


'''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    UpperCAmelCase					(       __snake_case    ):




			def __init__(						self							:		Union[str, Any]       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					data





			def __iter__(						self							:		Optional[Any]						):







						"""simple docstring"""







						for element in self.data:
									yield element






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]=True	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Accelerator       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		bool = False	):





			"""simple docstring"""




			if iterable:
						UpperCamelCase         =					DummyIterableDataset(torch.as_tensor(range(SCREAMING_SNAKE_CASE_	)	)	)
			else:
						UpperCamelCase         =					TensorDataset(torch.as_tensor(range(SCREAMING_SNAKE_CASE_	)	)	)

			UpperCamelCase         =					DataLoader(SCREAMING_SNAKE_CASE_       ,  batch_size=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					accelerator.prepare(SCREAMING_SNAKE_CASE_	)

			return dl






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Accelerator       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		List[int]       ,  SCREAMING_SNAKE_CASE_   :		List[int]       ,  ):





			"""simple docstring"""




			UpperCamelCase         =					create_dataloader(accelerator=SCREAMING_SNAKE_CASE_       ,  dataset_size=SCREAMING_SNAKE_CASE_       ,  batch_size=SCREAMING_SNAKE_CASE_	)

			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 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					create_accelerator()

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






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					create_accelerator(even_batches=SCREAMING_SNAKE_CASE_	)

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

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






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					create_accelerator(even_batches=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					torch.nn.Linear(1       ,  1	)
			UpperCamelCase         =					accelerator.prepare(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					create_dataloader(SCREAMING_SNAKE_CASE_       ,  dataset_size=3       ,  batch_size=1	)

			UpperCamelCase         =					[]
			with accelerator.join_uneven_inputs([ddp_model]	):
						for batch_idx, batch in enumerate(SCREAMING_SNAKE_CASE_	):
									UpperCamelCase         =					ddp_model(batch[0].float()	)
									UpperCamelCase         =					output.sum()
									loss.backward()
									batch_idxs.append(SCREAMING_SNAKE_CASE_	)

			accelerator.wait_for_everyone()

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




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

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






def 							_lowercase    (		):





			"""simple docstring"""




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

			with accelerator.join_uneven_inputs([ddp_model]       ,  even_batches=SCREAMING_SNAKE_CASE_	):
						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 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					True
			UpperCamelCase         =					False
			UpperCamelCase         =					create_accelerator(even_batches=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					torch.nn.Linear(1       ,  1	)
			UpperCamelCase         =					accelerator.prepare(SCREAMING_SNAKE_CASE_	)
			create_dataloader(SCREAMING_SNAKE_CASE_       ,  dataset_size=3       ,  batch_size=1       ,  iterable=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					create_dataloader(SCREAMING_SNAKE_CASE_       ,  dataset_size=3       ,  batch_size=1	)

			with warnings.catch_warnings():
						warnings.filterwarnings("""ignore"""	)
						try:
									with accelerator.join_uneven_inputs([ddp_model]       ,  even_batches=SCREAMING_SNAKE_CASE_	):
												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 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					create_accelerator()
			UpperCamelCase         =					torch.nn.Linear(1       ,  1	)
			UpperCamelCase         =					accelerator.prepare(SCREAMING_SNAKE_CASE_	)
			create_dataloader(SCREAMING_SNAKE_CASE_       ,  dataset_size=3       ,  batch_size=1       ,  iterable=SCREAMING_SNAKE_CASE_	)

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

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






def 							_lowercase    (		):





			"""simple docstring"""




			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(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					original_state


if __name__ == "__main__":
			main()








def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			return [
			    txt[:a] + txt[a].upper() + txt[a + 1 :]
			    for a in range(len(SCREAMING_SNAKE_CASE_	)	)
			    if txt[a].isalpha()
			]


if __name__ == "__main__":
			__import__("doctest").testmod()




