


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

						# 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							(					lowercase__	:List[Any]=2   ,    lowercase__	:Any=True   ,    lowercase__	:Union[str, Any]=False					)							->	List[Any]:
						__magic_name__					:				List[str]				=   Accelerator(step_scheduler_with_optimizer=lowercase__   ,    split_batches=lowercase__					)
						__magic_name__					:				int				=   torch.nn.Linear(2   ,    4					)
						__magic_name__					:				Union[str, Any]				=   torch.optim.AdamW(model.parameters()   ,    lr=1.0					)
						__magic_name__					:				Optional[int]				=   torch.optim.lr_scheduler.LambdaLR(lowercase__   ,    lr_lambda=lambda lowercase__					: 1 - n / 1_0					)
						__magic_name__		,			__magic_name__		,			__magic_name__					:				Optional[int]				=   accelerator.prepare(lowercase__   ,    lowercase__   ,    lowercase__					)

						# Optimizer has stepped
						__magic_name__					:				Any				=   False
						scheduler.step()
						__magic_name__					:				List[str]				=   1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
						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
						__magic_name__					:				str				=   True
						scheduler.step()
						if not step_scheduler_with_optimizer:
												__magic_name__					:				Dict				=   1 - 2 / 1_0
						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							(					lowercase__	:int = 2					)							->	Optional[Any]:
						from transformers import get_linear_schedule_with_warmup

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

												__magic_name__		,			__magic_name__		,			__magic_name__					:				Tuple				=   accelerator.prepare(lowercase__   ,    lowercase__   ,    lowercase__					)

												for i in range(1_0 * num_steps					):
																		with accelerator.accumulate(lowercase__					):
																								optimizer.step()
																								scheduler.step()

																		if i == (1_0 * 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 			UpperCamelCase_    (	self   ):




										"""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 			UpperCamelCase_    (	self   ):




										"""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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										debug_launcher(partial(_UpperCAmelCase  ,   num_processes=1  ,   step_scheduler_with_optimizer=_UpperCAmelCase   )  ,   num_processes=1   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										debug_launcher(partial(_UpperCAmelCase  ,   num_processes=1  ,   step_scheduler_with_optimizer=_UpperCAmelCase   )  ,   num_processes=1   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										AcceleratorState._reset_state(_UpperCAmelCase   )
										debug_launcher(_UpperCAmelCase   )
										debug_launcher(partial(_UpperCAmelCase  ,   num_processes=1  ,   split_batches=_UpperCAmelCase   )  ,   num_processes=1   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										AcceleratorState._reset_state(_UpperCAmelCase   )
										debug_launcher(_UpperCAmelCase   )
										debug_launcher(partial(_UpperCAmelCase  ,   num_processes=1  ,   split_batches=_UpperCAmelCase   )  ,   num_processes=1   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										AcceleratorState._reset_state(_UpperCAmelCase   )
										debug_launcher(partial(_UpperCAmelCase  ,   step_scheduler_with_optimizer=_UpperCAmelCase   )   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




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





				@require_huggingface_suite
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




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






from __future__ import annotations

import random
import string



class      snake_case__	:
				def __init__(	self  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   passcode or self.__passcode_creator()
										__magic_name__					:				Any				=   self.__make_key_list()
										__magic_name__					:				Tuple				=   self.__make_shift_key()
				def __str__(	self   ):




										"""simple docstring"""




										return "".join(self.__passcode   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										for i in range(1  ,   len(_UpperCAmelCase   )  ,   2   ):
																iterlist[i] *= -1
										return iterlist
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   string.ascii_letters + string.digits
										__magic_name__					:				Optional[Any]				=   [random.choice(_UpperCAmelCase   ) for _ in range(random.randint(10  ,   20   )   )]
										return password
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   (
										    string.ascii_letters + string.digits + string.punctuation + ' \t\n'
										)

										__magic_name__					:				List[str]				=   []

										# creates points known as breakpoints to break the key_list_options at those
										# points and pivot each substring
										__magic_name__					:				List[Any]				=   sorted(set(self.__passcode   )   )
										__magic_name__					:				list[str]				=   []

										# algorithm for creating a new shuffled list, keys_l, out of key_list_options
										for i in key_list_options:
																temp_list.extend(_UpperCAmelCase   )

																# checking breakpoints at which to pivot temporary sublist and add it into
																# keys_l
																if i in breakpoints or i == key_list_options[-1]:
																						keys_l.extend(temp_list[::-1]   )
																						temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
										return keys_l
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   sum(self.__neg_pos([ord(_UpperCAmelCase   ) for x in self.__passcode]   )   )
										return num if num > 0 else len(self.__passcode   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   ''

										# decoding shift like Caesar cipher algorithm implementing negative shift or
										# reverse shift or left shift
										for i in encoded_message:
																__magic_name__					:				int				=   self.__key_list.index(_UpperCAmelCase   )
																decoded_message += self.__key_list[
																    (position - self.__shift_key) % -len(self.__key_list   )
																]

										return decoded_message





				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   ''

										# encoding shift like Caesar cipher algorithm implementing positive shift or
										# forward shift or right shift
										for i in plaintext:
																__magic_name__					:				Dict				=   self.__key_list.index(_UpperCAmelCase   )
																encoded_message += self.__key_list[
																    (position + self.__shift_key) % len(self.__key_list   )
																]

										return encoded_message






def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:str = "Hello, this is a modified Caesar cipher"					)							->	str:
						__magic_name__					:				List[str]				=   ShuffledShiftCipher()
						return cipa.decrypt(cipa.encrypt(lowercase__					)					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


