# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# Copyright 2020 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 logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()


def _dump_articles(path: Path, articles: list):
    content = "\n".join(articles)
    Path(path).open("w").writelines(content)


T5_TINY = "patrickvonplaten/t5-tiny-random"
BART_TINY = "sshleifer/bart-tiny-random"
MBART_TINY = "sshleifer/tiny-mbart"

stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


class TestTheRest(TestCasePlus):
    def run_eval_tester(self, model):
        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
        output_file_name = input_file_name.parent / "utest_output.txt"
        assert not output_file_name.exists()
        articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
        _dump_articles(input_file_name, articles)

        score_path = str(Path(self.get_auto_remove_tmp_dir()) / "scores.json")
        task = "translation_en_to_de" if model == T5_TINY else "summarization"
        testargs = f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

        with patch.object(sys, "argv", testargs):
            run_generate()
            assert Path(output_file_name).exists()
            # os.remove(Path(output_file_name))

    # test one model to quickly (no-@slow) catch simple problems and do an
    # extensive testing of functionality with multiple models as @slow separately
    def test_run_eval(self):
        self.run_eval_tester(T5_TINY)

    # any extra models should go into the list here - can be slow
    @parameterized.expand([BART_TINY, MBART_TINY])
    @slow
    def test_run_eval_slow(self, model):
        self.run_eval_tester(model)

    # testing with 2 models to validate: 1. translation (t5) 2. summarization (mbart)
    @parameterized.expand([T5_TINY, MBART_TINY])
    @slow
    def test_run_eval_search(self, model):
        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
        output_file_name = input_file_name.parent / "utest_output.txt"
        assert not output_file_name.exists()

        text = {
            "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
            "de": [
                "Maschinelles Lernen ist großartig, oder?",
                "Ich esse gerne Bananen",
                "Morgen ist wieder ein toller Tag!",
            ],
        }

        tmp_dir = Path(self.get_auto_remove_tmp_dir())
        score_path = str(tmp_dir / "scores.json")
        reference_path = str(tmp_dir / "val.target")
        _dump_articles(input_file_name, text["en"])
        _dump_articles(reference_path, text["de"])
        task = "translation_en_to_de" if model == T5_TINY else "summarization"
        testargs = f"""
            run_eval_search.py
            {model}
            {str(input_file_name)}
            {str(output_file_name)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
        testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"])

        with patch.object(sys, "argv", testargs):
            with CaptureStdout() as cs:
                run_search()
            expected_strings = [" num_beams | length_penalty", model, "Best score args"]
            un_expected_strings = ["Info"]
            if "translation" in task:
                expected_strings.append("bleu")
            else:
                expected_strings.extend(ROUGE_KEYS)
            for w in expected_strings:
                assert w in cs.out
            for w in un_expected_strings:
                assert w not in cs.out
            assert Path(output_file_name).exists()
            os.remove(Path(output_file_name))

# Copyright 2020 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 logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()


def _dump_articles(path: Path, articles: list):
    content = "\n".join(articles)
    Path(path).open("w").writelines(content)


T5_TINY = "patrickvonplaten/t5-tiny-random"
BART_TINY = "sshleifer/bart-tiny-random"
MBART_TINY = "sshleifer/tiny-mbart"

stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


class TestTheRest(TestCasePlus):
    def run_eval_tester(self, model):
        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
        output_file_name = input_file_name.parent / "utest_output.txt"
        assert not output_file_name.exists()
        articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
        _dump_articles(input_file_name, articles)

        score_path = str(Path(self.get_auto_remove_tmp_dir()) / "scores.json")
        task = "translation_en_to_de" if model == T5_TINY else "summarization"
        testargs = f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

        with patch.object(sys, "argv", testargs):
            run_generate()
            assert Path(output_file_name).exists()
            # os.remove(Path(output_file_name))

    # test one model to quickly (no-@slow) catch simple problems and do an
    # extensive testing of functionality with multiple models as @slow separately
    def test_run_eval(self):
        self.run_eval_tester(T5_TINY)

    # any extra models should go into the list here - can be slow
    @parameterized.expand([BART_TINY, MBART_TINY])
    @slow
    def test_run_eval_slow(self, model):
        self.run_eval_tester(model)

    # testing with 2 models to validate: 1. translation (t5) 2. summarization (mbart)
    @parameterized.expand([T5_TINY, MBART_TINY])
    @slow
    def test_run_eval_search(self, model):
        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
        output_file_name = input_file_name.parent / "utest_output.txt"
        assert not output_file_name.exists()

        text = {
            "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
            "de": [
                "Maschinelles Lernen ist großartig, oder?",
                "Ich esse gerne Bananen",
                "Morgen ist wieder ein toller Tag!",
            ],
        }

        tmp_dir = Path(self.get_auto_remove_tmp_dir())
        score_path = str(tmp_dir / "scores.json")
        reference_path = str(tmp_dir / "val.target")
        _dump_articles(input_file_name, text["en"])
        _dump_articles(reference_path, text["de"])
        task = "translation_en_to_de" if model == T5_TINY else "summarization"
        testargs = f"""
            run_eval_search.py
            {model}
            {str(input_file_name)}
            {str(output_file_name)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
        testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"])

        with patch.object(sys, "argv", testargs):
            with CaptureStdout() as cs:
                run_search()
            expected_strings = [" num_beams | length_penalty", model, "Best score args"]
            un_expected_strings = ["Info"]
            if "translation" in task:
                expected_strings.append("bleu")
            else:
                expected_strings.extend(ROUGE_KEYS)
            for w in expected_strings:
                assert w in cs.out
            for w in un_expected_strings:
                assert w not in cs.out
            assert Path(output_file_name).exists()
            os.remove(Path(output_file_name))
