






"""simple docstring"""



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        lowercase__      (							snake_case__       ):


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  Dict ):
										lowerCamelCase_	:							Any									=data


			def __iter__(				self					:  int ):
										for element in self.data:
																	yield element


def 			_snake_case    (					lowerCamelCase__     :   Dict=True  )    ->     Tuple:
							lowerCamelCase_	:							Dict									=Accelerator(even_batches=lowerCamelCase__  )
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator

def 			_snake_case    (					lowerCamelCase__     :   Accelerator    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   bool = False  )    ->     Optional[Any]:
							if iterable:
														lowerCamelCase_	:							int									=DummyIterableDataset(torch.as_tensor(range(lowerCamelCase__  )  )  )
							else:
														lowerCamelCase_	:							Dict									=TensorDataset(torch.as_tensor(range(lowerCamelCase__  )  )  )

							lowerCamelCase_	:							List[str]									=DataLoader(lowerCamelCase__    ,						batch_size=lowerCamelCase__  )
							lowerCamelCase_	:							List[Any]									=accelerator.prepare(lowerCamelCase__  )

							return dl

def 			_snake_case    (					lowerCamelCase__     :   Accelerator    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   List[int]    ,						lowerCamelCase__     :   List[int]    ,						)    ->     Tuple:
							lowerCamelCase_	:							Any									=create_dataloader(accelerator=lowerCamelCase__    ,						dataset_size=lowerCamelCase__    ,						batch_size=lowerCamelCase__  )

							lowerCamelCase_	:							Optional[int]									=[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 			_snake_case    (					)    ->     str:
							lowerCamelCase_	:							Optional[int]									=create_accelerator()

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

def 			_snake_case    (					)    ->     str:
							lowerCamelCase_	:							Dict									=create_accelerator(even_batches=lowerCamelCase__  )

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

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

def 			_snake_case    (					)    ->     List[Any]:
							lowerCamelCase_	:							List[Any]									=create_accelerator(even_batches=lowerCamelCase__  )

							lowerCamelCase_	:							Tuple									=torch.nn.Linear(1    ,						1  )
							lowerCamelCase_	:							Dict									=accelerator.prepare(lowerCamelCase__  )

							lowerCamelCase_	:							Optional[int]									=create_dataloader(lowerCamelCase__    ,						dataset_size=3    ,						batch_size=1  )

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

							accelerator.wait_for_everyone()

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

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

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

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

							with accelerator.join_uneven_inputs([ddp_model]    ,						even_batches=lowerCamelCase__  ):
														lowerCamelCase_	:							Tuple									=train_dl.batch_sampler.even_batches
														lowerCamelCase_	:							int									=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 			_snake_case    (					)    ->     int:
							lowerCamelCase_	:							Optional[Any]									=True
							lowerCamelCase_	:							Optional[Any]									=False
							lowerCamelCase_	:							Any									=create_accelerator(even_batches=lowerCamelCase__  )
							lowerCamelCase_	:							Union[str, Any]									=torch.nn.Linear(1    ,						1  )
							lowerCamelCase_	:							List[Any]									=accelerator.prepare(lowerCamelCase__  )
							create_dataloader(lowerCamelCase__    ,						dataset_size=3    ,						batch_size=1    ,						iterable=lowerCamelCase__  )
							lowerCamelCase_	:							Union[str, Any]									=create_dataloader(lowerCamelCase__    ,						dataset_size=3    ,						batch_size=1  )

							with warnings.catch_warnings():
														warnings.filterwarnings("ignore"  )
														try:
																					with accelerator.join_uneven_inputs([ddp_model]    ,						even_batches=lowerCamelCase__  ):
																												lowerCamelCase_	:							List[Any]									=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 			_snake_case    (					)    ->     int:
							lowerCamelCase_	:							Union[str, Any]									=create_accelerator()
							lowerCamelCase_	:							List[str]									=torch.nn.Linear(1    ,						1  )
							lowerCamelCase_	:							int									=accelerator.prepare(lowerCamelCase__  )
							create_dataloader(lowerCamelCase__    ,						dataset_size=3    ,						batch_size=1    ,						iterable=lowerCamelCase__  )

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

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

def 			_snake_case    (					)    ->     Union[str, Any]:
							lowerCamelCase_	:							Optional[int]									=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_	:							Optional[int]									=accelerator.state.distributed_type
							lowerCamelCase_	:							Optional[Any]									=DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase__  )
							lowerCamelCase_	:							Optional[int]									=original_state


if __name__ == "__main__":
			main()











"""simple docstring"""



import torch

def 			_snake_case    (					)    ->     Union[str, Any]:
							if torch.cuda.is_available():
														lowerCamelCase_	:							int									=torch.cuda.device_count()
							else:
														lowerCamelCase_	:							List[str]									=0
							print(F"""Successfully ran on {num_gpus} GPUs"""  )


if __name__ == "__main__":
						main()



