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e99f0efedc
* add support for MLFLOW_FLATTEN_PARAMS * ensure key is str * fix style and update warning msg * Empty commit to trigger CI * fix bug in check_inits.py * add unittest for flatten_dict utils * fix 'NoneType' object is not callable on __del__ * add generic flatten_dict unittest to SPECIAL_MODULE_TO_TEST_MAP * fix style
46 lines
2.1 KiB
Python
46 lines
2.1 KiB
Python
# coding=utf-8
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# Copyright 2019-present, the HuggingFace Inc. team.
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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.utils import flatten_dict
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class GenericTester(unittest.TestCase):
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def test_flatten_dict(self):
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input_dict = {
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"task_specific_params": {
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"summarization": {"length_penalty": 1.0, "max_length": 128, "min_length": 12, "num_beams": 4},
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"summarization_cnn": {"length_penalty": 2.0, "max_length": 142, "min_length": 56, "num_beams": 4},
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"summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
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}
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}
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expected_dict = {
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"task_specific_params.summarization.length_penalty": 1.0,
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"task_specific_params.summarization.max_length": 128,
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"task_specific_params.summarization.min_length": 12,
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"task_specific_params.summarization.num_beams": 4,
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"task_specific_params.summarization_cnn.length_penalty": 2.0,
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"task_specific_params.summarization_cnn.max_length": 142,
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"task_specific_params.summarization_cnn.min_length": 56,
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"task_specific_params.summarization_cnn.num_beams": 4,
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"task_specific_params.summarization_xsum.length_penalty": 1.0,
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"task_specific_params.summarization_xsum.max_length": 62,
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"task_specific_params.summarization_xsum.min_length": 11,
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"task_specific_params.summarization_xsum.num_beams": 6,
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}
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self.assertEqual(flatten_dict(input_dict), expected_dict)
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