# Copyright 2024 The Qwen team, Alibaba Group and The HuggingFace Inc. 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. """Testing suite for the PyTorch GotOcr2 model.""" import unittest from transformers import ( AutoProcessor, GotOcr2Config, is_torch_available, is_vision_available, ) from transformers.testing_utils import cleanup, require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( GotOcr2ForConditionalGeneration, GotOcr2Model, ) if is_vision_available(): from transformers.image_utils import load_image class GotOcr2VisionText2TextModelTester: def __init__( self, parent, batch_size=3, seq_length=7, num_channels=3, ignore_index=-100, image_size=64, image_token_index=1, model_type="got_ocr2", is_training=True, text_config={ "model_type": "qwen2", "vocab_size": 99, "hidden_size": 128, "intermediate_size": 37, "num_hidden_layers": 2, "num_attention_heads": 4, "num_key_value_heads": 2, "output_channels": 64, "hidden_act": "silu", "max_position_embeddings": 512, "rope_theta": 10000, "mlp_ratio": 4, "tie_word_embeddings": True, "bos_token_id": 2, "eos_token_id": 3, "pad_token_id": 4, }, vision_config={ "num_hidden_layers": 2, "output_channels": 64, "hidden_act": "quick_gelu", "hidden_size": 32, "mlp_dim": 128, "num_attention_heads": 4, "patch_size": 2, "image_size": 64, }, ): self.parent = parent self.ignore_index = ignore_index self.bos_token_id = text_config["bos_token_id"] self.eos_token_id = text_config["eos_token_id"] self.pad_token_id = text_config["pad_token_id"] self.image_token_index = image_token_index self.model_type = model_type self.text_config = text_config self.vision_config = vision_config self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.is_training = is_training self.num_image_tokens = 64 self.seq_length = seq_length + self.num_image_tokens self.num_hidden_layers = text_config["num_hidden_layers"] self.vocab_size = text_config["vocab_size"] self.hidden_size = text_config["hidden_size"] self.num_attention_heads = text_config["num_attention_heads"] def get_config(self): return GotOcr2Config( text_config=self.text_config, vision_config=self.vision_config, model_type=self.model_type, image_token_index=self.image_token_index, ) def prepare_config_and_inputs(self): config = self.get_config() pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) return config, pixel_values def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) input_ids[input_ids == self.image_token_index] = self.pad_token_id input_ids[:, : self.num_image_tokens] = self.image_token_index inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class GotOcr2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( GotOcr2Model, GotOcr2ForConditionalGeneration, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "image-to-text": GotOcr2ForConditionalGeneration, "image-text-to-text": GotOcr2ForConditionalGeneration, "any-to-any": GotOcr2ForConditionalGeneration, } if is_torch_available() else {} ) def setUp(self): self.model_tester = GotOcr2VisionText2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=GotOcr2Config, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() @require_torch class GotOcr2IntegrationTest(unittest.TestCase): def setUp(self): self.processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf") def tearDown(self): cleanup(torch_device, gc_collect=True) @slow def test_small_model_integration_test_got_ocr_stop_strings(self): model_id = "stepfun-ai/GOT-OCR-2.0-hf" model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device) image = load_image( "https://huggingface.co/datasets/hf-internal-testing/fixtures_ocr/resolve/main/iam_picture.jpeg" ) inputs = self.processor(image, return_tensors="pt").to(torch_device) generate_ids = model.generate( **inputs, do_sample=False, num_beams=1, tokenizer=self.processor.tokenizer, stop_strings="<|im_end|>", max_new_tokens=4096, ) decoded_output = self.processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_output = "industre" self.assertEqual(decoded_output, expected_output) @slow def test_small_model_integration_test_got_ocr_format(self): model_id = "stepfun-ai/GOT-OCR-2.0-hf" model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device) image = load_image( "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg" ) inputs = self.processor(image, return_tensors="pt", format=True).to(torch_device) generate_ids = model.generate(**inputs, do_sample=False, num_beams=1, max_new_tokens=4) decoded_output = self.processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_output = "\\title{\nR" self.assertEqual(decoded_output, expected_output) @slow def test_small_model_integration_test_got_ocr_fine_grained(self): model_id = "stepfun-ai/GOT-OCR-2.0-hf" model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device) image = load_image( "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png" ) inputs = self.processor(image, return_tensors="pt", color="green").to(torch_device) generate_ids = model.generate(**inputs, do_sample=False, num_beams=1, max_new_tokens=4) decoded_output = self.processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_output = "You should keep in" self.assertEqual(decoded_output, expected_output) @slow def test_small_model_integration_test_got_ocr_crop_to_patches(self): model_id = "stepfun-ai/GOT-OCR-2.0-hf" model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device) image = load_image( "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/one_column.png" ) inputs = self.processor(image, return_tensors="pt", crop_to_patches=True).to(torch_device) generate_ids = model.generate(**inputs, do_sample=False, num_beams=1, max_new_tokens=4) decoded_output = self.processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_output = "on developing architectural improvements" self.assertEqual(decoded_output, expected_output) @slow def test_small_model_integration_test_got_ocr_multi_pages(self): model_id = "stepfun-ai/GOT-OCR-2.0-hf" model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device) image1 = load_image( "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/one_column.png" ) image2 = load_image( "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png" ) inputs = self.processor([image1, image2], return_tensors="pt", multi_page=True).to(torch_device) generate_ids = model.generate(**inputs, do_sample=False, num_beams=1, max_new_tokens=4) decoded_output = self.processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_output = "on developing architectural improvements" self.assertEqual(decoded_output, expected_output) @slow def test_small_model_integration_test_got_ocr_batched(self): model_id = "stepfun-ai/GOT-OCR-2.0-hf" model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device) image1 = load_image( "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png" ) image2 = load_image( "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg" ) inputs = self.processor([image1, image2], return_tensors="pt").to(torch_device) generate_ids = model.generate(**inputs, do_sample=False, num_beams=1, max_new_tokens=4) decoded_output = self.processor.batch_decode( generate_ids[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_output = ["Reducing the number", "R&D QUALITY"] self.assertEqual(decoded_output, expected_output)