



'''simple docstring'''
from __future__ import annotations

import random
import string


class   SCREAMING_SNAKE_CASE__				:
 """simple docstring"""


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


 def __str__(		self			) ->							List[Any]:
    return "".join(self.__passcode			)


 def      a__ (		self					,   A			) ->							int:
    for i in range(1					,   len(A_			)					,   2			):
       iterlist[i] *= -1
    return iterlist


 def      a__ (		self			) ->							Optional[int]:
    A:      int														=   string.ascii_letters + string.digits
    A:      str														=   [random.choice(A_			) for _ in range(random.randint(10					,   20			)			)]
    return password


 def      a__ (		self			) ->							Union[str, Any]:
    A:      Dict														=   (
        string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
    )

    A:      str														=   []

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

    # 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      a__ (		self			) ->							List[Any]:
    A:      Union[str, Any]														=   sum(self.__neg_pos([ord(A_			) for x in self.__passcode]			)			)
    return num if num > 0 else len(self.__passcode			)


 def      a__ (		self					,   A			) ->							Union[str, Any]:
    A:      Optional[Any]														=   """"""

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

    return decoded_message





 def      a__ (		self					,   A			) ->							Any:
    A:      Optional[Any]														=   """"""

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

    return encoded_message







def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      str = "Hello, this is a modified Caesar cipher"				):


   '''simple docstring'''







   A:      Union[str, Any]														=   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_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE :      Dict             ={
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE :      Dict             =[
					    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'NystromformerForMaskedLM',
					    'NystromformerForMultipleChoice',
					    'NystromformerForQuestionAnswering',
					    'NystromformerForSequenceClassification',
					    'NystromformerForTokenClassification',
					    'NystromformerLayer',
					    'NystromformerModel',
					    'NystromformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nystromformer import (
										    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NystromformerForMaskedLM,
										    NystromformerForMultipleChoice,
										    NystromformerForQuestionAnswering,
										    NystromformerForSequenceClassification,
										    NystromformerForTokenClassification,
										    NystromformerLayer,
										    NystromformerModel,
										    NystromformerPreTrainedModel,
										)


else:
					import sys

					__SCREAMING_SNAKE_CASE :      Dict             =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





