huggingface--transformers
wehub-resource-sync
e06fe8e8c6
Secret Leaks / trufflehog (push) Failing after 1s
Build documentation / build (push) Failing after 1s
Build documentation / build_other_lang (push) Failing after 0s
CodeQL Security Analysis / CodeQL Analysis (push) Failing after 0s
PR CI / pr-ci (push) Failing after 1s
Slow tests on important models (on Push - A10) / Get all modified files (push) Failing after 1s
Slow tests on important models (on Push - A10) / Model CI (push) Has been skipped
Self-hosted runner (benchmark) / Benchmark (aws-g5-4xlarge-cache) (push) Has been cancelled
New model PR merged notification / Notify new model (push) Has been cancelled
Update Transformers metadata / build_and_package (push) Has been cancelled
60 行
2.3 KiB
Python
60 行
2.3 KiB
Python
# Copyright 2022 HuggingFace Inc.
|
||
#
|
||
# 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 DonutProcessor
|
||
|
||
from ...test_processing_common import ProcessorTesterMixin
|
||
|
||
|
||
class DonutProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||
# Tiny processor created with make_tiny_processor.py from "naver-clova-ix/donut-base"
|
||
tiny_model_id = "hf-internal-testing/tiny-processor-donut"
|
||
processor_class = DonutProcessor
|
||
|
||
@classmethod
|
||
def _setup_image_processor(cls):
|
||
image_processor_class = cls._get_component_class_from_processor("image_processor")
|
||
# Default size=2560×1920 is the document-scanning resolution (~59 MB per image as float32).
|
||
# Use 64×64 for tests — no assertions check spatial dimensions.
|
||
return image_processor_class.from_pretrained(cls.tiny_model_id, size={"height": 64, "width": 64})
|
||
|
||
def test_token2json(self):
|
||
expected_json = {
|
||
"name": "John Doe",
|
||
"age": "99",
|
||
"city": "Atlanta",
|
||
"state": "GA",
|
||
"zip": "30301",
|
||
"phone": "123-4567",
|
||
"nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
|
||
"multiline": "text\nwith\nnewlines",
|
||
"empty": "",
|
||
}
|
||
|
||
sequence = (
|
||
"<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
|
||
"<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
|
||
"<s_nicknames><s_nickname>Johnny</s_nickname>"
|
||
"<sep/><s_nickname>JD</s_nickname></s_nicknames>"
|
||
"<s_multiline>text\nwith\nnewlines</s_multiline>"
|
||
"<s_empty></s_empty>"
|
||
)
|
||
processor = self.get_processor()
|
||
actual_json = processor.token2json(sequence)
|
||
|
||
self.assertDictEqual(actual_json, expected_json)
|