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
from typing import List, Optional

from ...file_utils import add_start_docstrings
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, 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 def save_pretrained(self, save_directory): if os.path.isfile(save_directory): raise ValueError("Provided path ({}) should be a directory, not a file".format(save_directory)) 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.question_encoder(*args, **kwargs) def batch_decode(self, *args, **kwargs): return self.generator.batch_decode(*args, **kwargs)
[docs] @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING) 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=True, **kwargs, ) -> BatchEncoding: if max_length is None: max_length = self.question_encoder.model_max_length model_inputs: BatchEncoding = self.question_encoder( 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 if max_target_length is None: max_target_length = self.generator.model_max_length labels = self.generator( tgt_texts, add_special_tokens=True, return_tensors=return_tensors, padding=padding, max_length=max_target_length, truncation=truncation, **kwargs, )["input_ids"] model_inputs["labels"] = labels return model_inputs