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Grounded-Segment-Anything
/
transformers_4_35_0
/models
/bert_generation
/tokenization_bert_generation.py
# coding=utf-8 | |
# Copyright (c) 2020, NVIDIA CORPORATION. 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. | |
""" Tokenization class for model BertGeneration.""" | |
import os | |
from shutil import copyfile | |
from typing import Any, Dict, List, Optional, Tuple | |
import sentencepiece as spm | |
from ...tokenization_utils import PreTrainedTokenizer | |
from ...utils import logging | |
logger = logging.get_logger(__name__) | |
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} | |
PRETRAINED_VOCAB_FILES_MAP = { | |
"vocab_file": { | |
"bert_for_seq_generation": ( | |
"https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model" | |
), | |
} | |
} | |
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"bert_for_seq_generation": 512} | |
class BertGenerationTokenizer(PreTrainedTokenizer): | |
""" | |
Construct a BertGeneration tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). | |
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`): | |
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that | |
contains the vocabulary necessary to instantiate a tokenizer. | |
eos_token (`str`, *optional*, defaults to `"</s>"`): | |
The end of sequence token. | |
bos_token (`str`, *optional*, defaults to `"<s>"`): | |
The begin of sequence token. | |
unk_token (`str`, *optional*, defaults to `"<unk>"`): | |
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. | |
pad_token (`str`, *optional*, defaults to `"<pad>"`): | |
The token used for padding, for example when batching sequences of different lengths. | |
sp_model_kwargs (`dict`, *optional*): | |
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for | |
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, | |
to set: | |
- `enable_sampling`: Enable subword regularization. | |
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. | |
- `nbest_size = {0,1}`: No sampling is performed. | |
- `nbest_size > 1`: samples from the nbest_size results. | |
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) | |
using forward-filtering-and-backward-sampling algorithm. | |
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for | |
BPE-dropout. | |
""" | |
vocab_files_names = VOCAB_FILES_NAMES | |
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP | |
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES | |
prefix_tokens: List[int] = [] | |
model_input_names = ["input_ids", "attention_mask"] | |
def __init__( | |
self, | |
vocab_file, | |
bos_token="<s>", | |
eos_token="</s>", | |
unk_token="<unk>", | |
pad_token="<pad>", | |
sep_token="<::::>", | |
sp_model_kwargs: Optional[Dict[str, Any]] = None, | |
**kwargs, | |
) -> None: | |
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs | |
self.vocab_file = vocab_file | |
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) | |
self.sp_model.Load(vocab_file) | |
# Add extra_ids to the special token list | |
super().__init__( | |
bos_token=bos_token, | |
eos_token=eos_token, | |
unk_token=unk_token, | |
pad_token=pad_token, | |
sep_token=sep_token, | |
sp_model_kwargs=self.sp_model_kwargs, | |
**kwargs, | |
) | |
def vocab_size(self): | |
return self.sp_model.get_piece_size() | |
def get_vocab(self): | |
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} | |
vocab.update(self.added_tokens_encoder) | |
return vocab | |
def __getstate__(self): | |
state = self.__dict__.copy() | |
state["sp_model"] = None | |
return state | |
def __setstate__(self, d): | |
self.__dict__ = d | |
# for backward compatibility | |
if not hasattr(self, "sp_model_kwargs"): | |
self.sp_model_kwargs = {} | |
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) | |
self.sp_model.Load(self.vocab_file) | |
def _tokenize(self, text: str) -> List[str]: | |
"""Take as input a string and return a list of strings (tokens) for words/sub-words""" | |
return self.sp_model.encode(text, out_type=str) | |
def _convert_token_to_id(self, token): | |
"""Converts a token (str) in an id using the vocab.""" | |
return self.sp_model.piece_to_id(token) | |
def _convert_id_to_token(self, index): | |
"""Converts an index (integer) in a token (str) using the vocab.""" | |
token = self.sp_model.IdToPiece(index) | |
return token | |
def convert_tokens_to_string(self, tokens): | |
"""Converts a sequence of tokens (string) in a single string.""" | |
current_sub_tokens = [] | |
out_string = "" | |
for token in tokens: | |
# make sure that special tokens are not decoded using sentencepiece model | |
if token in self.all_special_tokens: | |
out_string += self.sp_model.decode(current_sub_tokens) + token | |
current_sub_tokens = [] | |
else: | |
current_sub_tokens.append(token) | |
out_string += self.sp_model.decode(current_sub_tokens) | |
return out_string.strip() | |
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 | |
out_vocab_file = os.path.join( | |
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] | |
) | |
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): | |
copyfile(self.vocab_file, out_vocab_file) | |
elif not os.path.isfile(self.vocab_file): | |
with open(out_vocab_file, "wb") as fi: | |
content_spiece_model = self.sp_model.serialized_model_proto() | |
fi.write(content_spiece_model) | |
return (out_vocab_file,) | |