222 lines
9.0 KiB
Python
222 lines
9.0 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 unittest
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from transformers.data.processors.squad import SquadExample
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from transformers.pipelines import Pipeline, QuestionAnsweringArgumentHandler
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from .test_pipelines_common import CustomInputPipelineCommonMixin
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class QAPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "question-answering"
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pipeline_running_kwargs = {
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"padding": "max_length",
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"max_seq_len": 25,
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"doc_stride": 5,
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} # Default is 'longest' but we use 'max_length' to test equivalence between slow/fast tokenizers
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small_models = [
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"sshleifer/tiny-distilbert-base-cased-distilled-squad"
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] # Models tested without the @slow decorator
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large_models = [] # Models tested with the @slow decorator
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valid_inputs = [
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{"question": "Where was HuggingFace founded ?", "context": "HuggingFace was founded in Paris."},
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{
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"question": "In what field is HuggingFace working ?",
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"context": "HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
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},
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{
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"question": ["In what field is HuggingFace working ?", "In what field is HuggingFace working ?"],
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"context": "HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
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},
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{
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"question": ["In what field is HuggingFace working ?", "In what field is HuggingFace working ?"],
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"context": [
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"HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
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"HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
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],
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},
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]
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def _test_pipeline(self, nlp: Pipeline):
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output_keys = {"score", "answer", "start", "end"}
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valid_inputs = [
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{"question": "Where was HuggingFace founded ?", "context": "HuggingFace was founded in Paris."},
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{
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"question": "In what field is HuggingFace working ?",
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"context": "HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
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},
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]
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invalid_inputs = [
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{"question": "", "context": "This is a test to try empty question edge case"},
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{"question": None, "context": "This is a test to try empty question edge case"},
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{"question": "What is does with empty context ?", "context": ""},
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{"question": "What is does with empty context ?", "context": None},
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]
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self.assertIsNotNone(nlp)
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mono_result = nlp(valid_inputs[0])
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self.assertIsInstance(mono_result, dict)
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for key in output_keys:
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self.assertIn(key, mono_result)
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multi_result = nlp(valid_inputs)
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], dict)
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for result in multi_result:
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for key in output_keys:
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self.assertIn(key, result)
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for bad_input in invalid_inputs:
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self.assertRaises(ValueError, nlp, bad_input)
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self.assertRaises(ValueError, nlp, invalid_inputs)
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def test_argument_handler(self):
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qa = QuestionAnsweringArgumentHandler()
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Q = "Where was HuggingFace founded ?"
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C = "HuggingFace was founded in Paris"
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normalized = qa(Q, C)
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 1)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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normalized = qa(question=Q, context=C)
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 1)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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normalized = qa(question=Q, context=C)
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 1)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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normalized = qa(question=[Q, Q], context=C)
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 2)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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normalized = qa({"question": Q, "context": C})
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 1)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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normalized = qa([{"question": Q, "context": C}])
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 1)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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normalized = qa([{"question": Q, "context": C}, {"question": Q, "context": C}])
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 2)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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normalized = qa(X={"question": Q, "context": C})
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 1)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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normalized = qa(X=[{"question": Q, "context": C}])
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 1)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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normalized = qa(data={"question": Q, "context": C})
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 1)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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def test_argument_handler_error_handling(self):
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qa = QuestionAnsweringArgumentHandler()
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Q = "Where was HuggingFace founded ?"
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C = "HuggingFace was founded in Paris"
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with self.assertRaises(KeyError):
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qa({"context": C})
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with self.assertRaises(KeyError):
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qa({"question": Q})
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with self.assertRaises(KeyError):
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qa([{"context": C}])
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with self.assertRaises(ValueError):
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qa(None, C)
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with self.assertRaises(ValueError):
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qa("", C)
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with self.assertRaises(ValueError):
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qa(Q, None)
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with self.assertRaises(ValueError):
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qa(Q, "")
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with self.assertRaises(ValueError):
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qa(question=None, context=C)
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with self.assertRaises(ValueError):
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qa(question="", context=C)
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with self.assertRaises(ValueError):
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qa(question=Q, context=None)
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with self.assertRaises(ValueError):
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qa(question=Q, context="")
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with self.assertRaises(ValueError):
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qa({"question": None, "context": C})
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with self.assertRaises(ValueError):
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qa({"question": "", "context": C})
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with self.assertRaises(ValueError):
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qa({"question": Q, "context": None})
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with self.assertRaises(ValueError):
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qa({"question": Q, "context": ""})
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with self.assertRaises(ValueError):
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qa([{"question": Q, "context": C}, {"question": None, "context": C}])
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with self.assertRaises(ValueError):
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qa([{"question": Q, "context": C}, {"question": "", "context": C}])
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with self.assertRaises(ValueError):
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qa([{"question": Q, "context": C}, {"question": Q, "context": None}])
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with self.assertRaises(ValueError):
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qa([{"question": Q, "context": C}, {"question": Q, "context": ""}])
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with self.assertRaises(ValueError):
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qa(question={"This": "Is weird"}, context="This is a context")
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with self.assertRaises(ValueError):
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qa(question=[Q, Q], context=[C, C, C])
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with self.assertRaises(ValueError):
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qa(question=[Q, Q, Q], context=[C, C])
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def test_argument_handler_old_format(self):
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qa = QuestionAnsweringArgumentHandler()
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Q = "Where was HuggingFace founded ?"
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C = "HuggingFace was founded in Paris"
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# Backward compatibility for this
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normalized = qa(question=[Q, Q], context=[C, C])
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self.assertEqual(type(normalized), list)
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self.assertEqual(len(normalized), 2)
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self.assertEqual({type(el) for el in normalized}, {SquadExample})
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def test_argument_handler_error_handling_odd(self):
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qa = QuestionAnsweringArgumentHandler()
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with self.assertRaises(ValueError):
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qa(None)
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with self.assertRaises(ValueError):
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qa(Y=None)
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with self.assertRaises(ValueError):
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qa(1)
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