

"""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      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			int					,A		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					data




  def __iter__(  self		:			int    ):






   '''simple docstring'''
   for element in self.data:
    yield element







def   lowerCAmelCase						(					__UpperCamelCase=True      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Any					   =					Accelerator(even_batches=__UpperCamelCase      )
 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:
  UpperCAmelCase__					:  Dict					   =					DummyIterableDataset(torch.as_tensor(range(__UpperCamelCase      )      )      )
 else:
  UpperCAmelCase__					:  List[Any]					   =					TensorDataset(torch.as_tensor(range(__UpperCamelCase      )      )      )

 UpperCAmelCase__					:  Any					   =					DataLoader(__UpperCamelCase							,    batch_size=__UpperCamelCase      )
 UpperCAmelCase__					:  Optional[Any]					   =					accelerator.prepare(__UpperCamelCase      )

 return dl
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    ):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[int]					   =					create_dataloader(accelerator=__UpperCamelCase							,    dataset_size=__UpperCamelCase							,    batch_size=__UpperCamelCase      )

 UpperCAmelCase__					:  Union[str, Any]					   =					[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'''





 UpperCAmelCase__					:  Optional[Any]					   =					create_accelerator()

 # without padding, we would expect a different number of batches
 verify_dataloader_batch_sizes(
     __UpperCamelCase							,    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(
     __UpperCamelCase							,    dataset_size=7							,    batch_size=2							,    process_0_expected_batch_sizes=[2, 2]							,    process_1_expected_batch_sizes=[2, 2]							,    )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					create_accelerator(even_batches=__UpperCamelCase      )

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

 verify_dataloader_batch_sizes(
     __UpperCamelCase							,    dataset_size=7							,    batch_size=2							,    process_0_expected_batch_sizes=[2, 2]							,    process_1_expected_batch_sizes=[2, 1]							,    )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Any					   =					create_accelerator(even_batches=__UpperCamelCase      )

 UpperCAmelCase__					:  Dict					   =					torch.nn.Linear(1							,    1      )
 UpperCAmelCase__					:  str					   =					accelerator.prepare(__UpperCamelCase      )

 UpperCAmelCase__					:  List[Any]					   =					create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1      )

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

 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=__UpperCamelCase      ) as w:
  with accelerator.join_uneven_inputs([Mock()]      ):
   pass

  assert issubclass(w[-1].category							,    __UpperCamelCase      )
  assert "only supported for multi-GPU" in str(w[-1].message      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Union[str, Any]					   =					True
 UpperCAmelCase__					:  List[str]					   =					False
 UpperCAmelCase__					:  str					   =					create_accelerator(even_batches=__UpperCamelCase      )
 UpperCAmelCase__					:  Any					   =					torch.nn.Linear(1							,    1      )
 UpperCAmelCase__					:  Optional[int]					   =					accelerator.prepare(__UpperCamelCase      )
 UpperCAmelCase__					:  List[Any]					   =					create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1      )
 UpperCAmelCase__					:  str					   =					create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1      )

 with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=__UpperCamelCase      ):
  UpperCAmelCase__					:  int					   =					train_dl.batch_sampler.even_batches
  UpperCAmelCase__					:  List[str]					   =					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'''





 UpperCAmelCase__					:  Tuple					   =					True
 UpperCAmelCase__					:  str					   =					False
 UpperCAmelCase__					:  Optional[Any]					   =					create_accelerator(even_batches=__UpperCamelCase      )
 UpperCAmelCase__					:  int					   =					torch.nn.Linear(1							,    1      )
 UpperCAmelCase__					:  str					   =					accelerator.prepare(__UpperCamelCase      )
 create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1							,    iterable=__UpperCamelCase      )
 UpperCAmelCase__					:  Any					   =					create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1      )

 with warnings.catch_warnings():
  warnings.filterwarnings("""ignore"""      )
  try:
   with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=__UpperCamelCase      ):
    UpperCAmelCase__					:  List[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						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  List[Any]					   =					create_accelerator()
 UpperCAmelCase__					:  int					   =					torch.nn.Linear(1							,    1      )
 UpperCAmelCase__					:  Tuple					   =					accelerator.prepare(__UpperCamelCase      )
 create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1							,    iterable=__UpperCamelCase      )

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

  assert issubclass(w[-1].category							,    __UpperCamelCase      )
  assert "only supported for map-style datasets" in str(w[-1].message      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					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"""      )
 UpperCAmelCase__					:  int					   =					accelerator.state.distributed_type
 UpperCAmelCase__					:  Any					   =					DistributedType.FSDP
 test_join_raises_warning_for_non_ddp_distributed(__UpperCamelCase      )
 UpperCAmelCase__					:  str					   =					original_state


if __name__ == "__main__":
    main()








"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase =				logging.get_logger(__name__)







class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       """timm_backbone"""

  def __init__(  self		:			List[str]					,A		:			Any=None					,A		:			List[Any]=3					,A		:			Any=True					,A		:			Union[str, Any]=True					,A		:			List[Any]=None					,**A		:			Optional[int]					,):






   '''simple docstring'''
   super().__init__(**A    )
   UpperCAmelCase__					:  Optional[int]					   =					backbone
   UpperCAmelCase__					:  Dict					   =					num_channels
   UpperCAmelCase__					:  Optional[int]					   =					features_only
   UpperCAmelCase__					:  Tuple					   =					use_pretrained_backbone
   UpperCAmelCase__					:  str					   =					True
   UpperCAmelCase__					:  List[str]					   =					out_indices if out_indices is not None else (-1,)





