






"""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        lowercase__      (							snake_case__       ):


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  Dict ):
										lowerCamelCase_	:							Any									=data


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


def 			_snake_case    (					lowerCamelCase__     :   Dict=True  )    ->     Tuple:
							lowerCamelCase_	:							Dict									=Accelerator(even_batches=lowerCamelCase__  )
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator

def 			_snake_case    (					lowerCamelCase__     :   Accelerator    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   bool = False  )    ->     Optional[Any]:
							if iterable:
														lowerCamelCase_	:							int									=DummyIterableDataset(torch.as_tensor(range(lowerCamelCase__  )  )  )
							else:
														lowerCamelCase_	:							Dict									=TensorDataset(torch.as_tensor(range(lowerCamelCase__  )  )  )

							lowerCamelCase_	:							List[str]									=DataLoader(lowerCamelCase__    ,						batch_size=lowerCamelCase__  )
							lowerCamelCase_	:							List[Any]									=accelerator.prepare(lowerCamelCase__  )

							return dl

def 			_snake_case    (					lowerCamelCase__     :   Accelerator    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   List[int]    ,						lowerCamelCase__     :   List[int]    ,						)    ->     Tuple:
							lowerCamelCase_	:							Any									=create_dataloader(accelerator=lowerCamelCase__    ,						dataset_size=lowerCamelCase__    ,						batch_size=lowerCamelCase__  )

							lowerCamelCase_	:							Optional[int]									=[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 			_snake_case    (					)    ->     str:
							lowerCamelCase_	:							Optional[int]									=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 			_snake_case    (					)    ->     str:
							lowerCamelCase_	:							Dict									=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 			_snake_case    (					)    ->     List[Any]:
							lowerCamelCase_	:							List[Any]									=create_accelerator(even_batches=lowerCamelCase__  )

							lowerCamelCase_	:							Tuple									=torch.nn.Linear(1    ,						1  )
							lowerCamelCase_	:							Dict									=accelerator.prepare(lowerCamelCase__  )

							lowerCamelCase_	:							Optional[int]									=create_dataloader(lowerCamelCase__    ,						dataset_size=3    ,						batch_size=1  )

							lowerCamelCase_	:							Tuple									=[]
							with accelerator.join_uneven_inputs([ddp_model]  ):
														for batch_idx, batch in enumerate(lowerCamelCase__  ):
																					lowerCamelCase_	:							Dict									=ddp_model(batch[0].float()  )
																					lowerCamelCase_	:							Tuple									=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 			_snake_case    (					lowerCamelCase__     :   int  )    ->     Tuple:
							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 			_snake_case    (					)    ->     Dict:
							lowerCamelCase_	:							Union[str, Any]									=True
							lowerCamelCase_	:							Union[str, Any]									=False
							lowerCamelCase_	:							List[Any]									=create_accelerator(even_batches=lowerCamelCase__  )
							lowerCamelCase_	:							List[Any]									=torch.nn.Linear(1    ,						1  )
							lowerCamelCase_	:							Any									=accelerator.prepare(lowerCamelCase__  )
							lowerCamelCase_	:							Tuple									=create_dataloader(lowerCamelCase__    ,						dataset_size=3    ,						batch_size=1  )
							lowerCamelCase_	:							Optional[Any]									=create_dataloader(lowerCamelCase__    ,						dataset_size=3    ,						batch_size=1  )

							with accelerator.join_uneven_inputs([ddp_model]    ,						even_batches=lowerCamelCase__  ):
														lowerCamelCase_	:							Tuple									=train_dl.batch_sampler.even_batches
														lowerCamelCase_	:							int									=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 			_snake_case    (					)    ->     int:
							lowerCamelCase_	:							Optional[Any]									=True
							lowerCamelCase_	:							Optional[Any]									=False
							lowerCamelCase_	:							Any									=create_accelerator(even_batches=lowerCamelCase__  )
							lowerCamelCase_	:							Union[str, Any]									=torch.nn.Linear(1    ,						1  )
							lowerCamelCase_	:							List[Any]									=accelerator.prepare(lowerCamelCase__  )
							create_dataloader(lowerCamelCase__    ,						dataset_size=3    ,						batch_size=1    ,						iterable=lowerCamelCase__  )
							lowerCamelCase_	:							Union[str, Any]									=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__  ):
																												lowerCamelCase_	:							List[Any]									=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 			_snake_case    (					)    ->     int:
							lowerCamelCase_	:							Union[str, Any]									=create_accelerator()
							lowerCamelCase_	:							List[str]									=torch.nn.Linear(1    ,						1  )
							lowerCamelCase_	:							int									=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 			_snake_case    (					)    ->     Union[str, Any]:
							lowerCamelCase_	:							Optional[int]									=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"  )
							lowerCamelCase_	:							Optional[int]									=accelerator.state.distributed_type
							lowerCamelCase_	:							Optional[Any]									=DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase__  )
							lowerCamelCase_	:							Optional[int]									=original_state


if __name__ == "__main__":
						main()











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   list[int]  )    ->     bool:

							# 1. Validate that path exists between current and next vertices
							if graph[path[curr_ind - 1]][next_ver] == 0:
														return False

							# 2. Validate that next vertex is not already in path
							return not any(vertex == next_ver for vertex in path  )

def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   list[int]    ,						lowerCamelCase__     :   int  )    ->     bool:

							# Base Case
							if curr_ind == len(lowerCamelCase__  ):
														# return whether path exists between current and starting vertices
														return graph[path[curr_ind - 1]][path[0]] == 1

							# Recursive Step
							for next_ver in range(0    ,						len(lowerCamelCase__  )  ):
														if valid_connection(lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__  ):
																					# Insert current vertex  into path as next transition
																					lowerCamelCase_	:							Tuple									=next_ver
																					# Validate created path
																					if util_hamilton_cycle(lowerCamelCase__    ,						lowerCamelCase__    ,						curr_ind + 1  ):
																												return True
																					# Backtrack
																					lowerCamelCase_	:							int									=-1
							return False

def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   int = 0  )    ->     list[int]:
							lowerCamelCase_	:							Optional[Any]									=[-1] * (len(lowerCamelCase__  ) + 1)
							# initialize start and end of path with starting index
							lowerCamelCase_	:							Optional[int]									=start_index
							# evaluate and if we find answer return path either return empty array
							return path if util_hamilton_cycle(lowerCamelCase__    ,						lowerCamelCase__    ,						1  ) else []



