Upload tokenization_indictrans.py with huggingface_hub

tokenization_indictrans.py

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+import os
+import json
+
+from typing import Dict, List, Optional, Union, Tuple
+
+from transformers.utils import logging
+from sentencepiece import SentencePieceProcessor
+from transformers.tokenization_utils import PreTrainedTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+SPIECE_UNDERLINE = "▁"
+
+SPECIAL_TAGS = {
+    "_bt_",
+    "_ft_",
+    "asm_Beng",
+    "awa_Deva",
+    "ben_Beng",
+    "bho_Deva",
+    "brx_Deva",
+    "doi_Deva",
+    "eng_Latn",
+    "gom_Deva",
+    "gon_Deva",
+    "guj_Gujr",
+    "hin_Deva",
+    "hne_Deva",
+    "kan_Knda",
+    "kas_Arab",
+    "kas_Deva",
+    "kha_Latn",
+    "lus_Latn",
+    "mag_Deva",
+    "mai_Deva",
+    "mal_Mlym",
+    "mar_Deva",
+    "mni_Beng",
+    "mni_Mtei",
+    "npi_Deva",
+    "ory_Orya",
+    "pan_Guru",
+    "san_Deva",
+    "sat_Olck",
+    "snd_Arab",
+    "snd_Deva",
+    "tam_Taml",
+    "tel_Telu",
+    "urd_Arab",
+    "unr_Deva",
+}
+
+VOCAB_FILES_NAMES = {
+    "src_vocab_fp": "dict.SRC.json",
+    "tgt_vocab_fp": "dict.TGT.json",
+    "src_spm_fp": "model.SRC",
+    "tgt_spm_fp": "model.TGT",
+}
+
+
+class IndicTransTokenizer(PreTrainedTokenizer):
+    _added_tokens_encoder = {}
+    _added_tokens_decoder = {}
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        src_vocab_fp=None,
+        tgt_vocab_fp=None,
+        src_spm_fp=None,
+        tgt_spm_fp=None,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token="<pad>",
+        do_lower_case=False,
+        **kwargs,
+    ):
+
+        self.src = True
+
+        self.src_vocab_fp = src_vocab_fp
+        self.tgt_vocab_fp = tgt_vocab_fp
+        self.src_spm_fp = src_spm_fp
+        self.tgt_spm_fp = tgt_spm_fp
+
+        self.unk_token = unk_token
+        self.pad_token = pad_token
+        self.eos_token = eos_token
+        self.bos_token = bos_token
+
+        self.encoder = self._load_json(self.src_vocab_fp)
+        if self.unk_token not in self.encoder:
+            raise KeyError("<unk> token must be in vocab")
+        assert self.pad_token in self.encoder
+        self.encoder_rev = {v: k for k, v in self.encoder.items()}
+
+        self.decoder = self._load_json(self.tgt_vocab_fp)
+        if self.unk_token not in self.encoder:
+            raise KeyError("<unk> token must be in vocab")
+        assert self.pad_token in self.encoder
+        self.decoder_rev = {v: k for k, v in self.decoder.items()}
+
+        # load SentencePiece model for pre-processing
+        self.src_spm = self._load_spm(self.src_spm_fp)
+        self.tgt_spm = self._load_spm(self.tgt_spm_fp)
+
+        self.current_spm = self.src_spm
+        self.current_encoder = self.encoder
+        self.current_encoder_rev = self.encoder_rev
+
+        self.unk_token_id = self.encoder[self.unk_token]
+        self.pad_token_id = self.encoder[self.pad_token]
+        self.eos_token_id = self.encoder[self.eos_token]
+        self.bos_token_id = self.encoder[self.bos_token]
+
+        super().__init__(
+            src_vocab_file=self.src_vocab_fp,
+            tgt_vocab_file=self.src_vocab_fp,
+            do_lower_case=do_lower_case,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            **kwargs,
+        )
+
+    def add_new_special_tags(self, new_tags: List[str]):
+        SPECIAL_TAGS.update(new_tags)
+
+    def _switch_to_input_mode(self):
+        self.src = True
+        self.padding_side = "left"
+        self.current_spm = self.src_spm
+        self.current_encoder = self.encoder
+        self.current_encoder_rev = self.encoder_rev
+
+    def _switch_to_target_mode(self):
+        self.src = False
+        self.padding_side = "right"
+        self.current_spm = self.tgt_spm
+        self.current_encoder = self.decoder
+        self.current_encoder_rev = self.decoder_rev
+
+    def _load_spm(self, path: str) -> SentencePieceProcessor:
+        return SentencePieceProcessor(model_file=path)
+
+    def _save_json(self, data, path: str) -> None:
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump(data, f, indent=2)
+
+    def _load_json(self, path: str) -> Union[Dict, List]:
+        with open(path, "r", encoding="utf-8") as f:
+            return json.load(f)
+
+    def _split_tags(self, tokens: List[str]) -> Tuple[List[str], List[str]]:
+        tags = [token for token in tokens if token in SPECIAL_TAGS]
+        tokens = [token for token in tokens if token not in SPECIAL_TAGS]
+        return tags, tokens
+
+    def _split_pads(self, tokens: List[str]) -> Tuple[List[str], List[str]]:
+        pads = [token for token in tokens if token == self.pad_token]
+        tokens = [token for token in tokens if token != self.pad_token]
+        return pads, tokens
+
+    @property
+    def src_vocab_size(self) -> int:
+        return len(self.encoder)
+
+    @property
+    def tgt_vocab_size(self) -> int:
+        return len(self.decoder)
+
+    def get_src_vocab(self) -> Dict[str, int]:
+        return dict(self.encoder, **self.added_tokens_encoder)
+
+    def get_tgt_vocab(self) -> Dict[str, int]:
+        return dict(self.decoder, **self.added_tokens_decoder)
+
+    # hack override
+    def get_vocab(self) -> Dict[str, int]:
+        return self.get_src_vocab()
+
+    # hack override
+    @property
+    def vocab_size(self) -> int:
+        return self.src_vocab_size
+
+    def _convert_token_to_id(self, token: str) -> int:
+        """Converts an token (str) into an index (integer) using the source/target vocabulary map."""
+        return self.current_encoder.get(token, self.current_encoder[self.unk_token])
+
+    def _convert_id_to_token(self, index: int) -> str:
+        """Converts an index (integer) into a token (str) using the source/target vocabulary map."""
+        return self.current_encoder_rev.get(index, self.unk_token)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """Uses sentencepiece model for detokenization"""
+        pads, tokens = self._split_pads(tokens)
+
+        if self.src:
+
+            tags, non_tags = self._split_tags(tokens)
+
+            return (
+                " ".join(pads)
+                + " "
+                + " ".join(tags)
+                + " "
+                + "".join(non_tags).replace(SPIECE_UNDERLINE, " ").strip()
+            )
+
+        return (
+            "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
+            + " "
+            + " ".join(pads)
+        )
+
+    def _tokenize(self, text) -> List[str]:
+        if self.src:
+            tokens = text.split(" ")
+            tags, non_tags = self._split_tags(tokens)
+            text = " ".join(non_tags)
+            tokens = self.current_spm.EncodeAsPieces(text)
+            return tags + tokens
+        else:
+            return self.current_spm.EncodeAsPieces(text)
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        if token_ids_1 is None:
+            return token_ids_0 + [self.eos_token_id]
+        # We don't expect to process pairs, but leave the pair logic for API consistency
+        return token_ids_0 + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
+
+    def save_vocabulary(
+        self, save_directory: str, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+
+        src_spm_fp = os.path.join(save_directory, "model.SRC")
+        tgt_spm_fp = os.path.join(save_directory, "model.TGT")
+        src_vocab_fp = os.path.join(save_directory, "dict.SRC.json")
+        tgt_vocab_fp = os.path.join(save_directory, "dict.TGT.json")
+
+        self._save_json(self.encoder, src_vocab_fp)
+        self._save_json(self.decoder, tgt_vocab_fp)
+
+        with open(src_spm_fp, "wb") as f:
+            f.write(self.src_spm.serialized_model_proto())
+
+        with open(tgt_spm_fp, "wb") as f:
+            f.write(self.tgt_spm.serialized_model_proto())
+
+        return src_vocab_fp, tgt_vocab_fp, src_spm_fp, tgt_spm_fp