





# 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 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		data





			def __iter__(    self							):



				'''simple docstring'''







				for element in self.data:
					yield element





def lowerCAmelCase__(__snake_case=True							)				->      List[Any]:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case = False							)				->      List[str]:







	'''simple docstring'''







	if iterable:
		lowerCamelCase__								=		DummyIterableDataset(torch.as_tensor(range(__snake_case							)							)							)
	else:
		lowerCamelCase__								=		TensorDataset(torch.as_tensor(range(__snake_case							)							)							)

	lowerCamelCase__								=		DataLoader(__snake_case       ,batch_size=__snake_case							)
	lowerCamelCase__								=		accelerator.prepare(__snake_case							)

	return dl





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,)				->      Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		create_dataloader(accelerator=__snake_case       ,dataset_size=__snake_case       ,batch_size=__snake_case							)

	lowerCamelCase__								=		[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__()				->      Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		create_accelerator()

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





def lowerCAmelCase__()				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		create_accelerator(even_batches=__snake_case							)

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

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





def lowerCAmelCase__()				->      List[str]:







	'''simple docstring'''







	lowerCamelCase__								=		create_accelerator(even_batches=__snake_case							)

	lowerCamelCase__								=		torch.nn.Linear(1       ,1							)
	lowerCamelCase__								=		accelerator.prepare(__snake_case							)

	lowerCamelCase__								=		create_dataloader(__snake_case       ,dataset_size=3       ,batch_size=1							)

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

	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__(__snake_case							)				->      Union[str, Any]:







	'''simple docstring'''







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

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





def lowerCAmelCase__()				->      int:







	'''simple docstring'''







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

	with accelerator.join_uneven_inputs([ddp_model]       ,even_batches=__snake_case							):
		lowerCamelCase__								=		train_dl.batch_sampler.even_batches
		lowerCamelCase__								=		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__()				->      str:







	'''simple docstring'''







	lowerCamelCase__								=		True
	lowerCamelCase__								=		False
	lowerCamelCase__								=		create_accelerator(even_batches=__snake_case							)
	lowerCamelCase__								=		torch.nn.Linear(1       ,1							)
	lowerCamelCase__								=		accelerator.prepare(__snake_case							)
	create_dataloader(__snake_case       ,dataset_size=3       ,batch_size=1       ,iterable=__snake_case							)
	lowerCamelCase__								=		create_dataloader(__snake_case       ,dataset_size=3       ,batch_size=1							)

	with warnings.catch_warnings():
		warnings.filterwarnings('''ignore'''							)
		try:
			with accelerator.join_uneven_inputs([ddp_model]       ,even_batches=__snake_case							):
				lowerCamelCase__								=		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__()				->      Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		create_accelerator()
	lowerCamelCase__								=		torch.nn.Linear(1       ,1							)
	lowerCamelCase__								=		accelerator.prepare(__snake_case							)
	create_dataloader(__snake_case       ,dataset_size=3       ,batch_size=1       ,iterable=__snake_case							)

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

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





def lowerCAmelCase__()				->      Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		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__								=		accelerator.state.distributed_type
	lowerCamelCase__								=		DistributedType.FSDP
	test_join_raises_warning_for_non_ddp_distributed(__snake_case							)
	lowerCamelCase__								=		original_state


if __name__ == "__main__":
			main()










from __future__ import annotations

from random import random






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__								=		value
				lowerCamelCase__								=		random()
				lowerCamelCase__								=		None
				lowerCamelCase__								=		None





			def __repr__(    self							):



				'''simple docstring'''







				from pprint import pformat

				if self.left is None and self.right is None:
					return F'\'{self.value}: {self.prior:.5}\''
				else:
					return pformat(
					    {F'{self.value}: {self.prior:.5}': (self.left, self.right)}       ,  indent=1							)





			def __str__(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		str(self.value							) + ''' '''
				lowerCamelCase__								=		str(self.left or ''''''							)
				lowerCamelCase__								=		str(self.right or ''''''							)
				return value + left + right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      tuple[Node | None, Node | None]:







	'''simple docstring'''







	if root is None:  # None tree is split into 2 Nones
		return None, None
	elif root.value is None:
		return None, None
	else:
		if value < root.value:
			lowerCamelCase__				,	lowerCamelCase__								=		split(root.left       ,__snake_case							)
			return left, root
		else:
			lowerCamelCase__				,	lowerCamelCase__								=		split(root.right       ,__snake_case							)
			return root, right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	if (not left) or (not right):  # If one node is None, return the other
		return left or right
	elif left.prior < right.prior:
		lowerCamelCase__								=		merge(left.right       ,__snake_case							)
		return left
	else:
		lowerCamelCase__								=		merge(__snake_case       ,right.left							)
		return right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	lowerCamelCase__								=		Node(__snake_case							)
	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,__snake_case							)
	return merge(merge(__snake_case       ,__snake_case							)       ,__snake_case							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,value - 1							)
	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,__snake_case							)
	return merge(__snake_case       ,__snake_case							)





def lowerCAmelCase__(__snake_case							)				->      None:







	'''simple docstring'''







	if not root:  # None
		return
	else:
		inorder(root.left							)
		print(root.value       ,end=''','''							)
		inorder(root.right							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	for arg in args.split():
		if arg[0] == "+":
			lowerCamelCase__								=		insert(__snake_case       ,int(arg[1:]							)							)

		elif arg[0] == "-":
			lowerCamelCase__								=		erase(__snake_case       ,int(arg[1:]							)							)

		else:
			print('''Unknown command'''							)

	return root





def lowerCAmelCase__()				->      None:







	'''simple docstring'''







	lowerCamelCase__								=		None
	print(
	    '''enter numbers to create a tree, + value to add value into treap, '''
	    '''- value to erase all nodes with value. \'q\' to quit. '''							)

	lowerCamelCase__								=		input()
	while args != "q":
		lowerCamelCase__								=		interact_treap(__snake_case       ,__snake_case							)
		print(__snake_case							)
		lowerCamelCase__								=		input()

	print('''good by!'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



