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# coding=utf-8
# 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 HAT."""
import torch
from transformers import RobertaTokenizer, BertTokenizer
from .configuration_hat import HATConfig
from transformers.utils import logging
try:
from nltk import sent_tokenize
except:
raise Exception('NLTK is not installed! Install it with `pip install nltk`...')
logger = logging.get_logger(__name__)
class HATTokenizer:
def __init__(self, tokenizer=None):
self._tokenizer = tokenizer
self.config = HATConfig.from_pretrained(self._tokenizer.name_or_path)
self._tokenizer.model_max_length = self.model_max_length
self.type2id = {'input_ids': (self._tokenizer.cls_token_id, self._tokenizer.pad_token_id),
'token_type_ids': (0, 0),
'attention_mask': (1, 0),
'special_tokens_mask': (1, -100)}
@property
def model_max_length(self):
return self.config.model_max_length
@property
def mask_token(self):
return self._tokenizer.mask_token
@property
def mask_token_id(self):
return self._tokenizer.mask_token_id
@property
def pad_token_id(self):
return self._tokenizer.pad_token_id
@property
def cls_token_id(self):
return self._tokenizer.cls_token_id
@property
def sep_token_id(self):
return self._tokenizer.sep_token_id
@property
def vocab(self):
return self._tokenizer.vocab
def __len__(self):
"""
Size of the full vocabulary with the added tokens.
"""
return len(self._tokenizer)
def pad(self, *args, **kwargs):
return self._tokenizer.pad(*args, **kwargs)
def convert_tokens_to_ids(self, *args, **kwargs):
return self._tokenizer.convert_tokens_to_ids(*args, **kwargs)
def batch_decode(self, *args, **kwargs):
return self._tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
return self._tokenizer.decode(*args, **kwargs)
def tokenize(self, text, **kwargs):
return self._tokenizer.tokenize(text, **kwargs)
def encode(self, text, **kwargs):
input_ids = self._tokenizer.encode_plus(text, add_special_tokens=False, **kwargs)
input_ids = self.chunks(input_ids[: self.model_max_length - self.config.max_sentences],
chunk_size=self.config.max_sentence_length, special_id=self.type2id['input_ids'])
return input_ids
def get_special_tokens_mask(self, *args, **kwargs):
return self._tokenizer.get_special_tokens_mask(*args, **kwargs)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
try:
tokenizer = RobertaTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
except:
tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
return cls(tokenizer=tokenizer)
def save_pretrained(self, *args, **kwargs):
return self._tokenizer.save_pretrained( *args, **kwargs)
def __call__(self, text, **kwargs):
greedy_chunking = kwargs.pop('greedy_chunking', None)
text_pair = kwargs.pop('text_pair', None)
if isinstance(text[0], list):
batch = self.auto_chunking(text, **kwargs)
elif greedy_chunking:
# fixed uniform chunking
batch = self.uniform_chunking(text, **kwargs)
else:
# dynamic sentence splitting and grouping
batch = self.sentence_splitting(text, **kwargs)
if text_pair:
batch_b = self._tokenizer(text_pair, add_special_tokens=False,
padding=False, truncation=False)
for idx, sample in enumerate(batch['input_ids']):
n_sentences = sum(sample[::self.config.max_sentence_size])
for input_key in batch:
batch[input_key][idx][self.config.max_sentence_size * n_sentences:
self.config.max_sentence_size * (n_sentences + 1)] = \
self.pad_sentence(batch_b[input_key][idx],
special_id=(self.sep_token_id, self.pad_token_id)
if input_key == 'input_ids' else self.type2id[input_key])
return batch
def uniform_chunking(self, texts, **kwargs):
original_batch = self._tokenizer(texts, add_special_tokens=False, **kwargs)
batch = {input_type: [] for input_type in original_batch}
for input_type in original_batch:
fixed_batch = []
for example in original_batch[input_type]:
fixed_batch.append(self.chunks(example[: self.model_max_length - self.config.max_sentences],
chunk_size=self.config.max_sentence_length,
special_id=self.type2id[input_type]))
batch[input_type] = fixed_batch if isinstance(fixed_batch[0], list) else torch.stack(fixed_batch)
if kwargs['padding']:
batch = self.pad(batch,
padding=kwargs['padding'],
max_length=kwargs['max_length'],
pad_to_multiple_of=kwargs['max_length'])
return batch
def auto_chunking(self, texts, **kwargs):
batch = {}
for text_idx, text in enumerate(texts):
example_batch = self._tokenizer(text, add_special_tokens=False, **kwargs)
for input_key in example_batch:
key_inputs_list = []
for idx, example in enumerate(example_batch[input_key][:self.config.max_sentences]):
key_inputs_list.append(self.pad_sentence(example, special_id=self.type2id[input_key]))
if isinstance(key_inputs_list[0], list):
key_inputs_list = [token for sentence in key_inputs_list for token in sentence]
else:
key_inputs_list = torch.stack([token for sentence in key_inputs_list for token in sentence])
if input_key in batch:
batch[input_key].append(key_inputs_list)
else:
batch[input_key] = [key_inputs_list]
if kwargs['padding']:
batch = self.pad(batch,
padding=kwargs['padding'],
max_length=kwargs['max_length'],
pad_to_multiple_of=kwargs['max_length'])
return batch
def chunks(self, flat_inputs, chunk_size=128, special_id=0):
if isinstance(flat_inputs, list):
return self.list_chunks(flat_inputs, chunk_size, special_id)
else:
return self.tensor_chunks(flat_inputs, chunk_size, special_id)
def list_chunks(self, flat_inputs, chunk_size=128, special_id=(0, 0)):
"""Yield successive n-sized chunks from lst."""
structured_inputs = [[special_id[0] if sum(flat_inputs[i:i + chunk_size-1]) else special_id[1]]
+ flat_inputs[i:i + chunk_size-1] for i in range(0, len(flat_inputs), chunk_size-1)]
return [token_input for sentence_inputs in structured_inputs for token_input in sentence_inputs]
def tensor_chunks(self, flat_inputs, chunk_size=128, special_id=(0, 0)):
"""Yield successive n-sized chunks from lst."""
structured_inputs = torch.stack([torch.cat((torch.tensor([special_id[0] if flat_inputs[i:i + chunk_size-1].sum() else special_id[1]], dtype=torch.int),
flat_inputs[i:i + chunk_size-1])) for i in range(0, len(flat_inputs), chunk_size-1)])
return structured_inputs.reshape(-1)
def sentence_splitting(self, texts, **kwargs):
fixed_batch = []
doc_out = {}
for text in texts:
# sentence splitting
sentences = sent_tokenize(text)
# tokenization of sentences
sentences = self._tokenizer(sentences, add_special_tokens=False, padding=False, truncation=False)
# sentence grouping - merging short sentences to minimize padding
doc_out = self.sentence_grouping(sentences)
fixed_batch.append(doc_out)
# batchify examples
batch = {input_type: [] for input_type in doc_out}
for input_type in batch:
batch[input_type] = [example[input_type] for example in fixed_batch]
if not isinstance(batch[input_type][0], list):
batch[input_type] = torch.stack(batch[input_type])
if kwargs['padding']:
batch = self.pad(batch,
padding=kwargs['padding'],
max_length=kwargs['max_length'],
pad_to_multiple_of=kwargs['max_length'])
return batch
def sentence_grouping(self, sentences):
doc_out = {input_type: [] for input_type in sentences}
for input_type in sentences:
tmp_doc = []
tmp_sentence = []
for example in sentences[input_type]:
if len(tmp_doc) >= self.config.max_sentences:
break
if len(tmp_sentence) + len(example) <= self.config.max_sentence_length - 1:
tmp_sentence.extend(example)
else:
tmp_doc.append(self.pad_sentence(tmp_sentence if len(tmp_sentence) else example,
chunk_size=self.config.max_sentence_length,
special_id=self.type2id[input_type]))
tmp_sentence = example if len(tmp_sentence) else example[self.config.max_sentence_length:]
if len(tmp_sentence) and len(tmp_doc) < self.config.max_sentences:
tmp_doc.append(self.pad_sentence(tmp_sentence,
chunk_size=self.config.max_sentence_length,
special_id=self.type2id[input_type]))
doc_out[input_type] = [token for sentence in tmp_doc for token in sentence]
return doc_out
def pad_sentence(self, flat_input, chunk_size=128, special_id=(0, 0)):
if isinstance(flat_input, list):
return [special_id[0]] + flat_input[:chunk_size-1] + [self.pad_token_id] * max(0, chunk_size - len(flat_input) - 1)
else:
return torch.cat((torch.tensor([special_id[0] if flat_input[:chunk_size-1].sum()
else special_id[1]], dtype=torch.int),
flat_input[:chunk_size-1],
torch.tensor([self.pad_token_id] * max(0, chunk_size - len(flat_input) - 1), dtype=torch.int)
))
@classmethod
def register_for_auto_class(cls, auto_class="AutoModel"):
"""
Register this class with a given auto class. This should only be used for custom models as the ones in the
library are already mapped with an auto class.
<Tip warning={true}>
This API is experimental and may have some slight breaking changes in the next releases.
</Tip>
Args:
auto_class (`str` or `type`, *optional*, defaults to `"TFAutoModel"`):
The auto class to register this new model with.
"""
if not isinstance(auto_class, str):
auto_class = auto_class.__name__
import transformers.models.auto as auto_module
if not hasattr(auto_module, auto_class):
raise ValueError(f"{auto_class} is not a valid auto class.")
cls._auto_class = auto_class
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