
import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE		,						unittest.TestCase				):
						__A:						Dict				   =      PreTrainedTokenizerFast
						__A:						Optional[int]				   =      False
						__A:						Dict				   =      True
						__A:						Optional[int]				   =      """tokenizer_file"""

						def 						a__ (					self		:			Optional[int]			):
													_UpperCAmelCase  :			Optional[int]	       =		False  # because we don't have pretrained_vocab_files_map
													super().setUp()
													_UpperCAmelCase  :			Any	       =		True

													_UpperCAmelCase  :			int	       =		["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
													_UpperCAmelCase  :			Optional[int]	       =		"SaulLu/dummy-tokenizer-bytelevel-bpe"

													# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
													_UpperCAmelCase  :			Union[str, Any]	       =		[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

													_UpperCAmelCase  :			int	       =		PreTrainedTokenizerFast.from_pretrained(model_paths[0]			)
													tokenizer.save_pretrained(self.tmpdirname			)

						def 						a__ (					self		:			Optional[Any]			):
													# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
													# model
													pass

						def 						a__ (					self		:			List[str]			):
													# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
													# model
													pass

						def 						a__ (					self		:			Dict			):
													# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
													# model
													pass

						def 						a__ (					self		:			int			):
													# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
													pass

						def 						a__ (					self		:			Dict			):
													_UpperCAmelCase  :			int	       =		self.tmpdirname
													# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
																											try:
																																		_UpperCAmelCase  :			List[str]	       =		tempfile.mkdtemp()
																																		_UpperCAmelCase  :			str	       =		self.rust_tokenizer_class.from_pretrained(_lowerCamelCase	,						**_lowerCamelCase			)

																																		tokenizer.save_pretrained(self.tmpdirname			)
																																		super().test_training_new_tokenizer()
																											finally:
																																		# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																																		# is restored
																																		shutil.rmtree(self.tmpdirname			)
																																		_UpperCAmelCase  :			Dict	       =		tmpdirname_orig

						def 						a__ (					self		:			Dict			):
													_UpperCAmelCase  :			str	       =		self.tmpdirname
													# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
																											try:
																																		_UpperCAmelCase  :			Dict	       =		tempfile.mkdtemp()
																																		_UpperCAmelCase  :			str	       =		self.rust_tokenizer_class.from_pretrained(_lowerCamelCase	,						**_lowerCamelCase			)

																																		tokenizer.save_pretrained(self.tmpdirname			)
																																		super().test_training_new_tokenizer_with_special_tokens_change()
																											finally:
																																		# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																																		# is restored
																																		shutil.rmtree(self.tmpdirname			)
																																		_UpperCAmelCase  :			Tuple	       =		tmpdirname_orig






						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name			)

													_UpperCAmelCase  :			Union[str, Any]	       =		("a" for _ in range(10_00			))
													_UpperCAmelCase  :			Union[str, Any]	       =		tokenizer.train_new_from_iterator(text_iterator=_lowerCamelCase	,						length=10_00	,						vocab_size=50			)

													_UpperCAmelCase  :			List[Any]	       =		new_tokenizer.encode("a🤗"			)
													self.assertEqual(_lowerCamelCase	,						[64, 1_72, 2_53, 97, 2_45]			)







@require_tokenizers
class    _UpperCamelCase(   unittest.TestCase				):

						def 						a__ (					self		:			Tuple			):
													_UpperCAmelCase  :			List[str]	       =		AutoTokenizer.from_pretrained("bert-base-cased"			)
													_UpperCAmelCase  :			List[Any]	       =		json.loads(tokenizer._tokenizer.to_str()			)
													_UpperCAmelCase  :			int	       =		len(_lowerCamelCase			)

													with tempfile.TemporaryDirectory() as tmp_dir:
																				# Hack to save this in the tokenizer_config.json
																				_UpperCAmelCase  :			Optional[int]	       =		["tokenizer.4.0.0.json"]
																				tokenizer.save_pretrained(_lowerCamelCase			)
																				json.dump(_lowerCamelCase	,						open(os.path.join(_lowerCamelCase	,						"tokenizer.4.0.0.json"			)	,						"w"			)			)

																				# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
																				_UpperCAmelCase  :			Tuple	       =		AutoTokenizer.from_pretrained(_lowerCamelCase			)
																				self.assertEqual(len(_lowerCamelCase			)	,						len(_lowerCamelCase			) + 1			)
																				_UpperCAmelCase  :			Tuple	       =		json.loads(new_tokenizer._tokenizer.to_str()			)
																				self.assertIn("huggingface"	,						json_tokenizer["model"]["vocab"]			)

																				# Will need to be adjusted if we reach v42 and this test is still here.
																				# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
																				shutil.move(os.path.join(_lowerCamelCase	,						"tokenizer.4.0.0.json"			)	,						os.path.join(_lowerCamelCase	,						"tokenizer.42.0.0.json"			)			)
																				_UpperCAmelCase  :			List[str]	       =		["tokenizer.42.0.0.json"]
																				tokenizer.save_pretrained(_lowerCamelCase			)
																				_UpperCAmelCase  :			str	       =		AutoTokenizer.from_pretrained(_lowerCamelCase			)
																				self.assertEqual(len(_lowerCamelCase			)	,						len(_lowerCamelCase			)			)
																				_UpperCAmelCase  :			Dict	       =		json.loads(new_tokenizer._tokenizer.to_str()			)
																				self.assertNotIn("huggingface"	,						json_tokenizer["model"]["vocab"]			)






						def 						a__ (					self		:			str			):
													# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
													_UpperCAmelCase  :			Optional[int]	       =		"hf-internal-testing/test-two-tokenizers"

													# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
													_UpperCAmelCase  :			Dict	       =		AutoTokenizer.from_pretrained(_lowerCamelCase			)
													self.assertEqual(len(_lowerCamelCase			)	,						2_89_97			)
													_UpperCAmelCase  :			Optional[int]	       =		json.loads(tokenizer._tokenizer.to_str()			)
													self.assertIn("huggingface"	,						json_tokenizer["model"]["vocab"]			)

													# Testing an older version by monkey-patching the version in the module it's used.
													import transformers as old_transformers

													_UpperCAmelCase  :			Dict	       =		"3.0.0"
													_UpperCAmelCase  :			int	       =		old_transformers.models.auto.AutoTokenizer.from_pretrained(_lowerCamelCase			)
													self.assertEqual(len(_lowerCamelCase			)	,						2_89_96			)
													_UpperCAmelCase  :			int	       =		json.loads(old_tokenizer._tokenizer.to_str()			)
													self.assertNotIn("huggingface"	,						json_tokenizer["model"]["vocab"]			)







@require_tokenizers
class    _UpperCamelCase(   unittest.TestCase				):

						def 						a__ (					self		:			Tuple			):
													# See https://github.com/huggingface/transformers/pull/12550
													# and https://github.com/huggingface/tokenizers/issues/537
													_UpperCAmelCase  :			str	       =		PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"			)
													_UpperCAmelCase  :			List[str]	       =		"The Matrix is a 1999 science fiction action film."

													with concurrent.futures.ThreadPoolExecutor() as executor:
																				_UpperCAmelCase  :			List[str]	       =		[executor.submit(self.fetch	,						_lowerCamelCase	,						_lowerCamelCase			) for i in range(10			)]
																				_UpperCAmelCase  :			Optional[int]	       =		[future.result() for future in futures]
																				self.assertEqual(_lowerCamelCase	,						[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10			)]			)






						def 						a__ (					self		:			Dict	,						_lowerCamelCase		:			Dict	,						_lowerCamelCase		:			str			):
													return tokenizer.encode(_lowerCamelCase	,						truncation="longest_first"	,						padding="longest"			)






from __future__ import annotations

from string import ascii_letters




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE = None  )   ->      str:

							"""simple docstring"""






							_UpperCAmelCase  :			List[Any]	       =		alphabet or ascii_letters

							# The final result string
							_UpperCAmelCase  :			Optional[Any]	       =		""

							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
																					_UpperCAmelCase  :			Any	       =		(alpha.index(_SCREAMING_SNAKE_CASE  ) + key) % len(_SCREAMING_SNAKE_CASE  )

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

							return result




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE = None  )   ->      str:

							"""simple docstring"""






							key *= -1

							return encrypt(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE = None  )   ->      dict[int, str]:

							"""simple docstring"""






							_UpperCAmelCase  :			Any	       =		alphabet or ascii_letters

							# To store data on all the combinations
							_UpperCAmelCase  :			Dict	       =		{}

							# Cycle through each combination
							for key in range(1      ,	len(_SCREAMING_SNAKE_CASE  ) + 1  ):
														# Decrypt the message and store the result in the data
														_UpperCAmelCase  :			Union[str, Any]	       =		decrypt(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_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
								__lowerCamelCase								=					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":
												__lowerCamelCase								=					input('Please enter the string to be encrypted: ')
												__lowerCamelCase								=					int(input('Please enter off-set: ').strip())

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

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

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

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




