



from __future__ import annotations

import random
import string



class 	__lowerCAmelCase       :




	def __init__(  self		,			__UpperCAmelCase = 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     lowerCamelCase (  self		,			__UpperCAmelCase			):



				'''simple docstring'''
				for i in range(1		,			len(__UpperCAmelCase			)		,			2			):
							iterlist[i] *= -1
				return iterlist




	def     lowerCamelCase (  self			):



				'''simple docstring'''
				__lowerCamelCase  							=      string.ascii_letters + string.digits
				__lowerCamelCase  							=      [random.choice(__UpperCAmelCase			) for _ in range(random.randint(10		,			20			)			)]
				return password




	def     lowerCamelCase (  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(__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     lowerCamelCase (  self			):



				'''simple docstring'''
				__lowerCamelCase  							=      sum(self.__neg_pos([ord(__UpperCAmelCase			) for x in self.__passcode]			)			)
				return num if num > 0 else len(self.__passcode			)




	def     lowerCamelCase (  self		,			__UpperCAmelCase			):



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

				return decoded_message




	def     lowerCamelCase (  self		,			__UpperCAmelCase			):



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

				return encoded_message





def 						a__     (						_UpperCamelCase     :	str = "Hello, this is a modified Caesar cipher"							):
			__lowerCamelCase  							=      ShuffledShiftCipher()
			return cipa.decrypt(cipa.encrypt(snake_case__							)							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()











import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.electra.modeling_flax_electra import (
      FlaxElectraForCausalLM,
      FlaxElectraForMaskedLM,
      FlaxElectraForMultipleChoice,
      FlaxElectraForPreTraining,
      FlaxElectraForQuestionAnswering,
      FlaxElectraForSequenceClassification,
      FlaxElectraForTokenClassification,
      FlaxElectraModel,
  )



class 	__lowerCAmelCase       (		unittest.TestCase				):




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase=13		,			__UpperCAmelCase=7		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=99		,			__UpperCAmelCase=24		,			__UpperCAmelCase=32		,			__UpperCAmelCase=5		,			__UpperCAmelCase=4		,			__UpperCAmelCase=37		,			__UpperCAmelCase="gelu"		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=512		,			__UpperCAmelCase=16		,			__UpperCAmelCase=2		,			__UpperCAmelCase=0.02		,			__UpperCAmelCase=4		,			):



    '''simple docstring'''
    __lowerCamelCase  							=      parent
    __lowerCamelCase  							=      batch_size
    __lowerCamelCase  							=      seq_length
    __lowerCamelCase  							=      is_training
    __lowerCamelCase  							=      use_attention_mask
    __lowerCamelCase  							=      use_token_type_ids
    __lowerCamelCase  							=      use_labels
    __lowerCamelCase  							=      vocab_size
    __lowerCamelCase  							=      hidden_size
    __lowerCamelCase  							=      embedding_size
    __lowerCamelCase  							=      num_hidden_layers
    __lowerCamelCase  							=      num_attention_heads
    __lowerCamelCase  							=      intermediate_size
    __lowerCamelCase  							=      hidden_act
    __lowerCamelCase  							=      hidden_dropout_prob
    __lowerCamelCase  							=      attention_probs_dropout_prob
    __lowerCamelCase  							=      max_position_embeddings
    __lowerCamelCase  							=      type_vocab_size
    __lowerCamelCase  							=      type_sequence_label_size
    __lowerCamelCase  							=      initializer_range
    __lowerCamelCase  							=      num_choices




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

    __lowerCamelCase  							=      None
    if self.use_attention_mask:
       __lowerCamelCase  							=      random_attention_mask([self.batch_size, self.seq_length]			)

    __lowerCamelCase  							=      None
    if self.use_token_type_ids:
       __lowerCamelCase  							=      ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size			)

    __lowerCamelCase  							=      ElectraConfig(
        vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			embedding_size=self.embedding_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			max_position_embeddings=self.max_position_embeddings		,			type_vocab_size=self.type_vocab_size		,			initializer_range=self.initializer_range		,			)

    return config, input_ids, token_type_ids, attention_mask




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.prepare_config_and_inputs()
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase  							=      config_and_inputs
    __lowerCamelCase  							=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict



@require_flax
class 	__lowerCAmelCase       (		lowerCAmelCase__					,		unittest.TestCase				):
 lowerCAmelCase__       =					True

 lowerCAmelCase__       =					(
     (
         FlaxElectraModel,
         FlaxElectraForCausalLM,
         FlaxElectraForMaskedLM,
         FlaxElectraForPreTraining,
         FlaxElectraForTokenClassification,
         FlaxElectraForQuestionAnswering,
         FlaxElectraForMultipleChoice,
         FlaxElectraForSequenceClassification,
     )
     if is_flax_available()
     else ()
 )




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      FlaxElectraModelTester(self			)




 @slow
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    for model_class_name in self.all_model_classes:
       if model_class_name == FlaxElectraForMaskedLM:
          __lowerCamelCase  							=      model_class_name.from_pretrained('''google/electra-small-generator'''			)
       else:
          __lowerCamelCase  							=      model_class_name.from_pretrained('''google/electra-small-discriminator'''			)
       __lowerCamelCase  							=      model(np.ones((1, 1)			)			)
       self.assertIsNotNone(__UpperCAmelCase			)






