
import random
import sys

lowercase__						:Any					    =       "ABCDEFGHIJKLMNOPQRSTUVWXYZ"



def 			UpperCamelCase       (				):
					'''simple docstring'''
					lowercase		      =		input('''Enter message: '''						)
					lowercase		      =		'''LFWOAYUISVKMNXPBDCRJTQEGHZ'''
					lowercase		      =		input('''Encrypt/Decrypt [e/d]: '''						)

					check_valid_key(lowerCAmelCase__						)

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

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



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		list(lowerCAmelCase__						)
					lowercase		      =		list(lowerCAmelCase__						)
					key_list.sort()
					letters_list.sort()

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



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					return translate_message(lowerCAmelCase__					,     lowerCAmelCase__					,     '''encrypt'''						)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					return translate_message(lowerCAmelCase__					,     lowerCAmelCase__					,     '''decrypt'''						)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		''''''
					lowercase		      =		LETTERS
					lowercase		      =		key

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

					for symbol in message:
										if symbol.upper() in chars_a:
															lowercase		      =		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 			UpperCamelCase       (				):
					'''simple docstring'''
					lowercase		      =		list(lowerCAmelCase__						)
					random.shuffle(lowerCAmelCase__						)
					return "".join(lowerCAmelCase__						)


if __name__ == "__main__":
							main()






import torch

from accelerate import Accelerator, DistributedType







class 							lowercase						:




							def __enter__(							self):
												if self.enabled:
																	lowercase		      =		self.model.no_sync()
																	self.model_sync_obj.__enter__()

												return self




							def __exit__(							self  ,A__  ,A__  ,A__):
												if self.enabled:
																	# Average all models on exit
																	self._sync_and_avg_model_params()
																	self.model_sync_obj.__exit__(A__  ,A__  ,A__)




							def __init__(							self  ,A__  ,A__  ,A__  ,A__ = True):
												if accelerator.distributed_type not in [
												    DistributedType.NO,
												    DistributedType.MULTI_CPU,
												    DistributedType.MULTI_GPU,
												]:
																	raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''')
												lowercase		      =		enabled and accelerator.distributed_type != DistributedType.NO
												lowercase		      =		0
												if self.enabled:
																	lowercase		      =		accelerator
																	lowercase		      =		model
																	lowercase		      =		local_sgd_steps




							def 					A__ (							self):
												self.num_steps += 1
												if not self.enabled:
																	return

												if self.num_steps % self.local_sgd_steps == 0:
																	self._sync_and_avg_model_params()





							def 					A__ (							self):
												self.accelerator.wait_for_everyone()
												with self.accelerator.autocast():
																	for param in self.model.parameters():
																						lowercase		      =		self.accelerator.reduce(param.data  ,reduction='''mean''')




