File size: 2,115 Bytes
a9bd396 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | # Copyright 2023 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.testing_utils import require_vision
from transformers.utils import is_vision_available
from ...test_processing_common import ProcessorTesterMixin
if is_vision_available():
from transformers import AlignProcessor
@require_vision
class AlignProcessorTest(ProcessorTesterMixin, unittest.TestCase):
processor_class = AlignProcessor
@classmethod
def _setup_tokenizer(cls):
tokenizer_class = cls._get_component_class_from_processor("tokenizer")
vocab_tokens = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
vocab_file = f"{cls.tmpdirname}/vocab.txt"
with open(vocab_file, "w", encoding="utf-8") as f:
f.write("\n".join(vocab_tokens))
tokenizer = tokenizer_class(vocab_file)
return tokenizer
@classmethod
def _setup_image_processor(cls):
image_processor_class = cls._get_component_class_from_processor("image_processor")
image_processor = image_processor_class(
do_resize=True,
size=20,
do_normalize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
)
return image_processor
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