
'''simple docstring'''




from __future__ import annotations

import random
import string






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase        =  passcode or self.__passcode_creator()
					lowerCAmelCase        =  self.__make_key_list()
					lowerCAmelCase        =  self.__make_shift_key()



			def __str__(       self):




					"""simple docstring"""


					return "".join(self.__passcode)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					for i in range(1	,   len(__lowerCAmelCase)	,   2):
							iterlist[i] *= -1
					return iterlist



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  string.ascii_letters + string.digits
					lowerCAmelCase        =  [random.choice(__lowerCAmelCase) for _ in range(random.randint(10	,   20))]
					return password



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  (
					    string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
					)

					lowerCAmelCase        =  []

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

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

							# 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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  sum(self.__neg_pos([ord(__lowerCAmelCase) for x in self.__passcode]))
					return num if num > 0 else len(self.__passcode)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  """"""

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

					return decoded_message







			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  """"""

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

					return encoded_message







def     snake_case__							(      _A: str = "Hello, this is a modified Caesar cipher"			)   ->     str:

		'''simple docstring'''


		lowerCAmelCase        =  ShuffledShiftCipher()
		return cipa.decrypt(cipa.encrypt(snake_case__			)			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





'''simple docstring'''




from typing import TYPE_CHECKING

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


__lowercase				   =  {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  ['''BartTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BartForCausalLM''',
						    '''BartForConditionalGeneration''',
						    '''BartForQuestionAnswering''',
						    '''BartForSequenceClassification''',
						    '''BartModel''',
						    '''BartPretrainedModel''',
						    '''PretrainedBartModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''TFBartForConditionalGeneration''',
						    '''TFBartForSequenceClassification''',
						    '''TFBartModel''',
						    '''TFBartPretrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''FlaxBartDecoderPreTrainedModel''',
						    '''FlaxBartForCausalLM''',
						    '''FlaxBartForConditionalGeneration''',
						    '''FlaxBartForQuestionAnswering''',
						    '''FlaxBartForSequenceClassification''',
						    '''FlaxBartModel''',
						    '''FlaxBartPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowercase				   =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




