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19420fd99e
* move test model folders (TODO: fix imports and others) * fix (potentially partially) imports (in model test modules) * fix (potentially partially) imports (in tokenization test modules) * fix (potentially partially) imports (in feature extraction test modules) * fix import utils.test_modeling_tf_core * fix path ../fixtures/ * fix imports about generation.test_generation_flax_utils * fix more imports * fix fixture path * fix get_test_dir * update module_to_test_file * fix get_tests_dir from wrong transformers.utils * update config.yml (CircleCI) * fix style * remove missing imports * update new model script * update check_repo * update SPECIAL_MODULE_TO_TEST_MAP * fix style * add __init__ * update self-scheduled * fix add_new_model scripts * check one way to get location back * python setup.py build install * fix import in test auto * update self-scheduled.yml * update slack notification script * Add comments about artifact names * fix for yolos Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
# coding=utf-8
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# Copyright 2020 Huggingface
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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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from transformers import (
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DPRContextEncoderTokenizer,
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DPRContextEncoderTokenizerFast,
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DPRQuestionEncoderTokenizer,
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DPRQuestionEncoderTokenizerFast,
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DPRReaderOutput,
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DPRReaderTokenizer,
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DPRReaderTokenizerFast,
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)
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from transformers.testing_utils import require_tokenizers, slow
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from transformers.tokenization_utils_base import BatchEncoding
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from ..bert.test_tokenization_bert import BertTokenizationTest
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@require_tokenizers
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class DPRContextEncoderTokenizationTest(BertTokenizationTest):
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tokenizer_class = DPRContextEncoderTokenizer
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rust_tokenizer_class = DPRContextEncoderTokenizerFast
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test_rust_tokenizer = True
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@require_tokenizers
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class DPRQuestionEncoderTokenizationTest(BertTokenizationTest):
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tokenizer_class = DPRQuestionEncoderTokenizer
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rust_tokenizer_class = DPRQuestionEncoderTokenizerFast
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test_rust_tokenizer = True
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@require_tokenizers
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class DPRReaderTokenizationTest(BertTokenizationTest):
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tokenizer_class = DPRReaderTokenizer
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rust_tokenizer_class = DPRReaderTokenizerFast
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test_rust_tokenizer = True
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@slow
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def test_decode_best_spans(self):
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tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
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text_1 = tokenizer.encode("question sequence", add_special_tokens=False)
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text_2 = tokenizer.encode("title sequence", add_special_tokens=False)
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text_3 = tokenizer.encode("text sequence " * 4, add_special_tokens=False)
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input_ids = [[101] + text_1 + [102] + text_2 + [102] + text_3]
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reader_input = BatchEncoding({"input_ids": input_ids})
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start_logits = [[0] * len(input_ids[0])]
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end_logits = [[0] * len(input_ids[0])]
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relevance_logits = [0]
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reader_output = DPRReaderOutput(start_logits, end_logits, relevance_logits)
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start_index, end_index = 8, 9
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start_logits[0][start_index] = 10
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end_logits[0][end_index] = 10
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predicted_spans = tokenizer.decode_best_spans(reader_input, reader_output)
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self.assertEqual(predicted_spans[0].start_index, start_index)
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self.assertEqual(predicted_spans[0].end_index, end_index)
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self.assertEqual(predicted_spans[0].doc_id, 0)
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@slow
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def test_call(self):
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tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
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text_1 = tokenizer.encode("question sequence", add_special_tokens=False)
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text_2 = tokenizer.encode("title sequence", add_special_tokens=False)
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text_3 = tokenizer.encode("text sequence", add_special_tokens=False)
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expected_input_ids = [101] + text_1 + [102] + text_2 + [102] + text_3
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encoded_input = tokenizer(questions=["question sequence"], titles=["title sequence"], texts=["text sequence"])
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self.assertIn("input_ids", encoded_input)
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self.assertIn("attention_mask", encoded_input)
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self.assertListEqual(encoded_input["input_ids"][0], expected_input_ids)
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