





"""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    _SCREAMING_SNAKE_CASE(  A    ):




       def __init__(		self						,SCREAMING_SNAKE_CASE__				)     -> List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE  :int													=    data




       def __iter__(		self				)     -> Optional[int]:





             """simple docstring"""






             for element in self.data:
                   yield element




def 	__lowerCamelCase  (				a_	:						List[str]=True				)     -> Tuple:
      __SCREAMING_SNAKE_CASE  :int													=    Accelerator(even_batches=a_				)
      assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
      return accelerator



def 	__lowerCamelCase  (				a_	:						int					,  a_	:						Optional[int]					,  a_	:						Optional[int]					,  a_	:						List[Any] = False				)     -> Dict:
      if iterable:
            __SCREAMING_SNAKE_CASE  :List[Any]													=    DummyIterableDataset(torch.as_tensor(range(a_				)				)				)
      else:
            __SCREAMING_SNAKE_CASE  :List[str]													=    TensorDataset(torch.as_tensor(range(a_				)				)				)

      __SCREAMING_SNAKE_CASE  :Union[str, Any]													=    DataLoader(a_					,  batch_size=a_				)
      __SCREAMING_SNAKE_CASE  :Optional[int]													=    accelerator.prepare(a_				)

      return dl



def 	__lowerCamelCase  (				a_	:						int					,  a_	:						Optional[int]					,  a_	:						Union[str, Any]					,  a_	:						Dict					,  a_	:						Dict					,  )     -> Optional[Any]:
      __SCREAMING_SNAKE_CASE  :Dict													=    create_dataloader(accelerator=a_					,  dataset_size=a_					,  batch_size=a_				)

      __SCREAMING_SNAKE_CASE  :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 	__lowerCamelCase  (				)     -> Dict:
      __SCREAMING_SNAKE_CASE  :str													=    create_accelerator()

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



def 	__lowerCamelCase  (				)     -> Union[str, Any]:
      __SCREAMING_SNAKE_CASE  :Dict													=    create_accelerator(even_batches=a_				)

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

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



def 	__lowerCamelCase  (				)     -> Optional[Any]:
      __SCREAMING_SNAKE_CASE  :Any													=    create_accelerator(even_batches=a_				)

      __SCREAMING_SNAKE_CASE  :List[Any]													=    torch.nn.Linear(1					,  1				)
      __SCREAMING_SNAKE_CASE  :List[str]													=    accelerator.prepare(a_				)

      __SCREAMING_SNAKE_CASE  :List[Any]													=    create_dataloader(a_					,  dataset_size=3					,  batch_size=1				)

      __SCREAMING_SNAKE_CASE  :List[Any]													=    []
      with accelerator.join_uneven_inputs([ddp_model]				):
            for batch_idx, batch in enumerate(a_				):
                  __SCREAMING_SNAKE_CASE  :Tuple													=    ddp_model(batch[0].float()				)
                  __SCREAMING_SNAKE_CASE  :Optional[int]													=    output.sum()
                  loss.backward()
                  batch_idxs.append(a_				)

      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  (				a_	:						Tuple				)     -> Optional[Any]:
      with warnings.catch_warnings(record=a_				) as w:
            with accelerator.join_uneven_inputs([Mock()]				):
                  pass

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



def 	__lowerCamelCase  (				)     -> int:
      __SCREAMING_SNAKE_CASE  :List[str]													=    True
      __SCREAMING_SNAKE_CASE  :Dict													=    False
      __SCREAMING_SNAKE_CASE  :Union[str, Any]													=    create_accelerator(even_batches=a_				)
      __SCREAMING_SNAKE_CASE  :Optional[int]													=    torch.nn.Linear(1					,  1				)
      __SCREAMING_SNAKE_CASE  :Dict													=    accelerator.prepare(a_				)
      __SCREAMING_SNAKE_CASE  :List[str]													=    create_dataloader(a_					,  dataset_size=3					,  batch_size=1				)
      __SCREAMING_SNAKE_CASE  :Any													=    create_dataloader(a_					,  dataset_size=3					,  batch_size=1				)

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

      with warnings.catch_warnings():
            warnings.filterwarnings('''ignore'''				)
            try:
                  with accelerator.join_uneven_inputs([ddp_model]					,  even_batches=a_				):
                        __SCREAMING_SNAKE_CASE  :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 	__lowerCamelCase  (				)     -> Any:
      __SCREAMING_SNAKE_CASE  :Tuple													=    create_accelerator()
      __SCREAMING_SNAKE_CASE  :Any													=    torch.nn.Linear(1					,  1				)
      __SCREAMING_SNAKE_CASE  :str													=    accelerator.prepare(a_				)
      create_dataloader(a_					,  dataset_size=3					,  batch_size=1					,  iterable=a_				)

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

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



def 	__lowerCamelCase  (				)     -> Optional[int]:
      __SCREAMING_SNAKE_CASE  :Any													=    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'''				)
      __SCREAMING_SNAKE_CASE  :List[Any]													=    accelerator.state.distributed_type
      __SCREAMING_SNAKE_CASE  :List[str]													=    DistributedType.FSDP
      test_join_raises_warning_for_non_ddp_distributed(a_				)
      __SCREAMING_SNAKE_CASE  :Optional[Any]													=    original_state


if __name__ == "__main__":
  main()



'''simple docstring'''
def 	_A  (       A							)  ->				list:

	if len(A							) <= 1:
		return [tuple(A							)]

	lowercase						:		Dict   					=		[]

	def generate(A							,A							):
		lowercase						:		List[Any]   					=		[0] * n
		res.append(tuple(A							)							)

		lowercase						:		Tuple   					=		0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					lowercase					,		lowercase						:		List[Any]   					=		arr[i], arr[0]
				else:
					lowercase					,		lowercase						:		Optional[Any]   					=		arr[i], arr[c[i]]
				res.append(tuple(A							)							)
				c[i] += 1
				lowercase						:		Optional[Any]   					=		0
			else:
				lowercase						:		Any   					=		0
				i += 1

	generate(len(A							)							,A							)
	return res


if __name__ == "__main__":
					lowerCAmelCase   : Any             =				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase   : List[str]             =				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))


