
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 ..utils import DummyObject, requires_backends


class 		__UpperCAmelCase   (      metaclass=_lowerCamelCase		):
							__lowercase   	= ["""torch""", """torchsde"""]


							def __init__( self					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(self					,     ['torch', 'torchsde']						)


							@classmethod
							def 				lowerCamelCase       ( cls					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(cls					,     ['torch', 'torchsde']						)




							@classmethod
							def 				lowerCamelCase       ( cls					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(cls					,     ['torch', 'torchsde']						)

