# 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).
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

logger = logging.getLogger()


def get_setup_file():
    parser = argparse.ArgumentParser()
    parser.add_argument("-f")
    args = parser.parse_args()
    return args.f


class DeeBertTests(TestCasePlus):
    def setup(self) -> None:
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

    def run_and_check(self, args):
        n_gpu = get_gpu_count()

        if n_gpu > 1:
            pass
            # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
            # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
            # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
            # cmd = [sys.executable] + distributed_args + args
            # execute_subprocess_async(cmd, env=self.get_env())
            # XXX: test the results - need to save them first into .json file
        else:
            args.insert(0, "run_glue_deebert.py")
            with patch.object(sys, "argv", args):
                result = run_glue_deebert.main()
                for value in result.values():
                    self.assertGreaterEqual(value, 0.666)

    @slow
    @require_torch_non_multi_gpu
    def test_glue_deebert_train(self):
        train_args = """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
        self.run_and_check(train_args)

        eval_args = """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(eval_args)

        entropy_eval_args = """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(entropy_eval_args)

import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

logger = logging.getLogger()


def get_setup_file():
    parser = argparse.ArgumentParser()
    parser.add_argument("-f")
    args = parser.parse_args()
    return args.f


class DeeBertTests(TestCasePlus):
    def setup(self) -> None:
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

    def run_and_check(self, args):
        n_gpu = get_gpu_count()

        if n_gpu > 1:
            pass
            # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
            # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
            # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
            # cmd = [sys.executable] + distributed_args + args
            # execute_subprocess_async(cmd, env=self.get_env())
            # XXX: test the results - need to save them first into .json file
        else:
            args.insert(0, "run_glue_deebert.py")
            with patch.object(sys, "argv", args):
                result = run_glue_deebert.main()
                for value in result.values():
                    self.assertGreaterEqual(value, 0.666)

    @slow
    @require_torch_non_multi_gpu
    def test_glue_deebert_train(self):
        train_args = """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
        self.run_and_check(train_args)

        eval_args = """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(eval_args)

        entropy_eval_args = """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(entropy_eval_args)
