



'''simple docstring'''





# 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    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Dict     ,      _a :      str  )   ->   List[str]:
    __lowerCamelCase			:			Union[str, Any]       			=					data




  def __iter__(					self :      Dict  )   ->   Any:
    for element in self.data:
      yield element




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

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

  __lowerCamelCase			:			List[Any]       			=					DataLoader(_lowerCAmelCase			,batch_size=_lowerCAmelCase			)
  __lowerCamelCase			:			List[str]       			=					accelerator.prepare(_lowerCAmelCase			)

  return dl

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,) -> str:
  __lowerCamelCase			:			List[str]       			=					create_dataloader(accelerator=_lowerCAmelCase			,dataset_size=_lowerCAmelCase			,batch_size=_lowerCAmelCase			)

  __lowerCamelCase			:			List[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 			a_	(				) -> Optional[int]:
  __lowerCamelCase			:			Tuple       			=					create_accelerator()

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

def 			a_	(				) -> Any:
  __lowerCamelCase			:			int       			=					create_accelerator(even_batches=_lowerCAmelCase			)

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

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

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

  __lowerCamelCase			:			Union[str, Any]       			=					torch.nn.Linear(1			,1			)
  __lowerCamelCase			:			Any       			=					accelerator.prepare(_lowerCAmelCase			)

  __lowerCamelCase			:			Dict       			=					create_dataloader(_lowerCAmelCase			,dataset_size=3			,batch_size=1			)

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

  accelerator.wait_for_everyone()

  if accelerator.process_index == 0:
    assert batch_idxs == [0, 1]
  elif accelerator.process_index == 1:
    assert batch_idxs == [0]

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

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

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

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

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

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

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

def 			a_	(				) -> Union[str, Any]:
  __lowerCamelCase			:			Optional[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'			)
  __lowerCamelCase			:			str       			=					accelerator.state.distributed_type
  __lowerCamelCase			:			Optional[Any]       			=					DistributedType.FSDP
  test_join_raises_warning_for_non_ddp_distributed(_lowerCAmelCase			)
  __lowerCamelCase			:			Tuple       			=					original_state


if __name__ == "__main__":
   main()











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase											=		{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		['BridgeTowerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BridgeTowerForContrastiveLearning',
       'BridgeTowerForImageAndTextRetrieval',
       'BridgeTowerForMaskedLM',
       'BridgeTowerModel',
       'BridgeTowerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=		_LazyModule(__name__, globals()['__file__'], _import_structure)






