# Copyright 2024 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 functools import cached_property from transformers import ( UdopProcessor, UdopTokenizer, UdopTokenizerFast, ) from transformers.testing_utils import ( require_pytesseract, require_sentencepiece, require_tokenizers, require_torch, slow, ) from transformers.utils import is_pytesseract_available, is_torch_available from ...test_processing_common import ProcessorTesterMixin if is_torch_available(): import torch if is_pytesseract_available(): from transformers import LayoutLMv3ImageProcessor @require_pytesseract @require_sentencepiece @require_tokenizers class UdopProcessorTest(ProcessorTesterMixin, unittest.TestCase): tokenizer_class = UdopTokenizer rust_tokenizer_class = UdopTokenizerFast processor_class = UdopProcessor maxDiff = None @classmethod def _setup_image_processor(cls): image_processor_class = cls._get_component_class_from_processor("image_processor") return image_processor_class( do_resize=True, size=224, apply_ocr=True, ) @classmethod def _setup_tokenizer(cls): tokenizer_class = cls._get_component_class_from_processor("tokenizer") return tokenizer_class.from_pretrained("microsoft/udop-large") @unittest.skip("UdopProcessor doesn't return pixel_values tensors") def test_image_processor_defaults(self): pass def test_text_target(self): image_processor = self.get_component("image_processor") tokenizer = self.get_component("tokenizer") processor = UdopProcessor(tokenizer=tokenizer, image_processor=image_processor) text = "hello world" expected_decoding = "hello world" encoding_processor = processor(text_target=text) encoding_tokenizer = tokenizer(text_target=text) self.assertListEqual(encoding_processor["input_ids"], [21820, 296, 1]) self.assertListEqual(encoding_processor["attention_mask"], [1, 1, 1]) self.assertDictEqual(dict(encoding_processor), dict(encoding_tokenizer)) self.assertEqual(tokenizer.decode(encoding_processor["input_ids"]), expected_decoding) @slow def test_overflowing_tokens(self): # In the case of overflowing tokens, test that we still have 1-to-1 mapping between the images and input_ids (sequences that are too long are broken down into multiple sequences). from datasets import load_dataset # set up datasets = load_dataset("nielsr/funsd") processor = UdopProcessor.from_pretrained("microsoft/udop-large", apply_ocr=False) def preprocess_data(examples): images = [image.convert("RGB") for image in examples["image"]] words = list(examples["words"]) boxes = list(examples["bboxes"]) word_labels = list(examples["ner_tags"]) encoded_inputs = processor( images, words, boxes=boxes, word_labels=word_labels, max_length=512, padding="max_length", truncation=True, return_overflowing_tokens=True, stride=50, return_offsets_mapping=True, return_tensors="pt", ) return encoded_inputs train_data = preprocess_data(datasets["train"]) self.assertEqual(len(train_data["pixel_values"]), len(train_data["input_ids"])) @unittest.skip("We will not support batch input with and without images for UDOP!") def test_processor_text_has_no_visual(self): pass # different use cases tests @require_sentencepiece @require_torch @require_pytesseract class UdopProcessorIntegrationTests(unittest.TestCase): @cached_property def get_images(self): # we verify our implementation on 2 document images from the DocVQA dataset from datasets import load_dataset ds = load_dataset("hf-internal-testing/fixtures_docvqa", split="test") return ds[0]["image"].convert("RGB"), ds[1]["image"].convert("RGB") @cached_property def get_tokenizers(self): slow_tokenizer = UdopTokenizer.from_pretrained("microsoft/udop-large") fast_tokenizer = UdopTokenizerFast.from_pretrained("microsoft/udop-large") return [slow_tokenizer, fast_tokenizer] @slow def test_processor_case_1(self): # case 1: document image classification (training, inference) + token classification (inference), apply_ocr = True image_processor = LayoutLMv3ImageProcessor() tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer) # not batched input_image_processor = image_processor(images[0], return_tensors="pt") input_processor = processor(images[0], return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(input_processor.keys()) self.assertListEqual(actual_keys, expected_keys) # verify pixel_values self.assertTrue( torch.allclose(input_image_processor["pixel_values"], input_processor["pixel_values"], atol=1e-2) ) # verify input_ids # this was obtained with Tesseract 4.1.1 # fmt: off expected_decoding = "11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer" # noqa: E231 # fmt: on decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched input_image_processor = image_processor(images, return_tensors="pt") input_processor = processor(images, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(input_processor.keys()) self.assertListEqual(actual_keys, expected_keys) # verify pixel_values self.assertTrue( torch.allclose(input_image_processor["pixel_values"], input_processor["pixel_values"], atol=1e-2) ) # verify input_ids # this was obtained with Tesseract 4.1.1 # fmt: off expected_decoding = "7 ITC Limited REPORT AND ACCOUNTS 2013 ITC’s Brands: An Asset for the Nation The consumer needs and aspirations they fulfil, the benefit they generate for millions across ITC’s value chains, the future-ready capabilities that support them, and the value that they create for the country, have made ITC’s brands national assets, adding to India’s competitiveness. It is ITC’s aspiration to be the No 1 FMCG player in the country, driven by its new FMCG businesses. A recent Nielsen report has highlighted that ITC's new FMCG businesses are the fastest growing among the top consumer goods companies operating in India. ITC takes justifiable pride that, along with generating economic value, these celebrated Indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. DI WILLS * ; LOVE DELIGHTFULLY SOFT SKIN? aia Ans Source: https://www.industrydocuments.ucsf.edu/docs/snbx0223" # noqa: E231 # fmt: on decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) @slow def test_processor_case_2(self): # case 2: document image classification (training, inference) + token classification (inference), apply_ocr=False image_processor = LayoutLMv3ImageProcessor(apply_ocr=False) tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer) # not batched words = ["hello", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] input_processor = processor(images[0], words, boxes=boxes, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = list(input_processor.keys()) for key in expected_keys: self.assertIn(key, actual_keys) # verify input_ids expected_decoding = "hello world" decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched words = [["hello", "world"], ["my", "name", "is", "niels"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]] input_processor = processor(images, words, boxes=boxes, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(input_processor.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "hello world" decoding = processor.decode(input_processor.input_ids[0].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox expected_bbox = [ [3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3], [1, 1, 2, 3], [1, 1, 2, 3], [1000, 1000, 1000, 1000], ] self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) @slow def test_processor_case_3(self): # case 3: token classification (training), apply_ocr=False image_processor = LayoutLMv3ImageProcessor(apply_ocr=False) tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer) # not batched words = ["weirdly", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] word_labels = [1, 2] input_processor = processor(images[0], words, boxes=boxes, word_labels=word_labels, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "labels", "pixel_values"] actual_keys = sorted(input_processor.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "weirdly world" decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify labels expected_labels = [1, -100, 2, -100] self.assertListEqual(input_processor.labels.squeeze().tolist(), expected_labels) # batched words = [["hello", "world"], ["my", "name", "is", "niels"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]] word_labels = [[1, 2], [6, 3, 10, 2]] input_processor = processor( images, words, boxes=boxes, word_labels=word_labels, padding=True, return_tensors="pt" ) # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "labels", "pixel_values"] actual_keys = sorted(input_processor.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "my name is niels" decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox expected_bbox = [ [3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3], [1, 1, 2, 3], [1, 1, 2, 3], [1000, 1000, 1000, 1000], ] self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) # verify labels expected_labels = [6, 3, 10, 2, -100, -100, -100] self.assertListEqual(input_processor.labels[1].tolist(), expected_labels) @slow def test_processor_case_4(self): # case 4: visual question answering (inference), apply_ocr=True image_processor = LayoutLMv3ImageProcessor() tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer) # not batched question = "What's his name?" input_processor = processor(images[0], question, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(input_processor.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids # this was obtained with Tesseract 4.1.1 # fmt: off expected_decoding = "What's his name? 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer" # noqa: E231 # fmt: on decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched questions = ["How old is he?", "what's the time"] input_processor = processor( images, questions, padding="max_length", max_length=20, truncation=True, return_tensors="pt" ) # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(input_processor.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids # this was obtained with Tesseract 4.1.1 expected_decoding = "what's the time 7 ITC Limited REPORT AND ACCOUNTS 2013 I" decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox # fmt: off expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [372, 59, 407, 66], [74, 136, 161, 158], [1000, 1000, 1000, 1000]] # noqa: E231 # fmt: on self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) @slow def test_processor_case_5(self): # case 5: visual question answering (inference), apply_ocr=False image_processor = LayoutLMv3ImageProcessor(apply_ocr=False) tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer) # not batched question = "What's his name?" words = ["hello", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] input_processor = processor(images[0], question, text_pair=words, boxes=boxes, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(input_processor.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "What's his name? hello world" decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched questions = ["How old is he?", "what's the time"] words = [["hello", "world"], ["my", "name", "is", "niels"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]] input_processor = processor( images, questions, text_pair=words, boxes=boxes, padding=True, return_tensors="pt" ) # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(input_processor.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "How old is he? hello world" decoding = processor.decode(input_processor.input_ids[0].tolist()) self.assertSequenceEqual(decoding, expected_decoding) expected_decoding = "what's the time my name is niels" decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox expected_bbox = [[3, 9, 2, 4], [1, 1, 2, 3], [1, 1, 2, 3], [1, 1, 2, 3], [1000, 1000, 1000, 1000]] self.assertListEqual(input_processor.bbox[1].tolist()[-5:], expected_bbox)