

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 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__						,	unittest.TestCase   ):







						"""simple docstring"""




						lowercase  :				Optional[Any]											=							PreTrainedTokenizerFast
						lowercase  :				Optional[int]											=							False
						lowercase  :				Union[str, Any]											=							True
						lowercase  :				Union[str, Any]											=							'tokenizer_file'

						def __lowerCamelCase					(				self    )							->	Tuple:




									'''simple docstring'''


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

									__UpperCamelCase    :    Union[str, Any]								=      ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
									__UpperCamelCase    :    Dict								=      "SaulLu/dummy-tokenizer-bytelevel-bpe"

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

									__UpperCamelCase    :    Any								=      PreTrainedTokenizerFast.from_pretrained(model_paths[0]    )
									tokenizer.save_pretrained(self.tmpdirname    )

						def __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									pass

						def __lowerCamelCase					(				self    )							->	str:




									'''simple docstring'''


									pass

						def __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									pass

						def __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


									pass

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      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    :    int								=      tempfile.mkdtemp()
																		__UpperCamelCase    :    int								=      self.rust_tokenizer_class.from_pretrained(__UpperCamelCase , **__UpperCamelCase    )

																		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    :    Tuple								=      tmpdirname_orig

						def __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      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    :    Union[str, Any]								=      tempfile.mkdtemp()
																		__UpperCamelCase    :    str								=      self.rust_tokenizer_class.from_pretrained(__UpperCamelCase , **__UpperCamelCase    )

																		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    :    Any								=      tmpdirname_orig




						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name    )

									__UpperCamelCase    :    List[str]								=      ("a" for _ in range(10_00    ))
									__UpperCamelCase    :    Optional[int]								=      tokenizer.train_new_from_iterator(text_iterator=__UpperCamelCase , length=10_00 , vocab_size=50    )

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







@require_tokenizers
class 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):







						"""simple docstring"""




						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      AutoTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Tuple								=      json.loads(tokenizer._tokenizer.to_str()    )
									__UpperCamelCase    :    int								=      len(__UpperCamelCase    )

									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(__UpperCamelCase    )
												json.dump(__UpperCamelCase , open(os.path.join(__UpperCamelCase , "tokenizer.4.0.0.json"    ) , "w"    )    )

												# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
												__UpperCamelCase    :    List[str]								=      AutoTokenizer.from_pretrained(__UpperCamelCase    )
												self.assertEqual(len(__UpperCamelCase    ) , len(__UpperCamelCase    ) + 1    )
												__UpperCamelCase    :    Optional[Any]								=      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(__UpperCamelCase , "tokenizer.4.0.0.json"    ) , os.path.join(__UpperCamelCase , "tokenizer.42.0.0.json"    )    )
												__UpperCamelCase    :    Dict								=      ["tokenizer.42.0.0.json"]
												tokenizer.save_pretrained(__UpperCamelCase    )
												__UpperCamelCase    :    int								=      AutoTokenizer.from_pretrained(__UpperCamelCase    )
												self.assertEqual(len(__UpperCamelCase    ) , len(__UpperCamelCase    )    )
												__UpperCamelCase    :    Dict								=      json.loads(new_tokenizer._tokenizer.to_str()    )
												self.assertNotIn("huggingface" , json_tokenizer["model"]["vocab"]    )




						def __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      "hf-internal-testing/test-two-tokenizers"

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									__UpperCamelCase    :    Any								=      AutoTokenizer.from_pretrained(__UpperCamelCase    )
									self.assertEqual(len(__UpperCamelCase    ) , 2_89_97    )
									__UpperCamelCase    :    Dict								=      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    :    int								=      "3.0.0"
									__UpperCamelCase    :    int								=      old_transformers.models.auto.AutoTokenizer.from_pretrained(__UpperCamelCase    )
									self.assertEqual(len(__UpperCamelCase    ) , 2_89_96    )
									__UpperCamelCase    :    int								=      json.loads(old_tokenizer._tokenizer.to_str()    )
									self.assertNotIn("huggingface" , json_tokenizer["model"]["vocab"]    )







@require_tokenizers
class 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):







						"""simple docstring"""




						def __lowerCamelCase					(				self    )							->	Tuple:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[int]								=      PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"    )
									__UpperCamelCase    :    Dict								=      "The Matrix is a 1999 science fiction action film."

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




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase    )							->	int:




									'''simple docstring'''


									return tokenizer.encode(__UpperCamelCase , truncation="longest_first" , padding="longest"    )


import random
import sys

from . import cryptomath_module as cryptomath

lowercase     :       Optional[Any]        =       (
    r" !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"
    r"abcdefghijklmnopqrstuvwxyz{|}~"
)







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		int     ,			_lowerCAmelCase :		str		):
			if mode == "encrypt":
						if key_a == 1:
									sys.exit(
									    "The affine cipher becomes weak when key "
									    "A is set to 1. Choose different key"		)
						if key_b == 0:
									sys.exit(
									    "The affine cipher becomes weak when key "
									    "B is set to 0. Choose different key"		)
			if key_a < 0 or key_b < 0 or key_b > len(_lowerCAmelCase		) - 1:
						sys.exit(
						    "Key A must be greater than 0 and key B must "
						    F'''be between 0 and {len(_lowerCAmelCase		) - 1}.'''		)
			if cryptomath.gcd(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		) != 1:
						sys.exit(
						    F'''Key A {key_a} and the symbol set size {len(_lowerCAmelCase		)} '''
						    "are not relatively prime. Choose a different key."		)







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		str		):
			__UpperCamelCase							,				__UpperCamelCase    :    int								=      divmod(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		)
			check_keys(_lowerCAmelCase     ,			_lowerCAmelCase     ,			"encrypt"		)
			__UpperCamelCase    :    List[Any]								=      ""
			for symbol in message:
						if symbol in SYMBOLS:
									__UpperCamelCase    :    Tuple								=      SYMBOLS.find(_lowerCAmelCase		)
									cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_lowerCAmelCase		)]
						else:
									cipher_text += symbol
			return cipher_text







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		str		):
			__UpperCamelCase							,				__UpperCamelCase    :    List[str]								=      divmod(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		)
			check_keys(_lowerCAmelCase     ,			_lowerCAmelCase     ,			"decrypt"		)
			__UpperCamelCase    :    List[Any]								=      ""
			__UpperCamelCase    :    Tuple								=      cryptomath.find_mod_inverse(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		)
			for symbol in message:
						if symbol in SYMBOLS:
									__UpperCamelCase    :    List[Any]								=      SYMBOLS.find(_lowerCAmelCase		)
									plain_text += SYMBOLS[
									    (sym_index - key_b) * mod_inverse_of_key_a % len(_lowerCAmelCase		)
									]
						else:
									plain_text += symbol
			return plain_text







def  UpperCAmelCase_   ():
			while True:
						__UpperCamelCase    :    Optional[int]								=      random.randint(2     ,			len(_lowerCAmelCase		)		)
						__UpperCamelCase    :    Optional[int]								=      random.randint(2     ,			len(_lowerCAmelCase		)		)
						if cryptomath.gcd(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		) == 1 and key_b % len(_lowerCAmelCase		) != 0:
									return key_b * len(_lowerCAmelCase		) + key_b







def  UpperCAmelCase_   ():
			__UpperCamelCase    :    List[Any]								=      input("Enter message: "		).strip()
			__UpperCamelCase    :    List[Any]								=      int(input("Enter key [2000 - 9000]: "		).strip()		)
			__UpperCamelCase    :    Optional[int]								=      input("Encrypt/Decrypt [E/D]: "		).strip().lower()

			if mode.startswith("e"		):
						__UpperCamelCase    :    str								=      "encrypt"
						__UpperCamelCase    :    str								=      encrypt_message(_lowerCAmelCase     ,			_lowerCAmelCase		)
			elif mode.startswith("d"		):
						__UpperCamelCase    :    int								=      "decrypt"
						__UpperCamelCase    :    Dict								=      decrypt_message(_lowerCAmelCase     ,			_lowerCAmelCase		)
			print(F'''\n{mode.title()}ed text: \n{translated}'''		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# main()