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Fix style

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This commit is contained in:
Anthony MOI
2019-12-24 12:43:27 -05:00
parent 951ae99bea
commit 31c56f2e0b
3 changed files with 125 additions and 72 deletions
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58
src/transformers/tokenization_bert.py
View File

@@ -20,7 +20,7 @@ import logging
import os
import unicodedata
from .tokenization_utils import PreTrainedTokenizer, FastPreTrainedTokenizer
from .tokenization_utils import FastPreTrainedTokenizer, PreTrainedTokenizer
logger = logging.getLogger(__name__)
@@ -526,42 +526,64 @@ def _is_punctuation(char):
return True
return False
class BertTokenizerFast(FastPreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self, vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None,
unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]",
mask_token="[MASK]", tokenize_chinese_chars=True,
max_length=None, pad_to_max_length=False, stride=0,
truncation_strategy='longest_first', add_special_tokens=True, **kwargs):
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
max_length=None,
pad_to_max_length=False,
stride=0,
truncation_strategy="longest_first",
add_special_tokens=True,
**kwargs
):
try:
from tokenizers import Tokenizer, models, pre_tokenizers, decoders, processors
super(BertTokenizerFast, self).__init__(unk_token=unk_token, sep_token=sep_token,
pad_token=pad_token, cls_token=cls_token,
mask_token=mask_token, **kwargs)
self._tokenizer = Tokenizer(models.WordPiece.from_files(
vocab_file,
unk_token=unk_token
))
super(BertTokenizerFast, self).__init__(
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
**kwargs
)
self._tokenizer = Tokenizer(models.WordPiece.from_files(vocab_file, unk_token=unk_token))
self._update_special_tokens()
self._tokenizer.with_pre_tokenizer(pre_tokenizers.BertPreTokenizer.new(
self._tokenizer.with_pre_tokenizer(
pre_tokenizers.BertPreTokenizer.new(
do_basic_tokenize=do_basic_tokenize,
do_lower_case=do_lower_case,
tokenize_chinese_chars=tokenize_chinese_chars,
never_split=never_split if never_split is not None else [],
))
)
)
self._tokenizer.with_decoder(decoders.WordPiece.new())
if add_special_tokens:
self._tokenizer.with_post_processor(processors.BertProcessing.new(
self._tokenizer.with_post_processor(
processors.BertProcessing.new(
(sep_token, self._tokenizer.token_to_id(sep_token)),
(cls_token, self._tokenizer.token_to_id(cls_token)),
))
)
)
if max_length is not None:
self._tokenizer.with_truncation(max_length, stride, truncation_strategy)
self._tokenizer.with_padding(
@@ -569,7 +591,7 @@ class BertTokenizerFast(FastPreTrainedTokenizer):
self.padding_side,
self.pad_token_id,
self.pad_token_type_id,
self.pad_token
self.pad_token,
)
self._decoder = decoders.WordPiece.new()

26
src/transformers/tokenization_gpt2.py
View File

@@ -22,7 +22,7 @@ from functools import lru_cache
import regex as re
from .tokenization_utils import PreTrainedTokenizer, FastPreTrainedTokenizer
from .tokenization_utils import FastPreTrainedTokenizer, PreTrainedTokenizer
logger = logging.getLogger(__name__)
@@ -247,19 +247,33 @@ class GPT2Tokenizer(PreTrainedTokenizer):
return vocab_file, merge_file
class GPT2TokenizerFast(FastPreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self, vocab_file, merges_file, unk_token="<|endoftext|>", bos_token="<|endoftext|>",
eos_token="<|endoftext|>", pad_to_max_length=False, add_prefix_space=False,
max_length=None, stride=0, truncation_strategy='longest_first', **kwargs):
def __init__(
self,
vocab_file,
merges_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
pad_to_max_length=False,
add_prefix_space=False,
max_length=None,
stride=0,
truncation_strategy="longest_first",
**kwargs
):
try:
from tokenizers import Tokenizer, models, pre_tokenizers, decoders
super(GPT2TokenizerFast, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
super(GPT2TokenizerFast, self).__init__(
bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs
)
self._tokenizer = Tokenizer(models.BPE.from_files(vocab_file, merges_file))
self._update_special_tokens()
@@ -272,7 +286,7 @@ class GPT2TokenizerFast(FastPreTrainedTokenizer):
self.padding_side,
self.pad_token_id if self.pad_token_id is not None else 0,
self.pad_token_type_id,
self.pad_token if self.pad_token is not None else ""
self.pad_token if self.pad_token is not None else "",
)
self._decoder = decoders.ByteLevel.new()

51
src/transformers/tokenization_utils.py
View File

@@ -1411,6 +1411,7 @@ class PreTrainedTokenizer(object):
)
return out_string
class FastPreTrainedTokenizer(PreTrainedTokenizer):
def __init__(self, **kwargs):
super(FastPreTrainedTokenizer, self).__init__(**kwargs)
@@ -1438,12 +1439,14 @@ class FastPreTrainedTokenizer(PreTrainedTokenizer):
self.tokenizer.add_special_tokens(self.all_special_tokens)
@staticmethod
def _convert_encoding(encoding,
def _convert_encoding(
encoding,
return_tensors=None,
return_token_type_ids=True,
return_attention_mask=True,
return_overflowing_tokens=False,
return_special_tokens_mask=False):
return_special_tokens_mask=False,
):
encoding_dict = {
"input_ids": encoding.ids,
}
@@ -1458,14 +1461,14 @@ class FastPreTrainedTokenizer(PreTrainedTokenizer):
encoding_dict["special_tokens_mask"] = encoding.special_tokens_mask
# Prepare inputs as tensors if asked
if return_tensors == 'tf' and is_tf_available():
if return_tensors == "tf" and is_tf_available():
encoding_dict["input_ids"] = tf.constant([encoding_dict["input_ids"]])
encoding_dict["token_type_ids"] = tf.constant([encoding_dict["token_type_ids"]])
if "attention_mask" in encoding_dict:
encoding_dict["attention_mask"] = tf.constant([encoding_dict["attention_mask"]])
elif return_tensors == 'pt' and is_torch_available():
elif return_tensors == "pt" and is_torch_available():
encoding_dict["input_ids"] = torch.tensor([encoding_dict["input_ids"]])
encoding_dict["token_type_ids"] = torch.tensor([encoding_dict["token_type_ids"]])
@@ -1474,11 +1477,14 @@ class FastPreTrainedTokenizer(PreTrainedTokenizer):
elif return_tensors is not None:
logger.warning(
"Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(
return_tensors))
return_tensors
)
)
return encoding_dict
def encode_plus(self,
def encode_plus(
self,
text,
text_pair=None,
return_tensors=None,
@@ -1486,14 +1492,17 @@ class FastPreTrainedTokenizer(PreTrainedTokenizer):
return_attention_mask=True,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
**kwargs):
**kwargs
):
encoding = self.tokenizer.encode(text, text_pair)
return self._convert_encoding(encoding,
return self._convert_encoding(
encoding,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask)
return_special_tokens_mask=return_special_tokens_mask,
)
def tokenize(self, text):
return self.tokenizer.encode(text).tokens
@@ -1510,19 +1519,26 @@ class FastPreTrainedTokenizer(PreTrainedTokenizer):
def add_tokens(self, new_tokens):
self.tokenizer.add_tokens(new_tokens)
def encode_batch(self, texts,
def encode_batch(
self,
texts,
return_tensors=None,
return_token_type_ids=True,
return_attention_mask=True,
return_overflowing_tokens=False,
return_special_tokens_mask=False):
return [self._convert_encoding(encoding,
return_special_tokens_mask=False,
):
return [
self._convert_encoding(
encoding,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask)
for encoding in self.tokenizer.encode_batch(texts)]
return_special_tokens_mask=return_special_tokens_mask,
)
for encoding in self.tokenizer.encode_batch(texts)
]
def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
text = self.tokenizer.decode(token_ids, skip_special_tokens)
@@ -1534,6 +1550,7 @@ class FastPreTrainedTokenizer(PreTrainedTokenizer):
return text
def decode_batch(self, ids_batch, skip_special_tokens=False, clear_up_tokenization_spaces=True):
return [self.clean_up_tokenization(text)
if clear_up_tokenization_spaces else text
for text in self.tokenizer.decode_batch(ids_batch, skip_special_tokens)]
return [
self.clean_up_tokenization(text) if clear_up_tokenization_spaces else text
for text in self.tokenizer.decode_batch(ids_batch, skip_special_tokens)
]