




'''simple docstring'''





from __future__ import annotations

import random
import string




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      _a				:							List[Any]							       =      passcode or self.__passcode_creator()
      _a				:							List[str]							       =      self.__make_key_list()
      _a				:							Optional[int]							       =      self.__make_shift_key()



  def __str__(			self ):







      '''simple docstring'''


      return "".join(self.__passcode )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      for i in range(1		,     len(__lowerCAmelCase )		,     2 ):
          iterlist[i] *= -1
      return iterlist



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      string.ascii_letters + string.digits
      _a				:							str							       =      [random.choice(__lowerCAmelCase ) for _ in range(random.randint(10		,     20 ) )]
      return password



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      (
          string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
      )

      _a				:							str							       =      []

      # creates points known as breakpoints to break the key_list_options at those
      # points and pivot each substring
      _a				:							Dict							       =      sorted(set(self.__passcode ) )
      _a				:							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(__lowerCAmelCase )

          # 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 ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      sum(self.__neg_pos([ord(__lowerCAmelCase ) for x in self.__passcode] ) )
      return num if num > 0 else len(self.__passcode )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Tuple							       =      """"""

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

      return decoded_message



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[str]							       =      """"""

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

      return encoded_message






def UpperCAmelCase    (       UpperCAmelCase_ = "Hello, this is a modified Caesar cipher"						):
    _a				:							Union[str, Any]							       =      ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(UpperCAmelCase_						)						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



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

  # Optimizer has stepped
  lowercase				:							Any									=   False
  scheduler.step()
  lowercase				:							Optional[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
  lowercase				:							List[Any]									=   True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    lowercase				:							List[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 (					__UpperCAmelCase  :					int = 2			)							->	Dict:
  from transformers import get_linear_schedule_with_warmup

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

    lowercase				,      lowercase				,      lowercase				:							Dict									=   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 _lowerCAmelCase			(	unittest.TestCase					):







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







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







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







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







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







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







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







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