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# coding=utf-8
# Copyright 2020 Huggingface
#
# 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.
from transformers import (
DPRContextEncoderTokenizer,
DPRContextEncoderTokenizerFast,
DPRQuestionEncoderTokenizer,
DPRQuestionEncoderTokenizerFast,
DPRReaderOutput,
DPRReaderTokenizer,
DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class DPRContextEncoderTokenizationTest(BertTokenizationTest):
tokenizer_class = DPRContextEncoderTokenizer
rust_tokenizer_class = DPRContextEncoderTokenizerFast
test_rust_tokenizer = True
@require_tokenizers
class DPRQuestionEncoderTokenizationTest(BertTokenizationTest):
tokenizer_class = DPRQuestionEncoderTokenizer
rust_tokenizer_class = DPRQuestionEncoderTokenizerFast
test_rust_tokenizer = True
@require_tokenizers
class DPRReaderTokenizationTest(BertTokenizationTest):
tokenizer_class = DPRReaderTokenizer
rust_tokenizer_class = DPRReaderTokenizerFast
test_rust_tokenizer = True
@slow
def test_decode_best_spans(self):
tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
text_1 = tokenizer.encode("question sequence", add_special_tokens=False)
text_2 = tokenizer.encode("title sequence", add_special_tokens=False)
text_3 = tokenizer.encode("text sequence " * 4, add_special_tokens=False)
input_ids = [[101] + text_1 + [102] + text_2 + [102] + text_3]
reader_input = BatchEncoding({"input_ids": input_ids})
start_logits = [[0] * len(input_ids[0])]
end_logits = [[0] * len(input_ids[0])]
relevance_logits = [0]
reader_output = DPRReaderOutput(start_logits, end_logits, relevance_logits)
start_index, end_index = 8, 9
start_logits[0][start_index] = 10
end_logits[0][end_index] = 10
predicted_spans = tokenizer.decode_best_spans(reader_input, reader_output)
self.assertEqual(predicted_spans[0].start_index, start_index)
self.assertEqual(predicted_spans[0].end_index, end_index)
self.assertEqual(predicted_spans[0].doc_id, 0)
@slow
def test_call(self):
tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
text_1 = tokenizer.encode("question sequence", add_special_tokens=False)
text_2 = tokenizer.encode("title sequence", add_special_tokens=False)
text_3 = tokenizer.encode("text sequence", add_special_tokens=False)
expected_input_ids = [101] + text_1 + [102] + text_2 + [102] + text_3
encoded_input = tokenizer(questions=["question sequence"], titles=["title sequence"], texts=["text sequence"])
self.assertIn("input_ids", encoded_input)
self.assertIn("attention_mask", encoded_input)
self.assertListEqual(encoded_input["input_ids"][0], expected_input_ids)
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