


import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				if isinstance(UpperCamelCase__     ,   (list, tuple)		):
								return honor_type(UpperCamelCase__     ,   (move_to_device(UpperCamelCase__     ,   UpperCamelCase__		) for t in state)		)
				elif isinstance(UpperCamelCase__     ,   UpperCamelCase__		):
								return type(UpperCamelCase__		)({k: move_to_device(UpperCamelCase__     ,   UpperCamelCase__		) for k, v in state.items()}		)
				elif isinstance(UpperCamelCase__     ,   torch.Tensor		):
								return state.to(UpperCamelCase__		)
				return state






class 		_snake_case    (				torch.optim.Optimizer		):






						'''simple docstring'''







						def __init__(  self:	Tuple      ,							__UpperCamelCase:	Any      ,							__UpperCamelCase:	Dict=True      ,							__UpperCamelCase:	int=None     )							->	int:
										__magic_name__				:					Union[str, Any]						       =       optimizer
										__magic_name__				:					str						       =       scaler
										__magic_name__				:					List[Any]						       =       AcceleratorState()
										__magic_name__				:					List[str]						       =       GradientState()
										__magic_name__				:					Optional[int]						       =       device_placement
										__magic_name__				:					str						       =       False
										__magic_name__				:					int						       =       None

										# Handle device placement
										if device_placement:
														__magic_name__				:					str						       =       self.optimizer.state_dict()
														if self.accelerator_state.distributed_type == DistributedType.TPU:
																		xm.send_cpu_data_to_device(__UpperCamelCase      ,							self.accelerator_state.device     )
														else:
																		__magic_name__				:					List[Any]						       =       move_to_device(__UpperCamelCase      ,							self.accelerator_state.device     )
														self.optimizer.load_state_dict(__UpperCamelCase     )







						@property
						def lowerCAmelCase__ (  self:	Union[str, Any]     )							->	Optional[Any]:
										return self.optimizer.state







						@state.setter
						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							__UpperCamelCase:	Optional[Any]     )							->	str:
										__magic_name__				:					str						       =       state







						@property
						def lowerCAmelCase__ (  self:	int     )							->	int:
										return self.optimizer.param_groups







						@param_groups.setter
						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							__UpperCamelCase:	Dict     )							->	str:
										__magic_name__				:					List[Any]						       =       param_groups







						@property
						def lowerCAmelCase__ (  self:	Tuple     )							->	str:
										return self.optimizer.defaults







						@defaults.setter
						def lowerCAmelCase__ (  self:	Optional[int]      ,							__UpperCamelCase:	Union[str, Any]     )							->	Dict:
										__magic_name__				:					Tuple						       =       defaults







						def lowerCAmelCase__ (  self:	Tuple      ,							__UpperCamelCase:	List[str]     )							->	Dict:
										self.optimizer.add_param_group(__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	int      ,							__UpperCamelCase:	str     )							->	Dict:







										if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
														xm.send_cpu_data_to_device(__UpperCamelCase      ,							self.accelerator_state.device     )
										self.optimizer.load_state_dict(__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	str:
										return self.optimizer.state_dict()







						def lowerCAmelCase__ (  self:	Dict      ,							__UpperCamelCase:	Dict=None     )							->	Dict:







										if self.gradient_state.sync_gradients:
														__magic_name__				:					str						       =       "set_to_none" in inspect.signature(self.optimizer.zero_grad     ).parameters
														if accept_arg:
																		if set_to_none is None:
																						__magic_name__				:					str						       =       False
																		self.optimizer.zero_grad(set_to_none=__UpperCamelCase     )
														else:
																		if set_to_none is not None:
																						raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."     )
																		self.optimizer.zero_grad()







						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							__UpperCamelCase:	Optional[int]=None     )							->	int:







										if self.gradient_state.sync_gradients:
														if self.accelerator_state.distributed_type == DistributedType.TPU:
																		__magic_name__				:					Dict						       =       {"closure": closure} if closure is not None else {}
																		xm.optimizer_step(self.optimizer      ,							optimizer_args=__UpperCamelCase     )
														elif self.scaler is not None:
																		__magic_name__				:					Tuple						       =       False
																		if self._last_scale is None:
																						# `get_scale` is an async operation requiring full synchronization
																						# on CPU and GPUs before finishing. As a result, we store away
																						# the prior one to reduce the call overhead
																						__magic_name__				:					List[str]						       =       self.scaler.get_scale()
																						__magic_name__				:					Union[str, Any]						       =       True
																		self.scaler.step(self.optimizer      ,							__UpperCamelCase     )
																		self.scaler.update()
																		__magic_name__				:					Optional[Any]						       =       self.scaler.get_scale()
																		if not new_scale:
																						# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																						__magic_name__				:					Union[str, Any]						       =       scale_after < self._last_scale
																		__magic_name__				:					Optional[int]						       =       scale_after

														else:
																		self.optimizer.step(__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							__UpperCamelCase:	Dict     )							->	Dict:







										for param_group in self.optimizer.param_groups:
														__magic_name__				:					Tuple						       =       [parameters_map.get(__UpperCamelCase      ,							__UpperCamelCase     ) for p in param_group["params"]]







						@property
						def lowerCAmelCase__ (  self:	Dict     )							->	List[str]:
										warnings.warn(
										    "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
										    "`optimizer.step_was_skipped` instead."      ,							__UpperCamelCase      ,							)
										return self._is_overflow







						@property
						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Optional[Any]:
										return self._is_overflow







						def __getstate__(  self:	Optional[Any]     )							->	int:
										return self.__dict__.copy()







						def __setstate__(  self:	Optional[int]      ,							__UpperCamelCase:	int     )							->	Tuple:
										self.__dict__.update(__UpperCamelCase     )



'''simple docstring'''







def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				if not isinstance(UpperCamelCase__     ,   UpperCamelCase__		):
								raise TypeError("plaintext must be a string"		)
				if not isinstance(UpperCamelCase__     ,   UpperCamelCase__		):
								raise TypeError("key must be a string"		)

				if not plaintext:
								raise ValueError("plaintext is empty"		)
				if not key:
								raise ValueError("key is empty"		)

				key += plaintext
				__magic_name__				:					List[str]						       =       plaintext.lower()
				__magic_name__				:					Tuple						       =       key.lower()
				__magic_name__				:					Dict						       =       0
				__magic_name__				:					Optional[int]						       =       0
				__magic_name__				:					List[str]						       =       ""
				while plaintext_iterator < len(UpperCamelCase__		):
								if (
								    ord(plaintext[plaintext_iterator]		) < 97
								    or ord(plaintext[plaintext_iterator]		) > 122
								):
												ciphertext += plaintext[plaintext_iterator]
												plaintext_iterator += 1
								elif ord(key[key_iterator]		) < 97 or ord(key[key_iterator]		) > 122:
												key_iterator += 1
								else:
												ciphertext += chr(
												    (
												        (ord(plaintext[plaintext_iterator]		) - 97 + ord(key[key_iterator]		))
												        - 97
												    )
												    % 26
												    + 97		)
												key_iterator += 1
												plaintext_iterator += 1
				return ciphertext



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				if not isinstance(UpperCamelCase__     ,   UpperCamelCase__		):
								raise TypeError("ciphertext must be a string"		)
				if not isinstance(UpperCamelCase__     ,   UpperCamelCase__		):
								raise TypeError("key must be a string"		)

				if not ciphertext:
								raise ValueError("ciphertext is empty"		)
				if not key:
								raise ValueError("key is empty"		)

				__magic_name__				:					List[str]						       =       key.lower()
				__magic_name__				:					str						       =       0
				__magic_name__				:					Optional[Any]						       =       0
				__magic_name__				:					Tuple						       =       ""
				while ciphertext_iterator < len(UpperCamelCase__		):
								if (
								    ord(ciphertext[ciphertext_iterator]		) < 97
								    or ord(ciphertext[ciphertext_iterator]		) > 122
								):
												plaintext += ciphertext[ciphertext_iterator]
								else:
												plaintext += chr(
												    (ord(ciphertext[ciphertext_iterator]		) - ord(key[key_iterator]		)) % 26
												    + 97		)
												key += chr(
												    (ord(ciphertext[ciphertext_iterator]		) - ord(key[key_iterator]		)) % 26
												    + 97		)
												key_iterator += 1
								ciphertext_iterator += 1
				return plaintext


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		_SCREAMING_SNAKE_CASE       :   Tuple        = int(input("Type 1 to encrypt or 2 to decrypt:"))
		if operation == 1:
				_SCREAMING_SNAKE_CASE       :   Optional[Any]        = input("Typeplaintext to be encrypted:\n")
				_SCREAMING_SNAKE_CASE       :   Tuple        = input("Type the key:\n")
				print(encrypt(plaintext, key))
		elif operation == 2:
				_SCREAMING_SNAKE_CASE       :   Optional[Any]        = input("Type the ciphertext to be decrypted:\n")
				_SCREAMING_SNAKE_CASE       :   List[str]        = input("Type the key:\n")
				print(decrypt(ciphertext, key))
		decrypt("jsqqs avvwo", "coffee")