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  __lowerCamelCase						(		lowerCAmelCase__=2      ,    lowerCAmelCase__=True      ,    lowerCAmelCase__=False      ):
       lowerCAmelCase__	      =   Accelerator(step_scheduler_with_optimizer=UpperCAmelCase_      ,    split_batches=UpperCAmelCase_      )
       lowerCAmelCase__	      =   torch.nn.Linear(2      ,    4      )
       lowerCAmelCase__	      =   torch.optim.AdamW(model.parameters()      ,    lr=1.0      )
       lowerCAmelCase__	      =   torch.optim.lr_scheduler.OneCycleLR(UpperCAmelCase_      ,    max_lr=0.0_1      ,    steps_per_epoch=2      ,    epochs=1      )
       lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__	      =   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  __lowerCamelCase						(		lowerCAmelCase__=2      ,    lowerCAmelCase__=True      ,    lowerCAmelCase__=False      ):
       lowerCAmelCase__	      =   Accelerator(step_scheduler_with_optimizer=UpperCAmelCase_      ,    split_batches=UpperCAmelCase_      )
       lowerCAmelCase__	      =   torch.nn.Linear(2      ,    4      )
       lowerCAmelCase__	      =   torch.optim.AdamW(model.parameters()      ,    lr=1.0      )
       lowerCAmelCase__	      =   torch.optim.lr_scheduler.LambdaLR(UpperCAmelCase_      ,    lr_lambda=lambda lowerCAmelCase__      : 1 - n / 1_0      )
       lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__	      =   accelerator.prepare(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      )

       # Optimizer has stepped
       lowerCAmelCase__	      =   False
       scheduler.step()
       lowerCAmelCase__	      =   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
       lowerCAmelCase__	      =   True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              lowerCAmelCase__	      =   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  __lowerCamelCase						(		lowerCAmelCase__ = 2      ):
       from transformers import get_linear_schedule_with_warmup

       lowerCAmelCase__	      =   [1, 2, 4]
       for num_steps in steps:
              lowerCAmelCase__	      =   GradientAccumulationPlugin(num_steps=UpperCAmelCase_      ,    adjust_scheduler=num_steps > 1      )
              lowerCAmelCase__	      =   Accelerator(gradient_accumulation_plugin=UpperCAmelCase_      )
              lowerCAmelCase__	      =   torch.nn.Linear(2      ,    4      )
              lowerCAmelCase__	      =   torch.optim.AdamW(model.parameters()      ,    lr=1_0.0      )
              lowerCAmelCase__	      =   get_linear_schedule_with_warmup(optimizer=UpperCAmelCase_      ,    num_warmup_steps=0      ,    num_training_steps=2_0      )

              lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__	      =   accelerator.prepare(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      )

              for i in range(1_0 * num_steps      ):
                     with accelerator.accumulate(UpperCAmelCase_      ):
                            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 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 def 						__snake_case		(     self   :    Optional[int]):







        '''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 						__snake_case		(     self   :    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 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



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




 def 						__snake_case		(     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 						__snake_case		(     self   :    Any):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



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




 @require_huggingface_suite
 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



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






from __future__ import annotations





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      dict[str, str]      )->			str:

       """simple docstring"""




       a   ,      a   ,      a			   ="""""", """""", """"""
       a			   =[]

       for character in message_part:
              tmp.append(character_to_number[character]      )

       for each in tmp:
              one += each[0]
              two += each[1]
              three += each[2]

       return one + two + three





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      dict[str, str]      )->			tuple[str, str, str]:

       """simple docstring"""




       a   ,      a			   ="""""", """"""
       a			   =[]

       for character in message_part:
              this_part += character_to_number[character]

       for digit in this_part:
              tmp += digit
              if len(UpperCAmelCase_      ) == len(UpperCAmelCase_      ):
                     result.append(UpperCAmelCase_      )
                     a			   =""""""

       return result[0], result[1], result[2]





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str      )->			tuple[str, str, dict[str, str], dict[str, str]]:

       """simple docstring"""




       a			   =alphabet.replace(""" """   ,       """"""      ).upper()
       a			   =message.replace(""" """   ,       """"""      ).upper()

       # Check length and characters
       if len(UpperCAmelCase_      ) != 27:
              raise KeyError("""Length of alphabet has to be 27."""      )
       for each in message:
              if each not in alphabet:
                     raise ValueError("""Each message character has to be included in alphabet!"""      )

    # Generate dictionares
       a			   =(
           """111""",
           """112""",
           """113""",
           """121""",
           """122""",
           """123""",
           """131""",
           """132""",
           """133""",
           """211""",
           """212""",
           """213""",
           """221""",
           """222""",
           """223""",
           """231""",
           """232""",
           """233""",
           """311""",
           """312""",
           """313""",
           """321""",
           """322""",
           """323""",
           """331""",
           """332""",
           """333""",
       )
       a			   ={}
       a			   ={}
       for letter, number in zip(UpperCAmelCase_   ,       UpperCAmelCase_      ):
              a			   =number
              a			   =letter

       return message, alphabet, character_to_number, number_to_character





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,       UpperCAmelCase_	:      int = 5      )->			str:

       """simple docstring"""




       a   ,      a   ,      a   ,      a			   =__prepare(
           UpperCAmelCase_   ,       UpperCAmelCase_      )
       a   ,      a			   ="""""", """"""

       for i in range(0   ,       len(UpperCAmelCase_      ) + 1   ,       UpperCAmelCase_      ):
              encrypted_numeric += __encrypt_part(
                  message[i : i + period]   ,       UpperCAmelCase_      )

       for i in range(0   ,       len(UpperCAmelCase_      )   ,       3      ):
              encrypted += number_to_character[encrypted_numeric[i : i + 3]]

       return encrypted





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,       UpperCAmelCase_	:      int = 5      )->			str:

       """simple docstring"""




       a   ,      a   ,      a   ,      a			   =__prepare(
           UpperCAmelCase_   ,       UpperCAmelCase_      )
       a			   =[]
       a			   =""""""

       for i in range(0   ,       len(UpperCAmelCase_      ) + 1   ,       UpperCAmelCase_      ):
              a   ,      a   ,      a			   =__decrypt_part(message[i : i + period]   ,       UpperCAmelCase_      )

              for j in range(0   ,       len(UpperCAmelCase_      )      ):
                     decrypted_numeric.append(a[j] + b[j] + c[j]      )

       for each in decrypted_numeric:
              decrypted += number_to_character[each]

       return decrypted


if __name__ == "__main__":
    _lowerCamelCase							=      '''DEFEND THE EAST WALL OF THE CASTLE.'''
    _lowerCamelCase							=      encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
    _lowerCamelCase							=      decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
    print(f"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
