






'''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       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Any							,			A__      :	Tuple				)					-> Union[str, Any]:
						lowercase         = data





		def __iter__(self      :	Tuple				)					-> Optional[int]:
						for element in self.data:
										yield element







def 							UpperCAmelCase_    (	lowerCAmelCase_=True							):

				"""simple docstring"""






				lowercase         = Accelerator(even_batches=lowerCAmelCase_							)
				assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
				return accelerator







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_ = False							):

				"""simple docstring"""






				if iterable:
								lowercase         = DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase_							)							)							)
				else:
								lowercase         = TensorDataset(torch.as_tensor(range(lowerCAmelCase_							)							)							)

				lowercase         = DataLoader(lowerCAmelCase_		,							batch_size=lowerCAmelCase_							)
				lowercase         = accelerator.prepare(lowerCAmelCase_							)

				return dl







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							):

				"""simple docstring"""






				lowercase         = create_dataloader(accelerator=lowerCAmelCase_		,							dataset_size=lowerCAmelCase_		,							batch_size=lowerCAmelCase_							)

				lowercase         = [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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = 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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = 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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = create_accelerator(even_batches=lowerCAmelCase_							)

				lowercase         = torch.nn.Linear(1		,							1							)
				lowercase         = accelerator.prepare(lowerCAmelCase_							)

				lowercase         = create_dataloader(lowerCAmelCase_		,							dataset_size=3		,							batch_size=1							)

				lowercase         = []
				with accelerator.join_uneven_inputs([ddp_model]							):
								for batch_idx, batch in enumerate(lowerCAmelCase_							):
												lowercase         = ddp_model(batch[0].float()							)
												lowercase         = 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 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = True
				lowercase         = False
				lowercase         = create_accelerator(even_batches=lowerCAmelCase_							)
				lowercase         = torch.nn.Linear(1		,							1							)
				lowercase         = accelerator.prepare(lowerCAmelCase_							)
				lowercase         = create_dataloader(lowerCAmelCase_		,							dataset_size=3		,							batch_size=1							)
				lowercase         = create_dataloader(lowerCAmelCase_		,							dataset_size=3		,							batch_size=1							)

				with accelerator.join_uneven_inputs([ddp_model]		,							even_batches=lowerCAmelCase_							):
								lowercase         = train_dl.batch_sampler.even_batches
								lowercase         = 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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = True
				lowercase         = False
				lowercase         = create_accelerator(even_batches=lowerCAmelCase_							)
				lowercase         = torch.nn.Linear(1		,							1							)
				lowercase         = accelerator.prepare(lowerCAmelCase_							)
				create_dataloader(lowerCAmelCase_		,							dataset_size=3		,							batch_size=1		,							iterable=lowerCAmelCase_							)
				lowercase         = 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_							):
																lowercase         = 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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = create_accelerator()
				lowercase         = torch.nn.Linear(1		,							1							)
				lowercase         = 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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = 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"							)
				lowercase         = accelerator.state.distributed_type
				lowercase         = DistributedType.FSDP
				test_join_raises_warning_for_non_ddp_distributed(lowerCAmelCase_							)
				lowercase         = original_state


if __name__ == "__main__":
			main()











'''simple docstring'''

__lowerCamelCase : str											=			8.314_4598







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				if temperature < 0:
								raise Exception("Temperature cannot be less than 0 K"							)
				if molar_mass <= 0:
								raise Exception("Molar mass cannot be less than or equal to 0 kg/mol"							)
				else:
								return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
			import doctest

			# run doctest
			doctest.testmod()

			# example
			__lowerCamelCase : Optional[Any]											=			300
			__lowerCamelCase : Tuple											=			28
			__lowerCamelCase : str											=			rms_speed_of_molecule(temperature, molar_mass)
			print(f"Vrms of Nitrogen gas at 300 K is {vrms} m/s")




