





'''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    ( lowerCAmelCase__  ):







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							data


	def __iter__(self	)							->							Optional[Any]:







				'''simple docstring'''





				for element in self.data:
							yield element





def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]=True			):
			__lowercase			  =							Accelerator(even_batches=lowerCamelCase_			)
			assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
			return accelerator




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Accelerator						,    lowerCamelCase_     :					int						,    lowerCamelCase_     :					int						,    lowerCamelCase_     :					bool = False			):
			if iterable:
						__lowercase			  =							DummyIterableDataset(torch.as_tensor(range(lowerCamelCase_			)			)			)
			else:
						__lowercase			  =							TensorDataset(torch.as_tensor(range(lowerCamelCase_			)			)			)

			__lowercase			  =							DataLoader(lowerCamelCase_						,    batch_size=lowerCamelCase_			)
			__lowercase			  =							accelerator.prepare(lowerCamelCase_			)

			return dl




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Accelerator						,    lowerCamelCase_     :					int						,    lowerCamelCase_     :					int						,    lowerCamelCase_     :					List[int]						,    lowerCamelCase_     :					List[int]						,    ):
			__lowercase			  =							create_dataloader(accelerator=lowerCamelCase_						,    dataset_size=lowerCamelCase_						,    batch_size=lowerCamelCase_			)

			__lowercase			  =							[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 				_lowerCAmelCase  (			):
			__lowercase			  =							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 				_lowerCAmelCase  (			):
			__lowercase			  =							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 				_lowerCAmelCase  (			):
			__lowercase			  =							create_accelerator(even_batches=lowerCamelCase_			)

			__lowercase			  =							torch.nn.Linear(1						,    1			)
			__lowercase			  =							accelerator.prepare(lowerCamelCase_			)

			__lowercase			  =							create_dataloader(lowerCamelCase_						,    dataset_size=3						,    batch_size=1			)

			__lowercase			  =							[]
			with accelerator.join_uneven_inputs([ddp_model]			):
						for batch_idx, batch in enumerate(lowerCamelCase_			):
									__lowercase			  =							ddp_model(batch[0].float()			)
									__lowercase			  =							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 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]			):
			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 				_lowerCAmelCase  (			):
			__lowercase			  =							True
			__lowercase			  =							False
			__lowercase			  =							create_accelerator(even_batches=lowerCamelCase_			)
			__lowercase			  =							torch.nn.Linear(1						,    1			)
			__lowercase			  =							accelerator.prepare(lowerCamelCase_			)
			__lowercase			  =							create_dataloader(lowerCamelCase_						,    dataset_size=3						,    batch_size=1			)
			__lowercase			  =							create_dataloader(lowerCamelCase_						,    dataset_size=3						,    batch_size=1			)

			with accelerator.join_uneven_inputs([ddp_model]						,    even_batches=lowerCamelCase_			):
						__lowercase			  =							train_dl.batch_sampler.even_batches
						__lowercase			  =							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 				_lowerCAmelCase  (			):
			__lowercase			  =							True
			__lowercase			  =							False
			__lowercase			  =							create_accelerator(even_batches=lowerCamelCase_			)
			__lowercase			  =							torch.nn.Linear(1						,    1			)
			__lowercase			  =							accelerator.prepare(lowerCamelCase_			)
			create_dataloader(lowerCamelCase_						,    dataset_size=3						,    batch_size=1						,    iterable=lowerCamelCase_			)
			__lowercase			  =							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_			):
												__lowercase			  =							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 				_lowerCAmelCase  (			):
			__lowercase			  =							create_accelerator()
			__lowercase			  =							torch.nn.Linear(1						,    1			)
			__lowercase			  =							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 				_lowerCAmelCase  (			):
			__lowercase			  =							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'''			)
			__lowercase			  =							accelerator.state.distributed_type
			__lowercase			  =							DistributedType.FSDP
			test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase_			)
			__lowercase			  =							original_state


if __name__ == "__main__":
	main()












'''simple docstring'''



_SCREAMING_SNAKE_CASE   =      {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							set()
			# keep track of all the paths to be checked
			__lowercase			  =							[[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						__lowercase			  =							queue.pop(0			)
						# get the last node from the path
						__lowercase			  =							path[-1]
						if node not in explored:
									__lowercase			  =							graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__lowercase			  =							list(lowerCamelCase_			)
												new_path.append(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(lowerCamelCase_			)

    # in case there's no path between the 2 nodes
			return []




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			__lowercase			  =							[start]
			__lowercase			  =							set(lowerCamelCase_			)
			# Keep tab on distances from `start` node.
			__lowercase			  =							{start: 0, target: -1}
			while queue:
						__lowercase			  =							queue.pop(0			)
						if node == target:
									__lowercase			  =							(
									    dist[node] if dist[target] == -1 else min(dist[target]						,    dist[node]			)
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												__lowercase			  =							dist[node] + 1
			return dist[target]


if __name__ == "__main__":
	print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
	print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4





