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import os |
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import json |
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import regex as re |
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from typing import Dict, List, Optional, Tuple, Union |
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from transformers import PreTrainedTokenizer |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class AksharaTokenizer(PreTrainedTokenizer): |
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""" |
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Akshara tokenizer for processing Indic language text. |
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This tokenizer handles characters at the akshara (syllable) level. |
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""" |
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vocab_files_names = {"vocab_file": "vocab.json", "merges_file": "merges.txt"} |
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model_input_names = ["input_ids", "attention_mask"] |
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def __init__( |
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self, |
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vocab_file, |
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merges_file=None, |
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unk_token="<unk>", |
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bos_token="<s>", |
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eos_token="</s>", |
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pad_token="<pad>", |
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mask_token="<mask>", |
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add_prefix_space=False, |
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**kwargs |
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): |
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super().__init__( |
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unk_token=unk_token, |
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bos_token=bos_token, |
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eos_token=eos_token, |
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pad_token=pad_token, |
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mask_token=mask_token, |
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add_prefix_space=add_prefix_space, |
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**kwargs, |
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) |
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with open(vocab_file, encoding="utf-8") as vocab_handle: |
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self.encoder = json.load(vocab_handle) |
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self.decoder = {v: k for k, v in self.encoder.items()} |
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self.merges = {} |
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if merges_file is not None and os.path.isfile(merges_file): |
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with open(merges_file, encoding="utf-8") as merges_handle: |
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merges = merges_handle.read().split("\n") |
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self.merges = {tuple(merge.split()): i for i, merge in enumerate(merges) if merge} |
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self.add_prefix_space = add_prefix_space |
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self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") |
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@property |
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def vocab_size(self): |
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return len(self.encoder) |
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def get_vocab(self): |
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return dict(self.encoder, **self.added_tokens_encoder) |
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def _tokenize(self, text): |
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"""Tokenize text into akshara units.""" |
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if self.add_prefix_space and not text.startswith(" "): |
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text = " " + text |
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tokens = re.findall(self.pat, text) |
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return tokens |
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def _convert_token_to_id(self, token): |
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"""Convert a token to its ID in the vocabulary.""" |
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return self.encoder.get(token, self.encoder.get(self.unk_token)) |
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def _convert_id_to_token(self, index): |
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"""Convert an ID to its token in the vocabulary.""" |
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return self.decoder.get(index, self.unk_token) |
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def convert_tokens_to_string(self, tokens): |
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"""Convert a sequence of tokens to a single string.""" |
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text = "".join(tokens) |
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text = text.replace(" ", "").replace("▁", " ").strip() |
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return text |
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): |
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"""Build model inputs from a sequence by appending eos_token_id.""" |
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if token_ids_1 is None: |
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return [self.bos_token_id] + token_ids_0 + [self.eos_token_id] |
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return [self.bos_token_id] + token_ids_0 + [self.eos_token_id] + token_ids_1 + [self.eos_token_id] |
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def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): |
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"""Get list where entries are [1] if a token is special and [0] otherwise.""" |
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if already_has_special_tokens: |
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return super().get_special_tokens_mask( |
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True |
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) |
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if token_ids_1 is None: |
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return [1] + ([0] * len(token_ids_0)) + [1] |
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] |
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def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): |
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"""Create a mask from the two sequences for sequence classification tasks.""" |
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eos = [self.eos_token_id] |
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bos = [self.bos_token_id] |
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if token_ids_1 is None: |
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return len(bos + token_ids_0 + eos) * [0] |
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return len(bos + token_ids_0 + eos) * [0] + len(token_ids_1 + eos) * [1] |
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def save_vocabulary(self, save_directory, filename_prefix=None): |
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"""Save the vocabulary and merges files to a directory.""" |
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if not os.path.isdir(save_directory): |
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logger.error(f"Vocabulary path ({save_directory}) should be a directory") |
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return |
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vocab_file = os.path.join( |
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save_directory, (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"] |
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) |
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with open(vocab_file, "w", encoding="utf-8") as f: |
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f.write(json.dumps(self.encoder, ensure_ascii=False)) |
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return (vocab_file,) |
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from transformers import AutoTokenizer |
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AutoTokenizer.register("akshara", AksharaTokenizer) |
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from transformers.models.auto.configuration_auto import CONFIG_MAPPING |
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if "akshara" not in CONFIG_MAPPING: |
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from transformers import PretrainedConfig |
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class AksharaConfig(PretrainedConfig): |
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model_type = "akshara" |
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CONFIG_MAPPING.register("akshara", AksharaConfig) |