


# 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_       (				lowerCAmelCase__				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   data






					def __iter__(				self						)   ->			str:







								'''simple docstring'''

								for element in self.data:
											yield element




def 		lowercase_  (     _UpperCamelCase=True  ):


			'''simple docstring'''







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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase = False  ):


			'''simple docstring'''







			if iterable:
						__lowercase 					=   DummyIterableDataset(torch.as_tensor(range(_UpperCamelCase  )  )  )
			else:
						__lowercase 					=   TensorDataset(torch.as_tensor(range(_UpperCamelCase  )  )  )

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

			return dl


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			):


			'''simple docstring'''







			__lowercase 					=   create_dataloader(accelerator=_UpperCamelCase       ,			dataset_size=_UpperCamelCase       ,			batch_size=_UpperCamelCase  )

			__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 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   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 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   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 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   create_accelerator(even_batches=_UpperCamelCase  )

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

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

			__lowercase 					=   []
			with accelerator.join_uneven_inputs([ddp_model]  ):
						for batch_idx, batch in enumerate(_UpperCamelCase  ):
									__lowercase 					=   ddp_model(batch[0].float()  )
									__lowercase 					=   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 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			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 		lowercase_  (     ):


			'''simple docstring'''







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

			with accelerator.join_uneven_inputs([ddp_model]       ,			even_batches=_UpperCamelCase  ):
						__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 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   True
			__lowercase 					=   False
			__lowercase 					=   create_accelerator(even_batches=_UpperCamelCase  )
			__lowercase 					=   torch.nn.Linear(1       ,			1  )
			__lowercase 					=   accelerator.prepare(_UpperCamelCase  )
			create_dataloader(_UpperCamelCase       ,			dataset_size=3       ,			batch_size=1       ,			iterable=_UpperCamelCase  )
			__lowercase 					=   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  ):
												__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 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   create_accelerator()
			__lowercase 					=   torch.nn.Linear(1       ,			1  )
			__lowercase 					=   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 		lowercase_  (     ):


			'''simple docstring'''







			__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(_UpperCamelCase  )
			__lowercase 					=   original_state


if __name__ == "__main__":
	main()






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a : Optional[int]		=				typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a : Union[str, Any]		=				typing.Union[np.floataa, int, float]  # noqa: UP007


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return np.sqrt(np.sum((np.asarray(_UpperCamelCase  ) - np.asarray(_UpperCamelCase  )) ** 2  )  )


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return sum((va - va) ** 2 for va, va in zip(_UpperCamelCase       ,			_UpperCamelCase  )  ) ** (1 / 2)


if __name__ == "__main__":


	def 		lowercase_  (     ):


				'''simple docstring'''







				from timeit import timeit

				print('''Without Numpy'''  )
				print(
				    timeit(
				        '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''       ,			number=1_00_00       ,			globals=globals()       ,			)  )
				print('''With Numpy'''  )
				print(
				    timeit(
				        '''euclidean_distance([1, 2, 3], [4, 5, 6])'''       ,			number=1_00_00       ,			globals=globals()       ,			)  )





	benchmark()


