


"""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_     (			UpperCamelCase):

	"""simple docstring"""





	def __init__(			self      :    Optional[int]      ,      UpperCAmelCase__      :    Any       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    data




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




def        UpperCAmelCase__	(lowerCAmelCase_=True       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = False       ):




					'''simple docstring'''


					if iterable:
										__SCREAMING_SNAKE_CASE						       =    DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase_       )       )       )
					else:
										__SCREAMING_SNAKE_CASE						       =    TensorDataset(torch.as_tensor(range(lowerCAmelCase_       )       )       )

					__SCREAMING_SNAKE_CASE						       =    DataLoader(lowerCAmelCase_  ,						batch_size=lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    accelerator.prepare(lowerCAmelCase_       )

					return dl




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    create_dataloader(accelerator=lowerCAmelCase_  ,						dataset_size=lowerCAmelCase_  ,						batch_size=lowerCAmelCase_       )

					__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        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    create_accelerator()

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




def        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    create_accelerator(even_batches=lowerCAmelCase_       )

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

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




def        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    create_accelerator(even_batches=lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    torch.nn.Linear(1  ,						1       )
					__SCREAMING_SNAKE_CASE						       =    accelerator.prepare(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    create_dataloader(lowerCAmelCase_  ,						dataset_size=3  ,						batch_size=1       )

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

					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__	(lowerCAmelCase_       ):




					'''simple docstring'''


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

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




def        UpperCAmelCase__	():




					'''simple docstring'''


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

					with accelerator.join_uneven_inputs([ddp_model]  ,						even_batches=lowerCAmelCase_       ):
										__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        UpperCAmelCase__	():




					'''simple docstring'''


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

					with warnings.catch_warnings():
										warnings.filterwarnings("ignore"       )
										try:
															with accelerator.join_uneven_inputs([ddp_model]  ,						even_batches=lowerCAmelCase_       ):
																				__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        UpperCAmelCase__	():




					'''simple docstring'''


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

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

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




def        UpperCAmelCase__	():




					'''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(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    original_state


if __name__ == "__main__":
		main()




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import _LazyModule


a__    :							Optional[int]            =					{'''tokenization_byt5''': ['''ByT5Tokenizer''']}


if TYPE_CHECKING:
		from .tokenization_byta import ByTaTokenizer
else:
		import sys

		a__    :							Any            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
