


# 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__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       data







			def __iter__(							self     ):

						'''simple docstring'''


						for element in self.data:
									yield element



def        UpperCamelCase_(				_A   :List[str]=True  )->		Union[str, Any]:
			UpperCamelCase__					=       Accelerator(even_batches=_A  )
			assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
			return accelerator
def        UpperCamelCase_(				_A   :Accelerator				,     _A   :int				,     _A   :int				,     _A   :bool = False  )->		Any:
			if iterable:
						UpperCamelCase__					=       DummyIterableDataset(torch.as_tensor(range(_A  )  )  )
			else:
						UpperCamelCase__					=       TensorDataset(torch.as_tensor(range(_A  )  )  )

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

			return dl
def        UpperCamelCase_(				_A   :Accelerator				,     _A   :int				,     _A   :int				,     _A   :List[int]				,     _A   :List[int]				,     )->		str:
			UpperCamelCase__					=       create_dataloader(accelerator=_A				,     dataset_size=_A				,     batch_size=_A  )

			UpperCamelCase__					=       [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        UpperCamelCase_(				)->		Dict:
			UpperCamelCase__					=       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        UpperCamelCase_(				)->		str:
			UpperCamelCase__					=       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        UpperCamelCase_(				)->		str:
			UpperCamelCase__					=       create_accelerator(even_batches=_A  )

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

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

			UpperCamelCase__					=       []
			with accelerator.join_uneven_inputs([ddp_model]  ):
						for batch_idx, batch in enumerate(_A  ):
									UpperCamelCase__					=       ddp_model(batch[0].float()  )
									UpperCamelCase__					=       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        UpperCamelCase_(				_A   :Tuple  )->		Any:
			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        UpperCamelCase_(				)->		Optional[int]:
			UpperCamelCase__					=       True
			UpperCamelCase__					=       False
			UpperCamelCase__					=       create_accelerator(even_batches=_A  )
			UpperCamelCase__					=       torch.nn.Linear(1				,     1  )
			UpperCamelCase__					=       accelerator.prepare(_A  )
			UpperCamelCase__					=       create_dataloader(_A				,     dataset_size=3				,     batch_size=1  )
			UpperCamelCase__					=       create_dataloader(_A				,     dataset_size=3				,     batch_size=1  )

			with accelerator.join_uneven_inputs([ddp_model]				,     even_batches=_A  ):
						UpperCamelCase__					=       train_dl.batch_sampler.even_batches
						UpperCamelCase__					=       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        UpperCamelCase_(				)->		Optional[int]:
			UpperCamelCase__					=       True
			UpperCamelCase__					=       False
			UpperCamelCase__					=       create_accelerator(even_batches=_A  )
			UpperCamelCase__					=       torch.nn.Linear(1				,     1  )
			UpperCamelCase__					=       accelerator.prepare(_A  )
			create_dataloader(_A				,     dataset_size=3				,     batch_size=1				,     iterable=_A  )
			UpperCamelCase__					=       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  ):
												UpperCamelCase__					=       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        UpperCamelCase_(				)->		Tuple:
			UpperCamelCase__					=       create_accelerator()
			UpperCamelCase__					=       torch.nn.Linear(1				,     1  )
			UpperCamelCase__					=       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        UpperCamelCase_(				)->		Union[str, Any]:
			UpperCamelCase__					=       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__					=       accelerator.state.distributed_type
			UpperCamelCase__					=       DistributedType.FSDP
			test_join_raises_warning_for_non_ddp_distributed(_A  )
			UpperCamelCase__					=       original_state


if __name__ == "__main__":
						main()






from typing import TYPE_CHECKING

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


__UpperCamelCase    		=       {
    'configuration_mobilenet_v2': [
        'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV2Config',
        'MobileNetV2OnnxConfig',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       ['MobileNetV2FeatureExtractor']
						__UpperCamelCase    		=       ['MobileNetV2ImageProcessor']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MobileNetV2ForImageClassification',
						    'MobileNetV2ForSemanticSegmentation',
						    'MobileNetV2Model',
						    'MobileNetV2PreTrainedModel',
						    'load_tf_weights_in_mobilenet_v2',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

						__UpperCamelCase    		=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


