
"""simple docstring"""


import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin



def        _lowerCamelCase(__UpperCamelCase=2       ,					__UpperCamelCase=True       ,					__UpperCamelCase=False			)							->					Dict:
	_lowerCAmelCase								=Accelerator(step_scheduler_with_optimizer=__UpperCamelCase       ,					split_batches=__UpperCamelCase			)
	_lowerCAmelCase								=torch.nn.Linear(2       ,					4			)
	_lowerCAmelCase								=torch.optim.AdamW(model.parameters()       ,					lr=1.0			)
	_lowerCAmelCase								=torch.optim.lr_scheduler.OneCycleLR(__UpperCamelCase       ,					max_lr=0.01       ,					steps_per_epoch=2       ,					epochs=1			)
	_lowerCAmelCase		,				_lowerCAmelCase		,				_lowerCAmelCase								=accelerator.prepare(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)

	# Optimizer has stepped
	scheduler.step()
	if step_scheduler_with_optimizer or (num_processes == 1):
		assert (
		    scheduler.scheduler.last_epoch == num_processes
		), F'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
	else:
		assert (
		    scheduler.scheduler.last_epoch != num_processes
		), F'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''



def        _lowerCamelCase(__UpperCamelCase=2       ,					__UpperCamelCase=True       ,					__UpperCamelCase=False			)							->					int:
	_lowerCAmelCase								=Accelerator(step_scheduler_with_optimizer=__UpperCamelCase       ,					split_batches=__UpperCamelCase			)
	_lowerCAmelCase								=torch.nn.Linear(2       ,					4			)
	_lowerCAmelCase								=torch.optim.AdamW(model.parameters()       ,					lr=1.0			)
	_lowerCAmelCase								=torch.optim.lr_scheduler.LambdaLR(__UpperCamelCase       ,					lr_lambda=lambda __UpperCamelCase			: 1 - n / 10			)
	_lowerCAmelCase		,				_lowerCAmelCase		,				_lowerCAmelCase								=accelerator.prepare(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)

	# Optimizer has stepped
	_lowerCAmelCase								=False
	scheduler.step()
	_lowerCAmelCase								=1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), F'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

	# Optimizer has not stepped
	_lowerCAmelCase								=True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		_lowerCAmelCase								=1 - 2 / 10
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), F'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''



def        _lowerCamelCase(__UpperCamelCase = 2			)							->					Tuple:
	from transformers import get_linear_schedule_with_warmup

	_lowerCAmelCase								=[1, 2, 4]
	for num_steps in steps:
		_lowerCAmelCase								=GradientAccumulationPlugin(num_steps=__UpperCamelCase       ,					adjust_scheduler=num_steps > 1			)
		_lowerCAmelCase								=Accelerator(gradient_accumulation_plugin=__UpperCamelCase			)
		_lowerCAmelCase								=torch.nn.Linear(2       ,					4			)
		_lowerCAmelCase								=torch.optim.AdamW(model.parameters()       ,					lr=10.0			)
		_lowerCAmelCase								=get_linear_schedule_with_warmup(optimizer=__UpperCamelCase       ,					num_warmup_steps=0       ,					num_training_steps=20			)

		_lowerCAmelCase		,				_lowerCAmelCase		,				_lowerCAmelCase								=accelerator.prepare(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)

		for i in range(10 * num_steps			):
			with accelerator.accumulate(__UpperCamelCase			):
				optimizer.step()
				scheduler.step()

			if i == (10 * num_steps - 2):
				assert (
				    scheduler.get_last_lr()[0] != 0
				), F'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
		assert (
		    scheduler.get_last_lr()[0] == 0
		), F'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
		GradientState._reset_state()







@require_cpu
class 						lowerCamelCase__   (   unittest.TestCase							):





			'''simple docstring'''



			def    _lowerCAmelCase						(				self    )					->	Any:
				debug_launcher(partial(__UpperCAmelCase	,	num_processes=1    )	,	num_processes=1    )
				debug_launcher(partial(__UpperCAmelCase	,	num_processes=1	,	split_batches=__UpperCAmelCase    )	,	num_processes=1    )



			def    _lowerCAmelCase						(				self    )					->	int:
				debug_launcher(partial(__UpperCAmelCase	,	num_processes=1    )	,	num_processes=1    )
				debug_launcher(partial(__UpperCAmelCase	,	num_processes=1	,	split_batches=__UpperCAmelCase    )	,	num_processes=1    )



			def    _lowerCAmelCase						(				self    )					->	Optional[Any]:
				debug_launcher(partial(__UpperCAmelCase	,	num_processes=1	,	step_scheduler_with_optimizer=__UpperCAmelCase    )	,	num_processes=1    )



			def    _lowerCAmelCase						(				self    )					->	str:
				debug_launcher(partial(__UpperCAmelCase	,	num_processes=1	,	step_scheduler_with_optimizer=__UpperCAmelCase    )	,	num_processes=1    )



			def    _lowerCAmelCase						(				self    )					->	Optional[Any]:
				AcceleratorState._reset_state(__UpperCAmelCase    )
				debug_launcher(__UpperCAmelCase    )
				debug_launcher(partial(__UpperCAmelCase	,	num_processes=1	,	split_batches=__UpperCAmelCase    )	,	num_processes=1    )



			def    _lowerCAmelCase						(				self    )					->	Any:
				AcceleratorState._reset_state(__UpperCAmelCase    )
				debug_launcher(__UpperCAmelCase    )
				debug_launcher(partial(__UpperCAmelCase	,	num_processes=1	,	split_batches=__UpperCAmelCase    )	,	num_processes=1    )



			def    _lowerCAmelCase						(				self    )					->	Optional[Any]:
				AcceleratorState._reset_state(__UpperCAmelCase    )
				debug_launcher(partial(__UpperCAmelCase	,	step_scheduler_with_optimizer=__UpperCAmelCase    )    )



			def    _lowerCAmelCase						(				self    )					->	List[Any]:
				AcceleratorState._reset_state(__UpperCAmelCase    )
				debug_launcher(partial(__UpperCAmelCase	,	step_scheduler_with_optimizer=__UpperCAmelCase    )    )



			@require_huggingface_suite
			def    _lowerCAmelCase						(				self    )					->	List[str]:
				AcceleratorState._reset_state(__UpperCAmelCase    )
				debug_launcher(partial(__UpperCAmelCase	,	num_processes=1    )    )
				debug_launcher(__UpperCAmelCase    )





"""simple docstring"""


def        _lowerCamelCase(__UpperCamelCase = 20			)							->					int:

	try:
		_lowerCAmelCase								=int(__UpperCamelCase			)
	except (TypeError, ValueError):
		raise TypeError("""Parameter n must be int or castable to int."""			)
	if n <= 0:
		raise ValueError("""Parameter n must be greater than or equal to one."""			)
	_lowerCAmelCase								=0
	while 1:
		i += n * (n - 1)
		_lowerCAmelCase								=0
		for j in range(2       ,					__UpperCamelCase			):
			if i % j != 0:
				_lowerCAmelCase								=1
				break
		if nfound == 0:
			if i == 0:
				_lowerCAmelCase								=1
			return i
	return None


if __name__ == "__main__":
			print(F"""{solution() = }""")



