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# coding=utf-8
# Copyright 2022 The Hugging Face Team.
#
# 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 transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MarkupLMForQuestionAnswering,
MarkupLMForSequenceClassification,
MarkupLMForTokenClassification,
MarkupLMModel,
)
# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer
class MarkupLMModelTester:
"""You can also import this e.g from .test_modeling_markuplm import MarkupLMModelTester"""
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
scope=None,
max_xpath_tag_unit_embeddings=20,
max_xpath_subs_unit_embeddings=30,
tag_pad_id=2,
subs_pad_id=2,
max_depth=10,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
self.tag_pad_id = tag_pad_id
self.subs_pad_id = subs_pad_id
self.max_depth = max_depth
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
xpath_tags_seq = ids_tensor(
[self.batch_size, self.seq_length, self.max_depth], self.max_xpath_tag_unit_embeddings
)
xpath_subs_seq = ids_tensor(
[self.batch_size, self.seq_length, self.max_depth], self.max_xpath_subs_unit_embeddings
)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
config = self.get_config()
return (
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
)
def get_config(self):
return MarkupLMConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings,
max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings,
tag_pad_id=self.tag_pad_id,
subs_pad_id=self.subs_pad_id,
max_depth=self.max_depth,
)
def create_and_check_model(
self,
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
model = MarkupLMModel(config=config)
model.to(torch_device)
model.eval()
print("Configs:", model.config.tag_pad_id, model.config.subs_pad_id)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
config.num_labels = self.num_labels
model = MarkupLMForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self,
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
config.num_labels = self.num_labels
model = MarkupLMForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self,
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
model = MarkupLMForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"xpath_tags_seq": xpath_tags_seq,
"xpath_subs_seq": xpath_subs_seq,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class MarkupLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
MarkupLMModel,
MarkupLMForSequenceClassification,
MarkupLMForTokenClassification,
MarkupLMForQuestionAnswering,
)
if is_torch_available()
else None
)
pipeline_model_mapping = (
{
"feature-extraction": MarkupLMModel,
"question-answering": MarkupLMForQuestionAnswering,
"text-classification": MarkupLMForSequenceClassification,
"token-classification": MarkupLMForTokenClassification,
"zero-shot": MarkupLMForSequenceClassification,
}
if is_torch_available()
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
# (batch of pretokenized examples).
return True
def setUp(self):
self.model_tester = MarkupLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=MarkupLMConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def prepare_html_string():
html_string = """
<!DOCTYPE html>
<html>
<head>
<title>Page Title</title>
</head>
<body>
<h1>This is a Heading</h1>
<p>This is a paragraph.</p>
</body>
</html>
"""
return html_string
@require_torch
class MarkupLMModelIntegrationTest(unittest.TestCase):
@cached_property
def default_processor(self):
# TODO use from_pretrained here
feature_extractor = MarkupLMFeatureExtractor()
tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")
return MarkupLMProcessor(feature_extractor, tokenizer)
@slow
def test_forward_pass_no_head(self):
model = MarkupLMModel.from_pretrained("microsoft/markuplm-base").to(torch_device)
processor = self.default_processor
inputs = processor(prepare_html_string(), return_tensors="pt")
inputs = inputs.to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the last hidden states
expected_shape = torch.Size([1, 14, 768])
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
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