
'''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   A			(							A_				:       Optional[Any]=2     ,				A_				:       Tuple=True     ,				A_				:       str=False							):
						snake_case							:  Optional[int]			=     Accelerator(step_scheduler_with_optimizer=__lowerCamelCase     ,				split_batches=__lowerCamelCase							)
						snake_case							:  Tuple			=     torch.nn.Linear(2     ,				4							)
						snake_case							:  str			=     torch.optim.AdamW(model.parameters()     ,				lr=1.0							)
						snake_case							:  List[Any]			=     torch.optim.lr_scheduler.OneCycleLR(__lowerCamelCase     ,				max_lr=0.01     ,				steps_per_epoch=2     ,				epochs=1							)
						snake_case,       snake_case,       snake_case							:  int			=     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   A			(							A_				:       List[Any]=2     ,				A_				:       Union[str, Any]=True     ,				A_				:       int=False							):
						snake_case							:  Any			=     Accelerator(step_scheduler_with_optimizer=__lowerCamelCase     ,				split_batches=__lowerCamelCase							)
						snake_case							:  str			=     torch.nn.Linear(2     ,				4							)
						snake_case							:  Union[str, Any]			=     torch.optim.AdamW(model.parameters()     ,				lr=1.0							)
						snake_case							:  Optional[Any]			=     torch.optim.lr_scheduler.LambdaLR(__lowerCamelCase     ,				lr_lambda=lambda A_							: 1 - n / 10							)
						snake_case,       snake_case,       snake_case							:  Any			=     accelerator.prepare(__lowerCamelCase     ,				__lowerCamelCase     ,				__lowerCamelCase							)

						# Optimizer has stepped
						snake_case							:  Optional[Any]			=     False
						scheduler.step()
						snake_case							:  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
						snake_case							:  str			=     True
						scheduler.step()
						if not step_scheduler_with_optimizer:
												snake_case							:  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   A			(							A_				:       int = 2							):
						from transformers import get_linear_schedule_with_warmup

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

												snake_case,       snake_case,       snake_case							:  Tuple			=     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 						__snake_case				(				self						:					Dict   ):
										debug_launcher(partial(__UpperCamelCase,     num_processes=1   ),     num_processes=1   )
										debug_launcher(partial(__UpperCamelCase,     num_processes=1,     split_batches=__UpperCamelCase   ),     num_processes=1   )




				def 						__snake_case				(				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 						__snake_case				(				self						:					Union[str, Any]   ):
										debug_launcher(partial(__UpperCamelCase,     num_processes=1,     step_scheduler_with_optimizer=__UpperCamelCase   ),     num_processes=1   )




				def 						__snake_case				(				self						:					str   ):
										debug_launcher(partial(__UpperCamelCase,     num_processes=1,     step_scheduler_with_optimizer=__UpperCamelCase   ),     num_processes=1   )




				def 						__snake_case				(				self						:					Any   ):
										AcceleratorState._reset_state(__UpperCamelCase   )
										debug_launcher(__UpperCamelCase   )
										debug_launcher(partial(__UpperCamelCase,     num_processes=1,     split_batches=__UpperCamelCase   ),     num_processes=1   )




				def 						__snake_case				(				self						:					Optional[int]   ):
										AcceleratorState._reset_state(__UpperCamelCase   )
										debug_launcher(__UpperCamelCase   )
										debug_launcher(partial(__UpperCamelCase,     num_processes=1,     split_batches=__UpperCamelCase   ),     num_processes=1   )




				def 						__snake_case				(				self						:					List[Any]   ):
										AcceleratorState._reset_state(__UpperCamelCase   )
										debug_launcher(partial(__UpperCamelCase,     step_scheduler_with_optimizer=__UpperCamelCase   )   )




				def 						__snake_case				(				self						:					Optional[Any]   ):
										AcceleratorState._reset_state(__UpperCamelCase   )
										debug_launcher(partial(__UpperCamelCase,     step_scheduler_with_optimizer=__UpperCamelCase   )   )




				@require_huggingface_suite
				def 						__snake_case				(				self						:					Optional[int]   ):
										AcceleratorState._reset_state(__UpperCamelCase   )
										debug_launcher(partial(__UpperCamelCase,     num_processes=1   )   )
										debug_launcher(__UpperCamelCase   )





'''simple docstring'''

from __future__ import annotations






def __UpperCamelCase (      __lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	)   ->  list[float | int]:







     '''simple docstring'''






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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   lowercase__					  =		float(input("Enter the last number (n term) of the Geometric Series"))
   lowercase__					  =		float(input("Enter the starting term (a) of the Geometric Series"))
   lowercase__					  =		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))

