2020 the huggingface 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 orglicenseslicense2 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 putting it there for consistency but tqa do not have fast tokenizer which are needed to generate automatic tests self assertnotequalsequentialoutputs2 batchoutputs2 self assertnotequalsequentialoutputs2 batchoutputs2 2020 the huggingface 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 putting it there for consistency but tqa do not have fast tokenizer which are needed to generate automatic tests self assertnotequal sequential_outputs 2 batch_outputs 2 self assertnotequal sequential_outputs 2 batch_outputs 2
import unittest

from transformers import (
    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
    AutoModelForTableQuestionAnswering,
    AutoTokenizer,
    TableQuestionAnsweringPipeline,
    TFAutoModelForTableQuestionAnswering,
    is_torch_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    require_pandas,
    require_tensorflow_probability,
    require_tf,
    require_torch,
    slow,
)


if is_torch_available():
    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_12
else:
    is_torch_greater_or_equal_than_1_12 = False


@is_pipeline_test
class TQAPipelineTests(unittest.TestCase):
    
    
    model_mapping = MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING

    @require_tensorflow_probability
    @require_pandas
    @require_tf
    @require_torch
    def test_small_model_tf(self):
        model_id = "lysandre/tiny-tapas-random-wtq"
        model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_id, from_pt=True)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        self.assertIsInstance(model.config.aggregation_labels, dict)
        self.assertIsInstance(model.config.no_aggregation_label_index, int)

        table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
        outputs = table_querier(
            table={
                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
                "age": ["56", "45", "59"],
                "number of movies": ["87", "53", "69"],
                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
            },
            query="how many movies has george clooney played in?",
        )
        self.assertEqual(
            outputs,
            {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
        )
        outputs = table_querier(
            table={
                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
                "age": ["56", "45", "59"],
                "number of movies": ["87", "53", "69"],
                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
            },
            query=["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
        )
        self.assertEqual(
            outputs,
            [
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
            ],
        )
        outputs = table_querier(
            table={
                "Repository": ["Transformers", "Datasets", "Tokenizers"],
                "Stars": ["36542", "4512", "3934"],
                "Contributors": ["651", "77", "34"],
                "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
            },
            query=[
                "What repository has the largest number of stars?",
                "Given that the numbers of stars defines if a repository is active, what repository is the most"
                " active?",
                "What is the number of repositories?",
                "What is the average number of stars?",
                "What is the total amount of stars?",
            ],
        )
        self.assertEqual(
            outputs,
            [
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
            ],
        )

        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table=None)
        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table="")
        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table={})
        with self.assertRaises(ValueError):
            table_querier(
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                }
            )
        with self.assertRaises(ValueError):
            table_querier(
                query="",
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                },
            )
        with self.assertRaises(ValueError):
            table_querier(
                query=None,
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                },
            )

    @unittest.skipIf(not is_torch_greater_or_equal_than_1_12, reason="Tapas is only available in torch v1.12+")
    @require_torch
    def test_small_model_pt(self):
        model_id = "lysandre/tiny-tapas-random-wtq"
        model = AutoModelForTableQuestionAnswering.from_pretrained(model_id)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        self.assertIsInstance(model.config.aggregation_labels, dict)
        self.assertIsInstance(model.config.no_aggregation_label_index, int)

        table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
        outputs = table_querier(
            table={
                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
                "age": ["56", "45", "59"],
                "number of movies": ["87", "53", "69"],
                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
            },
            query="how many movies has george clooney played in?",
        )
        self.assertEqual(
            outputs,
            {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
        )
        outputs = table_querier(
            table={
                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
                "age": ["56", "45", "59"],
                "number of movies": ["87", "53", "69"],
                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
            },
            query=["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
        )
        self.assertEqual(
            outputs,
            [
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
            ],
        )
        outputs = table_querier(
            table={
                "Repository": ["Transformers", "Datasets", "Tokenizers"],
                "Stars": ["36542", "4512", "3934"],
                "Contributors": ["651", "77", "34"],
                "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
            },
            query=[
                "What repository has the largest number of stars?",
                "Given that the numbers of stars defines if a repository is active, what repository is the most"
                " active?",
                "What is the number of repositories?",
                "What is the average number of stars?",
                "What is the total amount of stars?",
            ],
        )
        self.assertEqual(
            outputs,
            [
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
                {"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
            ],
        )

        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table=None)
        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table="")
        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table={})
        with self.assertRaises(ValueError):
            table_querier(
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                }
            )
        with self.assertRaises(ValueError):
            table_querier(
                query="",
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                },
            )
        with self.assertRaises(ValueError):
            table_querier(
                query=None,
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                },
            )

    @unittest.skipIf(not is_torch_greater_or_equal_than_1_12, reason="Tapas is only available in torch v1.12+")
    @require_torch
    def test_slow_tokenizer_sqa_pt(self):
        model_id = "lysandre/tiny-tapas-random-sqa"
        model = AutoModelForTableQuestionAnswering.from_pretrained(model_id)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)

        inputs = {
            "table": {
                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
                "age": ["56", "45", "59"],
                "number of movies": ["87", "53", "69"],
                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
            },
            "query": ["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
        }
        sequential_outputs = table_querier(**inputs, sequential=True)
        batch_outputs = table_querier(**inputs, sequential=False)

        self.assertEqual(len(sequential_outputs), 3)
        self.assertEqual(len(batch_outputs), 3)
        self.assertEqual(sequential_outputs[0], batch_outputs[0])
        self.assertNotEqual(sequential_outputs[1], batch_outputs[1])
        

        table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
        outputs = table_querier(
            table={
                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
                "age": ["56", "45", "59"],
                "number of movies": ["87", "53", "69"],
                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
            },
            query="how many movies has george clooney played in?",
        )
        self.assertEqual(
            outputs,
            {"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
        )
        outputs = table_querier(
            table={
                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
                "age": ["56", "45", "59"],
                "number of movies": ["87", "53", "69"],
                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
            },
            query=["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
        )
        self.assertEqual(
            outputs,
            [
                {"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
                {"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
                {"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
            ],
        )
        outputs = table_querier(
            table={
                "Repository": ["Transformers", "Datasets", "Tokenizers"],
                "Stars": ["36542", "4512", "3934"],
                "Contributors": ["651", "77", "34"],
                "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
            },
            query=[
                "What repository has the largest number of stars?",
                "Given that the numbers of stars defines if a repository is active, what repository is the most"
                " active?",
                "What is the number of repositories?",
                "What is the average number of stars?",
                "What is the total amount of stars?",
            ],
        )
        self.assertEqual(
            outputs,
            [
                {"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
                {"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
                {"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
                {"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
                {"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
            ],
        )

        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table=None)
        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table="")
        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table={})
        with self.assertRaises(ValueError):
            table_querier(
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                }
            )
        with self.assertRaises(ValueError):
            table_querier(
                query="",
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                },
            )
        with self.assertRaises(ValueError):
            table_querier(
                query=None,
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                },
            )

    @require_tf
    @require_tensorflow_probability
    @require_pandas
    @require_torch
    def test_slow_tokenizer_sqa_tf(self):
        model_id = "lysandre/tiny-tapas-random-sqa"
        model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_id, from_pt=True)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)

        inputs = {
            "table": {
                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
                "age": ["56", "45", "59"],
                "number of movies": ["87", "53", "69"],
                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
            },
            "query": ["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
        }
        sequential_outputs = table_querier(**inputs, sequential=True)
        batch_outputs = table_querier(**inputs, sequential=False)

        self.assertEqual(len(sequential_outputs), 3)
        self.assertEqual(len(batch_outputs), 3)
        self.assertEqual(sequential_outputs[0], batch_outputs[0])
        self.assertNotEqual(sequential_outputs[1], batch_outputs[1])
        

        table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
        outputs = table_querier(
            table={
                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
                "age": ["56", "45", "59"],
                "number of movies": ["87", "53", "69"],
                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
            },
            query="how many movies has george clooney played in?",
        )
        self.assertEqual(
            outputs,
            {"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
        )
        outputs = table_querier(
            table={
                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
                "age": ["56", "45", "59"],
                "number of movies": ["87", "53", "69"],
                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
            },
            query=["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
        )
        self.assertEqual(
            outputs,
            [
                {"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
                {"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
                {"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
            ],
        )
        outputs = table_querier(
            table={
                "Repository": ["Transformers", "Datasets", "Tokenizers"],
                "Stars": ["36542", "4512", "3934"],
                "Contributors": ["651", "77", "34"],
                "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
            },
            query=[
                "What repository has the largest number of stars?",
                "Given that the numbers of stars defines if a repository is active, what repository is the most"
                " active?",
                "What is the number of repositories?",
                "What is the average number of stars?",
                "What is the total amount of stars?",
            ],
        )
        self.assertEqual(
            outputs,
            [
                {"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
                {"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
                {"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
                {"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
                {"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
            ],
        )

        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table=None)
        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table="")
        with self.assertRaises(ValueError):
            table_querier(query="What does it do with empty context ?", table={})
        with self.assertRaises(ValueError):
            table_querier(
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                }
            )
        with self.assertRaises(ValueError):
            table_querier(
                query="",
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                },
            )
        with self.assertRaises(ValueError):
            table_querier(
                query=None,
                table={
                    "Repository": ["Transformers", "Datasets", "Tokenizers"],
                    "Stars": ["36542", "4512", "3934"],
                    "Contributors": ["651", "77", "34"],
                    "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
                },
            )

    @unittest.skipIf(not is_torch_greater_or_equal_than_1_12, reason="Tapas is only available in torch v1.12+")
    @slow
    @require_torch
    def test_integration_wtq_pt(self):
        table_querier = pipeline("table-question-answering")

        data = {
            "Repository": ["Transformers", "Datasets", "Tokenizers"],
            "Stars": ["36542", "4512", "3934"],
            "Contributors": ["651", "77", "34"],
            "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
        }
        queries = [
            "What repository has the largest number of stars?",
            "Given that the numbers of stars defines if a repository is active, what repository is the most active?",
            "What is the number of repositories?",
            "What is the average number of stars?",
            "What is the total amount of stars?",
        ]

        results = table_querier(data, queries)

        expected_results = [
            {"answer": "Transformers", "coordinates": [(0, 0)], "cells": ["Transformers"], "aggregator": "NONE"},
            {"answer": "Transformers", "coordinates": [(0, 0)], "cells": ["Transformers"], "aggregator": "NONE"},
            {
                "answer": "COUNT > Transformers, Datasets, Tokenizers",
                "coordinates": [(0, 0), (1, 0), (2, 0)],
                "cells": ["Transformers", "Datasets", "Tokenizers"],
                "aggregator": "COUNT",
            },
            {
                "answer": "AVERAGE > 36542, 4512, 3934",
                "coordinates": [(0, 1), (1, 1), (2, 1)],
                "cells": ["36542", "4512", "3934"],
                "aggregator": "AVERAGE",
            },
            {
                "answer": "SUM > 36542, 4512, 3934",
                "coordinates": [(0, 1), (1, 1), (2, 1)],
                "cells": ["36542", "4512", "3934"],
                "aggregator": "SUM",
            },
        ]
        self.assertListEqual(results, expected_results)

    @slow
    @require_tensorflow_probability
    @require_pandas
    def test_integration_wtq_tf(self):
        model_id = "google/tapas-base-finetuned-wtq"
        model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_id)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        table_querier = pipeline("table-question-answering", model=model, tokenizer=tokenizer)

        data = {
            "Repository": ["Transformers", "Datasets", "Tokenizers"],
            "Stars": ["36542", "4512", "3934"],
            "Contributors": ["651", "77", "34"],
            "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
        }
        queries = [
            "What repository has the largest number of stars?",
            "Given that the numbers of stars defines if a repository is active, what repository is the most active?",
            "What is the number of repositories?",
            "What is the average number of stars?",
            "What is the total amount of stars?",
        ]

        results = table_querier(data, queries)

        expected_results = [
            {"answer": "Transformers", "coordinates": [(0, 0)], "cells": ["Transformers"], "aggregator": "NONE"},
            {"answer": "Transformers", "coordinates": [(0, 0)], "cells": ["Transformers"], "aggregator": "NONE"},
            {
                "answer": "COUNT > Transformers, Datasets, Tokenizers",
                "coordinates": [(0, 0), (1, 0), (2, 0)],
                "cells": ["Transformers", "Datasets", "Tokenizers"],
                "aggregator": "COUNT",
            },
            {
                "answer": "AVERAGE > 36542, 4512, 3934",
                "coordinates": [(0, 1), (1, 1), (2, 1)],
                "cells": ["36542", "4512", "3934"],
                "aggregator": "AVERAGE",
            },
            {
                "answer": "SUM > 36542, 4512, 3934",
                "coordinates": [(0, 1), (1, 1), (2, 1)],
                "cells": ["36542", "4512", "3934"],
                "aggregator": "SUM",
            },
        ]
        self.assertListEqual(results, expected_results)

    @unittest.skipIf(not is_torch_greater_or_equal_than_1_12, reason="Tapas is only available in torch v1.12+")
    @slow
    @require_torch
    def test_integration_sqa_pt(self):
        table_querier = pipeline(
            "table-question-answering",
            model="google/tapas-base-finetuned-sqa",
            tokenizer="google/tapas-base-finetuned-sqa",
        )
        data = {
            "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
            "Age": ["56", "45", "59"],
            "Number of movies": ["87", "53", "69"],
            "Date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
        }
        queries = ["How many movies has George Clooney played in?", "How old is he?", "What's his date of birth?"]
        results = table_querier(data, queries, sequential=True)

        expected_results = [
            {"answer": "69", "coordinates": [(2, 2)], "cells": ["69"]},
            {"answer": "59", "coordinates": [(2, 1)], "cells": ["59"]},
            {"answer": "28 november 1967", "coordinates": [(2, 3)], "cells": ["28 november 1967"]},
        ]
        self.assertListEqual(results, expected_results)

    @slow
    @require_tensorflow_probability
    @require_pandas
    def test_integration_sqa_tf(self):
        model_id = "google/tapas-base-finetuned-sqa"
        model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_id)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        table_querier = pipeline(
            "table-question-answering",
            model=model,
            tokenizer=tokenizer,
        )
        data = {
            "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
            "Age": ["56", "45", "59"],
            "Number of movies": ["87", "53", "69"],
            "Date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
        }
        queries = ["How many movies has George Clooney played in?", "How old is he?", "What's his date of birth?"]
        results = table_querier(data, queries, sequential=True)

        expected_results = [
            {"answer": "69", "coordinates": [(2, 2)], "cells": ["69"]},
            {"answer": "59", "coordinates": [(2, 1)], "cells": ["59"]},
            {"answer": "28 november 1967", "coordinates": [(2, 3)], "cells": ["28 november 1967"]},
        ]
        self.assertListEqual(results, expected_results)

    @slow
    @require_torch
    def test_large_model_pt_tapex(self):
        model_id = "microsoft/tapex-large-finetuned-wtq"
        table_querier = pipeline(
            "table-question-answering",
            model=model_id,
        )
        data = {
            "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
            "Age": ["56", "45", "59"],
            "Number of movies": ["87", "53", "69"],
            "Date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
        }
        queries = [
            "How many movies has George Clooney played in?",
            "How old is Mr Clooney ?",
            "What's the date of birth of Leonardo ?",
        ]
        results = table_querier(data, queries, sequential=True)

        expected_results = [
            {"answer": " 69"},
            {"answer": " 59"},
            {"answer": " 10 june 1996"},
        ]
        self.assertListEqual(results, expected_results)