






"""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						(					__snake_case		):






    def __init__(       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Tuple:
      lowerCamelCase_         =						data






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







def      lowerCamelCase__       (							_lowerCamelCase       :		int=True )      ->				Optional[Any]:
  lowerCamelCase_         =						Accelerator(even_batches=lowercase__ )
  assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
  return accelerator







def      lowerCamelCase__       (							_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		Optional[int] = False )      ->				str:
  if iterable:
    lowerCamelCase_         =						DummyIterableDataset(torch.as_tensor(range(lowercase__ ) ) )
  else:
    lowerCamelCase_         =						TensorDataset(torch.as_tensor(range(lowercase__ ) ) )

  lowerCamelCase_         =						DataLoader(lowercase__		,				batch_size=lowercase__ )
  lowerCamelCase_         =						accelerator.prepare(lowercase__ )

  return dl







def      lowerCamelCase__       (							_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		str		,				_lowerCamelCase       :		Dict		,				)      ->				Optional[Any]:
  lowerCamelCase_         =						create_dataloader(accelerator=lowercase__		,				dataset_size=lowercase__		,				batch_size=lowercase__ )

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

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







def      lowerCamelCase__       (							)      ->				Optional[Any]:
  lowerCamelCase_         =						create_accelerator(even_batches=lowercase__ )

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

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







def      lowerCamelCase__       (							)      ->				Union[str, Any]:
  lowerCamelCase_         =						create_accelerator(even_batches=lowercase__ )

  lowerCamelCase_         =						torch.nn.Linear(1		,				1 )
  lowerCamelCase_         =						accelerator.prepare(lowercase__ )

  lowerCamelCase_         =						create_dataloader(lowercase__		,				dataset_size=3		,				batch_size=1 )

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

  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__       (							_lowerCamelCase       :		Optional[Any] )      ->				List[str]:
  with warnings.catch_warnings(record=lowercase__ ) as w:
    with accelerator.join_uneven_inputs([Mock()] ):
      pass

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







def      lowerCamelCase__       (							)      ->				int:
  lowerCamelCase_         =						True
  lowerCamelCase_         =						False
  lowerCamelCase_         =						create_accelerator(even_batches=lowercase__ )
  lowerCamelCase_         =						torch.nn.Linear(1		,				1 )
  lowerCamelCase_         =						accelerator.prepare(lowercase__ )
  lowerCamelCase_         =						create_dataloader(lowercase__		,				dataset_size=3		,				batch_size=1 )
  lowerCamelCase_         =						create_dataloader(lowercase__		,				dataset_size=3		,				batch_size=1 )

  with accelerator.join_uneven_inputs([ddp_model]		,				even_batches=lowercase__ ):
    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      lowerCamelCase__       (							)      ->				str:
  lowerCamelCase_         =						True
  lowerCamelCase_         =						False
  lowerCamelCase_         =						create_accelerator(even_batches=lowercase__ )
  lowerCamelCase_         =						torch.nn.Linear(1		,				1 )
  lowerCamelCase_         =						accelerator.prepare(lowercase__ )
  create_dataloader(lowercase__		,				dataset_size=3		,				batch_size=1		,				iterable=lowercase__ )
  lowerCamelCase_         =						create_dataloader(lowercase__		,				dataset_size=3		,				batch_size=1 )

  with warnings.catch_warnings():
    warnings.filterwarnings('ignore' )
    try:
      with accelerator.join_uneven_inputs([ddp_model]		,				even_batches=lowercase__ ):
        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      lowerCamelCase__       (							)      ->				Optional[int]:
  lowerCamelCase_         =						create_accelerator()
  lowerCamelCase_         =						torch.nn.Linear(1		,				1 )
  lowerCamelCase_         =						accelerator.prepare(lowercase__ )
  create_dataloader(lowercase__		,				dataset_size=3		,				batch_size=1		,				iterable=lowercase__ )

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

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







def      lowerCamelCase__       (							)      ->				Union[str, Any]:
  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(lowercase__ )
  lowerCamelCase_         =						original_state


if __name__ == "__main__":
  main()










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__     						=						{"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """ViTMSNModel""",
			    """ViTMSNForImageClassification""",
			    """ViTMSNPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit_msn import (
						    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTMSNForImageClassification,
						    ViTMSNModel,
						    ViTMSNPreTrainedModel,
						)

else:
			import sys

			lowercase__     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)