Update tokenization_bn.py

tokenization_bn.py

@@ -1,110 +1,81 @@
+
+"""
+Forked from the file src/transformers/models/bert_generation/tokenization_bert_generation.py from the HuggingFace Transformers library.
+Permalink: https://github.com/huggingface/transformers/blob/04ab5605fbb4ef207b10bf2772d88c53fc242e83/src/transformers/models/bert_generation/tokenization_bert_generation.py
+
+"""
 import os
+import sentencepiece as spm
 from shutil import copyfile
+from transformers import PreTrainedTokenizer
 from typing import Any, Dict, List, Optional, Tuple
-
-import sentencepiece as spm
-from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {},
-    "tokenizer_file": {},
-}
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
-
+VOCAB_FILES_NAMES = {'vocab_file': 'tokenizer.model'}
 
 class BNTokenizer(PreTrainedTokenizer):
     """
-    Construct a Yi tokenizer. Based on byte-level Byte-Pair-Encoding.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-    """
-
+      Construct a ReplitLMTokenizer tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
+      This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods.
+
+      Args:
+          vocab_file (`str`):
+              [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
+              contains the vocabulary necessary to instantiate a tokenizer.
+          eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+              The end of sequence token.
+          bos_token (`str`, *optional*, defaults to `None`):
+              The begin of sequence token.
+          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.
+          pad_token (`str`, *optional*, defaults to `"<|pad|>"`):
+              The token used for padding, for example when batching sequences of different lengths.
+          sp_model_kwargs (`dict`, *optional*):
+              Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+              SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+              to set:
+              - `enable_sampling`: Enable subword regularization.
+              - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+                - `nbest_size = {0,1}`: No sampling is performed.
+                - `nbest_size > 1`: samples from the nbest_size results.
+                - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                  using forward-filtering-and-backward-sampling algorithm.
+              - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+                BPE-dropout.
+      """
     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"]
+    prefix_tokens: List[int] = []
+    model_input_names = ['input_ids', 'attention_mask']
 
-    def __init__(
-        self,
-        vocab_file,
-        unk_token="<unk>",
-        bos_token="<|startoftext|>",
-        eos_token="<|endoftext|>",
-        pad_token="<unk>",
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        add_bos_token=True,
-        add_eos_token=False,
-        clean_up_tokenization_spaces=False,
-        **kwargs,
-    ):
+    def __init__(self, vocab_file, bos_token=None, eos_token='</s>', unk_token='<unk>', pad_token='<|reserved001|>', sep_token=None, sp_model_kwargs: Optional[Dict[str, Any]]=None, **kwargs) -> None:
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        bos_token = (
-            AddedToken(bos_token, lstrip=False, rstrip=False)
-            if isinstance(bos_token, str)
-            else bos_token
-        )
-        eos_token = (
-            AddedToken(eos_token, lstrip=False, rstrip=False)
-            if isinstance(eos_token, str)
-            else eos_token
-        )
-        unk_token = (
-            AddedToken(unk_token, lstrip=False, rstrip=False)
-            if isinstance(unk_token, str)
-            else unk_token
-        )
-        pad_token = (
-            AddedToken(pad_token, lstrip=False, rstrip=False)
-            if isinstance(pad_token, str)
-            else pad_token
-        )
+        super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs)
         self.vocab_file = vocab_file
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
         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,
-            **kwargs,
-        )
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        return state
-
-    def __setstate__(self, d):
-        self.__dict__ = d
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(self.vocab_file)
 
     @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):
-        """Returns a tokenized string."""
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state['sp_model'] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        if not hasattr(self, 'sp_model_kwargs'):
+            self.sp_model_kwargs = {}
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.load(self.vocab_file)
+
+    def _tokenize(self, text: str) -> List[str]:
+        """Take as input a string and return a list of strings (tokens) for words/sub-words"""
         return self.sp_model.encode(text, out_type=str)
 
     def _convert_token_to_id(self, token):
@@ -113,143 +84,21 @@ class BNTokenizer(PreTrainedTokenizer):
 
     def _convert_id_to_token(self, index):
         """Converts an index (integer) in a token (str) using the vocab."""
-        token = self.sp_model.IdToPiece(index)
+        token = self.sp_model.id_to_piece(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):
-            # make sure that special tokens are not decoded using sentencepiece model
-            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
+        return self.sp_model.decode(tokens)
 
-    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.
-        """
+    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
-        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):
+            raise ValueError(f'Vocabulary path ({save_directory}) should be a directory')
+        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:
+            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,)
-
-    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-
-        output = bos_token_id + token_ids_0 + eos_token_id
-
-        if token_ids_1 is not None:
-            output = output + bos_token_id + token_ids_1 + eos_token_id
-
-        return output
-
-    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,
-            )
-
-        bos_token_id = [1] if self.add_bos_token else []
-        eos_token_id = [1] if self.add_eos_token else []
-
-        if token_ids_1 is None:
-            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
-        return (
-            bos_token_id
-            + ([0] * len(token_ids_0))
-            + eos_token_id
-            + bos_token_id
-            + ([0] * len(token_ids_1))
-            + eos_token_id
-        )
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
-        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, 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).
-        """
-        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-
-        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
-
-        if token_ids_1 is not None:
-            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
-
-        return output
+        return (out_vocab_file,)