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import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from transformers.tokenization_utils_base import BatchEncoding
from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
from transformers.utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"tokenizer_file": {
"smallcloudai/codify_medium_multi": "https://huggingface.co/smallcloudai/codify_medium_multi/blob/main/tokenizer.json",
"smallcloudai/codify_3b_multi": "https://huggingface.co/smallcloudai/codify_3b_multi/blob/main/tokenizer.json",
},
}
class CodifyTokenizerFast(PreTrainedTokenizerFast):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = None
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,
**kwargs
):
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,
**kwargs,
)
pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
pre_tok_state["add_prefix_space"] = add_prefix_space
self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
self.add_prefix_space = add_prefix_space
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
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)
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
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)
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 _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
"""This corresponds to DialoGPT variants of models."""
input_ids = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(text, add_special_tokens=False) + [self.eos_token_id])
if len(input_ids) > self.model_max_length:
input_ids = input_ids[-self.model_max_length :]
return input_ids
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