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

   # Optimizer has stepped
   UpperCAmelCase_   :   int     	=       False
   scheduler.step()
   UpperCAmelCase_   :   int     	=       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
   UpperCAmelCase_   :   Optional[Any]     	=       True
   scheduler.step()
   if not step_scheduler_with_optimizer:
      UpperCAmelCase_   :   Dict     	=       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	(						__lowerCamelCase = 2    ):
   from transformers import get_linear_schedule_with_warmup

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

      UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       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 ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1    )							,  num_processes=1    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1    )							,  num_processes=1    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1							,  step_scheduler_with_optimizer=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1							,  step_scheduler_with_optimizer=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(lowercase_    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(lowercase_    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(partial(lowercase_							,  step_scheduler_with_optimizer=lowercase_    )    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(partial(lowercase_							,  step_scheduler_with_optimizer=lowercase_    )    )







  @require_huggingface_suite
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(partial(lowercase_							,  num_processes=1    )    )
     debug_launcher(lowercase_    )










"""simple docstring"""





UpperCamelCase					=     [chr(i) for i in range(32, 1_26)]
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(reversed(range(len(alphabets))))
UpperCamelCase					=     []
UpperCamelCase					=     UpperCamelCase					=     UpperCamelCase					=     0






def 							A(    ):
       """simple docstring"""





       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       lowercase__:							int         =     gear_one[0]
       gear_one.append(snake_case_						)
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(snake_case_						)						) == 0:
              lowercase__:							List[str]         =     gear_two[0]
              gear_two.append(snake_case_						)
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(snake_case_						)						) == 0:
                     lowercase__:							Dict         =     gear_three[0]
                     gear_three.append(snake_case_						)
                     del gear_three[0]
                     gear_three_pos += 1






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							List[Any]         =     alphabets.index(snake_case_						)
       lowercase__:							Dict         =     gear_one[target]
       lowercase__:							Tuple         =     gear_two[target]
       lowercase__:							str         =     gear_three[target]
       lowercase__:							Any         =     reflector[target]
       lowercase__:							Any         =     gear_three.index(snake_case_						)
       lowercase__:							Tuple         =     gear_two.index(snake_case_						)
       lowercase__:							Union[str, Any]         =     gear_one.index(snake_case_						)
       code.append(alphabets[target]						)
       rotator()


if __name__ == "__main__":
  UpperCamelCase					=     list(input("""Type your message:\n"""))
  while True:
    try:
      UpperCamelCase					=     int(input("""Please set token:(must be only digits)\n"""))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print("""\n""" + """""".join(code))
  print(
      F"\nYour Token is {token} please write it down.\nIf you want to decode "
      """this message again you should input same digits as token!"""
  )






