






"""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      snake_case    (A_ :Optional[int]=2      ,     A_ :List[str]=True      ,     A_ :List[Any]=False ):







  '''simple docstring'''

  a	:     Union[str, Any]							=  Accelerator(step_scheduler_with_optimizer=lowercase__      ,     split_batches=lowercase__ )
  a	:     List[str]							=  torch.nn.Linear(2      ,     4 )
  a	:     Optional[Any]							=  torch.optim.AdamW(model.parameters()      ,     lr=1.0 )
  a	:     Dict							=  torch.optim.lr_scheduler.OneCycleLR(lowercase__      ,     max_lr=0.01      ,     steps_per_epoch=2      ,     epochs=1 )
  a	:     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      snake_case    (A_ :List[Any]=2      ,     A_ :Any=True      ,     A_ :Union[str, Any]=False ):







  '''simple docstring'''

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

  # Optimizer has stepped
  a	:     Any							=  False
  scheduler.step()
  a	:     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
  a	:     str							=  True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    a	:     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      snake_case    (A_ :int = 2 ):







  '''simple docstring'''

  from transformers import get_linear_schedule_with_warmup

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

    a	:     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 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''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 							lowerCamelCase__ (				self      :   Any     ):


      '''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 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




      debug_launcher(partial(_UpperCAmelCase							,						num_processes=1							,						step_scheduler_with_optimizer=_UpperCAmelCase     )							,						num_processes=1     )





    def 							lowerCamelCase__ (				self      :   Tuple     ):


      '''simple docstring'''




      debug_launcher(partial(_UpperCAmelCase							,						num_processes=1							,						step_scheduler_with_optimizer=_UpperCAmelCase     )							,						num_processes=1     )





    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




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





    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




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





    def 							lowerCamelCase__ (				self      :   Tuple     ):


      '''simple docstring'''




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





    def 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




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





    @require_huggingface_suite
    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''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()


