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# coding=utf-8
# Copyright 2024 The Emu team, BAAI and The HuggingFace Inc. team. All rights reserved.
#
# 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.
#
# Adapted from https://github.com/huggingface/transformers/blob/52daf4ec768fb9ffe84a0c373834172a7c54aecc/src/transformers/models/qwen2/tokenization_qwen2.py
#
"""Tokenization classes for Emu3."""
import base64
import logging
import os
import unicodedata
from typing import Collection, Dict, List, Optional, Set, Tuple, Union
import tiktoken
from transformers import PreTrainedTokenizer, AddedToken
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "emu3.tiktoken",
"special_tokens_file": "emu3_vision_tokens.txt",
}
PAT_STR = 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+"""
ENDOFTEXT = "<|endoftext|>"
IMSTART = "<|im_start|>"
IMEND = "<|im_end|>"
# as the default behavior is changed to allow special tokens in
# regular texts, the surface forms of special tokens need to be
# as different as possible to minimize the impact
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
# changed to use actual index to avoid misconfiguration with vocabulary expansion
SPECIAL_START_ID = 151643
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
with open(tiktoken_bpe_file, "rb") as f:
contents = f.read()
return {
base64.b64decode(token): int(rank)
for token, rank in (line.split() for line in contents.splitlines() if line)
}
class Emu3Tokenizer(PreTrainedTokenizer):
"""Emu3 tokenizer."""
vocab_files_names = VOCAB_FILES_NAMES
def __init__(
self,
vocab_file,
special_tokens_file,
errors="replace",
bos_token = "<|extra_203|>",
eos_token = "<|extra_204|>",
pad_token = "<|endoftext|>",
img_token = "<|image token|>",
boi_token = "<|image start|>",
eoi_token = "<|image end|>",
eol_token = "<|extra_200|>",
eof_token = "<|extra_201|>",
**kwargs,
):
super().__init__(**kwargs)
# how to handle errors in decoding UTF-8 byte sequences
# use ignore if you are in streaming inference
self.errors = errors
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)
vision_tokens = [t.strip() for t in open(special_tokens_file).readlines() if len(t.strip()) > 0]
SPECIAL_TOKENS = tuple(
enumerate(
(
(
ENDOFTEXT,
IMSTART,
IMEND,
)
+ EXTRAS
+ tuple(vision_tokens)
),
start=SPECIAL_START_ID,
)
)
self.special_tokens = {token: index for index, token in SPECIAL_TOKENS}
self.special_tokens_set = set(t for _, t in SPECIAL_TOKENS)
enc = tiktoken.Encoding(
"Emu3",
pat_str=PAT_STR,
mergeable_ranks=self.mergeable_ranks,
special_tokens=self.special_tokens,
)
assert (
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
self.decoder = {
v: k for k, v in self.mergeable_ranks.items()
}
self.decoder.update({v: k for k, v in self.special_tokens.items()})
self.tokenizer = enc
self.eod_id = self.tokenizer.eot_token
self.bos_token = bos_token
self.eos_token = eos_token
self.pad_token = pad_token
self.img_token = img_token
self.boi_token = boi_token
self.eoi_token = eoi_token
self.eol_token = eol_token
self.eof_token = eof_token
def __getstate__(self):
# for pickle lovers
state = self.__dict__.copy()
del state["tokenizer"]
return state
def __setstate__(self, state):
# tokenizer is not python native; don't pass it; rebuild it
self.__dict__.update(state)
enc = tiktoken.Encoding(
"Emu3",
pat_str=PAT_STR,
mergeable_ranks=self.mergeable_ranks,
special_tokens=self.special_tokens,
)
self.tokenizer = enc
def __len__(self) -> int:
return self.tokenizer.n_vocab
def get_vocab(self) -> Dict[bytes, int]:
return self.mergeable_ranks
def convert_tokens_to_ids(
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
) -> List[int]:
if isinstance(tokens, (str, bytes)):
if tokens in self.special_tokens:
return self.special_tokens[tokens]
else:
return self.mergeable_ranks.get(tokens)
ids = []
for token in tokens:
if token in self.special_tokens:
ids.append(self.special_tokens[token])
else:
ids.append(self.mergeable_ranks.get(token))
return ids
def _add_tokens(
self,
new_tokens: Union[List[str], List[AddedToken]],
special_tokens: bool = False,
) -> int:
if not special_tokens and new_tokens:
raise ValueError("Adding regular tokens is not supported")
for token in new_tokens:
surface_form = token.content if isinstance(token, AddedToken) else token
if surface_form not in self.special_tokens_set:
raise ValueError("Adding unknown special tokens is not supported")
return 0
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
"""
Save only the vocabulary of the tokenizer (vocabulary).
Returns:
`Tuple(str)`: Paths to the files saved.
"""
regular_file_path = os.path.join(save_directory, self.vocab_files_names["vocab_file"])
with open(regular_file_path,'w', encoding="utf8") as w:
for k, v in self.mergeable_ranks.items():
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
w.write(line)
excluded_special_tokens = set((ENDOFTEXT, IMSTART, IMEND,) + EXTRAS)
special_file_path = os.path.join(save_directory, self.vocab_files_names["special_tokens_file"])
with open(special_file_path, 'w', encoding="utf8") as w:
for k in self.special_tokens:
if k not in excluded_special_tokens:
print(k, file=w)
return (regular_file_path, special_file_path)
def tokenize(
self,
text: str,
allowed_special: Union[Set, str] = "all",
disallowed_special: Union[Collection, str] = (),
**kwargs,
) -> List[Union[bytes, str]]:
"""
Converts a string in a sequence of tokens.
Args:
text (`str`):
The sequence to be encoded.
allowed_special (`Literal["all"]` or `set`):
The surface forms of the tokens to be encoded as special tokens in regular texts.
Default to "all".
disallowed_special (`Literal["all"]` or `Collection`):
The surface forms of the tokens that should not be in regular texts and trigger errors.
Default to an empty tuple.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific encode method.
Returns:
`List[bytes|str]`: The list of tokens.
"""
tokens = []
text = unicodedata.normalize("NFC", text)
# this implementation takes a detour: text -> token id -> token surface forms
for t in self.tokenizer.encode(
text, allowed_special=allowed_special, disallowed_special=disallowed_special
):
tokens.append(self.decoder[t])
return tokens
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
"""
Converts a sequence of tokens in a single string.
"""
text = ""
temp = b""
for t in tokens:
if isinstance(t, str):
if temp:
text += temp.decode("utf-8", errors=self.errors)
temp = b""
text += t
elif isinstance(t, bytes):
temp += t
else:
raise TypeError("token should only be of type types or str")
if temp:
text += temp.decode("utf-8", errors=self.errors)
return text
@property
def vocab_size(self):
return self.tokenizer.n_vocab
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
"""Converts an id to a token, special tokens included"""
if index in self.decoder:
return self.decoder[index]
raise ValueError("unknown ids")
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
"""Converts a token to an id using the vocab, special tokens included"""
if token in self.special_tokens:
return self.special_tokens[token]
if token in self.mergeable_ranks:
return self.mergeable_ranks[token]
raise ValueError("unknown token")
def _decode(
self,
token_ids: Union[int, List[int]],
skip_special_tokens: bool = False,
errors: Optional[str] = None,
**kwargs,
) -> str:
if isinstance(token_ids, int):
token_ids = [token_ids]
if skip_special_tokens:
token_ids = [i for i in token_ids if i < self.eod_id]
return self.tokenizer.decode(token_ids, errors=errors or self.errors)
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