

'''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 					_SCREAMING_SNAKE_CASE		(						snake_case_=2							,       snake_case_=True							,       snake_case_=False	):
	_lowercase							=     Accelerator(step_scheduler_with_optimizer=__lowerCamelCase							,       split_batches=__lowerCamelCase	)
	_lowercase							=     torch.nn.Linear(2							,       4	)
	_lowercase							=     torch.optim.AdamW(model.parameters()							,       lr=1.0	)
	_lowercase							=     torch.optim.lr_scheduler.OneCycleLR(__lowerCamelCase							,       max_lr=0.01							,       steps_per_epoch=2							,       epochs=1	)
	_lowercase							=     accelerator.prepare(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase	)

	# 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 					_SCREAMING_SNAKE_CASE		(						snake_case_=2							,       snake_case_=True							,       snake_case_=False	):
	_lowercase							=     Accelerator(step_scheduler_with_optimizer=__lowerCamelCase							,       split_batches=__lowerCamelCase	)
	_lowercase							=     torch.nn.Linear(2							,       4	)
	_lowercase							=     torch.optim.AdamW(model.parameters()							,       lr=1.0	)
	_lowercase							=     torch.optim.lr_scheduler.LambdaLR(__lowerCamelCase							,       lr_lambda=lambda snake_case_	: 1 - n / 10	)
	_lowercase							=     accelerator.prepare(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase	)

	# Optimizer has stepped
	_lowercase							=     False
	scheduler.step()
	_lowercase							=     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
	_lowercase							=     True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		_lowercase							=     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 					_SCREAMING_SNAKE_CASE		(						snake_case_ = 2	):
	from transformers import get_linear_schedule_with_warmup

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

		_lowercase							=     accelerator.prepare(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase	)

		for i in range(10 * num_steps	):
			with accelerator.accumulate(__lowerCamelCase	):
				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 					__a   (		unittest.TestCase  ):

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->List[str]:

							"""simple docstring"""



							debug_launcher(partial(lowercase_    ,			num_processes=1)    ,			num_processes=1)
							debug_launcher(partial(lowercase_    ,			num_processes=1    ,			split_batches=lowercase_)    ,			num_processes=1)

						def 	_UpperCAmelCase							(		self	:						int)						->str:

							"""simple docstring"""



							debug_launcher(partial(lowercase_    ,			num_processes=1)    ,			num_processes=1)
							debug_launcher(partial(lowercase_    ,			num_processes=1    ,			split_batches=lowercase_)    ,			num_processes=1)

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->Tuple:

							"""simple docstring"""



							debug_launcher(partial(lowercase_    ,			num_processes=1    ,			step_scheduler_with_optimizer=lowercase_)    ,			num_processes=1)

						def 	_UpperCAmelCase							(		self	:						Dict)						->Union[str, Any]:

							"""simple docstring"""



							debug_launcher(partial(lowercase_    ,			num_processes=1    ,			step_scheduler_with_optimizer=lowercase_)    ,			num_processes=1)

						def 	_UpperCAmelCase							(		self	:						Any)						->Tuple:

							"""simple docstring"""



							AcceleratorState._reset_state(lowercase_)
							debug_launcher(lowercase_)
							debug_launcher(partial(lowercase_    ,			num_processes=1    ,			split_batches=lowercase_)    ,			num_processes=1)

						def 	_UpperCAmelCase							(		self	:						Union[str, Any])						->Tuple:

							"""simple docstring"""



							AcceleratorState._reset_state(lowercase_)
							debug_launcher(lowercase_)
							debug_launcher(partial(lowercase_    ,			num_processes=1    ,			split_batches=lowercase_)    ,			num_processes=1)

						def 	_UpperCAmelCase							(		self	:						Any)						->int:

							"""simple docstring"""



							AcceleratorState._reset_state(lowercase_)
							debug_launcher(partial(lowercase_    ,			step_scheduler_with_optimizer=lowercase_))

						def 	_UpperCAmelCase							(		self	:						Dict)						->Optional[Any]:

							"""simple docstring"""



							AcceleratorState._reset_state(lowercase_)
							debug_launcher(partial(lowercase_    ,			step_scheduler_with_optimizer=lowercase_))


						@require_huggingface_suite
						def 	_UpperCAmelCase							(		self	:						Tuple)						->Dict:

							"""simple docstring"""



							AcceleratorState._reset_state(lowercase_)
							debug_launcher(partial(lowercase_    ,			num_processes=1))
							debug_launcher(lowercase_)






"""simple docstring"""





def       __a	(						__lowerCamelCase    ):
   for i, _ in enumerate(__lowerCamelCase    ):
      if (i % 2 == 1) == (nums[i - 1] > nums[i]):
         UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       nums[i], nums[i - 1]

   return nums


if __name__ == "__main__":
    print('Enter the array elements:')
    _a									=				list(map(int, input().split()))
    print('The unsorted array is:')
    print(array)
    print('Array after Wiggle sort:')
    print(wiggle_sort(array))


