





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


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						:						Optional[int]    ,__A						:						int	)    ->       Union[str, Any]:
         _lowercase						    =			data







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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]=True  )   ->    Any:
  _lowercase						    =			Accelerator(even_batches=UpperCamelCase_  )
  assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
  return accelerator

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Accelerator      ,				snake_case__	:int      ,				snake_case__	:int      ,				snake_case__	:bool = False  )   ->    Optional[Any]:
  if iterable:
    _lowercase						    =			DummyIterableDataset(torch.as_tensor(range(UpperCamelCase_  )  )  )
  else:
    _lowercase						    =			TensorDataset(torch.as_tensor(range(UpperCamelCase_  )  )  )

  _lowercase						    =			DataLoader(UpperCamelCase_      ,				batch_size=UpperCamelCase_  )
  _lowercase						    =			accelerator.prepare(UpperCamelCase_  )

  return dl

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Accelerator      ,				snake_case__	:int      ,				snake_case__	:int      ,				snake_case__	:List[int]      ,				snake_case__	:List[int]      ,				)   ->    Union[str, Any]:
  _lowercase						    =			create_dataloader(accelerator=UpperCamelCase_      ,				dataset_size=UpperCamelCase_      ,				batch_size=UpperCamelCase_  )

  _lowercase						    =			[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 				SCREAMING_SNAKE_CASE__							(	)   ->    str:
  _lowercase						    =			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 				SCREAMING_SNAKE_CASE__							(	)   ->    Union[str, Any]:
  _lowercase						    =			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 				SCREAMING_SNAKE_CASE__							(	)   ->    Tuple:
  _lowercase						    =			create_accelerator(even_batches=UpperCamelCase_  )

  _lowercase						    =			torch.nn.Linear(1      ,				1  )
  _lowercase						    =			accelerator.prepare(UpperCamelCase_  )

  _lowercase						    =			create_dataloader(UpperCamelCase_      ,				dataset_size=3      ,				batch_size=1  )

  _lowercase						    =			[]
  with accelerator.join_uneven_inputs([ddp_model]  ):
    for batch_idx, batch in enumerate(UpperCamelCase_  ):
      _lowercase						    =			ddp_model(batch[0].float()  )
      _lowercase						    =			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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]  )   ->    str:
  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 				SCREAMING_SNAKE_CASE__							(	)   ->    Union[str, Any]:
  _lowercase						    =			True
  _lowercase						    =			False
  _lowercase						    =			create_accelerator(even_batches=UpperCamelCase_  )
  _lowercase						    =			torch.nn.Linear(1      ,				1  )
  _lowercase						    =			accelerator.prepare(UpperCamelCase_  )
  _lowercase						    =			create_dataloader(UpperCamelCase_      ,				dataset_size=3      ,				batch_size=1  )
  _lowercase						    =			create_dataloader(UpperCamelCase_      ,				dataset_size=3      ,				batch_size=1  )

  with accelerator.join_uneven_inputs([ddp_model]      ,				even_batches=UpperCamelCase_  ):
    _lowercase						    =			train_dl.batch_sampler.even_batches
    _lowercase						    =			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 				SCREAMING_SNAKE_CASE__							(	)   ->    int:
  _lowercase						    =			True
  _lowercase						    =			False
  _lowercase						    =			create_accelerator(even_batches=UpperCamelCase_  )
  _lowercase						    =			torch.nn.Linear(1      ,				1  )
  _lowercase						    =			accelerator.prepare(UpperCamelCase_  )
  create_dataloader(UpperCamelCase_      ,				dataset_size=3      ,				batch_size=1      ,				iterable=UpperCamelCase_  )
  _lowercase						    =			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_  ):
        _lowercase						    =			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 				SCREAMING_SNAKE_CASE__							(	)   ->    List[str]:
  _lowercase						    =			create_accelerator()
  _lowercase						    =			torch.nn.Linear(1      ,				1  )
  _lowercase						    =			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 				SCREAMING_SNAKE_CASE__							(	)   ->    Optional[int]:
  _lowercase						    =			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'  )
  _lowercase						    =			accelerator.state.distributed_type
  _lowercase						    =			DistributedType.FSDP
  test_join_raises_warning_for_non_ddp_distributed(UpperCamelCase_  )
  _lowercase						    =			original_state


if __name__ == "__main__":
      main()




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


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''timm_backbone'''




     def __init__(					self			,    a__=None			,    a__=3			,    a__=True			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(**a__)
         A__					=       backbone
         A__					=       num_channels
         A__					=       features_only
         A__					=       use_pretrained_backbone
         A__					=       True
         A__					=       out_indices if out_indices is not None else (-1,)
