


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_					(			UpperCamelCase__):






							"""simple docstring"""







							def __init__( self      ,     _UpperCAmelCase		):







													"""simple docstring"""


													a_		      =		data






							def __iter__( self		):







													"""simple docstring"""


													for element in self.data:
																			yield element




def 	lowerCamelCase_							(       UpperCAmelCase__=True   ):



						"""simple docstring"""




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




def 	lowerCamelCase_							(       UpperCAmelCase__							,     UpperCAmelCase__							,     UpperCAmelCase__							,     UpperCAmelCase__ = False   ):



						"""simple docstring"""




						if iterable:
												a_		      =		DummyIterableDataset(torch.as_tensor(range(SCREAMING_SNAKE_CASE_   )   )   )
						else:
												a_		      =		TensorDataset(torch.as_tensor(range(SCREAMING_SNAKE_CASE_   )   )   )

						a_		      =		DataLoader(SCREAMING_SNAKE_CASE_							,     batch_size=SCREAMING_SNAKE_CASE_   )
						a_		      =		accelerator.prepare(SCREAMING_SNAKE_CASE_   )

						return dl




def 	lowerCamelCase_							(       UpperCAmelCase__							,     UpperCAmelCase__							,     UpperCAmelCase__							,     UpperCAmelCase__							,     UpperCAmelCase__							,     ):



						"""simple docstring"""




						a_		      =		create_dataloader(accelerator=SCREAMING_SNAKE_CASE_							,     dataset_size=SCREAMING_SNAKE_CASE_							,     batch_size=SCREAMING_SNAKE_CASE_   )

						a_		      =		[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 	lowerCamelCase_							(       ):



						"""simple docstring"""




						a_		      =		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 	lowerCamelCase_							(       ):



						"""simple docstring"""




						a_		      =		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 	lowerCamelCase_							(       ):



						"""simple docstring"""




						a_		      =		create_accelerator(even_batches=SCREAMING_SNAKE_CASE_   )

						a_		      =		torch.nn.Linear(1							,     1   )
						a_		      =		accelerator.prepare(SCREAMING_SNAKE_CASE_   )

						a_		      =		create_dataloader(SCREAMING_SNAKE_CASE_							,     dataset_size=3							,     batch_size=1   )

						a_		      =		[]
						with accelerator.join_uneven_inputs([ddp_model]   ):
												for batch_idx, batch in enumerate(SCREAMING_SNAKE_CASE_   ):
																		a_		      =		ddp_model(batch[0].float()   )
																		a_		      =		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 	lowerCamelCase_							(       UpperCAmelCase__   ):



						"""simple docstring"""




						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 	lowerCamelCase_							(       ):



						"""simple docstring"""




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

						with accelerator.join_uneven_inputs([ddp_model]							,     even_batches=SCREAMING_SNAKE_CASE_   ):
												a_		      =		train_dl.batch_sampler.even_batches
												a_		      =		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 	lowerCamelCase_							(       ):



						"""simple docstring"""




						a_		      =		True
						a_		      =		False
						a_		      =		create_accelerator(even_batches=SCREAMING_SNAKE_CASE_   )
						a_		      =		torch.nn.Linear(1							,     1   )
						a_		      =		accelerator.prepare(SCREAMING_SNAKE_CASE_   )
						create_dataloader(SCREAMING_SNAKE_CASE_							,     dataset_size=3							,     batch_size=1							,     iterable=SCREAMING_SNAKE_CASE_   )
						a_		      =		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_   ):
																								a_		      =		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 	lowerCamelCase_							(       ):



						"""simple docstring"""




						a_		      =		create_accelerator()
						a_		      =		torch.nn.Linear(1							,     1   )
						a_		      =		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 	lowerCamelCase_							(       ):



						"""simple docstring"""




						a_		      =		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"""   )
						a_		      =		accelerator.state.distributed_type
						a_		      =		DistributedType.FSDP
						test_join_raises_warning_for_non_ddp_distributed(SCREAMING_SNAKE_CASE_   )
						a_		      =		original_state


if __name__ == "__main__":
	main()





'''simple docstring'''
from PIL import Image





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Image:

			def brightness(SCREAMING_SNAKE_CASE_     ) -> float:
						return 128 + level + (c - 128)

			if not -255.0 <= level <= 255.0:
						raise ValueError('level must be between -255.0 (black) and 255.0 (white)'     )
			return img.point(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change brightness to 100
														_SCREAMING_SNAKE_CASE			=						change_brightness(img, 1_00)
														brigt_img.save("image_data/lena_brightness.png", format="png")




