



'''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 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase							):
				A_	:						Optional[Any]	=     data


	def __iter__(self							):
				for element in self.data:
							yield element






def 	a			( lowerCamelCase__=True    ):
			'''simple docstring'''





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



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__ = False    ):
			'''simple docstring'''





			if iterable:
						A_	:						Optional[Any]	=     DummyIterableDataset(torch.as_tensor(range(lowerCamelCase__    )    )    )
			else:
						A_	:						Dict	=     TensorDataset(torch.as_tensor(range(lowerCamelCase__    )    )    )

			A_	:						Any	=     DataLoader(lowerCamelCase__						,      batch_size=lowerCamelCase__    )
			A_	:						Any	=     accelerator.prepare(lowerCamelCase__    )

			return dl



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):
			'''simple docstring'''





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

			A_	:						List[str]	=     [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			( ):
			'''simple docstring'''





			A_	:						Optional[Any]	=     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 	a			( ):
			'''simple docstring'''





			A_	:						str	=     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 	a			( ):
			'''simple docstring'''





			A_	:						Union[str, Any]	=     create_accelerator(even_batches=lowerCamelCase__    )

			A_	:						Any	=     torch.nn.Linear(1						,      1    )
			A_	:						str	=     accelerator.prepare(lowerCamelCase__    )

			A_	:						Union[str, Any]	=     create_dataloader(lowerCamelCase__						,      dataset_size=3						,      batch_size=1    )

			A_	:						Tuple	=     []
			with accelerator.join_uneven_inputs([ddp_model]    ):
						for batch_idx, batch in enumerate(lowerCamelCase__    ):
									A_	:						Union[str, Any]	=     ddp_model(batch[0].float()    )
									A_	:						Optional[Any]	=     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 	a			( 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 	a			( ):
			'''simple docstring'''





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

			with accelerator.join_uneven_inputs([ddp_model]						,      even_batches=lowerCamelCase__    ):
						A_	:						Optional[int]	=     train_dl.batch_sampler.even_batches
						A_	:						List[str]	=     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			( ):
			'''simple docstring'''





			A_	:						str	=     True
			A_	:						List[Any]	=     False
			A_	:						Tuple	=     create_accelerator(even_batches=lowerCamelCase__    )
			A_	:						List[str]	=     torch.nn.Linear(1						,      1    )
			A_	:						str	=     accelerator.prepare(lowerCamelCase__    )
			create_dataloader(lowerCamelCase__						,      dataset_size=3						,      batch_size=1						,      iterable=lowerCamelCase__    )
			A_	:						Dict	=     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__    ):
												A_	:						Tuple	=     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			( ):
			'''simple docstring'''





			A_	:						Union[str, Any]	=     create_accelerator()
			A_	:						str	=     torch.nn.Linear(1						,      1    )
			A_	:						List[str]	=     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 	a			( ):
			'''simple docstring'''





			A_	:						Union[str, Any]	=     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_	:						Any	=     accelerator.state.distributed_type
			A_	:						Tuple	=     DistributedType.FSDP
			test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase__    )
			A_	:						Dict	=     original_state


if __name__ == "__main__":
						main()




'''simple docstring'''




class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[Any]	=     [
				    [],
				    [],
				    [],
				]


	def     _a	(self       ,      lowercase       ,      lowercase							):
				try:
							if len(self.queues[priority]							) >= 100:
										raise OverflowError("""Maximum queue size is 100"""							)
							self.queues[priority].append(lowercase							)
				except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""							)


	def     _a	(self							):
				for queue in self.queues:
							if queue:
										return queue.pop(0							)
				raise UnderFlowError("""All queues are empty"""							)


	def __str__(self							):
				return "\n".join(F'Priority {i}: {q}' for i, q in enumerate(self.queues							)							)







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[str]	=     []


	def     _a	(self       ,      lowercase							):
				if len(self.queue							) == 100:
							raise OverFlowError("""Maximum queue size is 100"""							)
				self.queue.append(lowercase							)


	def     _a	(self							):
				if not self.queue:
							raise UnderFlowError("""The queue is empty"""							)
				else:
							A_	:						List[Any]	=     min(self.queue							)
							self.queue.remove(lowercase							)
							return data


	def __str__(self							):
				return str(self.queue							)






def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     FixedPriorityQueue()
			fpq.enqueue(0						,      10    )
			fpq.enqueue(1						,      70    )
			fpq.enqueue(0						,      1_00    )
			fpq.enqueue(2						,      1    )
			fpq.enqueue(2						,      5    )
			fpq.enqueue(1						,      7    )
			fpq.enqueue(2						,      4    )
			fpq.enqueue(1						,      64    )
			fpq.enqueue(0						,      1_28    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )



def 	a			( ):
			'''simple docstring'''





			A_	:						int	=     ElementPriorityQueue()
			epq.enqueue(10    )
			epq.enqueue(70    )
			epq.enqueue(1_00    )
			epq.enqueue(1    )
			epq.enqueue(5    )
			epq.enqueue(7    )
			epq.enqueue(4    )
			epq.enqueue(64    )
			epq.enqueue(1_28    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )


if __name__ == "__main__":
						fixed_priority_queue()
						element_priority_queue()