Delete tokenization_xtrimopglm.py

tokenization_xtrimopglm.py

@@ -1,140 +0,0 @@
-"""Tokenization classes for xTrimoPGLM."""
-
-import os
-from typing import List, Optional, Union, Dict, Any
-from torch import TensorType
-from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
-from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
-
-VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
-
-
-def load_vocab_file(vocab_file: str) -> List[str]:
-    with open(vocab_file, "r") as f:
-        lines = f.read().splitlines()
-        return [line.strip() for line in lines]
-
-
-class xTrimoPGLMTokenizer(PreTrainedTokenizer):
-    """
-    Constructs a xTrimoPGLM tokenizer.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    model_input_names = ["input_ids", "attention_mask", "position_ids"]
-    def __init__(
-        self,
-        vocab_file: str,
-        unk_token: str = "<unk>",
-        pad_token: str = "<pad>",
-        mask_token: str = "<mask>",
-        eos_token: str = "<eos>",
-        model_max_length: int = 2048,
-        additional_special_tokens: Optional[List[str]] = None,
-        **kwargs,
-    ):
-        self.all_tokens = load_vocab_file(vocab_file)
-        self._id_to_token = dict(enumerate(self.all_tokens))
-        self._token_to_id = {tok: ind for ind, tok in enumerate(self.all_tokens)}
-
-        if additional_special_tokens is None:
-            additional_special_tokens = ['<pad>', '<mask>', '<gmask>', '<smask>', '<eod>', '<sop>', '<eop>', '<eos>', '<unk>']
-
-        super().__init__(
-            unk_token=unk_token,
-            pad_token=pad_token,
-            mask_token=mask_token,
-            eos_token=eos_token,
-            model_max_length=model_max_length,
-            additional_special_tokens=additional_special_tokens,
-            **kwargs,
-        )
-
-        self.unique_no_split_tokens = self.all_tokens
-        self._update_trie(self.unique_no_split_tokens)
-
-    def _convert_id_to_token(self, index: int) -> str:
-        return self._id_to_token.get(index, self.unk_token)
-
-    def _convert_token_to_id(self, token: str) -> int:
-        return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))
-
-    def _tokenize(self, text: str, **kwargs) -> List[str]:
-        return text.split()
-
-    def get_vocab(self) -> dict:
-        base_vocab = self._token_to_id.copy()
-        base_vocab.update(self.added_tokens_encoder)
-        return base_vocab
-
-    def token_to_id(self, token: str) -> int:
-        return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))
-
-    def id_to_token(self, index: int) -> str:
-        return self._id_to_token.get(index, self.unk_token)
-
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        sep = [self.eos_token_id]  
-        if token_ids_1 is None:
-            if self.eos_token_id is None:
-                return token_ids_0
-            else:
-                return token_ids_0 + sep
-        elif self.eos_token_id is None:
-            raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!")
-        return token_ids_0 + sep + token_ids_1 + sep  # Multiple inputs always have an EOS token
-
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple:
-        vocab_file = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "tokenizer.model")
-        with open(vocab_file, "w") as f:
-            f.write("\n".join(self.all_tokens))
-        return (vocab_file,)
-
-    @property
-    def vocab_size(self) -> int:
-        return len(self.all_tokens)
-
-    def apply_chat_template(
-        self, 
-        query, 
-        add_generation_prompt: bool = True, 
-        tokenize: bool = True, 
-        padding: bool = False,
-        truncation: bool = False,
-        max_length: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_dict: bool = False,
-        tokenizer_kwargs: Optional[Dict[str, Any]] = None,
-        add_special_tokens: bool = True,
-        **kwargs,
-) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
-
-        generation_prompt = "<gmask><sop><eos>"
-        if isinstance(query, str):
-            query = [query]
-        prompt_query = []
-        if add_generation_prompt:
-            for each in query:
-                assert isinstance(each, str)
-                prompt_query.append(generation_prompt+each)
-        else:
-            prompt_query = query
-        if tokenize:
-            output = self.batch_encode_plus(
-                prompt_query,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                return_tensors=return_tensors,
-                is_split_into_words=True,
-                add_special_tokens=False
-            )
-            if return_dict:
-                return output
-            else:
-                return output["input_ids"]
-        else:
-            return prompt_query