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| # 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 json | |
| import os | |
| import shutil | |
| import tempfile | |
| import unittest | |
| import numpy as np | |
| import pytest | |
| from transformers import BertTokenizer, BertTokenizerFast | |
| from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES | |
| from transformers.testing_utils import require_vision | |
| from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available | |
| if is_vision_available(): | |
| from PIL import Image | |
| from transformers import AlignProcessor, EfficientNetImageProcessor | |
| class AlignProcessorTest(unittest.TestCase): | |
| def setUp(self): | |
| self.tmpdirname = tempfile.mkdtemp() | |
| vocab_tokens = [ | |
| "[UNK]", | |
| "[CLS]", | |
| "[SEP]", | |
| "[PAD]", | |
| "[MASK]", | |
| "want", | |
| "##want", | |
| "##ed", | |
| "wa", | |
| "un", | |
| "runn", | |
| "##ing", | |
| ",", | |
| "low", | |
| "lowest", | |
| ] | |
| self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) | |
| with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: | |
| vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) | |
| image_processor_map = { | |
| "do_resize": True, | |
| "size": 20, | |
| "do_center_crop": True, | |
| "crop_size": 18, | |
| "do_normalize": True, | |
| "image_mean": [0.48145466, 0.4578275, 0.40821073], | |
| "image_std": [0.26862954, 0.26130258, 0.27577711], | |
| } | |
| self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME) | |
| with open(self.image_processor_file, "w", encoding="utf-8") as fp: | |
| json.dump(image_processor_map, fp) | |
| def get_tokenizer(self, **kwargs): | |
| return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs) | |
| def get_rust_tokenizer(self, **kwargs): | |
| return BertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) | |
| def get_image_processor(self, **kwargs): | |
| return EfficientNetImageProcessor.from_pretrained(self.tmpdirname, **kwargs) | |
| def tearDown(self): | |
| shutil.rmtree(self.tmpdirname) | |
| def prepare_image_inputs(self): | |
| """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, | |
| or a list of PyTorch tensors if one specifies torchify=True. | |
| """ | |
| image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)] | |
| image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs] | |
| return image_inputs | |
| def test_save_load_pretrained_default(self): | |
| tokenizer_slow = self.get_tokenizer() | |
| tokenizer_fast = self.get_rust_tokenizer() | |
| image_processor = self.get_image_processor() | |
| processor_slow = AlignProcessor(tokenizer=tokenizer_slow, image_processor=image_processor) | |
| processor_slow.save_pretrained(self.tmpdirname) | |
| processor_slow = AlignProcessor.from_pretrained(self.tmpdirname, use_fast=False) | |
| processor_fast = AlignProcessor(tokenizer=tokenizer_fast, image_processor=image_processor) | |
| processor_fast.save_pretrained(self.tmpdirname) | |
| processor_fast = AlignProcessor.from_pretrained(self.tmpdirname) | |
| self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab()) | |
| self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab()) | |
| self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab()) | |
| self.assertIsInstance(processor_slow.tokenizer, BertTokenizer) | |
| self.assertIsInstance(processor_fast.tokenizer, BertTokenizerFast) | |
| self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string()) | |
| self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string()) | |
| self.assertIsInstance(processor_slow.image_processor, EfficientNetImageProcessor) | |
| self.assertIsInstance(processor_fast.image_processor, EfficientNetImageProcessor) | |
| def test_save_load_pretrained_additional_features(self): | |
| processor = AlignProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()) | |
| processor.save_pretrained(self.tmpdirname) | |
| tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") | |
| image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0) | |
| processor = AlignProcessor.from_pretrained( | |
| self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0 | |
| ) | |
| self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) | |
| self.assertIsInstance(processor.tokenizer, BertTokenizerFast) | |
| self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) | |
| self.assertIsInstance(processor.image_processor, EfficientNetImageProcessor) | |
| def test_image_processor(self): | |
| image_processor = self.get_image_processor() | |
| tokenizer = self.get_tokenizer() | |
| processor = AlignProcessor(tokenizer=tokenizer, image_processor=image_processor) | |
| image_input = self.prepare_image_inputs() | |
| input_image_proc = image_processor(image_input, return_tensors="np") | |
| input_processor = processor(images=image_input, return_tensors="np") | |
| for key in input_image_proc.keys(): | |
| self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2) | |
| def test_tokenizer(self): | |
| image_processor = self.get_image_processor() | |
| tokenizer = self.get_tokenizer() | |
| processor = AlignProcessor(tokenizer=tokenizer, image_processor=image_processor) | |
| input_str = "lower newer" | |
| encoded_processor = processor(text=input_str) | |
| encoded_tok = tokenizer(input_str, padding="max_length", max_length=64) | |
| for key in encoded_tok.keys(): | |
| self.assertListEqual(encoded_tok[key], encoded_processor[key]) | |
| def test_processor(self): | |
| image_processor = self.get_image_processor() | |
| tokenizer = self.get_tokenizer() | |
| processor = AlignProcessor(tokenizer=tokenizer, image_processor=image_processor) | |
| input_str = "lower newer" | |
| image_input = self.prepare_image_inputs() | |
| inputs = processor(text=input_str, images=image_input) | |
| self.assertListEqual(list(inputs.keys()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]) | |
| # test if it raises when no input is passed | |
| with pytest.raises(ValueError): | |
| processor() | |
| def test_tokenizer_decode(self): | |
| image_processor = self.get_image_processor() | |
| tokenizer = self.get_tokenizer() | |
| processor = AlignProcessor(tokenizer=tokenizer, image_processor=image_processor) | |
| predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] | |
| decoded_processor = processor.batch_decode(predicted_ids) | |
| decoded_tok = tokenizer.batch_decode(predicted_ids) | |
| self.assertListEqual(decoded_tok, decoded_processor) | |
| def test_model_input_names(self): | |
| image_processor = self.get_image_processor() | |
| tokenizer = self.get_tokenizer() | |
| processor = AlignProcessor(tokenizer=tokenizer, image_processor=image_processor) | |
| input_str = "lower newer" | |
| image_input = self.prepare_image_inputs() | |
| inputs = processor(text=input_str, images=image_input) | |
| self.assertListEqual(list(inputs.keys()), processor.model_input_names) | |