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 					UpperCamelCase__		(							A__=2     ,					A__=True     ,					A__=False       )  ->   List[str]:
				snake_case__   :      Any   				= Accelerator(step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE     ,					split_batches=_SCREAMING_SNAKE_CASE       )
				snake_case__   :      Dict   				= torch.nn.Linear(2     ,					4       )
				snake_case__   :      List[Any]   				= torch.optim.AdamW(model.parameters()     ,					lr=1.0       )
				snake_case__   :      List[Any]   				= torch.optim.lr_scheduler.OneCycleLR(_SCREAMING_SNAKE_CASE     ,					max_lr=0.0_1     ,					steps_per_epoch=2     ,					epochs=1       )
				snake_case__				,    snake_case__				,    snake_case__   :      Dict   				= accelerator.prepare(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE       )

				# 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 					UpperCamelCase__		(							A__=2     ,					A__=True     ,					A__=False       )  ->   str:
				snake_case__   :      Union[str, Any]   				= Accelerator(step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE     ,					split_batches=_SCREAMING_SNAKE_CASE       )
				snake_case__   :      List[Any]   				= torch.nn.Linear(2     ,					4       )
				snake_case__   :      Tuple   				= torch.optim.AdamW(model.parameters()     ,					lr=1.0       )
				snake_case__   :      List[Any]   				= torch.optim.lr_scheduler.LambdaLR(_SCREAMING_SNAKE_CASE     ,					lr_lambda=lambda A__       : 1 - n / 10       )
				snake_case__				,    snake_case__				,    snake_case__   :      Dict   				= accelerator.prepare(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE       )

				# Optimizer has stepped
				snake_case__   :      Dict   				= False
				scheduler.step()
				snake_case__   :      Any   				= 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
				snake_case__   :      Optional[int]   				= True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								snake_case__   :      Union[str, Any]   				= 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 					UpperCamelCase__		(							A__ = 2       )  ->   str:
				from transformers import get_linear_schedule_with_warmup

				snake_case__   :      str   				= [1, 2, 4]
				for num_steps in steps:
								snake_case__   :      Dict   				= GradientAccumulationPlugin(num_steps=_SCREAMING_SNAKE_CASE     ,					adjust_scheduler=num_steps > 1       )
								snake_case__   :      str   				= Accelerator(gradient_accumulation_plugin=_SCREAMING_SNAKE_CASE       )
								snake_case__   :      List[str]   				= torch.nn.Linear(2     ,					4       )
								snake_case__   :      Any   				= torch.optim.AdamW(model.parameters()     ,					lr=1_0.0       )
								snake_case__   :      str   				= get_linear_schedule_with_warmup(optimizer=_SCREAMING_SNAKE_CASE     ,					num_warmup_steps=0     ,					num_training_steps=20       )

								snake_case__				,    snake_case__				,    snake_case__   :      Any   				= accelerator.prepare(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE       )

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




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







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




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



									'''simple docstring'''







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




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







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




					def      __a     (  self						)	->    str:



									'''simple docstring'''







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




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







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




					def      __a     (  self						)	->    str:



									'''simple docstring'''







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




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







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




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



									'''simple docstring'''







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




					@require_huggingface_suite
					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







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






"""simple docstring"""
import math







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					list[int]:
							if not isinstance(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		f"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE					)
							if number < 1:
														snake_case_     							=		f"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		[number]
							while number != 1:
														if number % 2 == 0:
																					snake_case_     							=		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														else:
																					snake_case_     							=		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														sequence.append(_SCREAMING_SNAKE_CASE					)
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



