


'''simple docstring'''




from typing import Literal

__lowercase  :      Any      		=				"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""





def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->str:






				'''simple docstring'''

				__lowerCamelCase		:      Optional[int]											=					LETTERS if mode == "decrypt" else key
				__lowerCamelCase		:      Dict											=					key if mode == "decrypt" else LETTERS
				__lowerCamelCase		:      List[str]											=					""
				# loop through each symbol in the message
				for symbol in message:
								if symbol.upper() in chars_a:
												# encrypt/decrypt the symbol
												__lowerCamelCase		:      List[str]											=					chars_a.find(symbol.upper()    )
												if symbol.isupper():
																translated += chars_b[sym_index].upper()
												else:
																translated += chars_b[sym_index].lower()
								else:
												# symbol is not in LETTERS, just add it
												translated += symbol
				return translated





def 							a_       (						lowercase							,						lowercase    )			->str:






				'''simple docstring'''

				return translate_message(lowercase							,						lowercase							,						"encrypt"    )





def 							a_       (						lowercase							,						lowercase    )			->str:






				'''simple docstring'''

				return translate_message(lowercase							,						lowercase							,						"decrypt"    )





def 							a_       (						)			->None:






				'''simple docstring'''

				__lowerCamelCase		:      Tuple											=					"Hello World"
				__lowerCamelCase		:      List[Any]											=					"QWERTYUIOPASDFGHJKLZXCVBNM"
				__lowerCamelCase		:      List[Any]											=					"decrypt"  # set to 'encrypt' or 'decrypt'

				if mode == "encrypt":
								__lowerCamelCase		:      Union[str, Any]											=					encrypt_message(lowercase							,						lowercase    )
				elif mode == "decrypt":
								__lowerCamelCase		:      int											=					decrypt_message(lowercase							,						lowercase    )
				print(F'Using the key {key}, the {mode}ed message is: {translated}'    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()









'''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_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Optional[Any]:






				'''simple docstring'''

				__lowerCamelCase		:      List[str]											=					Accelerator(step_scheduler_with_optimizer=lowercase							,						split_batches=lowercase    )
				__lowerCamelCase		:      Union[str, Any]											=					torch.nn.Linear(2							,						4    )
				__lowerCamelCase		:      str											=					torch.optim.AdamW(model.parameters()							,						lr=1.0    )
				__lowerCamelCase		:      List[str]											=					torch.optim.lr_scheduler.OneCycleLR(lowercase							,						max_lr=0.0_1							,						steps_per_epoch=2							,						epochs=1    )
				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					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 							a_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Dict:






				'''simple docstring'''

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

				# Optimizer has stepped
				__lowerCamelCase		:      List[Any]											=					False
				scheduler.step()
				__lowerCamelCase		:      List[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		:      Any											=					True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								__lowerCamelCase		:      int											=					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 = 2    )			->Dict:






				'''simple docstring'''

				from transformers import get_linear_schedule_with_warmup

				__lowerCamelCase		:      str											=					[1, 2, 4]
				for num_steps in steps:
								__lowerCamelCase		:      List[Any]											=					GradientAccumulationPlugin(num_steps=lowercase							,						adjust_scheduler=num_steps > 1    )
								__lowerCamelCase		:      Dict											=					Accelerator(gradient_accumulation_plugin=lowercase    )
								__lowerCamelCase		:      Any											=					torch.nn.Linear(2							,						4    )
								__lowerCamelCase		:      List[Any]											=					torch.optim.AdamW(model.parameters()							,						lr=1_0.0    )
								__lowerCamelCase		:      Optional[Any]											=					get_linear_schedule_with_warmup(optimizer=lowercase							,						num_warmup_steps=0							,						num_training_steps=20    )

								__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )

								for i in range(10 * num_steps    ):
												with accelerator.accumulate(lowercase    ):
																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 _snake_case   ( unittest.TestCase   ):
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""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 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""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 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   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 		__SCREAMING_SNAKE_CASE(   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 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:



											"""simple docstring"""







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







							@require_huggingface_suite
							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







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





