# 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 UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     lowercase ):



						"""simple docstring"""





						A_		:    List[str]         =				data







				def __iter__(						self ):



						"""simple docstring"""





						for element in self.data:
								yield element




def     UpperCamelCase						(	__lowercase						:			int=True     ):
		'''simple docstring'''




		A_		:    Tuple         =				Accelerator(even_batches=__lowercase     )
		assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
		return accelerator




def     UpperCamelCase						(	__lowercase						:			Accelerator						,__lowercase						:			int						,__lowercase						:			int						,__lowercase						:			bool = False     ):
		'''simple docstring'''




		if iterable:
				A_		:    List[Any]         =				DummyIterableDataset(torch.as_tensor(range(__lowercase     )     )     )
		else:
				A_		:    Tuple         =				TensorDataset(torch.as_tensor(range(__lowercase     )     )     )

		A_		:    List[Any]         =				DataLoader(__lowercase						,batch_size=__lowercase     )
		A_		:    Union[str, Any]         =				accelerator.prepare(__lowercase     )

		return dl




def     UpperCamelCase						(	__lowercase						:			Accelerator						,__lowercase						:			int						,__lowercase						:			int						,__lowercase						:			List[int]						,__lowercase						:			List[int]						,):
		'''simple docstring'''




		A_		:    List[str]         =				create_dataloader(accelerator=__lowercase						,dataset_size=__lowercase						,batch_size=__lowercase     )

		A_		:    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     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Any         =				create_accelerator()

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




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Any         =				create_accelerator(even_batches=__lowercase     )

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

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




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[int]         =				create_accelerator(even_batches=__lowercase     )

		A_		:    List[str]         =				torch.nn.Linear(1						,1     )
		A_		:    Tuple         =				accelerator.prepare(__lowercase     )

		A_		:    str         =				create_dataloader(__lowercase						,dataset_size=3						,batch_size=1     )

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

		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						(	__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		with warnings.catch_warnings(record=__lowercase     ) as w:
				with accelerator.join_uneven_inputs([Mock()]     ):
						pass

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




def     UpperCamelCase						(	):
		'''simple docstring'''




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

		with accelerator.join_uneven_inputs([ddp_model]						,even_batches=__lowercase     ):
				A_		:    List[Any]         =				train_dl.batch_sampler.even_batches
				A_		:    Union[str, 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     UpperCamelCase						(	):
		'''simple docstring'''




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

		with warnings.catch_warnings():
				warnings.filterwarnings('ignore'     )
				try:
						with accelerator.join_uneven_inputs([ddp_model]						,even_batches=__lowercase     ):
								A_		:    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     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    List[Any]         =				create_accelerator()
		A_		:    Union[str, Any]         =				torch.nn.Linear(1						,1     )
		A_		:    str         =				accelerator.prepare(__lowercase     )
		create_dataloader(__lowercase						,dataset_size=3						,batch_size=1						,iterable=__lowercase     )

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

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




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[int]         =				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'     )
		A_		:    str         =				accelerator.state.distributed_type
		A_		:    Any         =				DistributedType.FSDP
		test_join_raises_warning_for_non_ddp_distributed(__lowercase     )
		A_		:    Tuple         =				original_state


if __name__ == "__main__":
	main()







import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						warnings.warn(
						    'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
						    ' use DonutImageProcessor instead.'		,     lowercase		,     )
						super().__init__(*lowercase		,     **lowercase )






