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import model
from transformers import PreTrainedTokenizerBase
from typing import Optional, Union, Any
from transformers.file_utils import PaddingStrategy
import re
import time
import random
from dataclasses import dataclass
import validators
tokenizer = None
@dataclass
class DataCollatorInvertTextNormalization:
tokenizer: PreTrainedTokenizerBase
model: Optional[Any] = None
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
label_pad_token_id: int = -100
return_tensors: str = "pt"
def __call__(self, features, return_tensors=None):
batch_src, batch_tgt = [], []
for item in features:
src_spans, tgt_spans = create_invert_text_norm(item['src'], item['tgt'])
batch_src.append(src_spans)
batch_tgt.append(tgt_spans)
# print("Batch: ", batch_src, batch_tgt)
features = preprocess_function({"src": batch_src, "tgt": batch_tgt})
# print("Preprocess batch: ", features)
if return_tensors is None:
return_tensors = self.return_tensors
labels = [feature["outputs"] for feature in features] if "outputs" in features[0].keys() else None
spoken_labels = [feature["spoken_label"] for feature in features] if "spoken_label" in features[0].keys() else None
spoken_idx = [feature["src_spoken_idx"] for feature in features] if "src_spoken_idx" in features[0].keys() else None
word_src_lengths = [feature["inputs_length"] for feature in features] if "inputs_length" in features[0].keys() else None
word_tgt_lengths = [feature["outputs_length"] for feature in features] if "outputs_length" in features[0].keys() else None
# print("Spoken labels: ", spoken_labels)
# We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
# same length to return tensors.
if labels is not None:
max_label_length = max(len(l) for l in labels)
max_src_length = max(len(l) for l in spoken_labels)
max_spoken_idx_length = max(len(l) for l in spoken_idx)
max_word_src_length = max(len(l) for l in word_src_lengths)
max_word_tgt_length = max(len(l) for l in word_tgt_lengths)
padding_side = self.tokenizer.padding_side
for feature in features:
remainder = [self.label_pad_token_id] * (max_label_length - len(feature["outputs"]))
remainder_word_tgt_length = [0] * (max_word_tgt_length - len(feature["outputs_length"]))
remainder_spoken = [self.label_pad_token_id] * (max_src_length - len(feature["spoken_label"]))
remainder_spoken_idx = [self.label_pad_token_id] * (max_spoken_idx_length - len(feature["src_spoken_idx"]))
remainder_word_src_length = [0] * (max_word_src_length - len(feature["inputs_length"]))
feature["labels"] = (
feature["outputs"] + [
self.tokenizer.eos_token_id] + remainder if padding_side == "right" else remainder + feature[
"outputs"] + [self.tokenizer.eos_token_id]
)
feature["spoken_label"] = [self.label_pad_token_id] + feature["spoken_label"] + [self.label_pad_token_id]
feature["spoken_label"] = feature["spoken_label"] + remainder_spoken if padding_side == "right" else remainder_spoken + feature["spoken_label"]
feature["src_spoken_idx"] = feature["src_spoken_idx"] + remainder_spoken_idx
feature['inputs_length'] = [1] + feature['inputs_length'] + [1]
feature['outputs_length'] = feature['outputs_length'] + [1]
feature["inputs_length"] = feature["inputs_length"] + remainder_word_src_length
feature["outputs_length"] = feature["outputs_length"] + remainder_word_tgt_length
# print("Features: ", features)
features_inputs = [{
"input_ids": [self.tokenizer.bos_token_id] + item["input_ids"] + [self.tokenizer.eos_token_id],
"attention_mask": [self.tokenizer.pad_token_id] + item["attention_mask"] + [self.tokenizer.pad_token_id]
} for item in features]
features_inputs = self.tokenizer.pad(
features_inputs,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors=return_tensors,
)
outputs = self.tokenizer.pad({"input_ids": [feature["labels"] for feature in features]},
return_tensors=return_tensors)['input_ids']
spoken_label = self.tokenizer.pad({"input_ids": [feature["spoken_label"] for feature in features]},
return_tensors=return_tensors)['input_ids']
spoken_idx = self.tokenizer.pad({"input_ids": [feature["src_spoken_idx"] for feature in features]},
return_tensors=return_tensors)['input_ids'] + 1 # 1 for bos token
word_src_lengths = self.tokenizer.pad({"input_ids": [feature["inputs_length"] for feature in features]},
return_tensors=return_tensors)['input_ids']
word_tgt_lengths = self.tokenizer.pad({"input_ids": [feature["outputs_length"] for feature in features]},
return_tensors=return_tensors)['input_ids']
features = {
"input_ids": features_inputs["input_ids"],
"spoken_label": spoken_label,
"spoken_idx": spoken_idx,
"word_src_lengths": word_src_lengths,
"word_tgt_lengths": word_tgt_lengths,
"attention_mask": features_inputs["attention_mask"],
"labels": outputs,
}
# prepare decoder_input_ids
if self.model is not None and hasattr(self.model, "prepare_decoder_input_ids_from_labels"):
decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(labels=features["labels"])
features["decoder_input_ids"] = decoder_input_ids
return features
def create_invert_text_norm(item_1, item_2):
src_list, tgt_list = [], []
for src, tgt in zip(item_1, item_2):
if len(src) == len(tgt):
if random.random() < 0.1:
src_list.append(src)
tgt_list.append(tgt)
else:
src_list.append(src)
tgt_list.append(tgt)
return src_list, tgt_list
# data init
def init_data():
dataset = datasets.load_dataset('VietAI/spoken_norm_assignment')
print("Dataset: ", dataset)
return dataset
def preprocess_function(batch):
global tokenizer
if tokenizer is None:
tokenizer = model.init_tokenizer()
features = []
for src_words, tgt_words in zip(batch["src"], batch["tgt"]):
src_ids, pad_ids, src_lengths, tgt_ids, tgt_lengths = [], [], [], [], []
# 0: "O", 1: "B", 2: "I"
src_spoken_label = []
src_spoken_idx = []
tgt_spoken_ids = []
for idx, (src, tgt) in enumerate(zip(src_words, tgt_words)):
# print("Src, Tgt: ", src, tgt)
is_remain = False
if src == tgt:
is_remain = True
src_tokenized = tokenizer(src)
# print("Src tokenized: ", src_tokenized)
if len(src_tokenized['input_ids']) < 3:
continue
# hardcode fix tokenizer email
if validators.email(tgt):
tgt_tokenized = tokenizer(tgt.replace('@', ' @'))
else:
tgt_tokenized = tokenizer(tgt)
if len(tgt_tokenized['input_ids']) < 3:
continue
src_ids.extend(src_tokenized["input_ids"][1:-1])
if is_remain:
src_spoken_label.extend([0 if random.random() < 0.5 else -100 for _ in range(len(src_tokenized["input_ids"][1:-1]))])
if random.random() < 0.1:
# Random pick normal word for spoken norm
src_spoken_idx.append(idx)
tgt_spoken_ids.append(tgt_tokenized["input_ids"][1:-1])
else:
src_spoken_label.extend([1] + [2] * (len(src_tokenized["input_ids"][1:-1]) - 1))
src_spoken_idx.append(idx)
tgt_spoken_ids.append(tgt_tokenized["input_ids"][1:-1])
pad_ids.extend(src_tokenized["attention_mask"][1:-1])
src_lengths.append(len(src_tokenized["input_ids"]) - 2)
tgt_ids.extend(tgt_tokenized["input_ids"][1:-1])
tgt_lengths.append(len(tgt_tokenized["input_ids"]) - 2)
if len(src_ids) > 80 or len(tgt_ids) > 80:
# print("Ignore sample")
break
if len(src_ids) < 1 or len(tgt_ids) < 1:
continue
features.append({
"input_ids": src_ids,
"attention_mask": pad_ids,
"spoken_label": src_spoken_label,
"inputs_length": src_lengths,
"outputs": tgt_ids,
"outputs_length": tgt_lengths,
"src_spoken_idx": src_spoken_idx,
"tgt_spoken_ids": tgt_spoken_ids
})
return features
if __name__ == "__main__":
split_datasets = init_data()
# model, model_tokenizer = model_handling.init_model()
# data_collator = DataCollatorForNormSeq2Seq(model_tokenizer, model=model)
if tokenizer is None:
tokenizer = model.init_tokenizer()
data_collator = DataCollatorInvertTextNormalization(tokenizer=tokenizer)
import time
start = time.time()
# batch = data_collator([split_datasets["train"][i] for i in [random.randint(0, 900) for _ in range(0, 64)]])
# print(batch)
print("Sample 0: ", split_datasets["train"][0])
data_collator([split_datasets["train"][0]])
# print("{}s".format(time.time() - start))
# print(split_datasets) |