






from __future__ import annotations

import random
import string





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    Any				,   a:    str | None = None)  ->str:






								'''simple docstring'''


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



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






								'''simple docstring'''


								return "".join(self.__passcode)



		def _lowerCAmelCase     (    self:    Any				,   a:    list[int])  ->Dict:






								'''simple docstring'''


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



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






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    Dict)  ->Optional[int]:






								'''simple docstring'''


								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(A_)

														# 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:    List[str])  ->Dict:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    List[Any]				,   a:    str)  ->Optional[Any]:






								'''simple docstring'''


								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(A_)
														decoded_message += self.__key_list[
														    (position - self.__shift_key) % -len(self.__key_list)
														]

								return decoded_message




		def _lowerCAmelCase     (    self:    Any				,   a:    str)  ->List[str]:






								'''simple docstring'''


								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(A_)
														encoded_message += self.__key_list[
														    (position + self.__shift_key) % len(self.__key_list)
														]

								return encoded_message





def 			__UpperCAmelCase				(lowercase__ = "Hello, this is a modified Caesar cipher"				)		->				int:

						'''simple docstring'''


						a_				     =   ShuffledShiftCipher()
						return cipa.decrypt(cipa.encrypt(lowercase__				)				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_      = {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = ['SegformerFeatureExtractor']
							a_      = ['SegformerImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = [
							    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SegformerDecodeHead',
							    'SegformerForImageClassification',
							    'SegformerForSemanticSegmentation',
							    'SegformerLayer',
							    'SegformerModel',
							    'SegformerPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = [
							    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSegformerDecodeHead',
							    'TFSegformerForImageClassification',
							    'TFSegformerForSemanticSegmentation',
							    'TFSegformerModel',
							    'TFSegformerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_segformer import SegformerFeatureExtractor
														from .image_processing_segformer import SegformerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_segformer import (
														    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SegformerDecodeHead,
														    SegformerForImageClassification,
														    SegformerForSemanticSegmentation,
														    SegformerLayer,
														    SegformerModel,
														    SegformerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_segformer import (
														    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSegformerDecodeHead,
														    TFSegformerForImageClassification,
														    TFSegformerForSemanticSegmentation,
														    TFSegformerModel,
														    TFSegformerPreTrainedModel,
														)

else:
							import sys

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



