
'''simple docstring'''







# 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 		__UpperCAmelCase   (      _lowerCamelCase		):


							def __init__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	data




							def __iter__( self						):





								"""simple docstring"""






								for element in self.data:
									yield element







def        SCREAMING_SNAKE_CASE__			(     __A=True			) ->			Optional[Any]:
	_snake_case     		=	Accelerator(even_batches=__A			)
	assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
	return accelerator







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A = False			) ->			Tuple:
	if iterable:
		_snake_case     		=	DummyIterableDataset(torch.as_tensor(range(__A			)			)			)
	else:
		_snake_case     		=	TensorDataset(torch.as_tensor(range(__A			)			)			)

	_snake_case     		=	DataLoader(__A    ,       batch_size=__A			)
	_snake_case     		=	accelerator.prepare(__A			)

	return dl







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       ) ->			Union[str, Any]:
	_snake_case     		=	create_dataloader(accelerator=__A    ,       dataset_size=__A    ,       batch_size=__A			)

	_snake_case     		=	[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__			(     ) ->			Dict:
	_snake_case     		=	create_accelerator()

	# without padding, we would expect a different number of batches
	verify_dataloader_batch_sizes(
	    __A    ,       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(
	    __A    ,       dataset_size=7    ,       batch_size=2    ,       process_0_expected_batch_sizes=[2, 2]    ,       process_1_expected_batch_sizes=[2, 2]    ,       )







def        SCREAMING_SNAKE_CASE__			(     ) ->			List[Any]:
	_snake_case     		=	create_accelerator(even_batches=__A			)

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

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







def        SCREAMING_SNAKE_CASE__			(     ) ->			Optional[int]:
	_snake_case     		=	create_accelerator(even_batches=__A			)

	_snake_case     		=	torch.nn.Linear(1    ,       1			)
	_snake_case     		=	accelerator.prepare(__A			)

	_snake_case     		=	create_dataloader(__A    ,       dataset_size=3    ,       batch_size=1			)

	_snake_case     		=	[]
	with accelerator.join_uneven_inputs([ddp_model]			):
		for batch_idx, batch in enumerate(__A			):
			_snake_case     		=	ddp_model(batch[0].float()			)
			_snake_case     		=	output.sum()
			loss.backward()
			batch_idxs.append(__A			)

	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__			(     __A			) ->			Optional[Any]:
	with warnings.catch_warnings(record=__A			) as w:
		with accelerator.join_uneven_inputs([Mock()]			):
			pass

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







def        SCREAMING_SNAKE_CASE__			(     ) ->			Optional[Any]:
	_snake_case     		=	True
	_snake_case     		=	False
	_snake_case     		=	create_accelerator(even_batches=__A			)
	_snake_case     		=	torch.nn.Linear(1    ,       1			)
	_snake_case     		=	accelerator.prepare(__A			)
	_snake_case     		=	create_dataloader(__A    ,       dataset_size=3    ,       batch_size=1			)
	_snake_case     		=	create_dataloader(__A    ,       dataset_size=3    ,       batch_size=1			)

	with accelerator.join_uneven_inputs([ddp_model]    ,       even_batches=__A			):
		_snake_case     		=	train_dl.batch_sampler.even_batches
		_snake_case     		=	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__			(     ) ->			str:
	_snake_case     		=	True
	_snake_case     		=	False
	_snake_case     		=	create_accelerator(even_batches=__A			)
	_snake_case     		=	torch.nn.Linear(1    ,       1			)
	_snake_case     		=	accelerator.prepare(__A			)
	create_dataloader(__A    ,       dataset_size=3    ,       batch_size=1    ,       iterable=__A			)
	_snake_case     		=	create_dataloader(__A    ,       dataset_size=3    ,       batch_size=1			)

	with warnings.catch_warnings():
		warnings.filterwarnings('ignore'			)
		try:
			with accelerator.join_uneven_inputs([ddp_model]    ,       even_batches=__A			):
				_snake_case     		=	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__			(     ) ->			int:
	_snake_case     		=	create_accelerator()
	_snake_case     		=	torch.nn.Linear(1    ,       1			)
	_snake_case     		=	accelerator.prepare(__A			)
	create_dataloader(__A    ,       dataset_size=3    ,       batch_size=1    ,       iterable=__A			)

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

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







def        SCREAMING_SNAKE_CASE__			(     ) ->			List[Any]:
	_snake_case     		=	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'			)
	_snake_case     		=	accelerator.state.distributed_type
	_snake_case     		=	DistributedType.FSDP
	test_join_raises_warning_for_non_ddp_distributed(__A			)
	_snake_case     		=	original_state


if __name__ == "__main__":
					main()



'''simple docstring'''







from __future__ import annotations







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	if not nums:
		return 0
	_snake_case     		=	nums[0]
	_snake_case     		=	0
	for num in nums[1:]:
		_snake_case		,				_snake_case     		=	(
		    max_excluding + num,
		    max(__A    ,       __A			),
		)
	return max(__A    ,       __A			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

