'''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__   (	lowerCamelCase__  ):





  def __init__(		self				:     Tuple							, _a				:     int			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =data





  def __iter__(		self				:     List[str]			)     ->   Dict:





       '''simple docstring'''






       for element in self.data:
            yield element


def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict=True    )				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =Accelerator(even_batches=_A    )
     assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
     return accelerator


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					Any							,			_UpperCamelCase						:					str							,			_UpperCamelCase						:					Dict = False    )				->      int:
     """simple docstring"""




     if iterable:
          _SCREAMING_SNAKE_CASE							   =DummyIterableDataset(torch.as_tensor(range(_A    )    )    )
     else:
          _SCREAMING_SNAKE_CASE							   =TensorDataset(torch.as_tensor(range(_A    )    )    )

     _SCREAMING_SNAKE_CASE							   =DataLoader(_A							,			batch_size=_A    )
     _SCREAMING_SNAKE_CASE							   =accelerator.prepare(_A    )

     return dl


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any							,			_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					str							,			_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Tuple							,			)				->      Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =create_dataloader(accelerator=_A							,			dataset_size=_A							,			batch_size=_A    )

     _SCREAMING_SNAKE_CASE							   =[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 (						)				->      List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =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 (						)				->      List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =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 (						)				->      str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =create_accelerator(even_batches=_A    )

     _SCREAMING_SNAKE_CASE							   =torch.nn.Linear(1							,			1    )
     _SCREAMING_SNAKE_CASE							   =accelerator.prepare(_A    )

     _SCREAMING_SNAKE_CASE							   =create_dataloader(_A							,			dataset_size=3							,			batch_size=1    )

     _SCREAMING_SNAKE_CASE							   =[]
     with accelerator.join_uneven_inputs([ddp_model]    ):
          for batch_idx, batch in enumerate(_A    ):
               _SCREAMING_SNAKE_CASE							   =ddp_model(batch[0].float()    )
               _SCREAMING_SNAKE_CASE							   =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 (						_UpperCamelCase						:					Any    )				->      Dict:
     """simple docstring"""




     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 (						)				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =True
     _SCREAMING_SNAKE_CASE							   =False
     _SCREAMING_SNAKE_CASE							   =create_accelerator(even_batches=_A    )
     _SCREAMING_SNAKE_CASE							   =torch.nn.Linear(1							,			1    )
     _SCREAMING_SNAKE_CASE							   =accelerator.prepare(_A    )
     _SCREAMING_SNAKE_CASE							   =create_dataloader(_A							,			dataset_size=3							,			batch_size=1    )
     _SCREAMING_SNAKE_CASE							   =create_dataloader(_A							,			dataset_size=3							,			batch_size=1    )

     with accelerator.join_uneven_inputs([ddp_model]							,			even_batches=_A    ):
          _SCREAMING_SNAKE_CASE							   =train_dl.batch_sampler.even_batches
          _SCREAMING_SNAKE_CASE							   =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 (						)				->      Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =True
     _SCREAMING_SNAKE_CASE							   =False
     _SCREAMING_SNAKE_CASE							   =create_accelerator(even_batches=_A    )
     _SCREAMING_SNAKE_CASE							   =torch.nn.Linear(1							,			1    )
     _SCREAMING_SNAKE_CASE							   =accelerator.prepare(_A    )
     create_dataloader(_A							,			dataset_size=3							,			batch_size=1							,			iterable=_A    )
     _SCREAMING_SNAKE_CASE							   =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							   =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 (						)				->      Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =create_accelerator()
     _SCREAMING_SNAKE_CASE							   =torch.nn.Linear(1							,			1    )
     _SCREAMING_SNAKE_CASE							   =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]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =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							   =accelerator.state.distributed_type
     _SCREAMING_SNAKE_CASE							   =DistributedType.FSDP
     test_join_raises_warning_for_non_ddp_distributed(_A    )
     _SCREAMING_SNAKE_CASE							   =original_state


if __name__ == "__main__":
     main()



'''simple docstring'''
# Copyright 2023 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase			:				Optional[int] 			=					{
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					["EfficientNetImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "EfficientNetForImageClassification",
         "EfficientNetModel",
         "EfficientNetPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_efficientnet import (
         EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EfficientNetConfig,
         EfficientNetOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_efficientnet import EfficientNetImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_efficientnet import (
              EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              EfficientNetForImageClassification,
              EfficientNetModel,
              EfficientNetPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				int 			=					_LazyModule(__name__, globals()["__file__"], _import_structure)
