Upload char_tokenizer.py

char_tokenizer.py

@@ -0,0 +1,173 @@
+"""
+Copypasted from 
+https://huggingface.co/IlyaGusev/ru-word-stress-transformer/blob/main/char_tokenizer.py
+with Apache 2.0 license
+"""
+
+import os
+from typing import Optional, Tuple, List
+from collections import OrderedDict
+
+from torch.utils.data import Dataset
+from transformers import PreTrainedTokenizer, AutoTokenizer
+
+
+def load_vocab(vocab_file):
+    vocab = OrderedDict()
+    with open(vocab_file, "r", encoding="utf-8") as reader:
+        tokens = reader.readlines()
+    for index, token in enumerate(tokens):
+        token = token.rstrip("\n")
+        vocab[token] = index
+    return vocab
+
+
+class CharTokenizer(PreTrainedTokenizer):
+    vocab_files_names = {"vocab_file": "vocab.txt"}
+
+    def __init__(
+        self,
+        vocab_file=None,
+        pad_token="[pad]",
+        unk_token="[unk]",
+        bos_token="[bos]",
+        eos_token="[eos]",
+        cls_token="[cls]",
+        sep_token="[sep]",
+        mask_token="[mask]",
+        space_token="▁",
+        do_lower_case=False,
+        *args,
+        **kwargs
+    ):
+        self.do_lower_case = do_lower_case
+        self.space_token = space_token
+        
+        if not vocab_file or not os.path.isfile(vocab_file):
+            self.vocab = OrderedDict()
+            self.ids_to_tokens = OrderedDict()
+        else:
+            self.vocab = load_vocab(vocab_file)
+            self.ids_to_tokens = OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
+        
+        super().__init__(
+            pad_token=pad_token,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            cls_token=cls_token,
+            sep_token=sep_token,  
+            mask_token=mask_token,
+            do_lower_case=do_lower_case,
+            **kwargs
+        )
+        self.do_lower_case = do_lower_case
+        self.space_token = space_token
+
+        if not vocab_file or not os.path.isfile(vocab_file):
+            self.vocab = OrderedDict()
+            self.ids_to_tokens = OrderedDict()
+        else:
+            self.vocab = load_vocab(vocab_file)
+            self.ids_to_tokens = OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
+
+    def train(self, file_path):
+        vocab = set()
+        with open(file_path) as r:
+            for line in r:
+                word = line.strip()
+                if self.do_lower_case:
+                    word = word.lower()
+                vocab |= set(word)
+        vocab = list(vocab)
+        vocab.sort()
+        special_tokens = [self.pad_token, self.unk_token, self.bos_token, self.eos_token]
+        vocab = special_tokens + vocab
+
+        for i, ch in enumerate(vocab):
+            self.vocab[ch] = i
+        self.ids_to_tokens = vocab
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+    def get_vocab(self):
+        return self.vocab
+
+    def _convert_token_to_id(self, token):
+        if self.do_lower_case:
+            token = token.lower()
+        return self.vocab.get(token, self.vocab[self.unk_token])
+
+    def _convert_id_to_token(self, index):
+        return self.ids_to_tokens[index]
+    
+    def prepare_for_tokenization(
+        self, text, is_split_into_words: bool = False, spaces=0, **kwargs
+    ):
+        if spaces:
+            pad = self.space_token * spaces
+            text = pad + pad.join(text) + pad
+        return (text, kwargs)
+
+    def _tokenize(self, text, spaces=0):
+        if self.do_lower_case:
+            text = text.lower()
+        return list(text)
+
+    def convert_tokens_to_string(self, tokens):
+        return "".join(tokens)
+
+    def build_inputs_with_special_tokens(
+        self,
+        token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        bos = [self.bos_token_id]
+        eos = [self.eos_token_id]
+        return bos + token_ids_0 + eos
+
+    def get_special_tokens_mask(
+         self,
+         token_ids_0: List[int],
+         token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        return [1] + ([0] * len(token_ids_0)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self,
+        token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        return (len(token_ids_0) + 2) * [0]
+
+    def save_vocabulary(
+        self,
+        save_directory: str,
+        filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        assert os.path.isdir(save_directory)
+        vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") +
+            self.vocab_files_names["vocab_file"]
+        )
+        index = 0
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+                assert index == token_index
+                writer.write(token + "\n")
+                index += 1
+        return (vocab_file,)
+    
+    def clean_up_tokenization(self, text, space='▁'):
+        res = []
+        prev = space
+        for c in text:
+            if c != prev and c != space:
+                res.append(c)
+            prev = c
+        return ''.join(res)
+
+AutoTokenizer.register("char_tokenizer", CharTokenizer)