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transformers/tests/models/dpr/test_tokenization_dpr.py

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+# 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 import test_tokenization_bert
+
+
+@require_tokenizers
+class DPRContextEncoderTokenizationTest(test_tokenization_bert.BertTokenizationTest):
+    tokenizer_class = DPRContextEncoderTokenizer
+    rust_tokenizer_class = DPRContextEncoderTokenizerFast
+    test_rust_tokenizer = True
+    from_pretrained_id = "facebook/dpr-ctx_encoder-single-nq-base"
+
+
+@require_tokenizers
+class DPRQuestionEncoderTokenizationTest(test_tokenization_bert.BertTokenizationTest):
+    tokenizer_class = DPRQuestionEncoderTokenizer
+    rust_tokenizer_class = DPRQuestionEncoderTokenizerFast
+    test_rust_tokenizer = True
+    from_pretrained_id = "facebook/dpr-ctx_encoder-single-nq-base"
+
+
+@require_tokenizers
+class DPRReaderTokenizationTest(test_tokenization_bert.BertTokenizationTest):
+    tokenizer_class = DPRReaderTokenizer
+    rust_tokenizer_class = DPRReaderTokenizerFast
+    test_rust_tokenizer = True
+    from_pretrained_id = "facebook/dpr-ctx_encoder-single-nq-base"
+
+    @slow
+    def test_decode_best_spans(self):
+        tokenizer = self.tokenizer_class.from_pretrained("google-bert/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("google-bert/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)