Delete tokenization_phobert_fast.py

tokenization_phobert_fast.py

@@ -1,328 +0,0 @@
-# coding=utf-8
-# Copyright (c) 2020, VinAI Research and the HuggingFace Inc. team.
-# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Tokenization classes for PhoBERT"""
-
-import os
-from collections import defaultdict
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-from transformers.tokenization_utils_base import EncodingFast
-
-from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
-from transformers.utils import logging
-from .tokenization_phobert import PhobertTokenizer
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {
-    "vocab_file": "vocab.txt",
-    "merges_file": "bpe.codes",
-    "tokenizer_file": "tokenizer.json",
-}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
-        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
-    },
-    "merges_file": {
-        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
-        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
-    },
-    "tokenizer_file": {
-        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/tokenizer.json",
-        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/tokenizer.json",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "vinai/phobert-base": 256,
-    "vinai/phobert-large": 256,
-}
-
-
-class PhobertTokenizerFast(PreTrainedTokenizerFast):
-    """
-    Construct a "Fast" BPE tokenizer for PhoBERT (backed by HuggingFace's *tokenizers* library).
-
-    Peculiarities:
-
-    - uses BERT's pre-tokenizer: BertPreTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of
-      a punctuation character will be treated separately.
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the methods. Users should refer to the
-    superclass for more information regarding methods.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        merges_file (`str`):
-            Path to the merges file.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "attention_mask"]
-    slow_tokenizer_class = PhobertTokenizer
-
-    def __init__(
-        self,
-        vocab_file=None,
-        merges_file=None,
-        tokenizer_file=None,
-        bos_token="<s>",
-        eos_token="</s>",
-        sep_token="</s>",
-        cls_token="<s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        mask_token="<mask>",
-        **kwargs
-    ):
-        super().__init__(
-            vocab_file,
-            merges_file,
-            tokenizer_file=tokenizer_file,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            sep_token=sep_token,
-            cls_token=cls_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            mask_token=mask_token,
-            **kwargs,
-        )
-
-        self.vocab_file = vocab_file
-        self.merges_file = merges_file
-        self.can_save_slow_tokenizer = False if not self.vocab_file else True
-
-    def get_added_vocab_hacking(self):
-        """
-        Returns the added tokens in the vocabulary as a dictionary of token to index.
-
-        Returns:
-            `Dict[str, int], Dict[int, int]`: The added tokens, and their original and new ids
-        """
-        base_vocab_size = self._tokenizer.get_vocab_size(with_added_tokens=False)
-        full_vocab_size = self._tokenizer.get_vocab_size(with_added_tokens=True)
-        if full_vocab_size == base_vocab_size:
-            return {}, {}
-
-        # Tokens in added_vocab should have ids that are equal to or larger than the size of base_vocab
-        added_vocab = dict(
-            (self._tokenizer.id_to_token(index), index + 1 - base_vocab_size + self.mask_token_id)
-            for index in range(base_vocab_size, full_vocab_size)
-        )
-
-        id_mapping = dict((index, self._tokenizer.token_to_id(tok)) for tok, index in added_vocab.items())
-
-        return added_vocab, id_mapping
-
-    def _decode(
-        self,
-        token_ids: Union[int, List[int]],
-        skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: bool = True,
-        **kwargs
-    ) -> str:
-        self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
-
-        if isinstance(token_ids, int):
-            token_ids = [token_ids]
-
-        # Mapping ids into their original values
-        _, id_mapping = self.get_added_vocab_hacking()
-        if len(id_mapping) > 0:
-            token_ids = [id_mapping[id] if id in id_mapping else id for id in token_ids]
-
-        text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
-
-        if clean_up_tokenization_spaces:
-            clean_text = self.clean_up_tokenization(text)
-            return clean_text
-        else:
-            return text
-
-    def _convert_encoding(
-        self,
-        encoding: EncodingFast,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-    ) -> Tuple[Dict[str, Any], List[EncodingFast]]:
-        """
-        Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list
-        of encodings, take care of building a batch from overflowing tokens.
-
-        Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are
-        lists (overflows) of lists (tokens).
-
-        Output shape: (overflows, sequence length)
-        """
-        if return_token_type_ids is None:
-            return_token_type_ids = "token_type_ids" in self.model_input_names
-        if return_attention_mask is None:
-            return_attention_mask = "attention_mask" in self.model_input_names
-
-        if return_overflowing_tokens and encoding.overflowing is not None:
-            encodings = [encoding] + encoding.overflowing
-        else:
-            encodings = [encoding]
-
-        encoding_dict = defaultdict(list)
-        added_vocab, _ = self.get_added_vocab_hacking()
-        for e in encodings:
-            # encoding_dict["input_ids"].append(e.ids)
-            # Reassign ids of tokens due to the hacking strategy
-            ids = []
-            for id, token in zip(e.ids, e.tokens):
-                if id <= self.mask_token_id:
-                    ids.append(id)
-                else:
-                    if token.strip() in added_vocab:
-                        ids.append(added_vocab[token.strip()])
-                    else:
-                        ids.append(self.unk_token_id)
-
-            encoding_dict["input_ids"].append(ids)
-
-            if return_token_type_ids:
-                encoding_dict["token_type_ids"].append(e.type_ids)
-            if return_attention_mask:
-                encoding_dict["attention_mask"].append(e.attention_mask)
-            if return_special_tokens_mask:
-                encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
-            if return_offsets_mapping:
-                encoding_dict["offset_mapping"].append(e.offsets)
-            if return_length:
-                # encoding_dict["length"].append(len(e.ids))
-                encoding_dict["length"].append(len(ids))
-
-        return encoding_dict, encodings
-
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A PhoBERT sequence has the following format:
-
-        - single sequence: `<s> X </s>`
-        - pair of sequences: `<s> A </s></s> B </s>`
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-        """
-
-        if token_ids_1 is None:
-            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
-        cls = [self.cls_token_id]
-        sep = [self.sep_token_id]
-        return cls + token_ids_0 + sep + sep + token_ids_1 + sep
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` method.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. PhoBERT does not
-        make use of token type ids, therefore a list of zeros is returned.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of zeros.
-
-        """
-
-        sep = [self.sep_token_id]
-        cls = [self.cls_token_id]
-
-        if token_ids_1 is None:
-            return len(cls + token_ids_0 + sep) * [0]
-        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not self.can_save_slow_tokenizer:
-            raise ValueError(
-                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
-                "tokenizer."
-            )
-
-        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"]
-        )
-
-        out_merges_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-
-        if os.path.abspath(self.merges_file) != os.path.abspath(out_merges_file):
-            copyfile(self.merges_file, out_merges_file)
-
-        return (out_vocab_file, out_merges_file)