





# Copyright 2021 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 warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType

class    lowerCAmelCase							(					__UpperCamelCase			):
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      int	)			->	int:
     lowerCamelCase__    :      Tuple					 =		data







 def __iter__(      self  :      Dict	)			->	List[str]:
     for element in self.data:
         yield element


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase=True )			->     int:
    lowerCamelCase__    :      Optional[int]					 =		Accelerator(even_batches=_UpperCAmelCase )
    assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
    return accelerator


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = False )			->     List[str]:
    if iterable:
        lowerCamelCase__    :      Optional[Any]					 =		DummyIterableDataset(torch.as_tensor(range(_UpperCAmelCase ) ) )
    else:
        lowerCamelCase__    :      Tuple					 =		TensorDataset(torch.as_tensor(range(_UpperCAmelCase ) ) )

    lowerCamelCase__    :      str					 =		DataLoader(_UpperCAmelCase						,	batch_size=_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		accelerator.prepare(_UpperCAmelCase )

    return dl


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	)			->     str:
    lowerCamelCase__    :      Any					 =		create_dataloader(accelerator=_UpperCAmelCase						,	dataset_size=_UpperCAmelCase						,	batch_size=_UpperCAmelCase )

    lowerCamelCase__    :      Any					 =		[len(batch[0] ) for batch in dl]

    if accelerator.process_index == 0:
        assert batch_sizes == process_0_expected_batch_sizes
    elif accelerator.process_index == 1:
        assert batch_sizes == process_1_expected_batch_sizes


def        SCREAMING_SNAKE_CASE      (       )			->     Optional[Any]:
    lowerCamelCase__    :      List[str]					 =		create_accelerator()

    # without padding, we would expect a different number of batches
    verify_dataloader_batch_sizes(
        _UpperCAmelCase						,	dataset_size=3						,	batch_size=1						,	process_0_expected_batch_sizes=[1, 1]						,	process_1_expected_batch_sizes=[1, 1]						,	)

    # without padding, we would expect the same number of batches, but different sizes
    verify_dataloader_batch_sizes(
        _UpperCAmelCase						,	dataset_size=7						,	batch_size=2						,	process_0_expected_batch_sizes=[2, 2]						,	process_1_expected_batch_sizes=[2, 2]						,	)


def        SCREAMING_SNAKE_CASE      (       )			->     Optional[Any]:
    lowerCamelCase__    :      Tuple					 =		create_accelerator(even_batches=_UpperCAmelCase )

    verify_dataloader_batch_sizes(
        _UpperCAmelCase						,	dataset_size=3						,	batch_size=1						,	process_0_expected_batch_sizes=[1, 1]						,	process_1_expected_batch_sizes=[1]						,	)

    verify_dataloader_batch_sizes(
        _UpperCAmelCase						,	dataset_size=7						,	batch_size=2						,	process_0_expected_batch_sizes=[2, 2]						,	process_1_expected_batch_sizes=[2, 1]						,	)


def        SCREAMING_SNAKE_CASE      (       )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		create_accelerator(even_batches=_UpperCAmelCase )

    lowerCamelCase__    :      Any					 =		torch.nn.Linear(1						,	1 )
    lowerCamelCase__    :      List[Any]					 =		accelerator.prepare(_UpperCAmelCase )

    lowerCamelCase__    :      Union[str, Any]					 =		create_dataloader(_UpperCAmelCase						,	dataset_size=3						,	batch_size=1 )

    lowerCamelCase__    :      List[str]					 =		[]
    with accelerator.join_uneven_inputs([ddp_model] ):
        for batch_idx, batch in enumerate(_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[int]					 =		ddp_model(batch[0].float() )
            lowerCamelCase__    :      int					 =		output.sum()
            loss.backward()
            batch_idxs.append(_UpperCAmelCase )

    accelerator.wait_for_everyone()

    if accelerator.process_index == 0:
        assert batch_idxs == [0, 1]
    elif accelerator.process_index == 1:
        assert batch_idxs == [0]


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    with warnings.catch_warnings(record=_UpperCAmelCase ) as w:
        with accelerator.join_uneven_inputs([Mock()] ):
            pass

        assert issubclass(w[-1].category						,	_UpperCAmelCase )
        assert "only supported for multi-GPU" in str(w[-1].message )


def        SCREAMING_SNAKE_CASE      (       )			->     Optional[Any]:
    lowerCamelCase__    :      List[Any]					 =		True
    lowerCamelCase__    :      Tuple					 =		False
    lowerCamelCase__    :      List[str]					 =		create_accelerator(even_batches=_UpperCAmelCase )
    lowerCamelCase__    :      Optional[Any]					 =		torch.nn.Linear(1						,	1 )
    lowerCamelCase__    :      List[Any]					 =		accelerator.prepare(_UpperCAmelCase )
    lowerCamelCase__    :      Union[str, Any]					 =		create_dataloader(_UpperCAmelCase						,	dataset_size=3						,	batch_size=1 )
    lowerCamelCase__    :      List[Any]					 =		create_dataloader(_UpperCAmelCase						,	dataset_size=3						,	batch_size=1 )

    with accelerator.join_uneven_inputs([ddp_model]						,	even_batches=_UpperCAmelCase ):
        lowerCamelCase__    :      Optional[int]					 =		train_dl.batch_sampler.even_batches
        lowerCamelCase__    :      Union[str, Any]					 =		valid_dl.batch_sampler.even_batches

    assert train_dl_overridden_value == overridden_even_batches
    assert valid_dl_overridden_value == overridden_even_batches
    assert train_dl.batch_sampler.even_batches == default_even_batches
    assert valid_dl.batch_sampler.even_batches == default_even_batches


def        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    lowerCamelCase__    :      List[Any]					 =		True
    lowerCamelCase__    :      Optional[int]					 =		False
    lowerCamelCase__    :      Any					 =		create_accelerator(even_batches=_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		torch.nn.Linear(1						,	1 )
    lowerCamelCase__    :      Tuple					 =		accelerator.prepare(_UpperCAmelCase )
    create_dataloader(_UpperCAmelCase						,	dataset_size=3						,	batch_size=1						,	iterable=_UpperCAmelCase )
    lowerCamelCase__    :      int					 =		create_dataloader(_UpperCAmelCase						,	dataset_size=3						,	batch_size=1 )

    with warnings.catch_warnings():
        warnings.filterwarnings('ignore' )
        try:
            with accelerator.join_uneven_inputs([ddp_model]						,	even_batches=_UpperCAmelCase ):
                lowerCamelCase__    :      str					 =		batch_dl.batch_sampler.even_batches
        except AttributeError:
            # ensure attribute error is not raised when processing iterable dl
            raise AssertionError

    assert batch_dl_overridden_value == overridden_even_batches
    assert batch_dl.batch_sampler.even_batches == default_even_batches


def        SCREAMING_SNAKE_CASE      (       )			->     Dict:
    lowerCamelCase__    :      int					 =		create_accelerator()
    lowerCamelCase__    :      str					 =		torch.nn.Linear(1						,	1 )
    lowerCamelCase__    :      Any					 =		accelerator.prepare(_UpperCAmelCase )
    create_dataloader(_UpperCAmelCase						,	dataset_size=3						,	batch_size=1						,	iterable=_UpperCAmelCase )

    with warnings.catch_warnings(record=_UpperCAmelCase ) as w:
        with accelerator.join_uneven_inputs([ddp_model]						,	even_batches=_UpperCAmelCase ):
            pass

        assert issubclass(w[-1].category						,	_UpperCAmelCase )
        assert "only supported for map-style datasets" in str(w[-1].message )


def        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    lowerCamelCase__    :      str					 =		create_accelerator()

    accelerator.print('Test that even_batches variable ensures uniform batches across processes' )
    test_default_ensures_even_batch_sizes()

    accelerator.print('Run tests with even_batches disabled' )
    test_can_disable_even_batches()

    accelerator.print('Test joining uneven inputs' )
    test_can_join_uneven_inputs()

    accelerator.print('Test overriding even_batches when joining uneven inputs' )
    test_join_can_override_even_batches()

    accelerator.print('Test overriding even_batches for mixed dataloader types' )
    test_join_can_override_for_mixed_type_dataloaders()

    accelerator.print('Test overriding even_batches raises a warning for iterable dataloaders' )
    test_join_raises_warning_for_iterable_when_overriding_even_batches()

    accelerator.print('Test join with non DDP distributed raises warning' )
    lowerCamelCase__    :      Dict					 =		accelerator.state.distributed_type
    lowerCamelCase__    :      List[Any]					 =		DistributedType.FSDP
    test_join_raises_warning_for_non_ddp_distributed(_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		original_state


if __name__ == "__main__":
    main()












# Copyright 2021 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 argparse
import os

from accelerate.test_utils import execute_subprocess_async


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase=None )			->     Tuple:
    if subparsers is not None:
        lowerCamelCase__    :      Any					 =		subparsers.add_parser('test' )
    else:
        lowerCamelCase__    :      int					 =		argparse.ArgumentParser('Accelerate test command' )

    parser.add_argument(
        '--config_file'						,	default=_UpperCAmelCase						,	help=(
            'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
            'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
            'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
            'with \'huggingface\'.'
        )						,	)

    if subparsers is not None:
        parser.set_defaults(func=_UpperCAmelCase )
    return parser


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['test_utils', 'scripts', 'test_script.py'] )

    if args.config_file is None:
        lowerCamelCase__    :      List[str]					 =		script_name
    else:
        lowerCamelCase__    :      List[Any]					 =		F"""--config_file={args.config_file} {script_name}"""

    lowerCamelCase__    :      str					 =		['accelerate-launch'] + test_args.split()
    lowerCamelCase__    :      Dict					 =		execute_subprocess_async(_UpperCAmelCase						,	env=os.environ.copy() )
    if result.returncode == 0:
        print('Test is a success! You are ready for your distributed training!' )


def        SCREAMING_SNAKE_CASE      (       )			->     Any:
    lowerCamelCase__    :      Any					 =		test_command_parser()
    lowerCamelCase__    :      List[Any]					 =		parser.parse_args()
    test_command(_UpperCAmelCase )


if __name__ == "__main__":
    main()





