
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_	( __lowercase     :			Any=2 ,      __lowercase     :			Tuple=True ,      __lowercase     :			Union[str, Any]=False				)				->						int:
			_snake_case						=    Accelerator(step_scheduler_with_optimizer=_UpperCamelCase ,      split_batches=_UpperCamelCase				)
			_snake_case						=    torch.nn.Linear(2 ,      4				)
			_snake_case						=    torch.optim.AdamW(model.parameters() ,      lr=1.0				)
			_snake_case						=    torch.optim.lr_scheduler.OneCycleLR(_UpperCamelCase ,      max_lr=0.0_1 ,      steps_per_epoch=2 ,      epochs=1				)
			_snake_case			,      _snake_case			,      _snake_case						=    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  a_	( __lowercase     :			Optional[int]=2 ,      __lowercase     :			List[str]=True ,      __lowercase     :			Tuple=False				)				->						Optional[Any]:
			_snake_case						=    Accelerator(step_scheduler_with_optimizer=_UpperCamelCase ,      split_batches=_UpperCamelCase				)
			_snake_case						=    torch.nn.Linear(2 ,      4				)
			_snake_case						=    torch.optim.AdamW(model.parameters() ,      lr=1.0				)
			_snake_case						=    torch.optim.lr_scheduler.LambdaLR(_UpperCamelCase ,      lr_lambda=lambda __lowercase				: 1 - n / 10				)
			_snake_case			,      _snake_case			,      _snake_case						=    accelerator.prepare(_UpperCamelCase ,      _UpperCamelCase ,      _UpperCamelCase				)

			# Optimizer has stepped
			_snake_case						=    False
			scheduler.step()
			_snake_case						=    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
			_snake_case						=    True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						_snake_case						=    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_	( __lowercase     :			int = 2				)				->						Optional[int]:
			from transformers import get_linear_schedule_with_warmup

			_snake_case						=    [1, 2, 4]
			for num_steps in steps:
						_snake_case						=    GradientAccumulationPlugin(num_steps=_UpperCamelCase ,      adjust_scheduler=num_steps > 1				)
						_snake_case						=    Accelerator(gradient_accumulation_plugin=_UpperCamelCase				)
						_snake_case						=    torch.nn.Linear(2 ,      4				)
						_snake_case						=    torch.optim.AdamW(model.parameters() ,      lr=10.0				)
						_snake_case						=    get_linear_schedule_with_warmup(optimizer=_UpperCamelCase ,      num_warmup_steps=0 ,      num_training_steps=20				)

						_snake_case			,      _snake_case			,      _snake_case						=    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      SCREAMING_SNAKE_CASE__  (			unittest.TestCase      ):





	'''simple docstring'''



	def 				A							(						self			:					int			):


				'''simple docstring'''


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



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


				'''simple docstring'''


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



	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


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



	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


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



	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


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



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


				'''simple docstring'''


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



	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


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



	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


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





	@require_huggingface_suite
	def 				A							(						self			:					Any			):


				'''simple docstring'''


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

import random
import sys

_lowerCamelCase :       Tuple     					=    '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''






def  a_	( )				->						None:
			_snake_case						=    input('Enter message: '				)
			_snake_case						=    'LFWOAYUISVKMNXPBDCRJTQEGHZ'
			_snake_case						=    input('Encrypt/Decrypt [e/d]: '				)

			check_valid_key(__lowercase				)

			if resp.lower().startswith('e'				):
						_snake_case						=    'encrypt'
						_snake_case						=    encrypt_message(__lowercase ,      __lowercase				)
			elif resp.lower().startswith('d'				):
						_snake_case						=    'decrypt'
						_snake_case						=    decrypt_message(__lowercase ,      __lowercase				)

			print(f'''\n{mode.title()}ion: \n{translated}'''				)






def  a_	( __lowercase     :			str				)				->						None:
			_snake_case						=    list(__lowercase				)
			_snake_case						=    list(__lowercase				)
			key_list.sort()
			letters_list.sort()

			if key_list != letters_list:
						sys.exit('Error in the key or symbol set.'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			return translate_message(__lowercase ,      __lowercase ,      'encrypt'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			return translate_message(__lowercase ,      __lowercase ,      'decrypt'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			_snake_case						=    ''
			_snake_case						=    LETTERS
			_snake_case						=    key

			if mode == "decrypt":
						_snake_case			,      _snake_case						=    chars_b, chars_a

			for symbol in message:
						if symbol.upper() in chars_a:
									_snake_case						=    chars_a.find(symbol.upper()				)
									if symbol.isupper():
												translated += chars_b[sym_index].upper()
									else:
												translated += chars_b[sym_index].lower()
						else:
									translated += symbol

			return translated






def  a_	( )				->						str:
			_snake_case						=    list(__lowercase				)
			random.shuffle(__lowercase				)
			return "".join(__lowercase				)


if __name__ == "__main__":
			main()