


SCREAMING_SNAKE_CASE__       			=				{
    'A': ('ABCDEFGHIJKLM', 'NOPQRSTUVWXYZ'),
    'B': ('ABCDEFGHIJKLM', 'NOPQRSTUVWXYZ'),
    'C': ('ABCDEFGHIJKLM', 'ZNOPQRSTUVWXY'),
    'D': ('ABCDEFGHIJKLM', 'ZNOPQRSTUVWXY'),
    'E': ('ABCDEFGHIJKLM', 'YZNOPQRSTUVWX'),
    'F': ('ABCDEFGHIJKLM', 'YZNOPQRSTUVWX'),
    'G': ('ABCDEFGHIJKLM', 'XYZNOPQRSTUVW'),
    'H': ('ABCDEFGHIJKLM', 'XYZNOPQRSTUVW'),
    'I': ('ABCDEFGHIJKLM', 'WXYZNOPQRSTUV'),
    'J': ('ABCDEFGHIJKLM', 'WXYZNOPQRSTUV'),
    'K': ('ABCDEFGHIJKLM', 'VWXYZNOPQRSTU'),
    'L': ('ABCDEFGHIJKLM', 'VWXYZNOPQRSTU'),
    'M': ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST'),
    'N': ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST'),
    'O': ('ABCDEFGHIJKLM', 'TUVWXYZNOPQRS'),
    'P': ('ABCDEFGHIJKLM', 'TUVWXYZNOPQRS'),
    'Q': ('ABCDEFGHIJKLM', 'STUVWXYZNOPQR'),
    'R': ('ABCDEFGHIJKLM', 'STUVWXYZNOPQR'),
    'S': ('ABCDEFGHIJKLM', 'RSTUVWXYZNOPQ'),
    'T': ('ABCDEFGHIJKLM', 'RSTUVWXYZNOPQ'),
    'U': ('ABCDEFGHIJKLM', 'QRSTUVWXYZNOP'),
    'V': ('ABCDEFGHIJKLM', 'QRSTUVWXYZNOP'),
    'W': ('ABCDEFGHIJKLM', 'PQRSTUVWXYZNO'),
    'X': ('ABCDEFGHIJKLM', 'PQRSTUVWXYZNO'),
    'Y': ('ABCDEFGHIJKLM', 'OPQRSTUVWXYZN'),
    'Z': ('ABCDEFGHIJKLM', 'OPQRSTUVWXYZN'),
}




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ):







	'''simple docstring'''



	return [alphabet[char] for char in key.upper()]




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		str  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



	lowercase_			 = ""
	lowercase_			 = 0
	lowercase_			 = generate_table(lowerCAmelCase_  )
	for char in words.upper():
		cipher += get_opponent(table[count]  ,			lowerCAmelCase_  )
		lowercase_			 = (count + 1) % len(lowerCAmelCase_  )
	return cipher




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		Dict  ):







	'''simple docstring'''



	return encrypt(lowerCAmelCase_  ,			lowerCAmelCase_  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Dict  ,			__lowerCamelCase:		Dict  ):







	'''simple docstring'''



	lowercase_			 = 0 if char in table[0] else 1
	lowercase_			 = table[row].index(lowerCAmelCase_  )
	return row, col




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		Union[str, Any]  ):







	'''simple docstring'''



	lowercase_ ,	lowercase_			 = get_position(lowerCAmelCase_  ,			char.upper()  )
	if row == 1:
		return table[0][col]
	else:
		return table[1][col] if row == 0 else char


if __name__ == "__main__":
							import doctest

							doctest.testmod()  # Fist ensure that all our tests are passing...
							SCREAMING_SNAKE_CASE__       			=				input("""Enter key: """).strip()
							SCREAMING_SNAKE_CASE__       			=				input("""Enter text to encrypt: """).strip()
							SCREAMING_SNAKE_CASE__       			=				encrypt(key, text)

							print(f"""Encrypted: {cipher_text}""")
							print(f"""Decrypted with key: {decrypt(key, cipher_text)}""")






'''simple docstring'''







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	):






   '''simple docstring'''

   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=5_12  ,    __UpperCamelCase=16  ,    __UpperCamelCase=2  ,    __UpperCamelCase=0.02  ,    __UpperCamelCase=4  ,    ):
       UpperCamelCase__	     =      parent
       UpperCamelCase__	     =      batch_size
       UpperCamelCase__	     =      seq_length
       UpperCamelCase__	     =      is_training
       UpperCamelCase__	     =      use_attention_mask
       UpperCamelCase__	     =      use_token_type_ids
       UpperCamelCase__	     =      use_labels
       UpperCamelCase__	     =      vocab_size
       UpperCamelCase__	     =      hidden_size
       UpperCamelCase__	     =      embedding_size
       UpperCamelCase__	     =      num_hidden_layers
       UpperCamelCase__	     =      num_attention_heads
       UpperCamelCase__	     =      intermediate_size
       UpperCamelCase__	     =      hidden_act
       UpperCamelCase__	     =      hidden_dropout_prob
       UpperCamelCase__	     =      attention_probs_dropout_prob
       UpperCamelCase__	     =      max_position_embeddings
       UpperCamelCase__	     =      type_vocab_size
       UpperCamelCase__	     =      type_sequence_label_size
       UpperCamelCase__	     =      initializer_range
       UpperCamelCase__	     =      num_choices


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size			)

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

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

       UpperCamelCase__	     =      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 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.prepare_config_and_inputs()
       UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__	     =      config_and_inputs
       UpperCamelCase__	     =      {"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	):






   '''simple docstring'''

   A			:  Dict					=						True

   A			:  Optional[Any]					=						(
       (
           FlaxElectraModel,
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForPreTraining,
           FlaxElectraForTokenClassification,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForMultipleChoice,
           FlaxElectraForSequenceClassification,
       )
       if is_flax_available()
       else ()
   )


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      FlaxElectraModelTester(self			)







   @slow
   def 						_UpperCamelCase							(	self			):
       for model_class_name in self.all_model_classes:
           if model_class_name == FlaxElectraForMaskedLM:
               UpperCamelCase__	     =      model_class_name.from_pretrained("google/electra-small-generator"			)
           else:
               UpperCamelCase__	     =      model_class_name.from_pretrained("google/electra-small-discriminator"			)
           UpperCamelCase__	     =      model(np.ones((1, 1)			)			)
           self.assertIsNotNone(__UpperCamelCase			)




