169 lines
7.2 KiB
Python
169 lines
7.2 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import shutil
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import tempfile
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from unittest import TestCase
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from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
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from transformers.models.bart.configuration_bart import BartConfig
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from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
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from transformers.models.dpr.configuration_dpr import DPRConfig
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from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
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from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
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from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
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if is_torch_available() and is_datasets_available() and is_faiss_available():
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from transformers.models.rag.configuration_rag import RagConfig
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from transformers.models.rag.tokenization_rag import RagTokenizer
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@require_faiss
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@require_torch
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class RagTokenizerTest(TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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self.retrieval_vector_size = 8
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# DPR tok
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vocab_tokens = [
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"[UNK]",
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"[CLS]",
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"[SEP]",
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"[PAD]",
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"[MASK]",
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"want",
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"##want",
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"##ed",
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"wa",
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"un",
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"runn",
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"##ing",
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",",
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"low",
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"lowest",
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]
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dpr_tokenizer_path = os.path.join(self.tmpdirname, "dpr_tokenizer")
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os.makedirs(dpr_tokenizer_path, exist_ok=True)
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self.vocab_file = os.path.join(dpr_tokenizer_path, DPR_VOCAB_FILES_NAMES["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
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vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
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# BART tok
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vocab = [
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"l",
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"o",
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"w",
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"e",
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"r",
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"s",
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"t",
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"i",
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"d",
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"n",
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"\u0120",
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"\u0120l",
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"\u0120n",
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"\u0120lo",
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"\u0120low",
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"er",
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"\u0120lowest",
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"\u0120newer",
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"\u0120wider",
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"<unk>",
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]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
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self.special_tokens_map = {"unk_token": "<unk>"}
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bart_tokenizer_path = os.path.join(self.tmpdirname, "bart_tokenizer")
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os.makedirs(bart_tokenizer_path, exist_ok=True)
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self.vocab_file = os.path.join(bart_tokenizer_path, BART_VOCAB_FILES_NAMES["vocab_file"])
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self.merges_file = os.path.join(bart_tokenizer_path, BART_VOCAB_FILES_NAMES["merges_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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with open(self.merges_file, "w", encoding="utf-8") as fp:
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fp.write("\n".join(merges))
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def get_dpr_tokenizer(self) -> DPRQuestionEncoderTokenizer:
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return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname, "dpr_tokenizer"))
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def get_bart_tokenizer(self) -> BartTokenizer:
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return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname, "bart_tokenizer"))
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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@require_tokenizers
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def test_save_load_pretrained_with_saved_config(self):
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save_dir = os.path.join(self.tmpdirname, "rag_tokenizer")
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rag_config = RagConfig(question_encoder=DPRConfig().to_dict(), generator=BartConfig().to_dict())
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rag_tokenizer = RagTokenizer(question_encoder=self.get_dpr_tokenizer(), generator=self.get_bart_tokenizer())
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rag_config.save_pretrained(save_dir)
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rag_tokenizer.save_pretrained(save_dir)
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new_rag_tokenizer = RagTokenizer.from_pretrained(save_dir, config=rag_config)
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self.assertIsInstance(new_rag_tokenizer.question_encoder, DPRQuestionEncoderTokenizerFast)
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self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab(), rag_tokenizer.question_encoder.get_vocab())
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self.assertIsInstance(new_rag_tokenizer.generator, BartTokenizerFast)
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self.assertEqual(new_rag_tokenizer.generator.get_vocab(), rag_tokenizer.generator.get_vocab())
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@slow
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def test_pretrained_token_nq_tokenizer(self):
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tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
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input_strings = [
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"who got the first nobel prize in physics",
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"when is the next deadpool movie being released",
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"which mode is used for short wave broadcast service",
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"who is the owner of reading football club",
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"when is the next scandal episode coming out",
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"when is the last time the philadelphia won the superbowl",
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"what is the most current adobe flash player version",
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"how many episodes are there in dragon ball z",
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"what is the first step in the evolution of the eye",
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"where is gall bladder situated in human body",
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"what is the main mineral in lithium batteries",
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"who is the president of usa right now",
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"where do the greasers live in the outsiders",
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"panda is a national animal of which country",
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"what is the name of manchester united stadium",
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]
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input_dict = tokenizer(input_strings)
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self.assertIsNotNone(input_dict)
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@slow
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def test_pretrained_sequence_nq_tokenizer(self):
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tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
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input_strings = [
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"who got the first nobel prize in physics",
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"when is the next deadpool movie being released",
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"which mode is used for short wave broadcast service",
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"who is the owner of reading football club",
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"when is the next scandal episode coming out",
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"when is the last time the philadelphia won the superbowl",
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"what is the most current adobe flash player version",
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"how many episodes are there in dragon ball z",
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"what is the first step in the evolution of the eye",
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"where is gall bladder situated in human body",
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"what is the main mineral in lithium batteries",
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"who is the president of usa right now",
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"where do the greasers live in the outsiders",
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"panda is a national animal of which country",
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"what is the name of manchester united stadium",
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]
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input_dict = tokenizer(input_strings)
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self.assertIsNotNone(input_dict)
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