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tokenization_rwkv_world.py

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+# 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.decoder.get(token.encode("utf-8"), self.unk_token_id)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.encoder.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