






# 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   __SCREAMING_SNAKE_CASE(	a_	):

							'''simple docstring'''





							def __init__(     self:      Tuple       ,	UpperCamelCase:      Optional[int]						)		->		Union[str, Any]:
											snake_case__									=     data


							def __iter__(     self:      List[str]						)		->		List[Any]:
											for element in self.data:
															yield element


def      a_ (      _A=True  )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     Accelerator(even_batches=_A  )
				assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
				return accelerator
def      a_ (      _A      ,   _A      ,   _A      ,   _A = False  )			->			int:







				"""simple docstring"""
				if iterable:
								snake_case__									=     DummyIterableDataset(torch.as_tensor(range(_A  )  )  )
				else:
								snake_case__									=     TensorDataset(torch.as_tensor(range(_A  )  )  )

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

				return dl
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A      ,   )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     create_dataloader(accelerator=_A      ,   dataset_size=_A      ,   batch_size=_A  )

				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      a_ (      )			->			int:







				"""simple docstring"""
				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      a_ (      )			->			str:







				"""simple docstring"""
				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      a_ (      )			->			int:







				"""simple docstring"""
				snake_case__									=     create_accelerator(even_batches=_A  )

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

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

				snake_case__									=     []
				with accelerator.join_uneven_inputs([ddp_model]  ):
								for batch_idx, batch in enumerate(_A  ):
												snake_case__									=     ddp_model(batch[0].float()  )
												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      a_ (      _A  )			->			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      a_ (      )			->			Optional[Any]:







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

				with accelerator.join_uneven_inputs([ddp_model]      ,   even_batches=_A  ):
								snake_case__									=     train_dl.batch_sampler.even_batches
								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      a_ (      )			->			Tuple:







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







				"""simple docstring"""
				snake_case__									=     create_accelerator()
				snake_case__									=     torch.nn.Linear(1      ,   1  )
				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      a_ (      )			->			str:







				"""simple docstring"""
				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'  )
				snake_case__									=     accelerator.state.distributed_type
				snake_case__									=     DistributedType.FSDP
				test_join_raises_warning_for_non_ddp_distributed(_A  )
				snake_case__									=     original_state


if __name__ == "__main__":
							main()












def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )
				binary_number += "0" * shift_amount
				return binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )[2:]
				if shift_amount >= len(_A  ):
								return "0b0"
				snake_case__									=     binary_number[: len(_A  ) - shift_amount]
				return "0b" + shifted_binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number >= 0:  # Get binary representation of positive number
								snake_case__									=     '0' + str(bin(_A  )  ).strip('-'  )[2:]
				else:  # Get binary (2's complement) representation of negative number
								snake_case__									=     len(bin(_A  )[3:]  )  # Find 2's complement of number
								snake_case__									=     bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
								snake_case__									=     (
								    '1' + '0' * (binary_number_length - len(_A  )) + binary_number
								)

				if shift_amount >= len(_A  ):
								return "0b" + binary_number[0] * len(_A  )
				return (
				    "0b"
				    + binary_number[0] * shift_amount
				    + binary_number[: len(_A  ) - shift_amount]
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




