Source code for transformers.models.rag.tokenization_rag

# coding=utf-8
# Copyright 2020, The RAG 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for RAG."""
import os
import warnings
from contextlib import contextmanager
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig

logger = logging.get_logger(__name__)

[docs]class RagTokenizer: def __init__(self, question_encoder, generator): self.question_encoder = question_encoder self.generator = generator self.current_tokenizer = self.question_encoder def save_pretrained(self, save_directory): if os.path.isfile(save_directory): raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file") os.makedirs(save_directory, exist_ok=True) question_encoder_path = os.path.join(save_directory, "question_encoder_tokenizer") generator_path = os.path.join(save_directory, "generator_tokenizer") self.question_encoder.save_pretrained(question_encoder_path) self.generator.save_pretrained(generator_path) @classmethod def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): # dynamically import AutoTokenizer from import AutoTokenizer config = kwargs.pop("config", None) if config is None: config = RagConfig.from_pretrained(pretrained_model_name_or_path) question_encoder = AutoTokenizer.from_pretrained( pretrained_model_name_or_path, config=config.question_encoder, subfolder="question_encoder_tokenizer" ) generator = AutoTokenizer.from_pretrained( pretrained_model_name_or_path, config=config.generator, subfolder="generator_tokenizer" ) return cls(question_encoder=question_encoder, generator=generator) def __call__(self, *args, **kwargs): return self.current_tokenizer(*args, **kwargs) def batch_decode(self, *args, **kwargs): return self.generator.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): return self.generator.decode(*args, **kwargs)
[docs] @contextmanager def as_target_tokenizer(self): """ Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to sequence-to-sequence models that need a slightly different processing for the labels. """ self.current_tokenizer = self.generator yield self.current_tokenizer = self.question_encoder
def prepare_seq2seq_batch( self, src_texts: List[str], tgt_texts: Optional[List[str]] = None, max_length: Optional[int] = None, max_target_length: Optional[int] = None, padding: str = "longest", return_tensors: str = None, truncation: bool = True, **kwargs, ) -> BatchEncoding: warnings.warn( "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the " "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` " "context manager to prepare your targets. See the documentation of your specific tokenizer for more " "details", FutureWarning, ) if max_length is None: max_length = self.current_tokenizer.model_max_length model_inputs = self( src_texts, add_special_tokens=True, return_tensors=return_tensors, max_length=max_length, padding=padding, truncation=truncation, **kwargs, ) if tgt_texts is None: return model_inputs # Process tgt_texts with self.as_target_tokenizer(): if max_target_length is None: max_target_length = self.current_tokenizer.model_max_length labels = self( tgt_texts, add_special_tokens=True, return_tensors=return_tensors, padding=padding, max_length=max_target_length, truncation=truncation, **kwargs, ) model_inputs["labels"] = labels["input_ids"] return model_inputs