



"""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 (  __UpperCAmelCase=2   ,				__UpperCAmelCase=True   ,				__UpperCAmelCase=False      ):
		_lowercase				: Dict     =   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase   ,				split_batches=__UpperCAmelCase      )
		_lowercase				: Union[str, Any]     =   torch.nn.Linear(2   ,				4      )
		_lowercase				: Any     =   torch.optim.AdamW(model.parameters()   ,				lr=1.0      )
		_lowercase				: Optional[int]     =   torch.optim.lr_scheduler.OneCycleLR(__UpperCAmelCase   ,				max_lr=0.0_1   ,				steps_per_epoch=2   ,				epochs=1      )
		_lowercase     ,  _lowercase     ,  _lowercase				: Any     =   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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase=2   ,				__UpperCAmelCase=True   ,				__UpperCAmelCase=False      ):
		_lowercase				: str     =   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase   ,				split_batches=__UpperCAmelCase      )
		_lowercase				: int     =   torch.nn.Linear(2   ,				4      )
		_lowercase				: int     =   torch.optim.AdamW(model.parameters()   ,				lr=1.0      )
		_lowercase				: int     =   torch.optim.lr_scheduler.LambdaLR(__UpperCAmelCase   ,				lr_lambda=lambda __UpperCAmelCase      : 1 - n / 10      )
		_lowercase     ,  _lowercase     ,  _lowercase				: List[Any]     =   accelerator.prepare(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )

		# Optimizer has stepped
		_lowercase				: List[Any]     =   False
		scheduler.step()
		_lowercase				: List[str]     =   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				: Optional[Any]     =   True
		scheduler.step()
		if not step_scheduler_with_optimizer:
				_lowercase				: Tuple     =   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 (  __UpperCAmelCase = 2      ):
		from transformers import get_linear_schedule_with_warmup

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

				_lowercase     ,  _lowercase     ,  _lowercase				: List[str]     =   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   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			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   ):
			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   ):
			debug_launcher(partial(UpperCAmelCase_				,num_processes=1				,step_scheduler_with_optimizer=UpperCAmelCase_   )				,num_processes=1   )






	def    lowerCamelCase__					(     self   ):
			debug_launcher(partial(UpperCAmelCase_				,num_processes=1				,step_scheduler_with_optimizer=UpperCAmelCase_   )				,num_processes=1   )






	def    lowerCamelCase__					(     self   ):
			AcceleratorState._reset_state(UpperCAmelCase_   )
			debug_launcher(UpperCAmelCase_   )
			debug_launcher(partial(UpperCAmelCase_				,num_processes=1				,split_batches=UpperCAmelCase_   )				,num_processes=1   )






	def    lowerCamelCase__					(     self   ):
			AcceleratorState._reset_state(UpperCAmelCase_   )
			debug_launcher(UpperCAmelCase_   )
			debug_launcher(partial(UpperCAmelCase_				,num_processes=1				,split_batches=UpperCAmelCase_   )				,num_processes=1   )






	def    lowerCamelCase__					(     self   ):
			AcceleratorState._reset_state(UpperCAmelCase_   )
			debug_launcher(partial(UpperCAmelCase_				,step_scheduler_with_optimizer=UpperCAmelCase_   )   )






	def    lowerCamelCase__					(     self   ):
			AcceleratorState._reset_state(UpperCAmelCase_   )
			debug_launcher(partial(UpperCAmelCase_				,step_scheduler_with_optimizer=UpperCAmelCase_   )   )






	@require_huggingface_suite
	def    lowerCamelCase__					(     self   ):
			AcceleratorState._reset_state(UpperCAmelCase_   )
			debug_launcher(partial(UpperCAmelCase_				,num_processes=1   )   )
			debug_launcher(UpperCAmelCase_   )











from __future__ import annotations


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						)->    list[float | int]:





						"""simple docstring"""



						if not all((nth_term, start_term_a, common_ratio_r)      ):
												return []
						_UpperCAmelCase							   =     []
						_UpperCAmelCase							   =     1
						_UpperCAmelCase							   =     common_ratio_r
						for _ in range(int(__lowerCAmelCase      )      ):
												if not series:
																		series.append(__lowerCAmelCase      )
												else:
																		power += 1
																		series.append(float(start_term_a * multiple      )      )
																		_UpperCAmelCase							   =     pow(float(__lowerCAmelCase      )					,						__lowerCAmelCase      )
						return series


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_a       =    float(input('''Enter the last number (n term) of the Geometric Series'''))
		_a       =    float(input('''Enter the starting term (a) of the Geometric Series'''))
		_a       =    float(
		    input('''Enter the common ratio between two terms (r) of the Geometric Series''')
		)
		print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
		print(geometric_series(nth_term, start_term_a, common_ratio_r))
