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Dream-v0-Instruct-7B

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-# coding=utf-8
-# Copyright 2024 The Dream team, HKUNLP Group and The HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on Qwen's implementations in this library.
-# 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 Dream."""
-import json
-import os
-import unicodedata
-from functools import lru_cache
-from typing import Optional, Tuple
-import regex as re
-from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
-from transformers.utils import logging
-logger = logging.get_logger(__name__)
-VOCAB_FILES_NAMES = {
-    "vocab_file": "vocab.json",
-    "merges_file": "merges.txt",
-}
-MAX_MODEL_INPUT_SIZES = {"dream/dream-tokenizer": 32768}
-PRETOKENIZE_REGEX = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
-@lru_cache()
-# Copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
-    characters the bpe code barfs on.
-    The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
-    if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
-    decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
-    tables between utf-8 bytes and unicode strings.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-# Copied from transformers.models.gpt2.tokenization_gpt2.get_pairs
-def get_pairs(word):
-    """
-    Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-class DreamTokenizer(PreTrainedTokenizer):
-    """
-    Construct a Dream tokenizer. Based on byte-level Byte-Pair-Encoding.
-    Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
-    be encoded differently whether it is at the beginning of the sentence (without space) or not:
-    ```python
-    >>> from transformers import AutoTokenizer
-    >>> tokenizer = AutoTokenizer.from_pretrained("Dream-org/Dream-v0-Base-7B", trust_remote_code=True)
-    >>> tokenizer("Hello world")["input_ids"]
-    [9707, 1879]
-    >>> tokenizer(" Hello world")["input_ids"]
-    [21927, 1879]
-    ```
-    This is expected.
-    You should not use GPT2Tokenizer instead, because of the different pretokenization rules.
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        merges_file (`str`):
-            Path to the merges file.
-        errors (`str`, *optional*, defaults to `"replace"`):
-            Paradigm to follow when decoding bytes to UTF-8. See
-            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
-        unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        bos_token (`str`, *optional*):
-            The beginning of sequence token. Not applicable for this tokenizer.
-        eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
-            The end of sequence token.
-        pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
-            Whether or not the model should cleanup the spaces that were added when splitting the input text during the
-            tokenization process. Not applicable to this tokenizer, since tokenization does not add spaces.
-        split_special_tokens (`bool`, *optional*, defaults to `False`):
-            Whether or not the special tokens should be split during the tokenization process. The default behavior is
-            to not split special tokens. This means that if `<|endoftext|>` is the `eos_token`, then `tokenizer.tokenize("<|endoftext|>") =
-            ['<|endoftext|>`]. Otherwise, if `split_special_tokens=True`, then `tokenizer.tokenize("<|endoftext|>")` will be give `['<',
-            '|', 'endo', 'ft', 'ext', '|', '>']`. This argument is only supported for `slow` tokenizers for the moment.
-    """
-    vocab_files_names = VOCAB_FILES_NAMES
-    model_input_names = ["input_ids", "attention_mask"]
-    def __init__(
-        self,
-        vocab_file,
-        merges_file,
-        errors="replace",
-        unk_token="<|endoftext|>",
-        bos_token=None,
-        eos_token="<|endoftext|>",
-        pad_token="<|endoftext|>",
-        clean_up_tokenization_spaces=False,
-        split_special_tokens=False,
-        **kwargs,
-    ):
-        # Dream vocab does not contain control tokens; added tokens need to be special
-        bos_token = (
-            AddedToken(bos_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(bos_token, str)
-            else bos_token
-        )
-        eos_token = (
-            AddedToken(eos_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(eos_token, str)
-            else eos_token
-        )
-        unk_token = (
-            AddedToken(unk_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(unk_token, str)
-            else unk_token
-        )
-        pad_token = (
-            AddedToken(pad_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(pad_token, str)
-            else pad_token
-        )
-        with open(vocab_file, encoding="utf-8") as vocab_handle:
-            self.encoder = json.load(vocab_handle)
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.errors = errors  # how to handle errors in decoding
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        bpe_merges = []
-        with open(merges_file, encoding="utf-8") as merges_handle:
-            for i, line in enumerate(merges_handle):
-                line = line.strip()
-                if (i == 0 and line.startswith("#version:")) or not line:
-                    continue
-                bpe_merges.append(tuple(line.split()))
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-        # NOTE: the cache can grow without bound and will get really large for long running processes
-        # (esp. for texts of language that do not use space between word, e.g. Chinese); technically
-        # not a memory leak but appears as one.
-        # GPT2Tokenizer has the same problem, so let's be consistent.
-        self.cache = {}
-        self.pat = re.compile(PRETOKENIZE_REGEX)
-        if kwargs.get("add_prefix_space", False):
-            logger.warning_once(
-                f"{self.__class__.__name} does not support `add_prefix_space`, setting it to True has no effect."
-            )
-        super().__init__(
-            errors=errors,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            pad_token=pad_token,
-            unk_token=unk_token,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            split_special_tokens=split_special_tokens,
-            **kwargs,
-        )
-    @property
-    def vocab_size(self) -> int:
-        return len(self.encoder)
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.get_vocab
-    def get_vocab(self):
-        return dict(self.encoder, **self.added_tokens_encoder)
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.bpe
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token)
-        pairs = get_pairs(word)
-        if not pairs:
-            return token
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                except ValueError:
-                    new_word.extend(word[i:])
-                    break
-                else:
-                    new_word.extend(word[i:j])
-                    i = j
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._tokenize
-    def _tokenize(self, text):
-        """Tokenize a string."""
-        bpe_tokens = []
-        for token in re.findall(self.pat, text):
-            token = "".join(
-                self.byte_encoder[b] for b in token.encode("utf-8")
-            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
-            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
-        return bpe_tokens
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_token_to_id
-    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))
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_id_to_token
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.decoder.get(index)
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.convert_tokens_to_string
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        text = "".join(tokens)
-        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
-        return text
-    def decode(
-        self,
-        token_ids,
-        skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: Optional[bool] = False,
-        spaces_between_special_tokens: bool = False,
-        **kwargs,
-    ) -> str:
-        # `spaces_between_special_tokens` defaults to True for _decode in slow tokenizers
-        # and cannot be configured elsewhere, but it should default to False for DreamTokenizer
-        return super().decode(
-            token_ids,
-            skip_special_tokens=skip_special_tokens,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            spaces_between_special_tokens=spaces_between_special_tokens,
-            **kwargs,
-        )
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.save_vocabulary
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        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"]
-        )
-        merge_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
-        )
-        with open(vocab_file, "w", encoding="utf-8") as f:
-            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
-        index = 0
-        with open(merge_file, "w", encoding="utf-8") as writer:
-            writer.write("#version: 0.2\n")
-            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning(
-                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
-                        " Please check that the tokenizer is not corrupted!"
-                    )
-                    index = token_index
-                writer.write(" ".join(bpe_tokens) + "\n")
-                index += 1
-        return vocab_file, merge_file
-    def prepare_for_tokenization(self, text, **kwargs):
-        text = unicodedata.normalize("NFC", text)
-        return (text, kwargs)