






from __future__ import annotations

import random
import string





class        a__      :






		def __init__(      self							, lowercase__ = None						)				->				None:
				__A    =							passcode or self.__passcode_creator()
				__A    =							self.__make_key_list()
				__A    =							self.__make_shift_key()






		def __str__(      self						)				->				str:
				return "".join(self.__passcode						)






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				list[int]:
				for i in range(1							, len(lowercase__						)							, 2						):
						iterlist[i] *= -1
				return iterlist






		def 					_lowerCamelCase					(      self						)				->				list[str]:
				__A    =							string.ascii_letters + string.digits
				__A    =							[random.choice(lowercase__						) for _ in range(random.randint(10							, 20						)						)]
				return password






		def 					_lowerCamelCase					(      self						)				->				list[str]:
				__A    =							(
				    string.ascii_letters + string.digits + string.punctuation + " \t\n"
				)

				__A    =							[]

				# creates points known as breakpoints to break the key_list_options at those
				# points and pivot each substring
				__A    =							sorted(set(self.__passcode						)						)
				__A    =							[]

				# algorithm for creating a new shuffled list, keys_l, out of key_list_options
				for i in key_list_options:
						temp_list.extend(lowercase__						)

						# checking breakpoints at which to pivot temporary sublist and add it into
						# keys_l
						if i in breakpoints or i == key_list_options[-1]:
								keys_l.extend(temp_list[::-1]						)
								temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
				return keys_l






		def 					_lowerCamelCase					(      self						)				->				int:
				__A    =							sum(self.__neg_pos([ord(lowercase__						) for x in self.__passcode]						)						)
				return num if num > 0 else len(self.__passcode						)






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				str:
				__A    =							""

				# decoding shift like Caesar cipher algorithm implementing negative shift or
				# reverse shift or left shift
				for i in encoded_message:
						__A    =							self.__key_list.index(lowercase__						)
						decoded_message += self.__key_list[
						    (position - self.__shift_key) % -len(self.__key_list						)
						]

				return decoded_message






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				str:
				__A    =							""

				# encoding shift like Caesar cipher algorithm implementing positive shift or
				# forward shift or right shift
				for i in plaintext:
						__A    =							self.__key_list.index(lowercase__						)
						encoded_message += self.__key_list[
						    (position + self.__shift_key) % len(self.__key_list						)
						]

				return encoded_message



def      UpperCAmelCase						( lowerCAmelCase__ = "Hello, this is a modified Caesar cipher"	):


		'''simple docstring'''

		__A    =							ShuffledShiftCipher()
		return cipa.decrypt(cipa.encrypt(lowerCAmelCase__	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_   :	Dict							      ={'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Any							      =['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[int]							      =[
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =[
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Tuple							      =[
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

					snake_case_   :	List[Any]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

