convert_token_to_id(self, token):
         """Converts a token (byte) to an id using the vocab."""
-        if not isinstance(token, bytes):
+        if token.startswith("b'\\"):
+            token = eval(token)
+        elif not isinstance(token, bytes):
             token = token.encode("utf-8", errors="replace")
         return self.encoder.get(token, self.unk_token_id)