




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 				lowercase_		(		_A      :   Tuple=2   , _A      :   List[Any]=True   , _A      :   Any=False						):

      """simple docstring"""




      lowerCamelCase__		:    Any   =  Accelerator(step_scheduler_with_optimizer=_A   , split_batches=_A						)
      lowerCamelCase__		:    int   =  torch.nn.Linear(2   , 4						)
      lowerCamelCase__		:    int   =  torch.optim.AdamW(model.parameters()   , lr=1.0						)
      lowerCamelCase__		:    Any   =  torch.optim.lr_scheduler.OneCycleLR(_A   , max_lr=0.01   , steps_per_epoch=2   , epochs=1						)
      lowerCamelCase__		:    Optional[Any]   =  accelerator.prepare(_A   , _A   , _A						)

      # 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 				lowercase_		(		_A      :   Union[str, Any]=2   , _A      :   List[Any]=True   , _A      :   str=False						):

      """simple docstring"""




      lowerCamelCase__		:    List[Any]   =  Accelerator(step_scheduler_with_optimizer=_A   , split_batches=_A						)
      lowerCamelCase__		:    str   =  torch.nn.Linear(2   , 4						)
      lowerCamelCase__		:    Union[str, Any]   =  torch.optim.AdamW(model.parameters()   , lr=1.0						)
      lowerCamelCase__		:    Dict   =  torch.optim.lr_scheduler.LambdaLR(_A   , lr_lambda=lambda _A						: 1 - n / 10						)
      lowerCamelCase__		:    Tuple   =  accelerator.prepare(_A   , _A   , _A						)

      # Optimizer has stepped
      lowerCamelCase__		:    List[Any]   =  False
      scheduler.step()
      lowerCamelCase__		:    Union[str, 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
      lowerCamelCase__		:    Union[str, Any]   =  True
      scheduler.step()
      if not step_scheduler_with_optimizer:
            lowerCamelCase__		:    Union[str, 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 				lowercase_		(		_A      :   int = 2						):

      """simple docstring"""




      from transformers import get_linear_schedule_with_warmup

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

            lowerCamelCase__		:    Optional[Any]   =  accelerator.prepare(_A   , _A   , _A						)

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


       """simple docstring"""




       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             debug_launcher(partial(__lowerCamelCase ,				num_processes=1	) ,				num_processes=1	)
             debug_launcher(partial(__lowerCamelCase ,				num_processes=1 ,				split_batches=__lowerCamelCase	) ,				num_processes=1	)





       def 						lowerCAmelCase					(       self							:      List[str]	):


             '''simple docstring'''



             debug_launcher(partial(__lowerCamelCase ,				num_processes=1	) ,				num_processes=1	)
             debug_launcher(partial(__lowerCamelCase ,				num_processes=1 ,				split_batches=__lowerCamelCase	) ,				num_processes=1	)





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



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





       @require_huggingface_suite
       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



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






A				:   Union[str, Any]		= {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}

def 				lowercase_		(		_A      :   str						):

      """simple docstring"""




      return [alphabet[char] for char in key.upper()]

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__		:    List[str]   =  ""
      lowerCamelCase__		:    Union[str, Any]   =  0
      lowerCamelCase__		:    Any   =  generate_table(_A						)
      for char in words.upper():
            cipher += get_opponent(table[count]   , _A						)
            lowerCamelCase__		:    Optional[int]   =  (count + 1) % len(_A						)
      return cipher

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




      return encrypt(_A   , _A						)

def 				lowercase_		(		_A      :   tuple[str, str]   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__		:    List[Any]   =  0 if char in table[0] else 1
      lowerCamelCase__		:    List[Any]   =  table[row].index(_A						)
      return row, col

def 				lowercase_		(		_A      :   tuple[str, str]   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__   ,       lowerCamelCase__		:    List[str]   =  get_position(_A   , char.upper()						)
      if row == 1:
            return table[0][col]
      else:
            return table[1][col] if row == 0 else char


if __name__ == "__main__":
    import doctest

    doctest.testmod()  # Fist ensure that all our tests are passing...
    A				:   List[str]		= input("Enter key: ").strip()
    A				:   Tuple		= input("Enter text to encrypt: ").strip()
    A				:   Dict		= encrypt(key, text)

    print(f'Encrypted: {cipher_text}')
    print(f'Decrypted with key: {decrypt(key, cipher_text)}')


