


'''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       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										__lowerCAmelCase							       =				data




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

def 							__magic_name__(						lowerCamelCase=True):
						__lowerCAmelCase							       =				Accelerator(even_batches=lowerCamelCase)
						assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
						return accelerator

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase = False):
						if iterable:
												__lowerCAmelCase							       =				DummyIterableDataset(torch.as_tensor(range(lowerCamelCase)))
						else:
												__lowerCAmelCase							       =				TensorDataset(torch.as_tensor(range(lowerCamelCase)))

						__lowerCAmelCase							       =				DataLoader(lowerCamelCase,				batch_size=lowerCamelCase)
						__lowerCAmelCase							       =				accelerator.prepare(lowerCamelCase)

						return dl

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				):
						__lowerCAmelCase							       =				create_dataloader(accelerator=lowerCamelCase,				dataset_size=lowerCamelCase,				batch_size=lowerCamelCase)

						__lowerCAmelCase							       =				[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 							__magic_name__(						):
						__lowerCAmelCase							       =				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 							__magic_name__(						):
						__lowerCAmelCase							       =				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 							__magic_name__(						):
						__lowerCAmelCase							       =				create_accelerator(even_batches=lowerCamelCase)

						__lowerCAmelCase							       =				torch.nn.Linear(1,				1)
						__lowerCAmelCase							       =				accelerator.prepare(lowerCamelCase)

						__lowerCAmelCase							       =				create_dataloader(lowerCamelCase,				dataset_size=3,				batch_size=1)

						__lowerCAmelCase							       =				[]
						with accelerator.join_uneven_inputs([ddp_model]):
												for batch_idx, batch in enumerate(lowerCamelCase):
																		__lowerCAmelCase							       =				ddp_model(batch[0].float())
																		__lowerCAmelCase							       =				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 							__magic_name__(						lowerCamelCase):
						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 							__magic_name__(						):
						__lowerCAmelCase							       =				True
						__lowerCAmelCase							       =				False
						__lowerCAmelCase							       =				create_accelerator(even_batches=lowerCamelCase)
						__lowerCAmelCase							       =				torch.nn.Linear(1,				1)
						__lowerCAmelCase							       =				accelerator.prepare(lowerCamelCase)
						__lowerCAmelCase							       =				create_dataloader(lowerCamelCase,				dataset_size=3,				batch_size=1)
						__lowerCAmelCase							       =				create_dataloader(lowerCamelCase,				dataset_size=3,				batch_size=1)

						with accelerator.join_uneven_inputs([ddp_model],				even_batches=lowerCamelCase):
												__lowerCAmelCase							       =				train_dl.batch_sampler.even_batches
												__lowerCAmelCase							       =				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 							__magic_name__(						):
						__lowerCAmelCase							       =				True
						__lowerCAmelCase							       =				False
						__lowerCAmelCase							       =				create_accelerator(even_batches=lowerCamelCase)
						__lowerCAmelCase							       =				torch.nn.Linear(1,				1)
						__lowerCAmelCase							       =				accelerator.prepare(lowerCamelCase)
						create_dataloader(lowerCamelCase,				dataset_size=3,				batch_size=1,				iterable=lowerCamelCase)
						__lowerCAmelCase							       =				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							       =				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 							__magic_name__(						):
						__lowerCAmelCase							       =				create_accelerator()
						__lowerCAmelCase							       =				torch.nn.Linear(1,				1)
						__lowerCAmelCase							       =				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 							__magic_name__(						):
						__lowerCAmelCase							       =				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							       =				accelerator.state.distributed_type
						__lowerCAmelCase							       =				DistributedType.FSDP
						test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase)
						__lowerCAmelCase							       =				original_state


if __name__ == "__main__":
	main()





'''simple docstring'''

from torch import nn

def 							__magic_name__(						lowerCamelCase):
						if act_fn in ["swish", "silu"]:
												return nn.SiLU()
						elif act_fn == "mish":
												return nn.Mish()
						elif act_fn == "gelu":
												return nn.GELU()
						else:
												raise ValueError(F"""Unsupported activation function: {act_fn}""")

