





"""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   (	lowercase_ ):


					def __init__(					self			:Any     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  data


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







def 					A__				(			UpperCamelCase=True       ):
							A		=  Accelerator(even_batches=UpperCamelCase       )
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase = False       ):
							if iterable:
														A		=  DummyIterableDataset(torch.as_tensor(range(UpperCamelCase       )       )       )
							else:
														A		=  TensorDataset(torch.as_tensor(range(UpperCamelCase       )       )       )

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

							return dl






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  ):
							A		=  create_dataloader(accelerator=UpperCamelCase					,  dataset_size=UpperCamelCase					,  batch_size=UpperCamelCase       )

							A		=  [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__				(			):
							A		=  create_accelerator()

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






def 					A__				(			):
							A		=  create_accelerator(even_batches=UpperCamelCase       )

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

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






def 					A__				(			):
							A		=  create_accelerator(even_batches=UpperCamelCase       )

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

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

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

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

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






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

							with accelerator.join_uneven_inputs([ddp_model]					,  even_batches=UpperCamelCase       ):
														A		=  train_dl.batch_sampler.even_batches
														A		=  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__				(			):
							A		=  True
							A		=  False
							A		=  create_accelerator(even_batches=UpperCamelCase       )
							A		=  torch.nn.Linear(1					,  1       )
							A		=  accelerator.prepare(UpperCamelCase       )
							create_dataloader(UpperCamelCase					,  dataset_size=3					,  batch_size=1					,  iterable=UpperCamelCase       )
							A		=  create_dataloader(UpperCamelCase					,  dataset_size=3					,  batch_size=1       )

							with warnings.catch_warnings():
														warnings.filterwarnings("ignore"       )
														try:
																					with accelerator.join_uneven_inputs([ddp_model]					,  even_batches=UpperCamelCase       ):
																												A		=  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__				(			):
							A		=  create_accelerator()
							A		=  torch.nn.Linear(1					,  1       )
							A		=  accelerator.prepare(UpperCamelCase       )
							create_dataloader(UpperCamelCase					,  dataset_size=3					,  batch_size=1					,  iterable=UpperCamelCase       )

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

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






def 					A__				(			):
							A		=  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		=  accelerator.state.distributed_type
							A		=  DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(UpperCamelCase       )
							A		=  original_state


if __name__ == "__main__":
		main()









"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 					A__				(			UpperCamelCase       ):
							A		=  [False] * len(UpperCamelCase       )
							A		=  [-1] * len(UpperCamelCase       )

							def dfs(UpperCamelCase					,  UpperCamelCase       ):
														A		=  True
														A		=  c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase					,  1 - c       )

							for i in range(len(UpperCamelCase       )       ):
														if not visited[i]:
																					dfs(UpperCamelCase					,  0       )

							for i in range(len(UpperCamelCase       )       ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_snake_case     :		str       						= {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))


