"""simple docstring"""







import string

import numpy
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	return b if a == 0 else greatest_common_divisor(b % a    ,							UpperCAmelCase_						)

class 	UpperCamelCase    :
				UpperCAmelCase       :   Any					=	string.ascii_uppercase + string.digits
				# This cipher takes alphanumerics into account
				# i.e. a total of 36 characters

				# take x and return x % len(key_string)
				UpperCAmelCase       :   List[Any]					=	numpy.vectorize(lambda lowercase		: x % 36		)

				UpperCAmelCase       :   Dict					=	numpy.vectorize(lowercase		)



				def __init__(self       :   str			,						_A       :   numpy.ndarray)					->			None:
					__snake_case    :							str   	=				self.modulus(_A)  # mod36 calc's on the encrypt key
					self.check_determinant()  # validate the determinant of the encryption key
					__snake_case    :							Optional[Any]   	=				encrypt_key.shape[0]



				def       _lowercase  (self       :   Any			,						_A       :   str)					->			int:
					return self.key_string.index(_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   int)					->			str:
					return self.key_string[round(_A)]



				def       _lowercase  (self       :   Optional[int])					->			None:
					__snake_case    :							Any   	=				round(numpy.linalg.det(self.encrypt_key))

					if det < 0:
						__snake_case    :							Any   	=				det % len(self.key_string)

					__snake_case    :							Tuple   	=				len(self.key_string)
					if greatest_common_divisor(_A			,						len(self.key_string)) != 1:
						__snake_case    :							List[str]   	=				(
						    f"determinant modular {req_l} of encryption key({det}) "
						    f"is not co prime w.r.t {req_l}.\nTry another key."
						)
						raise ValueError(_A)



				def       _lowercase  (self       :   Dict			,						_A       :   str)					->			str:
					__snake_case    :							str   	=				[char for char in text.upper() if char in self.key_string]

					__snake_case    :							int   	=				chars[-1]
					while len(_A) % self.break_key != 0:
						chars.append(_A)

					return "".join(_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			str:
					__snake_case    :							Any   	=				self.process_text(text.upper())
					__snake_case    :							Dict   	=				''

					for i in range(0			,						len(_A) - self.break_key + 1			,						self.break_key):
						__snake_case    :							Dict   	=				text[i : i + self.break_key]
						__snake_case    :							List[str]   	=				[self.replace_letters(_A) for char in batch]
						__snake_case    :							str   	=				numpy.array([vec]).T
						__snake_case    :							List[Any]   	=				self.modulus(self.encrypt_key.dot(_A)).T.tolist()[
						    0
						]
						__snake_case    :							str   	=				''.join(
						    self.replace_digits(_A) for num in batch_encrypted)
						encrypted += encrypted_batch

					return encrypted



				def       _lowercase  (self       :   Optional[int])					->			numpy.ndarray:
					__snake_case    :							List[Any]   	=				round(numpy.linalg.det(self.encrypt_key))

					if det < 0:
						__snake_case    :							int   	=				det % len(self.key_string)
					__snake_case    :							Optional[Any]   	=				None
					for i in range(len(self.key_string)):
						if (det * i) % len(self.key_string) == 1:
							__snake_case    :							Dict   	=				i
							break

					__snake_case    :							List[str]   	=				(
					    det_inv
					    * numpy.linalg.det(self.encrypt_key)
					    * numpy.linalg.inv(self.encrypt_key)
					)

					return self.to_int(self.modulus(_A))






				def       _lowercase  (self       :   int			,						_A       :   str)					->			str:
					__snake_case    :							int   	=				self.make_decrypt_key()
					__snake_case    :							List[str]   	=				self.process_text(text.upper())
					__snake_case    :							str   	=				''

					for i in range(0			,						len(_A) - self.break_key + 1			,						self.break_key):
						__snake_case    :							Optional[Any]   	=				text[i : i + self.break_key]
						__snake_case    :							Union[str, Any]   	=				[self.replace_letters(_A) for char in batch]
						__snake_case    :							Tuple   	=				numpy.array([vec]).T
						__snake_case    :							List[str]   	=				self.modulus(decrypt_key.dot(_A)).T.tolist()[0]
						__snake_case    :							str   	=				''.join(
						    self.replace_digits(_A) for num in batch_decrypted)
						decrypted += decrypted_batch

					return decrypted







def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				int(input('Enter the order of the encryption key: '						)						)
	__snake_case    :							str   	=				[]

	print('Enter each row of the encryption key with space separated integers'						)
	for _ in range(UpperCAmelCase_						):
		__snake_case    :							Union[str, Any]   	=				[int(UpperCAmelCase_						) for x in input().split()]
		hill_matrix.append(UpperCAmelCase_						)

	__snake_case    :							Dict   	=				HillCipher(numpy.array(UpperCAmelCase_						)						)

	print('Would you like to encrypt or decrypt some text? (1 or 2)'						)
	__snake_case    :							Optional[Any]   	=				input('\n1. Encrypt\n2. Decrypt\n'						)
	if option == "1":
		__snake_case    :							int   	=				input('What text would you like to encrypt?: '						)
		print('Your encrypted text is:'						)
		print(hc.encrypt(UpperCAmelCase_						)						)
	elif option == "2":
		__snake_case    :							Tuple   	=				input('What text would you like to decrypt?: '						)
		print('Your decrypted text is:'						)
		print(hc.decrypt(UpperCAmelCase_						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	main()






"""simple docstring"""







import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   List[Any]					=	MvpTokenizer
				UpperCAmelCase       :   int					=	MvpTokenizerFast
				UpperCAmelCase       :   Optional[Any]					=	True
				UpperCAmelCase       :   Tuple					=	filter_roberta_detectors



				def       _lowercase  (self       :   Any)					->			str:
					super().setUp()
					__snake_case    :							Optional[Any]   	=				[
					    'l',
					    'o',
					    'w',
					    'e',
					    'r',
					    's',
					    't',
					    'i',
					    'd',
					    'n',
					    '\u0120',
					    '\u0120l',
					    '\u0120n',
					    '\u0120lo',
					    '\u0120low',
					    'er',
					    '\u0120lowest',
					    '\u0120newer',
					    '\u0120wider',
					    '<unk>',
					]
					__snake_case    :							List[Any]   	=				dict(zip(_A			,						range(len(_A))))
					__snake_case    :							Optional[Any]   	=				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
					__snake_case    :							Optional[Any]   	=				{'unk_token': '<unk>'}

					__snake_case    :							int   	=				os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['vocab_file'])
					__snake_case    :							str   	=				os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['merges_file'])
					with open(self.vocab_file			,						'w'			,						encoding='utf-8') as fp:
						fp.write(json.dumps(_A) + '\n')
					with open(self.merges_file			,						'w'			,						encoding='utf-8') as fp:
						fp.write('\n'.join(_A))



				def       _lowercase  (self       :   List[str]			,						**_A       :   Any)					->			str:
					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   Optional[Any]			,						**_A       :   Tuple)					->			Any:
					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			Optional[Any]:
					return "lower newer", "lower newer"



				@cached_property
				def       _lowercase  (self       :   Tuple)					->			int:
					return MvpTokenizer.from_pretrained('RUCAIBox/mvp')



				@cached_property
				def       _lowercase  (self       :   int)					->			Tuple:
					return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp')



				@require_torch
				def       _lowercase  (self       :   Optional[int])					->			List[Any]:
					__snake_case    :							Optional[int]   	=				['A long paragraph for summarization.', 'Another paragraph for summarization.']
					__snake_case    :							Any   	=				[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							str   	=				tokenizer(_A			,						max_length=len(_A)			,						padding=_A			,						return_tensors='pt')
						self.assertIsInstance(_A			,						_A)

						self.assertEqual((2, 9)			,						batch.input_ids.shape)
						self.assertEqual((2, 9)			,						batch.attention_mask.shape)
						__snake_case    :							List[str]   	=				batch.input_ids.tolist()[0]
						self.assertListEqual(_A			,						_A)
						# Test that special tokens are reset



				@require_torch
				def       _lowercase  (self       :   List[str])					->			Any:
					__snake_case    :							List[Any]   	=				['A long paragraph for summarization.', 'Another paragraph for summarization.']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Tuple   	=				tokenizer(_A			,						padding=_A			,						return_tensors='pt')
						# check if input_ids are returned and no labels
						self.assertIn('input_ids'			,						_A)
						self.assertIn('attention_mask'			,						_A)
						self.assertNotIn('labels'			,						_A)
						self.assertNotIn('decoder_attention_mask'			,						_A)



				@require_torch
				def       _lowercase  (self       :   List[Any])					->			Tuple:
					__snake_case    :							List[Any]   	=				[
					    'Summary of the text.',
					    'Another summary.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Any   	=				tokenizer(text_target=_A			,						max_length=32			,						padding='max_length'			,						return_tensors='pt')
						self.assertEqual(32			,						targets['input_ids'].shape[1])



				@require_torch
				def       _lowercase  (self       :   Tuple)					->			Dict:
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Dict   	=				tokenizer(
						    ['I am a small frog' * 10_24, 'I am a small frog']			,						padding=_A			,						truncation=_A			,						return_tensors='pt')
						self.assertIsInstance(_A			,						_A)
						self.assertEqual(batch.input_ids.shape			,						(2, 10_24))



				@require_torch
				def       _lowercase  (self       :   Optional[int])					->			Dict:
					__snake_case    :							List[Any]   	=				['A long paragraph for summarization.']
					__snake_case    :							str   	=				[
					    'Summary of the text.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Any   	=				tokenizer(_A			,						text_target=_A			,						return_tensors='pt')
						__snake_case    :							str   	=				inputs['input_ids']
						__snake_case    :							Optional[Any]   	=				inputs['labels']
						self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
						self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



				def       _lowercase  (self       :   str)					->			Tuple:
					pass






				def       _lowercase  (self       :   str)					->			Any:
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
							__snake_case    :							Any   	=				self.rust_tokenizer_class.from_pretrained(_A			,						**_A)
							__snake_case    :							List[Any]   	=				self.tokenizer_class.from_pretrained(_A			,						**_A)
							__snake_case    :							List[Any]   	=				'A, <mask> AllenNLP sentence.'
							__snake_case    :							List[Any]   	=				tokenizer_r.encode_plus(_A			,						add_special_tokens=_A			,						return_token_type_ids=_A)
							__snake_case    :							int   	=				tokenizer_p.encode_plus(_A			,						add_special_tokens=_A			,						return_token_type_ids=_A)

							# token_type_ids should put 0 everywhere
							self.assertEqual(sum(tokens_r['token_type_ids'])			,						sum(tokens_p['token_type_ids']))

							# attention_mask should put 1 everywhere, so sum over length should be 1
							self.assertEqual(
							    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])			,						sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])			,						)

							__snake_case    :							Tuple   	=				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
							__snake_case    :							Union[str, Any]   	=				tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

							# Rust correctly handles the space before the mask while python doesnt
							self.assertSequenceEqual(tokens_p['input_ids']			,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
							self.assertSequenceEqual(tokens_r['input_ids']			,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

							self.assertSequenceEqual(
							    _A			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
							self.assertSequenceEqual(
							    _A			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])





