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R11484/source_code/transformers/tests/test_pipelines_text_generation.py
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test_pipelines_text_generation.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 import pipeline
from transformers.testing_utils import require_torch
from .test_pipelines_common import MonoInputPipelineCommonMixin
class TextGenerationPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
pipeline_task = "text-generation"
pipeline_running_kwargs = {"prefix": "This is "}
small_models = ["sshleifer/tiny-ctrl"] # Models tested without the @slow decorator
large_models = [] # Models tested with the @slow decorator
def test_simple_generation(self):
nlp = pipeline(task="text-generation", model=self.small_models[0])
# text-generation is non-deterministic by nature, we can't fully test the output
outputs = nlp("This is a test")
self.assertEqual(len(outputs), 1)
self.assertEqual(list(outputs[0].keys()), ["generated_text"])
self.assertEqual(type(outputs[0]["generated_text"]), str)
outputs = nlp(["This is a test", "This is a second test"])
self.assertEqual(len(outputs[0]), 1)
self.assertEqual(list(outputs[0][0].keys()), ["generated_text"])
self.assertEqual(type(outputs[0][0]["generated_text"]), str)
self.assertEqual(list(outputs[1][0].keys()), ["generated_text"])
self.assertEqual(type(outputs[1][0]["generated_text"]), str)
@require_torch
def test_generation_output_style(self):
text_generator = pipeline(task="text-generation", model=self.small_models[0])
# text-generation is non-deterministic by nature, we can't fully test the output
outputs = text_generator("This is a test")
self.assertIn("This is a test", outputs[0]["generated_text"])
outputs = text_generator("This is a test", return_full_text=False)
self.assertNotIn("This is a test", outputs[0]["generated_text"])
text_generator = pipeline(task="text-generation", model=self.small_models[0], return_full_text=False)
outputs = text_generator("This is a test")
self.assertNotIn("This is a test", outputs[0]["generated_text"])
outputs = text_generator("This is a test", return_full_text=True)
self.assertIn("This is a test", outputs[0]["generated_text"])
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