


from __future__ import annotations

import random
import string







class    lowerCamelCase__     :





					def __init__(self	,  UpperCAmelCase = None )  ->     None:
						_lowercase							   =passcode or self.__passcode_creator()
						_lowercase							   =self.__make_key_list()
						_lowercase							   =self.__make_shift_key()





					def __str__(self )  ->     str:
						return "".join(self.__passcode )





					def   __A      (self	,  UpperCAmelCase )  ->     list[int]:
						for i in range(1	,  len(UpperCAmelCase )	,  2 ):
							iterlist[i] *= -1
						return iterlist





					def   __A      (self )  ->     list[str]:
						_lowercase							   =string.ascii_letters + string.digits
						_lowercase							   =[random.choice(UpperCAmelCase ) for _ in range(random.randint(1_0	,  2_0 ) )]
						return password





					def   __A      (self )  ->     list[str]:
						_lowercase							   =(
						    string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
						)

						_lowercase							   =[]

						# creates points known as breakpoints to break the key_list_options at those
						# points and pivot each substring
						_lowercase							   =sorted(set(self.__passcode ) )
						_lowercase							   =[]

						# 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   __A      (self )  ->     int:
						_lowercase							   =sum(self.__neg_pos([ord(UpperCAmelCase ) for x in self.__passcode] ) )
						return num if num > 0 else len(self.__passcode )





					def   __A      (self	,  UpperCAmelCase )  ->     str:
						_lowercase							   =''''''

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

						return decoded_message





					def   __A      (self	,  UpperCAmelCase )  ->     str:
						_lowercase							   =''''''

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

						return encoded_message



def        UpperCAmelCase_      (							__snake_case = "Hello, this is a modified Caesar cipher"		)  ->    str:







	"""simple docstring"""


	_lowercase							   =ShuffledShiftCipher()
	return cipa.decrypt(cipa.encrypt(__snake_case		)		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





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_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Dict:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.OneCycleLR(__snake_case  ,		max_lr=0.01  ,		steps_per_epoch=2  ,		epochs=1		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__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_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Optional[int]:







 """simple docstring"""


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

 # 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        UpperCAmelCase_      (							__snake_case = 2		)  ->    Optional[int]:







 """simple docstring"""


 from transformers import get_linear_schedule_with_warmup

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

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

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





     def   __A      (self )  ->     Optional[Any]:
      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 )  ->     Any:
      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]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Dict:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Optional[Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     List[str]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     def   __A      (self )  ->     Union[str, Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     @require_huggingface_suite
     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 ) )
      debug_launcher(UpperCAmelCase )

