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R11484/source_code/transformers/tests/test_pipelines_question_answering.py
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test_pipelines_question_answering.py
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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.
import unittest
from transformers.data.processors.squad import SquadExample
from transformers.pipelines import Pipeline, QuestionAnsweringArgumentHandler
from .test_pipelines_common import CustomInputPipelineCommonMixin
class QAPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
pipeline_task = "question-answering"
pipeline_running_kwargs = {
"padding": "max_length",
"max_seq_len": 25,
"doc_stride": 5,
} # Default is 'longest' but we use 'max_length' to test equivalence between slow/fast tokenizers
small_models = [
"sshleifer/tiny-distilbert-base-cased-distilled-squad"
] # Models tested without the @slow decorator
large_models = [] # Models tested with the @slow decorator
valid_inputs = [
{"question": "Where was HuggingFace founded ?", "context": "HuggingFace was founded in Paris."},
{
"question": "In what field is HuggingFace working ?",
"context": "HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
},
{
"question": ["In what field is HuggingFace working ?", "In what field is HuggingFace working ?"],
"context": "HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
},
{
"question": ["In what field is HuggingFace working ?", "In what field is HuggingFace working ?"],
"context": [
"HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
"HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
],
},
]
def _test_pipeline(self, nlp: Pipeline):
output_keys = {"score", "answer", "start", "end"}
valid_inputs = [
{"question": "Where was HuggingFace founded ?", "context": "HuggingFace was founded in Paris."},
{
"question": "In what field is HuggingFace working ?",
"context": "HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
},
]
invalid_inputs = [
{"question": "", "context": "This is a test to try empty question edge case"},
{"question": None, "context": "This is a test to try empty question edge case"},
{"question": "What is does with empty context ?", "context": ""},
{"question": "What is does with empty context ?", "context": None},
]
self.assertIsNotNone(nlp)
mono_result = nlp(valid_inputs[0])
self.assertIsInstance(mono_result, dict)
for key in output_keys:
self.assertIn(key, mono_result)
multi_result = nlp(valid_inputs)
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], dict)
for result in multi_result:
for key in output_keys:
self.assertIn(key, result)
for bad_input in invalid_inputs:
self.assertRaises(ValueError, nlp, bad_input)
self.assertRaises(ValueError, nlp, invalid_inputs)
def test_argument_handler(self):
qa = QuestionAnsweringArgumentHandler()
Q = "Where was HuggingFace founded ?"
C = "HuggingFace was founded in Paris"
normalized = qa(Q, C)
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 1)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
normalized = qa(question=Q, context=C)
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 1)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
normalized = qa(question=Q, context=C)
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 1)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
normalized = qa(question=[Q, Q], context=C)
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 2)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
normalized = qa({"question": Q, "context": C})
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 1)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
normalized = qa([{"question": Q, "context": C}])
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 1)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
normalized = qa([{"question": Q, "context": C}, {"question": Q, "context": C}])
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 2)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
normalized = qa(X={"question": Q, "context": C})
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 1)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
normalized = qa(X=[{"question": Q, "context": C}])
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 1)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
normalized = qa(data={"question": Q, "context": C})
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 1)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
def test_argument_handler_error_handling(self):
qa = QuestionAnsweringArgumentHandler()
Q = "Where was HuggingFace founded ?"
C = "HuggingFace was founded in Paris"
with self.assertRaises(KeyError):
qa({"context": C})
with self.assertRaises(KeyError):
qa({"question": Q})
with self.assertRaises(KeyError):
qa([{"context": C}])
with self.assertRaises(ValueError):
qa(None, C)
with self.assertRaises(ValueError):
qa("", C)
with self.assertRaises(ValueError):
qa(Q, None)
with self.assertRaises(ValueError):
qa(Q, "")
with self.assertRaises(ValueError):
qa(question=None, context=C)
with self.assertRaises(ValueError):
qa(question="", context=C)
with self.assertRaises(ValueError):
qa(question=Q, context=None)
with self.assertRaises(ValueError):
qa(question=Q, context="")
with self.assertRaises(ValueError):
qa({"question": None, "context": C})
with self.assertRaises(ValueError):
qa({"question": "", "context": C})
with self.assertRaises(ValueError):
qa({"question": Q, "context": None})
with self.assertRaises(ValueError):
qa({"question": Q, "context": ""})
with self.assertRaises(ValueError):
qa([{"question": Q, "context": C}, {"question": None, "context": C}])
with self.assertRaises(ValueError):
qa([{"question": Q, "context": C}, {"question": "", "context": C}])
with self.assertRaises(ValueError):
qa([{"question": Q, "context": C}, {"question": Q, "context": None}])
with self.assertRaises(ValueError):
qa([{"question": Q, "context": C}, {"question": Q, "context": ""}])
with self.assertRaises(ValueError):
qa(question={"This": "Is weird"}, context="This is a context")
with self.assertRaises(ValueError):
qa(question=[Q, Q], context=[C, C, C])
with self.assertRaises(ValueError):
qa(question=[Q, Q, Q], context=[C, C])
def test_argument_handler_old_format(self):
qa = QuestionAnsweringArgumentHandler()
Q = "Where was HuggingFace founded ?"
C = "HuggingFace was founded in Paris"
# Backward compatibility for this
normalized = qa(question=[Q, Q], context=[C, C])
self.assertEqual(type(normalized), list)
self.assertEqual(len(normalized), 2)
self.assertEqual({type(el) for el in normalized}, {SquadExample})
def test_argument_handler_error_handling_odd(self):
qa = QuestionAnsweringArgumentHandler()
with self.assertRaises(ValueError):
qa(None)
with self.assertRaises(ValueError):
qa(Y=None)
with self.assertRaises(ValueError):
qa(1)
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