
"""simple docstring"""

from __future__ import annotations

import random
import string






class        _lowercase   :




	def __init__( self		, UpperCAmelCase_ = None					)		->							None:
			lowerCamelCase     :			Any		=    passcode or self.__passcode_creator()
			lowerCamelCase     :			int		=    self.__make_key_list()
			lowerCamelCase     :			Union[str, Any]		=    self.__make_shift_key()




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




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




	def 			_UpperCamelCase    ( self					)		->							list[str]:
			lowerCamelCase     :			Union[str, Any]		=    string.ascii_letters + string.digits
			lowerCamelCase     :			str		=    [random.choice(UpperCAmelCase_					) for _ in range(random.randint(10		, 20					)					)]
			return password




	def 			_UpperCamelCase    ( self					)		->							list[str]:
			lowerCamelCase     :			str		=    (
			    string.ascii_letters + string.digits + string.punctuation + ' \t\n'
			)

			lowerCamelCase     :			Union[str, Any]		=    []

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

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

					# 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 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			int		=    sum(self.__neg_pos([ord(UpperCAmelCase_					) for x in self.__passcode]					)					)
			return num if num > 0 else len(self.__passcode					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							str:
			lowerCamelCase     :			Any		=    ''

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

			return decoded_message




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							str:
			lowerCamelCase     :			int		=    ''

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

			return encoded_message


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





		'''simple docstring'''


		lowerCamelCase     :			List[str]		=    ShuffledShiftCipher()
		return cipa.decrypt(cipa.encrypt(a_			)			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()











from typing import TYPE_CHECKING

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


__A				    =						{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MPNetForMaskedLM''',
	    '''MPNetForMultipleChoice''',
	    '''MPNetForQuestionAnswering''',
	    '''MPNetForSequenceClassification''',
	    '''MPNetForTokenClassification''',
	    '''MPNetLayer''',
	    '''MPNetModel''',
	    '''MPNetPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFMPNetEmbeddings''',
	    '''TFMPNetForMaskedLM''',
	    '''TFMPNetForMultipleChoice''',
	    '''TFMPNetForQuestionAnswering''',
	    '''TFMPNetForSequenceClassification''',
	    '''TFMPNetForTokenClassification''',
	    '''TFMPNetMainLayer''',
	    '''TFMPNetModel''',
	    '''TFMPNetPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
	from .tokenization_mpnet import MPNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mpnet_fast import MPNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mpnet import (
		    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MPNetForMaskedLM,
		    MPNetForMultipleChoice,
		    MPNetForQuestionAnswering,
		    MPNetForSequenceClassification,
		    MPNetForTokenClassification,
		    MPNetLayer,
		    MPNetModel,
		    MPNetPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_mpnet import (
		    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFMPNetEmbeddings,
		    TFMPNetForMaskedLM,
		    TFMPNetForMultipleChoice,
		    TFMPNetForQuestionAnswering,
		    TFMPNetForSequenceClassification,
		    TFMPNetForTokenClassification,
		    TFMPNetMainLayer,
		    TFMPNetModel,
		    TFMPNetPreTrainedModel,
		)

else:
	import sys

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




