test_modeling_tapas.py
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test_modeling_tapas.py
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	# coding=utf-8
	# Copyright 2020 The HuggingFace Inc. 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 copy
	import unittest

	import numpy as np
	import pandas as pd

	from transformers import (
	MODEL_FOR_CAUSAL_LM_MAPPING,
	MODEL_FOR_MASKED_LM_MAPPING,
	MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
	MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
	MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	is_torch_available,
	)
	from transformers.file_utils import cached_property
	from transformers.testing_utils import require_scatter, require_torch, slow, torch_device

	from .test_configuration_common import ConfigTester
	from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


	if is_torch_available():
	import torch

	from transformers import (
	TapasConfig,
	TapasForMaskedLM,
	TapasForQuestionAnswering,
	TapasForSequenceClassification,
	TapasModel,
	TapasTokenizer,
	)
	from transformers.models.tapas.modeling_tapas import (
	IndexMap,
	ProductIndexMap,
	flatten,
	gather,
	range_index_map,
	reduce_max,
	reduce_mean,
	reduce_sum,
	)


	class TapasModelTester:
	"""You can also import this e.g from .test_modeling_tapas import TapasModelTester """

	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=5,
	num_attention_heads=4,
	intermediate_size=37,
	hidden_act="gelu",
	hidden_dropout_prob=0.1,
	attention_probs_dropout_prob=0.1,
	initializer_range=0.02,
	max_position_embeddings=512,
	type_vocab_sizes=[3, 256, 256, 2, 256, 256, 10],
	type_sequence_label_size=2,
	positive_weight=10.0,
	num_aggregation_labels=4,
	num_labels=2,
	aggregation_loss_importance=0.8,
	use_answer_as_supervision=True,
	answer_loss_importance=0.001,
	use_normalized_answer_loss=False,
	huber_loss_delta=25.0,
	temperature=1.0,
	agg_temperature=1.0,
	use_gumbel_for_cells=False,
	use_gumbel_for_agg=False,
	average_approximation_function="ratio",
	cell_selection_preference=0.5,
	answer_loss_cutoff=100,
	max_num_rows=64,
	max_num_columns=32,
	average_logits_per_cell=True,
	select_one_column=True,
	allow_empty_column_selection=False,
	init_cell_selection_weights_to_zero=False,
	reset_position_index_per_cell=True,
	disable_per_token_loss=False,
	scope=None,
	):
	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.initializer_range = initializer_range
	self.max_position_embeddings = max_position_embeddings
	self.type_vocab_sizes = type_vocab_sizes
	self.type_sequence_label_size = type_sequence_label_size
	self.positive_weight = positive_weight
	self.num_aggregation_labels = num_aggregation_labels
	self.num_labels = num_labels
	self.aggregation_loss_importance = aggregation_loss_importance
	self.use_answer_as_supervision = use_answer_as_supervision
	self.answer_loss_importance = answer_loss_importance
	self.use_normalized_answer_loss = use_normalized_answer_loss
	self.huber_loss_delta = huber_loss_delta
	self.temperature = temperature
	self.agg_temperature = agg_temperature
	self.use_gumbel_for_cells = use_gumbel_for_cells
	self.use_gumbel_for_agg = use_gumbel_for_agg
	self.average_approximation_function = average_approximation_function
	self.cell_selection_preference = cell_selection_preference
	self.answer_loss_cutoff = answer_loss_cutoff
	self.max_num_rows = max_num_rows
	self.max_num_columns = max_num_columns
	self.average_logits_per_cell = average_logits_per_cell
	self.select_one_column = select_one_column
	self.allow_empty_column_selection = allow_empty_column_selection
	self.init_cell_selection_weights_to_zero = init_cell_selection_weights_to_zero
	self.reset_position_index_per_cell = reset_position_index_per_cell
	self.disable_per_token_loss = disable_per_token_loss
	self.scope = scope

	def prepare_config_and_inputs(self):
	input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).to(torch_device)

	input_mask = None
	if self.use_input_mask:
	input_mask = random_attention_mask([self.batch_size, self.seq_length]).to(torch_device)

	token_type_ids = []
	for type_vocab_size in self.type_vocab_sizes:
	token_type_ids.append(ids_tensor(shape=[self.batch_size, self.seq_length], vocab_size=type_vocab_size))
	token_type_ids = torch.stack(token_type_ids, dim=2).to(torch_device)

	sequence_labels = None
	token_labels = None
	labels = None
	numeric_values = None
	numeric_values_scale = None
	float_answer = None
	aggregation_labels = None
	if self.use_labels:
	sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size).to(torch_device)
	token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels).to(torch_device)
	labels = ids_tensor([self.batch_size, self.seq_length], vocab_size=2).to(torch_device)
	numeric_values = floats_tensor([self.batch_size, self.seq_length]).to(torch_device)
	numeric_values_scale = floats_tensor([self.batch_size, self.seq_length]).to(torch_device)
	float_answer = floats_tensor([self.batch_size]).to(torch_device)
	aggregation_labels = ids_tensor([self.batch_size], self.num_aggregation_labels).to(torch_device)

	config = TapasConfig(
	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_sizes=self.type_vocab_sizes,
	initializer_range=self.initializer_range,
	positive_weight=self.positive_weight,
	num_aggregation_labels=self.num_aggregation_labels,
	num_labels=self.num_labels,
	aggregation_loss_importance=self.aggregation_loss_importance,
	use_answer_as_supervision=self.use_answer_as_supervision,
	answer_loss_importance=self.answer_loss_importance,
	use_normalized_answer_loss=self.use_normalized_answer_loss,
	huber_loss_delta=self.huber_loss_delta,
	temperature=self.temperature,
	agg_temperature=self.agg_temperature,
	use_gumbel_for_cells=self.use_gumbel_for_cells,
	use_gumbel_for_agg=self.use_gumbel_for_agg,
	average_approximation_function=self.average_approximation_function,
	cell_selection_preference=self.cell_selection_preference,
	answer_loss_cutoff=self.answer_loss_cutoff,
	max_num_rows=self.max_num_rows,
	max_num_columns=self.max_num_columns,
	average_logits_per_cell=self.average_logits_per_cell,
	select_one_column=self.select_one_column,
	allow_empty_column_selection=self.allow_empty_column_selection,
	init_cell_selection_weights_to_zero=self.init_cell_selection_weights_to_zero,
	reset_position_index_per_cell=self.reset_position_index_per_cell,
	disable_per_token_loss=self.disable_per_token_loss,
	)

	return (
	config,
	input_ids,
	input_mask,
	token_type_ids,
	sequence_labels,
	token_labels,
	labels,
	numeric_values,
	numeric_values_scale,
	float_answer,
	aggregation_labels,
	)

	def create_and_check_model(
	self,
	config,
	input_ids,
	input_mask,
	token_type_ids,
	sequence_labels,
	token_labels,
	labels,
	numeric_values,
	numeric_values_scale,
	float_answer,
	aggregation_labels,
	):
	model = TapasModel(config=config)
	model.to(torch_device)
	model.eval()
	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_masked_lm(
	self,
	config,
	input_ids,
	input_mask,
	token_type_ids,
	sequence_labels,
	token_labels,
	labels,
	numeric_values,
	numeric_values_scale,
	float_answer,
	aggregation_labels,
	):
	model = TapasForMaskedLM(config=config)
	model.to(torch_device)
	model.eval()
	result = model(input_ids, 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.vocab_size))

	def create_and_check_for_question_answering(
	self,
	config,
	input_ids,
	input_mask,
	token_type_ids,
	sequence_labels,
	token_labels,
	labels,
	numeric_values,
	numeric_values_scale,
	float_answer,
	aggregation_labels,
	):
	# inference: without aggregation head (SQA). Model only returns logits
	sqa_config = copy.copy(config)
	sqa_config.num_aggregation_labels = 0
	sqa_config.use_answer_as_supervision = False
	model = TapasForQuestionAnswering(config=sqa_config)
	model.to(torch_device)
	model.eval()
	result = model(
	input_ids=input_ids,
	attention_mask=input_mask,
	token_type_ids=token_type_ids,
	)
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))

	# inference: with aggregation head (WTQ, WikiSQL-supervised). Model returns logits and aggregation logits
	model = TapasForQuestionAnswering(config=config)
	model.to(torch_device)
	model.eval()
	result = model(
	input_ids=input_ids,
	attention_mask=input_mask,
	token_type_ids=token_type_ids,
	)
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
	self.parent.assertEqual(result.logits_aggregation.shape, (self.batch_size, self.num_aggregation_labels))

	# training: can happen in 3 main ways
	# case 1: conversational (SQA)
	model = TapasForQuestionAnswering(config=sqa_config)
	model.to(torch_device)
	model.eval()
	result = model(
	input_ids,
	attention_mask=input_mask,
	token_type_ids=token_type_ids,
	labels=labels,
	)
	self.parent.assertEqual(result.loss.shape, ())
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))

	# case 2: weak supervision for aggregation (WTQ)
	model = TapasForQuestionAnswering(config=config)
	model.to(torch_device)
	model.eval()
	result = model(
	input_ids=input_ids,
	attention_mask=input_mask,
	token_type_ids=token_type_ids,
	labels=labels,
	numeric_values=numeric_values,
	numeric_values_scale=numeric_values_scale,
	float_answer=float_answer,
	)
	self.parent.assertEqual(result.loss.shape, ())
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
	self.parent.assertEqual(result.logits_aggregation.shape, (self.batch_size, self.num_aggregation_labels))

	# case 3: strong supervision for aggregation (WikiSQL-supervised)
	wikisql_config = copy.copy(config)
	wikisql_config.use_answer_as_supervision = False
	model = TapasForQuestionAnswering(config=wikisql_config)
	model.to(torch_device)
	model.eval()
	result = model(
	input_ids,
	attention_mask=input_mask,
	token_type_ids=token_type_ids,
	labels=labels,
	aggregation_labels=aggregation_labels,
	)
	self.parent.assertEqual(result.loss.shape, ())
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
	self.parent.assertEqual(result.logits_aggregation.shape, (self.batch_size, self.num_aggregation_labels))

	def create_and_check_for_sequence_classification(
	self,
	config,
	input_ids,
	input_mask,
	token_type_ids,
	sequence_labels,
	token_labels,
	labels,
	numeric_values,
	numeric_values_scale,
	float_answer,
	aggregation_labels,
	):
	config.num_labels = self.num_labels
	model = TapasForSequenceClassification(config)
	model.to(torch_device)
	model.eval()
	result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

	def prepare_config_and_inputs_for_common(self):
	config_and_inputs = self.prepare_config_and_inputs()
	(
	config,
	input_ids,
	input_mask,
	token_type_ids,
	sequence_labels,
	token_labels,
	labels,
	numeric_values,
	numeric_values_scale,
	float_answer,
	aggregation_labels,
	) = config_and_inputs
	inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
	return config, inputs_dict


	@require_torch
	@require_scatter
	class TapasModelTest(ModelTesterMixin, unittest.TestCase):

	all_model_classes = (
	(
	TapasModel,
	TapasForMaskedLM,
	TapasForQuestionAnswering,
	TapasForSequenceClassification,
	)
	if is_torch_available()
	else None
	)
	test_pruning = False
	test_torchscript = False
	test_resize_embeddings = True
	test_head_masking = False

	def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
	inputs_dict = copy.deepcopy(inputs_dict)
	if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
	inputs_dict = {
	k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
	if isinstance(v, torch.Tensor) and v.ndim > 1
	else v
	for k, v in inputs_dict.items()
	}

	if return_labels:
	if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
	inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
	elif model_class in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.values():
	inputs_dict["labels"] = torch.zeros(
	(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
	)
	inputs_dict["aggregation_labels"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.long, device=torch_device
	)
	inputs_dict["numeric_values"] = torch.zeros(
	(self.model_tester.batch_size, self.model_tester.seq_length),
	dtype=torch.float,
	device=torch_device,
	)
	inputs_dict["numeric_values_scale"] = torch.zeros(
	(self.model_tester.batch_size, self.model_tester.seq_length),
	dtype=torch.float,
	device=torch_device,
	)
	inputs_dict["float_answer"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.float, device=torch_device
	)
	elif model_class in [
	*MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values(),
	*MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.values(),
	]:
	inputs_dict["labels"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.long, device=torch_device
	)
	elif model_class in [
	*MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.values(),
	*MODEL_FOR_CAUSAL_LM_MAPPING.values(),
	*MODEL_FOR_MASKED_LM_MAPPING.values(),
	*MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.values(),
	]:
	inputs_dict["labels"] = torch.zeros(
	(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
	)
	return inputs_dict

	def setUp(self):
	self.model_tester = TapasModelTester(self)
	self.config_tester = ConfigTester(self, config_class=TapasConfig, dim=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_masked_lm(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_for_masked_lm(*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 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 prepare_tapas_single_inputs_for_inference():
	# Here we prepare a single table-question pair to test TAPAS inference on:
	data = {
	"Footballer": ["Lionel Messi", "Cristiano Ronaldo"],
	"Age": ["33", "35"],
	}
	queries = "Which footballer is 33 years old?"
	table = pd.DataFrame.from_dict(data)

	return table, queries


	def prepare_tapas_batch_inputs_for_inference():
	# Here we prepare a batch of 2 table-question pairs to test TAPAS inference on:
	data = {
	"Footballer": ["Lionel Messi", "Cristiano Ronaldo"],
	"Age": ["33", "35"],
	"Number of goals": ["712", "750"],
	}
	queries = ["Which footballer is 33 years old?", "How many goals does Ronaldo have?"]
	table = pd.DataFrame.from_dict(data)

	return table, queries


	def prepare_tapas_batch_inputs_for_training():
	# Here we prepare a DIFFERENT batch of 2 table-question pairs to test TAPAS training on:
	data = {
	"Footballer": ["Lionel Messi", "Cristiano Ronaldo"],
	"Age": ["33", "35"],
	"Number of goals": ["712", "750"],
	}
	queries = ["Which footballer is 33 years old?", "What's the total number of goals?"]
	table = pd.DataFrame.from_dict(data)

	answer_coordinates = [[(0, 0)], [(0, 2), (1, 2)]]
	answer_text = [["Lionel Messi"], ["1462"]]
	float_answer = [float("NaN"), float("1462")]

	return table, queries, answer_coordinates, answer_text, float_answer


	@require_torch
	@require_scatter
	class TapasModelIntegrationTest(unittest.TestCase):
	@cached_property
	def default_tokenizer(self):
	return TapasTokenizer.from_pretrained("google/tapas-base-finetuned-wtq")

	@slow
	def test_inference_no_head(self):
	# ideally we want to test this with the weights of tapas_inter_masklm_base_reset,
	# but since it's not straightforward to do this with the TF 1 implementation, we test it with
	# the weights of the WTQ base model (i.e. tapas_wtq_wikisql_sqa_inter_masklm_base_reset)
	model = TapasModel.from_pretrained("google/tapas-base-finetuned-wtq").to(torch_device)

	tokenizer = self.default_tokenizer
	table, queries = prepare_tapas_single_inputs_for_inference()
	inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
	inputs = {k: v.to(torch_device) for k, v in inputs.items()}
	outputs = model(**inputs)
	# test the sequence output
	expected_slice = torch.tensor(
	[
	[
	[-0.141581565, -0.599805772, 0.747186482],
	[-0.143664181, -0.602008104, 0.749218345],
	[-0.15169853, -0.603363097, 0.741370678],
	]
	],
	device=torch_device,
	)

	self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :3, :3], expected_slice, atol=0.0005))

	# test the pooled output
	expected_slice = torch.tensor([[0.987518311, -0.970520139, -0.994303405]], device=torch_device)

	self.assertTrue(torch.allclose(outputs.pooler_output[:, :3], expected_slice, atol=0.0005))

	@unittest.skip(reason="Model not available yet")
	def test_inference_masked_lm(self):
	pass

	# TapasForQuestionAnswering has 3 possible ways of being fine-tuned:
	# - conversational set-up (SQA)
	# - weak supervision for aggregation (WTQ, WikiSQL)
	# - strong supervision for aggregation (WikiSQL-supervised)
	# We test all of them:
	@slow
	def test_inference_question_answering_head_conversational(self):
	# note that google/tapas-base-finetuned-sqa should correspond to tapas_sqa_inter_masklm_base_reset
	model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-sqa").to(torch_device)

	tokenizer = self.default_tokenizer
	table, queries = prepare_tapas_single_inputs_for_inference()
	inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
	inputs = {k: v.to(torch_device) for k, v in inputs.items()}
	outputs = model(**inputs)
	# test the logits
	logits = outputs.logits
	expected_shape = torch.Size((1, 21))
	self.assertEqual(logits.shape, expected_shape)

	expected_tensor = torch.tensor(
	[
	[
	-9997.22461,
	-9997.22461,
	-9997.22461,
	-9997.22461,
	-9997.22461,
	-9997.22461,
	-9997.22461,
	-9997.22461,
	-9997.22461,
	-16.2628059,
	-10004.082,
	15.4330549,
	15.4330549,
	15.4330549,
	-9990.42,
	-16.3270779,
	-16.3270779,
	-16.3270779,
	-16.3270779,
	-16.3270779,
	-10004.8506,
	]
	],
	device=torch_device,
	)

	self.assertTrue(torch.allclose(logits, expected_tensor, atol=0.015))

	@slow
	def test_inference_question_answering_head_conversational_absolute_embeddings(self):
	# note that google/tapas-small-finetuned-sqa should correspond to tapas_sqa_inter_masklm_small_reset
	# however here we test the version with absolute position embeddings
	model = TapasForQuestionAnswering.from_pretrained("google/tapas-small-finetuned-sqa", revision="no_reset").to(
	torch_device
	)

	tokenizer = self.default_tokenizer
	table, queries = prepare_tapas_single_inputs_for_inference()
	inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
	inputs = {k: v.to(torch_device) for k, v in inputs.items()}
	outputs = model(**inputs)
	# test the logits
	logits = outputs.logits
	expected_shape = torch.Size((1, 21))
	self.assertEqual(logits.shape, expected_shape)

	expected_tensor = torch.tensor(
	[
	[
	-10014.7793,
	-10014.7793,
	-10014.7793,
	-10014.7793,
	-10014.7793,
	-10014.7793,
	-10014.7793,
	-10014.7793,
	-10014.7793,
	-18.8419304,
	-10018.0391,
	17.7848816,
	17.7848816,
	17.7848816,
	-9981.02832,
	-16.4005489,
	-16.4005489,
	-16.4005489,
	-16.4005489,
	-16.4005489,
	-10013.4736,
	]
	],
	device=torch_device,
	)

	self.assertTrue(torch.allclose(logits, expected_tensor, atol=0.01))

	@slow
	def test_inference_question_answering_head_weak_supervision(self):
	# note that google/tapas-base-finetuned-wtq should correspond to tapas_wtq_wikisql_sqa_inter_masklm_base_reset
	model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq").to(torch_device)

	tokenizer = self.default_tokenizer
	# let's test on a batch
	table, queries = prepare_tapas_batch_inputs_for_inference()
	inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt")
	inputs_on_device = {k: v.to(torch_device) for k, v in inputs.items()}

	outputs = model(**inputs_on_device)
	# test the logits
	logits = outputs.logits
	expected_shape = torch.Size((2, 28))
	self.assertEqual(logits.shape, expected_shape)

	expected_slice = torch.tensor(
	[
	[-160.375504, -160.375504, -160.375504, -10072.3965, -10070.9414, -10094.9736],
	[-9861.6123, -9861.6123, -9861.6123, -9861.6123, -9891.01172, 146.600677],
	],
	device=torch_device,
	)

	self.assertTrue(torch.allclose(logits[:, -6:], expected_slice, atol=0.4))

	# test the aggregation logits
	logits_aggregation = outputs.logits_aggregation
	expected_shape = torch.Size((2, 4))
	self.assertEqual(logits_aggregation.shape, expected_shape)
	expected_tensor = torch.tensor(
	[[18.8545208, -9.76614857, -6.3128891, -2.93525243], [-4.05782509, 40.0351, -5.35329962, 23.3978653]],
	device=torch_device,
	)

	self.assertTrue(torch.allclose(logits_aggregation, expected_tensor, atol=0.001))

	# test the predicted answer coordinates and aggregation indices
	EXPECTED_PREDICTED_ANSWER_COORDINATES = [[(0, 0)], [(1, 2)]]
	EXPECTED_PREDICTED_AGGREGATION_INDICES = [0, 1]

	predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
	inputs, outputs.logits.detach().cpu(), outputs.logits_aggregation.detach().cpu()
	)

	self.assertEqual(EXPECTED_PREDICTED_ANSWER_COORDINATES, predicted_answer_coordinates)
	self.assertEqual(EXPECTED_PREDICTED_AGGREGATION_INDICES, predicted_aggregation_indices)

	@slow
	def test_training_question_answering_head_weak_supervision(self):
	# note that google/tapas-base-finetuned-wtq should correspond to tapas_wtq_wikisql_sqa_inter_masklm_base_reset
	model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq").to(torch_device)
	model.to(torch_device)
	# normally we should put the model in training mode but it's a pain to do this with the TF 1 implementation

	tokenizer = self.default_tokenizer
	# let's test on a batch
	table, queries, answer_coordinates, answer_text, float_answer = prepare_tapas_batch_inputs_for_training()
	inputs = tokenizer(
	table=table,
	queries=queries,
	answer_coordinates=answer_coordinates,
	answer_text=answer_text,
	padding="longest",
	return_tensors="pt",
	)

	# prepare data (created by the tokenizer) and move to torch_device
	input_ids = inputs["input_ids"].to(torch_device)
	attention_mask = inputs["attention_mask"].to(torch_device)
	token_type_ids = inputs["token_type_ids"].to(torch_device)
	labels = inputs["labels"].to(torch_device)
	numeric_values = inputs["numeric_values"].to(torch_device)
	numeric_values_scale = inputs["numeric_values_scale"].to(torch_device)

	# the answer should be prepared by the user
	float_answer = torch.FloatTensor(float_answer).to(torch_device)

	# forward pass to get loss + logits:
	outputs = model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	labels=labels,
	numeric_values=numeric_values,
	numeric_values_scale=numeric_values_scale,
	float_answer=float_answer,
	)

	# test the loss
	loss = outputs.loss
	expected_loss = torch.tensor(3.3527612686157227e-08, device=torch_device)
	self.assertTrue(torch.allclose(loss, expected_loss, atol=1e-6))

	# test the logits on the first example
	logits = outputs.logits
	expected_shape = torch.Size((2, 29))
	self.assertEqual(logits.shape, expected_shape)
	expected_slice = torch.tensor(
	[
	-160.0156,
	-160.0156,
	-160.0156,
	-160.0156,
	-160.0156,
	-10072.2266,
	-10070.8896,
	-10092.6006,
	-10092.6006,
	],
	device=torch_device,
	)

	self.assertTrue(torch.allclose(logits[0, -9:], expected_slice, atol=1e-6))

	# test the aggregation logits on the second example
	logits_aggregation = outputs.logits_aggregation
	expected_shape = torch.Size((2, 4))
	self.assertEqual(logits_aggregation.shape, expected_shape)
	expected_slice = torch.tensor([-4.0538, 40.0304, -5.3554, 23.3965], device=torch_device)

	self.assertTrue(torch.allclose(logits_aggregation[1, -4:], expected_slice, atol=1e-4))

	@slow
	def test_inference_question_answering_head_strong_supervision(self):
	# note that google/tapas-base-finetuned-wikisql-supervised should correspond to tapas_wikisql_sqa_inter_masklm_base_reset
	model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wikisql-supervised").to(
	torch_device
	)

	tokenizer = self.default_tokenizer
	table, queries = prepare_tapas_single_inputs_for_inference()
	inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
	inputs = {k: v.to(torch_device) for k, v in inputs.items()}
	outputs = model(**inputs)
	# test the logits
	logits = outputs.logits
	expected_shape = torch.Size((1, 21))
	self.assertEqual(logits.shape, expected_shape)
	expected_tensor = torch.tensor(
	[
	[
	-10011.1084,
	-10011.1084,
	-10011.1084,
	-10011.1084,
	-10011.1084,
	-10011.1084,
	-10011.1084,
	-10011.1084,
	-10011.1084,
	-18.6185989,
	-10008.7969,
	17.6355762,
	17.6355762,
	17.6355762,
	-10002.4404,
	-18.7111301,
	-18.7111301,
	-18.7111301,
	-18.7111301,
	-18.7111301,
	-10007.0977,
	]
	],
	device=torch_device,
	)

	self.assertTrue(torch.allclose(logits, expected_tensor, atol=0.02))

	# test the aggregation logits
	logits_aggregation = outputs.logits_aggregation
	expected_shape = torch.Size((1, 4))
	self.assertEqual(logits_aggregation.shape, expected_shape)
	expected_tensor = torch.tensor(
	[[16.5659733, -3.06624889, -2.34152961, -0.970244825]], device=torch_device
	) # PyTorch model outputs [[16.5679, -3.0668, -2.3442, -0.9674]]

	self.assertTrue(torch.allclose(logits_aggregation, expected_tensor, atol=0.003))

	@slow
	def test_inference_classification_head(self):
	# note that google/tapas-base-finetuned-tabfact should correspond to tapas_tabfact_inter_masklm_base_reset
	model = TapasForSequenceClassification.from_pretrained("google/tapas-base-finetuned-tabfact").to(torch_device)

	tokenizer = self.default_tokenizer
	table, queries = prepare_tapas_single_inputs_for_inference()
	inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt")
	inputs = {k: v.to(torch_device) for k, v in inputs.items()}
	outputs = model(**inputs)

	# test the classification logits
	logits = outputs.logits
	expected_shape = torch.Size((1, 2))
	self.assertEqual(logits.shape, expected_shape)
	expected_tensor = torch.tensor(
	[[0.795137286, 9.5572]], device=torch_device
	) # Note that the PyTorch model outputs [[0.8057, 9.5281]]

	self.assertTrue(torch.allclose(outputs.logits, expected_tensor, atol=0.05))


	# Below: tests for Tapas utilities which are defined in modeling_tapas.py.
	# These are based on segmented_tensor_test.py of the original implementation.
	# URL: https://github.com/google-research/tapas/blob/master/tapas/models/segmented_tensor_test.py
	@require_scatter
	class TapasUtilitiesTest(unittest.TestCase):
	def _prepare_tables(self):
	"""Prepares two tables, both with three distinct rows.
	The first table has two columns:
	1.0, 2.0 \| 3.0
	2.0, 0.0 \| 1.0
	1.0, 3.0 \| 4.0
	The second table has three columns:
	1.0 \| 2.0 \| 3.0
	2.0 \| 0.0 \| 1.0
	1.0 \| 3.0 \| 4.0
	Returns:
	SegmentedTensors with the tables.
	"""
	values = torch.tensor(
	[
	[[1.0, 2.0, 3.0], [2.0, 0.0, 1.0], [1.0, 3.0, 4.0]],
	[[1.0, 2.0, 3.0], [2.0, 0.0, 1.0], [1.0, 3.0, 4.0]],
	]
	)
	row_index = IndexMap(
	indices=torch.tensor(
	[
	[[0, 0, 0], [1, 1, 1], [2, 2, 2]],
	[[0, 0, 0], [1, 1, 1], [2, 2, 2]],
	]
	),
	num_segments=3,
	batch_dims=1,
	)
	col_index = IndexMap(
	indices=torch.tensor(
	[
	[[0, 0, 1], [0, 0, 1], [0, 0, 1]],
	[[0, 1, 2], [0, 1, 2], [0, 1, 2]],
	]
	),
	num_segments=3,
	batch_dims=1,
	)
	return values, row_index, col_index

	def test_product_index(self):
	_, row_index, col_index = self._prepare_tables()
	cell_index = ProductIndexMap(row_index, col_index)
	row_index_proj = cell_index.project_outer(cell_index)
	col_index_proj = cell_index.project_inner(cell_index)

	ind = cell_index.indices
	self.assertEqual(cell_index.num_segments, 9)

	# Projections should give back the original indices.
	# we use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
	np.testing.assert_array_equal(row_index.indices.numpy(), row_index_proj.indices.numpy())
	self.assertEqual(row_index.num_segments, row_index_proj.num_segments)
	self.assertEqual(row_index.batch_dims, row_index_proj.batch_dims)
	# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
	np.testing.assert_array_equal(col_index.indices.numpy(), col_index_proj.indices.numpy())
	self.assertEqual(col_index.batch_dims, col_index_proj.batch_dims)

	# The first and second "column" are identified in the first table.
	for i in range(3):
	self.assertEqual(ind[0, i, 0], ind[0, i, 1])
	self.assertNotEqual(ind[0, i, 0], ind[0, i, 2])

	# All rows are distinct in the first table.
	for i, i_2 in zip(range(3), range(3)):
	for j, j_2 in zip(range(3), range(3)):
	if i != i_2 and j != j_2:
	self.assertNotEqual(ind[0, i, j], ind[0, i_2, j_2])

	# All cells are distinct in the second table.
	for i, i_2 in zip(range(3), range(3)):
	for j, j_2 in zip(range(3), range(3)):
	if i != i_2 or j != j_2:
	self.assertNotEqual(ind[1, i, j], ind[1, i_2, j_2])

	def test_flatten(self):
	_, row_index, col_index = self._prepare_tables()
	row_index_flat = flatten(row_index)
	col_index_flat = flatten(col_index)

	shape = [3, 4, 5]
	batched_index = IndexMap(indices=torch.zeros(shape).type(torch.LongTensor), num_segments=1, batch_dims=3)
	batched_index_flat = flatten(batched_index)

	# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
	np.testing.assert_array_equal(
	row_index_flat.indices.numpy(), [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5]
	)
	np.testing.assert_array_equal(
	col_index_flat.indices.numpy(), [0, 0, 1, 0, 0, 1, 0, 0, 1, 3, 4, 5, 3, 4, 5, 3, 4, 5]
	)
	self.assertEqual(batched_index_flat.num_segments.numpy(), np.prod(shape))
	np.testing.assert_array_equal(batched_index_flat.indices.numpy(), range(np.prod(shape)))

	def test_range_index_map(self):
	batch_shape = [3, 4]
	num_segments = 5
	index = range_index_map(batch_shape, num_segments)

	self.assertEqual(num_segments, index.num_segments)
	self.assertEqual(2, index.batch_dims)
	indices = index.indices
	# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
	np.testing.assert_array_equal(list(indices.size()), [3, 4, 5])
	for i in range(batch_shape[0]):
	for j in range(batch_shape[1]):
	# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
	np.testing.assert_array_equal(indices[i, j, :].numpy(), range(num_segments))

	def test_reduce_sum(self):
	values, row_index, col_index = self._prepare_tables()
	cell_index = ProductIndexMap(row_index, col_index)
	row_sum, _ = reduce_sum(values, row_index)
	col_sum, _ = reduce_sum(values, col_index)
	cell_sum, _ = reduce_sum(values, cell_index)

	# We use np.testing.assert_allclose rather than Tensorflow's assertAllClose
	np.testing.assert_allclose(row_sum.numpy(), [[6.0, 3.0, 8.0], [6.0, 3.0, 8.0]])
	np.testing.assert_allclose(col_sum.numpy(), [[9.0, 8.0, 0.0], [4.0, 5.0, 8.0]])
	np.testing.assert_allclose(
	cell_sum.numpy(),
	[[3.0, 3.0, 0.0, 2.0, 1.0, 0.0, 4.0, 4.0, 0.0], [1.0, 2.0, 3.0, 2.0, 0.0, 1.0, 1.0, 3.0, 4.0]],
	)

	def test_reduce_mean(self):
	values, row_index, col_index = self._prepare_tables()
	cell_index = ProductIndexMap(row_index, col_index)
	row_mean, _ = reduce_mean(values, row_index)
	col_mean, _ = reduce_mean(values, col_index)
	cell_mean, _ = reduce_mean(values, cell_index)

	# We use np.testing.assert_allclose rather than Tensorflow's assertAllClose
	np.testing.assert_allclose(
	row_mean.numpy(), [[6.0 / 3.0, 3.0 / 3.0, 8.0 / 3.0], [6.0 / 3.0, 3.0 / 3.0, 8.0 / 3.0]]
	)
	np.testing.assert_allclose(col_mean.numpy(), [[9.0 / 6.0, 8.0 / 3.0, 0.0], [4.0 / 3.0, 5.0 / 3.0, 8.0 / 3.0]])
	np.testing.assert_allclose(
	cell_mean.numpy(),
	[
	[3.0 / 2.0, 3.0, 0.0, 2.0 / 2.0, 1.0, 0.0, 4.0 / 2.0, 4.0, 0.0],
	[1.0, 2.0, 3.0, 2.0, 0.0, 1.0, 1.0, 3.0, 4.0],
	],
	)

	def test_reduce_max(self):
	values = torch.as_tensor([2.0, 1.0, 0.0, 3.0])
	index = IndexMap(indices=torch.as_tensor([0, 1, 0, 1]), num_segments=2)
	maximum, _ = reduce_max(values, index)

	# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
	np.testing.assert_array_equal(maximum.numpy(), [2, 3])

	def test_reduce_sum_vectorized(self):
	values = torch.as_tensor([[1.0, 2.0, 3.0], [2.0, 3.0, 4.0], [3.0, 4.0, 5.0]])
	index = IndexMap(indices=torch.as_tensor([0, 0, 1]), num_segments=2, batch_dims=0)
	sums, new_index = reduce_sum(values, index)

	# We use np.testing.assert_allclose rather than Tensorflow's assertAllClose
	np.testing.assert_allclose(sums.numpy(), [[3.0, 5.0, 7.0], [3.0, 4.0, 5.0]])
	# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
	np.testing.assert_array_equal(new_index.indices.numpy(), [0, 1])
	np.testing.assert_array_equal(new_index.num_segments.numpy(), 2)
	np.testing.assert_array_equal(new_index.batch_dims, 0)

	def test_gather(self):
	values, row_index, col_index = self._prepare_tables()
	cell_index = ProductIndexMap(row_index, col_index)

	# Compute sums and then gather. The result should have the same shape as
	# the original table and each element should contain the sum the values in
	# its cell.
	sums, _ = reduce_sum(values, cell_index)
	cell_sum = gather(sums, cell_index)
	assert cell_sum.size() == values.size()

	# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
	np.testing.assert_allclose(
	cell_sum.numpy(),
	[[[3.0, 3.0, 3.0], [2.0, 2.0, 1.0], [4.0, 4.0, 4.0]], [[1.0, 2.0, 3.0], [2.0, 0.0, 1.0], [1.0, 3.0, 4.0]]],
	)

	def test_gather_vectorized(self):
	values = torch.as_tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
	index = IndexMap(indices=torch.as_tensor([[0, 1], [1, 0]]), num_segments=2, batch_dims=1)
	result = gather(values, index)

	# We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
	np.testing.assert_array_equal(result.numpy(), [[[1, 2], [3, 4]], [[7, 8], [5, 6]]])

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