code: 
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> str:
  
  
  
  				'''simple docstring'''
  
  
  
  
  
  
  				if not isinstance(_UpperCAmelCase,      _UpperCAmelCase				):
  								raise TypeError('plaintext must be a string'				)
  				if not isinstance(_UpperCAmelCase,      _UpperCAmelCase				):
  								raise TypeError('key must be a string'				)
  
  				if not plaintext:
  								raise ValueError('plaintext is empty'				)
  				if not key:
  								raise ValueError('key is empty'				)
  
  				key += plaintext
  				lowerCAmelCase				:			List[str]    			=      plaintext.lower()
  				lowerCAmelCase				:			Tuple    			=      key.lower()
  				lowerCAmelCase				:			Optional[Any]    			=      0
  				lowerCAmelCase				:			List[Any]    			=      0
  				lowerCAmelCase				:			List[Any]    			=      ''
  				while plaintext_iterator < len(_UpperCAmelCase				):
  								if (
  								    ord(plaintext[plaintext_iterator]				) < 97
  								    or ord(plaintext[plaintext_iterator]				) > 122
  								):
  												ciphertext += plaintext[plaintext_iterator]
  												plaintext_iterator += 1
  								elif ord(key[key_iterator]				) < 97 or ord(key[key_iterator]				) > 122:
  												key_iterator += 1
  								else:
  												ciphertext += chr(
  												    (
  												        (ord(plaintext[plaintext_iterator]				) - 97 + ord(key[key_iterator]				))
  												        - 97
  												    )
  												    % 26
  												    + 97				)
  												key_iterator += 1
  												plaintext_iterator += 1
  				return ciphertext
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> str:
  
  
  
  				'''simple docstring'''
  
  
  
  
  
  
  				if not isinstance(_UpperCAmelCase,      _UpperCAmelCase				):
  								raise TypeError('ciphertext must be a string'				)
  				if not isinstance(_UpperCAmelCase,      _UpperCAmelCase				):
  								raise TypeError('key must be a string'				)
  
  				if not ciphertext:
  								raise ValueError('ciphertext is empty'				)
  				if not key:
  								raise ValueError('key is empty'				)
  
  				lowerCAmelCase				:			List[Any]    			=      key.lower()
  				lowerCAmelCase				:			Any    			=      0
  				lowerCAmelCase				:			Optional[Any]    			=      0
  				lowerCAmelCase				:			Tuple    			=      ''
  				while ciphertext_iterator < len(_UpperCAmelCase				):
  								if (
  								    ord(ciphertext[ciphertext_iterator]				) < 97
  								    or ord(ciphertext[ciphertext_iterator]				) > 122
  								):
  												plaintext += ciphertext[ciphertext_iterator]
  								else:
  												plaintext += chr(
  												    (ord(ciphertext[ciphertext_iterator]				) - ord(key[key_iterator]				)) % 26
  												    + 97				)
  												key += chr(
  												    (ord(ciphertext[ciphertext_iterator]				) - ord(key[key_iterator]				)) % 26
  												    + 97				)
  												key_iterator += 1
  								ciphertext_iterator += 1
  				return plaintext
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  	__A   :     Any      =			int(input('''Type 1 to encrypt or 2 to decrypt:'''))
  	if operation == 1:
  		__A   :     Tuple      =			input('''Typeplaintext to be encrypted:\n''')
  		__A   :     str      =			input('''Type the key:\n''')
  		print(encrypt(plaintext, key))
  	elif operation == 2:
  		__A   :     List[str]      =			input('''Type the ciphertext to be decrypted:\n''')
  		__A   :     List[str]      =			input('''Type the key:\n''')
  		print(decrypt(ciphertext, key))
  	decrypt('''jsqqs avvwo''', '''coffee''')
  
  
  
  
  
  
style_context: 
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		List[str]						=							(KDPMaAncestralDiscreteScheduler,)
      lowerCAmelCase_							:		Dict						=							10
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ,				**UpperCAmelCase_			:    List[Any]    ):
          lowerCAmelCase				:			int    			=      {
              'num_train_timesteps': 1100,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
          }
  
          config.update(**UpperCAmelCase_    )
          return config
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ):
          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]    ,				[0.00_02, 0.0_02, 0.02]    ):
              self.check_over_configs(beta_start=UpperCAmelCase_    ,				beta_end=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          if torch_device == "mps":
              return
          lowerCAmelCase				:			Any    			=      self.scheduler_classes[0]
          lowerCAmelCase				:			Optional[int]    			=      self.get_scheduler_config()
          lowerCAmelCase				:			List[str]    			=      scheduler_class(**UpperCAmelCase_    )
  
          scheduler.set_timesteps(self.num_inference_steps    )
  
          lowerCAmelCase				:			Any    			=      torch.manual_seed(0    )
  
          lowerCAmelCase				:			List[Any]    			=      self.dummy_model()
          lowerCAmelCase				:			List[Any]    			=      self.dummy_sample_deter * scheduler.init_noise_sigma
          lowerCAmelCase				:			Optional[int]    			=      sample.to(UpperCAmelCase_    )
  
          for i, t in enumerate(scheduler.timesteps    ):
              lowerCAmelCase				:			str    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Any    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Optional[Any]    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
              lowerCAmelCase				:			Union[str, Any]    			=      output.prev_sample
  
          lowerCAmelCase				:			List[Any]    			=      torch.sum(torch.abs(UpperCAmelCase_    )    )
          lowerCAmelCase				:			Any    			=      torch.mean(torch.abs(UpperCAmelCase_    )    )
  
          assert abs(result_sum.item() - 1_38_49.38_77    ) < 1E-2
          assert abs(result_mean.item() - 18.03_31    ) < 5E-3
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          if torch_device == "mps":
              return
          lowerCAmelCase				:			Optional[Any]    			=      self.scheduler_classes[0]
          lowerCAmelCase				:			List[Any]    			=      self.get_scheduler_config(prediction_type='v_prediction'    )
          lowerCAmelCase				:			Any    			=      scheduler_class(**UpperCAmelCase_    )
  
          scheduler.set_timesteps(self.num_inference_steps    )
  
          lowerCAmelCase				:			Optional[int]    			=      self.dummy_model()
          lowerCAmelCase				:			Any    			=      self.dummy_sample_deter * scheduler.init_noise_sigma
          lowerCAmelCase				:			Optional[int]    			=      sample.to(UpperCAmelCase_    )
  
          lowerCAmelCase				:			Optional[int]    			=      torch.manual_seed(0    )
  
          for i, t in enumerate(scheduler.timesteps    ):
              lowerCAmelCase				:			Dict    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Optional[int]    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Optional[int]    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
              lowerCAmelCase				:			str    			=      output.prev_sample
  
          lowerCAmelCase				:			str    			=      torch.sum(torch.abs(UpperCAmelCase_    )    )
          lowerCAmelCase				:			Tuple    			=      torch.mean(torch.abs(UpperCAmelCase_    )    )
  
          assert abs(result_sum.item() - 3_28.99_70    ) < 1E-2
          assert abs(result_mean.item() - 0.42_84    ) < 1E-3
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          if torch_device == "mps":
              return
          lowerCAmelCase				:			str    			=      self.scheduler_classes[0]
          lowerCAmelCase				:			Dict    			=      self.get_scheduler_config()
          lowerCAmelCase				:			List[str]    			=      scheduler_class(**UpperCAmelCase_    )
  
          scheduler.set_timesteps(self.num_inference_steps    ,				device=UpperCAmelCase_    )
          lowerCAmelCase				:			int    			=      torch.manual_seed(0    )
  
          lowerCAmelCase				:			Tuple    			=      self.dummy_model()
          lowerCAmelCase				:			Optional[Any]    			=      self.dummy_sample_deter.to(UpperCAmelCase_    ) * scheduler.init_noise_sigma
  
          for t in scheduler.timesteps:
              lowerCAmelCase				:			str    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			List[Any]    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Dict    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
              lowerCAmelCase				:			Dict    			=      output.prev_sample
  
          lowerCAmelCase				:			Union[str, Any]    			=      torch.sum(torch.abs(UpperCAmelCase_    )    )
          lowerCAmelCase				:			str    			=      torch.mean(torch.abs(UpperCAmelCase_    )    )
  
          assert abs(result_sum.item() - 1_38_49.38_18    ) < 1E-1
          assert abs(result_mean.item() - 18.03_31    ) < 1E-3
  
  
  
  
  
  