# coding=utf-8 # Copyright 2022 The Salesforce authors, 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 OpenAI GPT.""" import re from typing import TYPE_CHECKING, List, Optional, Tuple, Union import numpy as np from ...utils import is_tf_available, is_torch_available, logging if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf from ...tokenization_utils_base import BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_codegen import CodeGenTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} class CodeGenTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" CodeGen tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ```python >>> from transformers import CodeGenTokenizerFast >>> tokenizer = CodeGenTokenizerFast.from_pretrained("Salesforce/codegen-350M-mono") >>> tokenizer("Hello world")["input_ids"] [15496, 995] >>> tokenizer(" Hello world")["input_ids"] [18435, 995] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since the model was not pretrained this way, it might yield a decrease in performance. When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. 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`, *optional*): Path to the vocabulary file. merges_file (`str`, *optional*): Path to the merges file. tokenizer_file (`str`, *optional*): Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that contains everything needed to load the tokenizer. unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`): 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. bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): The beginning of sequence token. eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): The end of sequence token. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (CodeGen tokenizer detect beginning of words by the preceding space). return_token_type_ids (`bool`, *optional*, defaults to `False`): Whether to return token type IDs. """ vocab_files_names = VOCAB_FILES_NAMES model_input_names = ["input_ids", "attention_mask"] slow_tokenizer_class = CodeGenTokenizer def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, unk_token="<|endoftext|>", bos_token="<|endoftext|>", eos_token="<|endoftext|>", add_prefix_space=False, return_token_type_ids=False, **kwargs, ): self.return_token_type_ids = return_token_type_ids if self.return_token_type_ids: self.model_input_names.append("token_type_ids") super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, add_prefix_space=add_prefix_space, return_token_type_ids=return_token_type_ids, **kwargs, ) if kwargs.pop("add_bos_token", False): model_id = kwargs.pop("name_or_path", "") raise ValueError( "Currenty GPT2's fast tokenizer does NOT support adding a BOS token. " "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n" f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n" f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n" "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005." " so that the fast tokenizer works correctly." ) def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*args, **kwargs) def _encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*args, **kwargs) # Copied from transformers.models.codegen.tokenization_codegen.CodeGenTokenizer.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 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] if self.sep_token_id is not None else [] cls = [self.cls_token_id] if self.sep_token_id is not None else [] 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] 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) def decode( self, token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: Optional[bool] = None, truncate_before_pattern: Optional[List[str]] = None, **kwargs, ) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`). truncate_before_pattern (`List[str]`, *optional*, defaults to `None`): A list of regular expression strings that will be used to truncate the returned string. This can be used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning of a new line). An example pattern could be `["^#", re.escape("<|endoftext|>"), "^'''", "\n\n\n"]`. kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str`: The decoded sentence. """ decoded_text = super().decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) if truncate_before_pattern is not None and len(truncate_before_pattern) > 0: decoded_text = self.truncate(decoded_text, truncate_before_pattern) return decoded_text def truncate(self, completion, truncate_before_pattern): def find_re(string, pattern, start_pos): m = pattern.search(string, start_pos) return m.start() if m else -1 terminals = [re.compile(pattern, re.MULTILINE) for pattern in truncate_before_pattern] prints = list(re.finditer("^print", completion, re.MULTILINE)) if len(prints) > 1: completion = completion[: prints[1].start()] defs = list(re.finditer("^def", completion, re.MULTILINE)) if len(defs) > 1: completion = completion[: defs[1].start()] start_pos = 0 terminals_pos = [ pos for pos in [find_re(completion, terminal, start_pos) for terminal in terminals] if pos != -1 ] if len(terminals_pos) > 0: return completion[: min(terminals_pos)] else: return completion __all__ = ["CodeGenTokenizerFast"]