# 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.
"""
A simple launcher script for TPU training

Inspired by https://github.com/pytorch/pytorch/blob/master/torch/distributed/launch.py

::
    >>> python xla_spawn.py --num_cores=NUM_CORES_YOU_HAVE
               YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
               arguments of your training script)

"""


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def parse_args():
    """
    Helper function parsing the command line options
    @retval ArgumentParser
    """
    parser = ArgumentParser(
        description=(
            "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
        )
    )

    # Optional arguments for the launch helper
    parser.add_argument("--num_cores", type=int, default=1, help="Number of TPU cores to use (1 or 8).")

    # positional
    parser.add_argument(
        "training_script",
        type=str,
        help=(
            "The full path to the single TPU training "
            "program/script to be launched in parallel, "
            "followed by all the arguments for the "
            "training script"
        ),
    )

    # rest from the training program
    parser.add_argument("training_script_args", nargs=REMAINDER)

    return parser.parse_args()


def main():
    args = parse_args()

    # Import training_script as a module.
    script_fpath = Path(args.training_script)
    sys.path.append(str(script_fpath.parent.resolve()))
    mod_name = script_fpath.stem
    mod = importlib.import_module(mod_name)

    # Patch sys.argv
    sys.argv = [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores)]

    xmp.spawn(mod._mp_fn, args=(), nprocs=args.num_cores)


if __name__ == "__main__":
    main()

# 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.
"""
A simple launcher script for TPU training

Inspired by https://github.com/pytorch/pytorch/blob/master/torch/distributed/launch.py

::
    >>> python xla_spawn.py --num_cores=NUM_CORES_YOU_HAVE
               YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
               arguments of your training script)

"""


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def parse_args():
    """
    Helper function parsing the command line options
    @retval ArgumentParser
    """
    parser = ArgumentParser(
        description=(
            "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
        )
    )

    # Optional arguments for the launch helper
    parser.add_argument("--num_cores", type=int, default=1, help="Number of TPU cores to use (1 or 8).")

    # positional
    parser.add_argument(
        "training_script",
        type=str,
        help=(
            "The full path to the single TPU training "
            "program/script to be launched in parallel, "
            "followed by all the arguments for the "
            "training script"
        ),
    )

    # rest from the training program
    parser.add_argument("training_script_args", nargs=REMAINDER)

    return parser.parse_args()


def main():
    args = parse_args()

    # Import training_script as a module.
    script_fpath = Path(args.training_script)
    sys.path.append(str(script_fpath.parent.resolve()))
    mod_name = script_fpath.stem
    mod = importlib.import_module(mod_name)

    # Patch sys.argv
    sys.argv = [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores)]

    xmp.spawn(mod._mp_fn, args=(), nprocs=args.num_cores)


if __name__ == "__main__":
    main()
