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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 shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
Pix2StructImageProcessor,
Pix2StructProcessor,
PreTrainedTokenizerFast,
T5Tokenizer,
)
@require_vision
@require_torch
class Pix2StructProcessorTest(unittest.TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
image_processor = Pix2StructImageProcessor()
tokenizer = T5Tokenizer.from_pretrained("t5-small")
processor = Pix2StructProcessor(image_processor, tokenizer)
processor.save_pretrained(self.tmpdirname)
def get_tokenizer(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
def get_image_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def prepare_image_inputs(self):
"""
This function prepares a list of random PIL images of the same fixed size.
"""
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_additional_features(self):
processor = Pix2StructProcessor(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 = Pix2StructProcessor.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, PreTrainedTokenizerFast)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, Pix2StructImageProcessor)
def test_image_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)
image_input = self.prepare_image_inputs()
input_feat_extract = image_processor(image_input, return_tensors="np")
input_processor = processor(images=image_input, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str, return_token_type_ids=False, add_special_tokens=False)
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 = Pix2StructProcessor(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()), ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]
)
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_processor_max_patches(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
max_patches = [512, 1024, 2048, 4096]
expected_hidden_size = [770, 770, 770, 770]
# with text
for i, max_patch in enumerate(max_patches):
inputs = processor(text=input_str, images=image_input, max_patches=max_patch)
self.assertEqual(inputs["flattened_patches"][0].shape[0], max_patch)
self.assertEqual(inputs["flattened_patches"][0].shape[1], expected_hidden_size[i])
# without text input
for i, max_patch in enumerate(max_patches):
inputs = processor(images=image_input, max_patches=max_patch)
self.assertEqual(inputs["flattened_patches"][0].shape[0], max_patch)
self.assertEqual(inputs["flattened_patches"][0].shape[1], expected_hidden_size[i])
def test_tokenizer_decode(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Pix2StructProcessor(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 = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
self.assertListEqual(
list(inputs.keys()), ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]
)
inputs = processor(text=input_str)
# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
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