# Copyright 2022 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 import numpy as np from transformers import ( IdeficsProcessor, ) from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_processing_common import ProcessorTesterMixin if is_torch_available(): pass if is_vision_available(): from PIL import Image @require_torch @require_vision class IdeficsProcessorTest(ProcessorTesterMixin, unittest.TestCase): processor_class = IdeficsProcessor input_keys = ["pixel_values", "input_ids", "attention_mask", "image_attention_mask"] @classmethod def _setup_image_processor(cls): image_processor_class = cls._get_component_class_from_processor("image_processor") return image_processor_class(return_tensors="pt") @classmethod def _setup_tokenizer(cls): tokenizer_class = cls._get_component_class_from_processor("tokenizer") return tokenizer_class.from_pretrained("HuggingFaceM4/tiny-random-idefics") def prepare_prompts(self): """This function prepares a list of PIL images""" num_images = 2 images = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8) for x in range(num_images)] images = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in images] # print([type(x) for x in images]) # die prompts = [ # text and 1 image [ "User:", images[0], "Describe this image.\nAssistant:", ], # text and images [ "User:", images[0], "Describe this image.\nAssistant: An image of two dogs.\n", "User:", images[1], "Describe this image.\nAssistant:", ], # only text [ "User:", "Describe this image.\nAssistant: An image of two kittens.\n", "User:", "Describe this image.\nAssistant:", ], # only images [ images[0], images[1], ], ] return prompts def test_save_load_pretrained_additional_features(self): tokenizer_add_kwargs = self.get_component("tokenizer", bos_token="(BOS)", eos_token="(EOS)") image_processor_add_kwargs = self.get_component("image_processor", do_normalize=False, padding_value=1.0) processor = IdeficsProcessor.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, self._get_component_class_from_processor("tokenizer")) self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) self.assertIsInstance(processor.image_processor, self._get_component_class_from_processor("image_processor")) def test_tokenizer_padding(self): image_processor = self.get_component("image_processor") tokenizer = self.get_component("tokenizer", padding_side="right") processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor, return_tensors="pt") predicted_tokens = [ "Describe this image.\nAssistant:", "Describe this image.\nAssistant:", ] predicted_attention_masks = [ ([1] * 10) + ([0] * 9), ([1] * 10) + ([0] * 10), ] prompts = [[prompt] for prompt in self.prepare_prompts()[2]] max_length = processor(text=prompts, padding="max_length", truncation=True, max_length=20, return_tensors="pt") longest = processor(text=prompts, padding="longest", truncation=True, max_length=30, return_tensors="pt") decoded_max_length = processor.tokenizer.decode(max_length["input_ids"][-1]) decoded_longest = processor.tokenizer.decode(longest["input_ids"][-1]) self.assertEqual(decoded_max_length, predicted_tokens[1]) self.assertEqual(decoded_longest, predicted_tokens[0]) self.assertListEqual(max_length["attention_mask"][-1].tolist(), predicted_attention_masks[1]) self.assertListEqual(longest["attention_mask"][-1].tolist(), predicted_attention_masks[0]) def test_tokenizer_left_padding(self): """Identical to test_tokenizer_padding, but with padding_side not explicitly set.""" processor = self.get_processor() predicted_tokens = [ "Describe this image.\nAssistant:", "Describe this image.\nAssistant:", ] predicted_attention_masks = [ ([0] * 9) + ([1] * 10), ([0] * 10) + ([1] * 10), ] prompts = [[prompt] for prompt in self.prepare_prompts()[2]] max_length = processor(text=prompts, padding="max_length", truncation=True, max_length=20) longest = processor(text=prompts, padding="longest", truncation=True, max_length=30) decoded_max_length = processor.tokenizer.decode(max_length["input_ids"][-1]) decoded_longest = processor.tokenizer.decode(longest["input_ids"][-1]) self.assertEqual(decoded_max_length, predicted_tokens[1]) self.assertEqual(decoded_longest, predicted_tokens[0]) self.assertListEqual(max_length["attention_mask"][-1].tolist(), predicted_attention_masks[1]) self.assertListEqual(longest["attention_mask"][-1].tolist(), predicted_attention_masks[0]) def test_tokenizer_defaults(self): # Override to account for the processor prefixing the BOS token to prompts. components = {attribute: self.get_component(attribute) for attribute in self.processor_class.get_attributes()} processor = self.processor_class(**components) tokenizer = components["tokenizer"] input_str = ["lower newer"] encoded_processor = processor(text=input_str, padding=False, return_tensors="pt") encoded_tok = tokenizer( [f"{tokenizer.bos_token}{input_str[0]}"], padding=False, add_special_tokens=False, return_tensors="pt" ) for key in encoded_tok: if key in encoded_processor: self.assertListEqual(encoded_tok[key].tolist(), encoded_processor[key].tolist())