


"""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        __A       (snake_case__):





							'''simple docstring'''

							def __init__(			self							: Tuple						,    UpperCAmelCase_							: List[Any]    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						data



							def __iter__(			self							: Union[str, Any]    )   ->Any:



														"""simple docstring"""



														for element in self.data:
																					yield element







def   _a			( _SCREAMING_SNAKE_CASE=True			)      ->			List[str]:
							snake_case_          =						Accelerator(even_batches=_SCREAMING_SNAKE_CASE			)
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = False			)      ->			Dict:
							if iterable:
														snake_case_          =						DummyIterableDataset(torch.as_tensor(range(_SCREAMING_SNAKE_CASE			)			)			)
							else:
														snake_case_          =						TensorDataset(torch.as_tensor(range(_SCREAMING_SNAKE_CASE			)			)			)

							snake_case_          =						DataLoader(_SCREAMING_SNAKE_CASE			,	batch_size=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						accelerator.prepare(_SCREAMING_SNAKE_CASE			)

							return dl







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	)      ->			Optional[int]:
							snake_case_          =						create_dataloader(accelerator=_SCREAMING_SNAKE_CASE			,	dataset_size=_SCREAMING_SNAKE_CASE			,	batch_size=_SCREAMING_SNAKE_CASE			)

							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   _a			( )      ->			Optional[int]:
							snake_case_          =						create_accelerator()

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







def   _a			( )      ->			List[Any]:
							snake_case_          =						create_accelerator(even_batches=_SCREAMING_SNAKE_CASE			)

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

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







def   _a			( )      ->			Dict:
							snake_case_          =						create_accelerator(even_batches=_SCREAMING_SNAKE_CASE			)

							snake_case_          =						torch.nn.Linear(1			,	1			)
							snake_case_          =						accelerator.prepare(_SCREAMING_SNAKE_CASE			)

							snake_case_          =						create_dataloader(_SCREAMING_SNAKE_CASE			,	dataset_size=3			,	batch_size=1			)

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

							accelerator.wait_for_everyone()

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







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							with warnings.catch_warnings(record=_SCREAMING_SNAKE_CASE			) as w:
														with accelerator.join_uneven_inputs([Mock()]			):
																					pass

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







def   _a			( )      ->			Any:
							snake_case_          =						True
							snake_case_          =						False
							snake_case_          =						create_accelerator(even_batches=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						torch.nn.Linear(1			,	1			)
							snake_case_          =						accelerator.prepare(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						create_dataloader(_SCREAMING_SNAKE_CASE			,	dataset_size=3			,	batch_size=1			)
							snake_case_          =						create_dataloader(_SCREAMING_SNAKE_CASE			,	dataset_size=3			,	batch_size=1			)

							with accelerator.join_uneven_inputs([ddp_model]			,	even_batches=_SCREAMING_SNAKE_CASE			):
														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   _a			( )      ->			Optional[Any]:
							snake_case_          =						True
							snake_case_          =						False
							snake_case_          =						create_accelerator(even_batches=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						torch.nn.Linear(1			,	1			)
							snake_case_          =						accelerator.prepare(_SCREAMING_SNAKE_CASE			)
							create_dataloader(_SCREAMING_SNAKE_CASE			,	dataset_size=3			,	batch_size=1			,	iterable=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						create_dataloader(_SCREAMING_SNAKE_CASE			,	dataset_size=3			,	batch_size=1			)

							with warnings.catch_warnings():
														warnings.filterwarnings("""ignore"""			)
														try:
																					with accelerator.join_uneven_inputs([ddp_model]			,	even_batches=_SCREAMING_SNAKE_CASE			):
																												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   _a			( )      ->			Dict:
							snake_case_          =						create_accelerator()
							snake_case_          =						torch.nn.Linear(1			,	1			)
							snake_case_          =						accelerator.prepare(_SCREAMING_SNAKE_CASE			)
							create_dataloader(_SCREAMING_SNAKE_CASE			,	dataset_size=3			,	batch_size=1			,	iterable=_SCREAMING_SNAKE_CASE			)

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

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







def   _a			( )      ->			Dict:
							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(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						original_state


if __name__ == "__main__":
				main()







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 0			)      ->			list:
							snake_case_          =						length or len(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						False
							for i in range(length - 1			):
														if list_data[i] > list_data[i + 1]:
																					snake_case_    ,  snake_case_          =						list_data[i + 1], list_data[i]
																					snake_case_          =						True

							return list_data if not swapped else bubble_sort(_SCREAMING_SNAKE_CASE			,	length - 1			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



