





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Union[str, Any]						    =   (EulerDiscreteScheduler,)
      _UpperCAmelCase	:Optional[Any]						    =   1_0





      def        __UpperCamelCase( self       ,							**A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   {
           "num_train_timesteps": 1100,
           "beta_start": 0.00_01,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**A_   )
       return config





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for timesteps in [10, 50, 100, 1000]:
        self.check_over_configs(num_train_timesteps=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       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=A_       ,							beta_end=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self.scheduler_classes[0]
       UpperCamelCase	:					Dict       	=   self.get_scheduler_config()
       UpperCamelCase	:					str       	=   scheduler_class(**A_   )

       scheduler.set_timesteps(self.num_inference_steps   )

       UpperCamelCase	:					List[str]       	=   torch.manual_seed(0   )

       UpperCamelCase	:					int       	=   self.dummy_model()
       UpperCamelCase	:					Any       	=   self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCamelCase	:					int       	=   sample.to(A_   )

       for i, t in enumerate(scheduler.timesteps   ):
        UpperCamelCase	:					Dict       	=   scheduler.scale_model_input(A_       ,							A_   )

        UpperCamelCase	:					Optional[Any]       	=   model(A_       ,							A_   )

        UpperCamelCase	:					Union[str, Any]       	=   scheduler.step(A_       ,							A_       ,							A_       ,							generator=A_   )
        UpperCamelCase	:					Any       	=   output.prev_sample

       UpperCamelCase	:					Optional[int]       	=   torch.sum(torch.abs(A_   )   )
       UpperCamelCase	:					List[Any]       	=   torch.mean(torch.abs(A_   )   )

       assert abs(result_sum.item() - 10.08_07   ) < 1e-2
       assert abs(result_mean.item() - 0.01_31   ) < 1e-3





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.scheduler_classes[0]
       UpperCamelCase	:					Optional[int]       	=   self.get_scheduler_config(prediction_type="v_prediction"   )
       UpperCamelCase	:					Dict       	=   scheduler_class(**A_   )

       scheduler.set_timesteps(self.num_inference_steps   )

       UpperCamelCase	:					Any       	=   torch.manual_seed(0   )

       UpperCamelCase	:					Tuple       	=   self.dummy_model()
       UpperCamelCase	:					Any       	=   self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCamelCase	:					Dict       	=   sample.to(A_   )

       for i, t in enumerate(scheduler.timesteps   ):
        UpperCamelCase	:					Any       	=   scheduler.scale_model_input(A_       ,							A_   )

        UpperCamelCase	:					List[Any]       	=   model(A_       ,							A_   )

        UpperCamelCase	:					Optional[int]       	=   scheduler.step(A_       ,							A_       ,							A_       ,							generator=A_   )
        UpperCamelCase	:					List[Any]       	=   output.prev_sample

       UpperCamelCase	:					List[str]       	=   torch.sum(torch.abs(A_   )   )
       UpperCamelCase	:					Tuple       	=   torch.mean(torch.abs(A_   )   )

       assert abs(result_sum.item() - 0.00_02   ) < 1e-2
       assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   self.scheduler_classes[0]
       UpperCamelCase	:					Optional[Any]       	=   self.get_scheduler_config()
       UpperCamelCase	:					List[Any]       	=   scheduler_class(**A_   )

       scheduler.set_timesteps(self.num_inference_steps       ,							device=A_   )

       UpperCamelCase	:					Optional[Any]       	=   torch.manual_seed(0   )

       UpperCamelCase	:					List[str]       	=   self.dummy_model()
       UpperCamelCase	:					List[Any]       	=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCamelCase	:					str       	=   sample.to(A_   )

       for t in scheduler.timesteps:
        UpperCamelCase	:					Dict       	=   scheduler.scale_model_input(A_       ,							A_   )

        UpperCamelCase	:					List[Any]       	=   model(A_       ,							A_   )

        UpperCamelCase	:					Optional[int]       	=   scheduler.step(A_       ,							A_       ,							A_       ,							generator=A_   )
        UpperCamelCase	:					Dict       	=   output.prev_sample

       UpperCamelCase	:					Tuple       	=   torch.sum(torch.abs(A_   )   )
       UpperCamelCase	:					List[str]       	=   torch.mean(torch.abs(A_   )   )

       assert abs(result_sum.item() - 10.08_07   ) < 1e-2
       assert abs(result_mean.item() - 0.01_31   ) < 1e-3





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.scheduler_classes[0]
       UpperCamelCase	:					Tuple       	=   self.get_scheduler_config()
       UpperCamelCase	:					Optional[int]       	=   scheduler_class(**A_       ,							use_karras_sigmas=A_   )

       scheduler.set_timesteps(self.num_inference_steps       ,							device=A_   )

       UpperCamelCase	:					Any       	=   torch.manual_seed(0   )

       UpperCamelCase	:					str       	=   self.dummy_model()
       UpperCamelCase	:					Dict       	=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCamelCase	:					Union[str, Any]       	=   sample.to(A_   )

       for t in scheduler.timesteps:
        UpperCamelCase	:					Any       	=   scheduler.scale_model_input(A_       ,							A_   )

        UpperCamelCase	:					List[Any]       	=   model(A_       ,							A_   )

        UpperCamelCase	:					Union[str, Any]       	=   scheduler.step(A_       ,							A_       ,							A_       ,							generator=A_   )
        UpperCamelCase	:					str       	=   output.prev_sample

       UpperCamelCase	:					Optional[Any]       	=   torch.sum(torch.abs(A_   )   )
       UpperCamelCase	:					List[str]       	=   torch.mean(torch.abs(A_   )   )

       assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19   ) < 1e-2
       assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63   ) < 1e-3







def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					List[Any]       	=   ""
 for ch in key:
  if ch == " " or ch not in key_no_dups and ch.isalpha():
   key_no_dups += ch
 return key_no_dups



def 		A_				(     _lowerCAmelCase     )						-> dict[str, str]:
 UpperCamelCase	:					Optional[Any]       	=   [chr(i + 65     ) for i in range(26     )]
 # Remove duplicate characters from key
 UpperCamelCase	:					Tuple       	=   remove_duplicates(key.upper()     )
 UpperCamelCase	:					int       	=   len(_lowerCAmelCase     )
 # First fill cipher with key characters
 UpperCamelCase	:					int       	=   {alphabet[i]: char for i, char in enumerate(_lowerCAmelCase     )}
 # Then map remaining characters in alphabet to
 # the alphabet from the beginning
 for i in range(len(_lowerCAmelCase     )							,   26     ):
  UpperCamelCase	:					Optional[Any]       	=   alphabet[i - offset]
  # Ensure we are not mapping letters to letters previously mapped
  while char in key:
   offset -= 1
   UpperCamelCase	:					List[str]       	=   alphabet[i - offset]
  UpperCamelCase	:					List[Any]       	=   char
 return cipher_alphabet



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 return "".join(cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Union[str, Any]       	=   {v: k for k, v in cipher_map.items()}
 return "".join(rev_cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     )						-> None:
 UpperCamelCase	:					int       	=   input("Enter message to encode or decode: "     ).strip()
 UpperCamelCase	:					str       	=   input("Enter keyword: "     ).strip()
 UpperCamelCase	:					Union[str, Any]       	=   input("Encipher or decipher? E/D:"     ).strip()[0].lower()
 try:
  UpperCamelCase	:					List[str]       	=   {"e": encipher, "d": decipher}[option]
 except KeyError:
  raise KeyError("invalid input option"     )
 UpperCamelCase	:					str       	=   create_cipher_map(_lowerCAmelCase     )
 print(func(_lowerCAmelCase							,   _lowerCAmelCase     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
