Delete tokenization_SEA_BPE.py

tokenization_SEA_BPE.py

@@ -1,197 +0,0 @@
-import os
-from shutil import copyfile
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
-import sentencepiece as spm
-from tokenizers import processors
-from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
-SPIECE_UNDERLINE = "▁"
-
-
-class SEABPETokenizer(PreTrainedTokenizer):
-    """
-    Construct the SEA BPE Tokenizer tailored for SEA languages. Based on the Byte-Pair-Encoding with an expanded voculabulary size
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        legacy (`bool`, *optional*, defaults to `True`):
-            Whether or not the `legacy` behaviour of the tokenizer should be used. Legacy is before the merge of #24622
-            which includes fixes to properly handle tokens that appear after special tokens.
-            legacy means we are not modifying existing tokenizers without knowing. (And we need to manually update those core tokenizers)
-
-            A simple example:
-
-            - `legacy=True`:
-            ```python
-            >>> from transformers import T5Tokenizer
-
-            >>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=True)
-            >>> tokenizer.encode("Hello <extra_id_0>.")
-            [8774, 32099, 3, 5, 1]
-            ```
-            - `legacy=False`:
-            ```python
-            >>> from transformers import T5Tokenizer
-
-            >>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=False)
-            >>> tokenizer.encode("Hello <extra_id_0>.")  # the extra space `[3]` is no longer here
-            [8774, 32099, 5, 1]
-            ```
-            Checkout the pull request and the issue [here](https://github.com/huggingface/transformers/pull/24565) for
-            more details.
-
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-
-    def __init__(
-        self,
-        vocab_file,
-        unk_token="<unk>",
-        bos_token=None,
-        eos_token="<|endoftext|>",
-        pad_token=None,
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        add_bos_token=False,
-        add_eos_token=False,
-        clean_up_tokenization_spaces=False,
-        legacy=None,
-        **kwargs,
-    ):
-        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(vocab_file)
-        super().__init__(
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            add_bos_token=add_bos_token,
-            add_eos_token=add_eos_token,
-            sp_model_kwargs=self.sp_model_kwargs,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            legacy=legacy,
-            **kwargs,
-        )
-        if legacy is None:
-            logger.warning_once(
-                f"You are using the default legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to read the related pull request available at https://github.com/huggingface/transformers/pull/24565, and set the legacy attribute accordingly."
-            )
-            legacy = True
-        self.legacy = legacy
-        self.vocab_file = vocab_file
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
-        return state
-
-    def __setstate__(self, d):
-        self.__dict__ = d
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
-
-    @property
-    def vocab_size(self):
-        """Returns vocab size"""
-        return self.sp_model.get_piece_size()
-
-    def get_vocab(self):
-        """Returns vocab as a dict"""
-        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    def tokenize(self, text, **kwargs) -> List[str]:
-        if not self.legacy:
-            text = SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " ")
-        return super().tokenize(text, **kwargs)
-
-    def _tokenize(self, text):
-        """
-        Returns a tokenized string.
-
-        Since the sentencepiece internal model always adds a SPIECE_UNDERLINE, at the beginning of the provided text,
-        we need to remove it by hand when the current text is a subsequence. This happens whenever the `self.tokenize`
-        function is called with specials tokens: the input is split on the special tokens, and each subsequence is
-        passed to `_tokenize`. Thus if a subsequence did not start with a `" "` or SPIECE_UNDERLINE, we have to remove
-        the extra `SPIECE_UNDERLINE` prepended.
-        """
-        if not self.legacy:
-            is_first = text.startswith(SPIECE_UNDERLINE)
-            if is_first:
-                text = text[1:]
-        tokens = self.sp_model.encode(text, out_type=str)
-        if (
-            not self.legacy
-            and (not is_first)
-            and (not text.startswith(" "))
-            and tokens[0].startswith(SPIECE_UNDERLINE)
-        ):
-            tokens = ([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]
-        return tokens
-
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.sp_model.piece_to_id(token)
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        token = self.sp_model.IdToPiece(index)
-        return token
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        current_sub_tokens = []
-        out_string = ""
-        prev_is_special = False
-        for i, token in enumerate(tokens):
-            if token in self.all_special_tokens:
-                if not prev_is_special and i != 0:
-                    out_string += " "
-                out_string += self.sp_model.decode(current_sub_tokens) + token
-                prev_is_special = True
-                current_sub_tokens = []
-            else:
-                current_sub_tokens.append(token)
-                prev_is_special = False
-        out_string += self.sp_model.decode(current_sub_tokens)
-        return out_string
-
-    def save_vocabulary(
-        self, save_directory, filename_prefix: Optional[str] = None
-    ) -> Tuple[str]:
-        """
-        Save the vocabulary and special tokens file to a directory.
-
-        Args:
-            save_directory (`str`):
-                The directory in which to save the vocabulary.
-
-        Returns:
-            `Tuple(str)`: Paths to the files saved.
-        """
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        out_vocab_file = os.path.join(
-            save_directory,
-            (filename_prefix + "-" if filename_prefix else "")
-            + VOCAB_FILES_NAMES["vocab_file"],
-        )
-        if os.path.abspath(self.vocab_file) != os.path.abspath(
-            out_vocab_file
-        ) and os.path.isfile(self.vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-        elif not os.path.isfile(self.vocab_file):
-            with open(out_vocab_file, "wb") as fi:
-                content_spiece_model = self.sp_model.serialized_model_proto()
-                fi.write(content_spiece_model)
-        return (out_vocab_file,)