from __future__ import annotations

from string import ascii_letters

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__ = None							)					->     str:
							lowerCAmelCase     		=		alphabet or ascii_letters

							# The final result string
							lowerCAmelCase     		=		''''''

							for character in input_string:
														if character not in alpha:
																					# Append without encryption if character is not in the alphabet
																					result += character
														else:
																					# Get the index of the new key and make sure it isn't too large
																					lowerCAmelCase     		=		(alpha.index(snake_case__							) + key) % len(snake_case__							)

																					# Append the encoded character to the alphabet
																					result += alpha[new_key]

							return result

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__ = None							)					->     str:
							key *= -1

							return encrypt(snake_case__       ,  snake_case__       ,  snake_case__							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__ = None							)					->     dict[int, str]:
							lowerCAmelCase     		=		alphabet or ascii_letters

							# To store data on all the combinations
							lowerCAmelCase     		=		{}

							# Cycle through each combination
							for key in range(1       ,  len(snake_case__							) + 1							):
														# Decrypt the message and store the result in the data
														lowerCAmelCase     		=		decrypt(snake_case__       ,  snake_case__       ,  snake_case__							)

							return brute_force_data


if __name__ == "__main__":
	while True:
		print(f'\n{"-" * 1_0}\n Menu\n{"-" * 1_0}')
		print(*['''1.Encrypt''', '''2.Decrypt''', '''3.BruteForce''', '''4.Quit'''], sep='''\n''')

		# get user input
		lowercase__      :    Union[str, Any]          =     input('''\nWhat would you like to do?: ''').strip() or '''4'''

		# run functions based on what the user chose
		if choice not in ("1", "2", "3", "4"):
			print('''Invalid choice, please enter a valid choice''')
		elif choice == "1":
			lowercase__      :    int          =     input('''Please enter the string to be encrypted: ''')
			lowercase__      :    Any          =     int(input('''Please enter off-set: ''').strip())

			print(encrypt(input_string, key))
		elif choice == "2":
			lowercase__      :    List[Any]          =     input('''Please enter the string to be decrypted: ''')
			lowercase__      :    List[str]          =     int(input('''Please enter off-set: ''').strip())

			print(decrypt(input_string, key))
		elif choice == "3":
			lowercase__      :    Dict          =     input('''Please enter the string to be decrypted: ''')
			lowercase__      :    int          =     brute_force(input_string)

			for key, value in brute_force_data.items():
				print(f'Key: {key} | Message: {value}')

		elif choice == "4":
			print('''Goodbye.''')
			break




# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Union[str, Any]:
								return cls




	@slow
	@require_torch_gpu
	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		self.get_auto_remove_tmp_dir()
								lowerCAmelCase     		=		F"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								lowerCAmelCase     		=		get_gpu_count()
								lowerCAmelCase     		=		F"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
								lowerCAmelCase     		=		[sys.executable] + distributed_args + args
								execute_subprocess_async(__SCREAMING_SNAKE_CASE		,      env=self.get_env()    )

								lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      '''test_bleu.json'''    )
								lowerCAmelCase     		=		load_json(__SCREAMING_SNAKE_CASE    )
								# print(metrics)
								self.assertGreaterEqual(metrics['''bleu''']		,      25    )



