Remove fast tokenizer

Author: VikParuchuri

surya/ocr_error/tokenizer.py

@@ -496,150 +496,4 @@ def tokenize(self, text):
                 output_tokens.append(self.unk_token)
             else:
                 output_tokens.extend(sub_tokens)
-        return output_tokens
-
-class DistilBertTokenizerFast(PreTrainedTokenizerFast):
-    r"""
-    Construct a "fast" DistilBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
-
-    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
-    refer to this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`):
-            File containing the vocabulary.
-        do_lower_case (`bool`, *optional*, defaults to `True`):
-            Whether or not to lowercase the input when tokenizing.
-        unk_token (`str`, *optional*, defaults to `"[UNK]"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        sep_token (`str`, *optional*, defaults to `"[SEP]"`):
-            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
-            sequence classification or for a text and a question for question answering. It is also used as the last
-            token of a sequence built with special tokens.
-        pad_token (`str`, *optional*, defaults to `"[PAD]"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        cls_token (`str`, *optional*, defaults to `"[CLS]"`):
-            The classifier token which is used when doing sequence classification (classification of the whole sequence
-            instead of per-token classification). It is the first token of the sequence when built with special tokens.
-        mask_token (`str`, *optional*, defaults to `"[MASK]"`):
-            The token used for masking values. This is the token used when training this model with masked language
-            modeling. This is the token which the model will try to predict.
-        clean_text (`bool`, *optional*, defaults to `True`):
-            Whether or not to clean the text before tokenization by removing any control characters and replacing all
-            whitespaces by the classic one.
-        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
-            Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
-            issue](https://github.com/huggingface/transformers/issues/328)).
-        strip_accents (`bool`, *optional*):
-            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
-            value for `lowercase` (as in the original BERT).
-        wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
-            The prefix for subwords.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    model_input_names = ["input_ids", "attention_mask"]
-    slow_tokenizer_class = DistilBertTokenizer
-
-    def __init__(
-        self,
-        vocab_file=None,
-        tokenizer_file=None,
-        do_lower_case=True,
-        unk_token="[UNK]",
-        sep_token="[SEP]",
-        pad_token="[PAD]",
-        cls_token="[CLS]",
-        mask_token="[MASK]",
-        tokenize_chinese_chars=True,
-        strip_accents=None,
-        **kwargs,
-    ):
-        super().__init__(
-            vocab_file,
-            tokenizer_file=tokenizer_file,
-            do_lower_case=do_lower_case,
-            unk_token=unk_token,
-            sep_token=sep_token,
-            pad_token=pad_token,
-            cls_token=cls_token,
-            mask_token=mask_token,
-            tokenize_chinese_chars=tokenize_chinese_chars,
-            strip_accents=strip_accents,
-            **kwargs,
-        )
-
-        normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
-        if (
-            normalizer_state.get("lowercase", do_lower_case) != do_lower_case
-            or normalizer_state.get("strip_accents", strip_accents) != strip_accents
-            or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars
-        ):
-            normalizer_class = getattr(normalizers, normalizer_state.pop("type"))
-            normalizer_state["lowercase"] = do_lower_case
-            normalizer_state["strip_accents"] = strip_accents
-            normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars
-            self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
-
-        self.do_lower_case = do_lower_case
-
-    # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.build_inputs_with_special_tokens
-    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A BERT sequence has the following format:
-
-        - single sequence: `[CLS] X [SEP]`
-        - pair of sequences: `[CLS] A [SEP] B [SEP]`
-
-        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.
-        """
-        output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
-
-        if token_ids_1 is not None:
-            output += token_ids_1 + [self.sep_token_id]
-
-        return output
-
-    # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.create_token_type_ids_from_sequences
-    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. A BERT sequence
-        pair mask has the following format:
-
-        ```
-        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
-        | first sequence    | second sequence |
-        ```
-
-        If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
-
-        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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
-        """
-        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) * [0] + len(token_ids_1 + sep) * [1]
-
-    # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.save_vocabulary
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
-        return tuple(files)
+        return output_tokens