





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							(CMStochasticIterativeScheduler,)
      a						       =							10
      def 		_lowerCamelCase      (		self      ,       **a__       ):
         A_     :				Optional[int]			      =	{
             """num_train_timesteps""": 201,
             """sigma_min""": 0.002,
             """sigma_max""": 80.0,
         }

         config.update(**a__       )
         return config
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	10

         A_     :				Optional[int]			      =	self.get_scheduler_config()
         A_     :				str			      =	self.scheduler_classes[0](**a__       )

         scheduler.set_timesteps(a__       )

         A_     :				Optional[int]			      =	scheduler.timesteps[0]
         A_     :				Optional[Any]			      =	scheduler.timesteps[1]

         A_     :				Optional[Any]			      =	self.dummy_sample
         A_     :				List[str]			      =	0.1 * sample

         A_     :				List[str]			      =	scheduler.step(a__      ,       a__      ,       a__       ).prev_sample
         A_     :				List[str]			      =	scheduler.step(a__      ,       a__      ,       a__       ).prev_sample

         self.assertEqual(output_a.shape      ,       sample.shape       )
         self.assertEqual(output_a.shape      ,       output_a.shape       )
      def 		_lowerCamelCase      (		self       ):
         for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=a__       )
      def 		_lowerCamelCase      (		self       ):
         for clip_denoised in [True, False]:
            self.check_over_configs(clip_denoised=a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	self.scheduler_classes[0]
         A_     :				Dict			      =	self.get_scheduler_config()
         A_     :				Optional[Any]			      =	scheduler_class(**a__       )

         A_     :				List[str]			      =	1
         scheduler.set_timesteps(a__       )
         A_     :				int			      =	scheduler.timesteps

         A_     :				List[Any]			      =	torch.manual_seed(0       )

         A_     :				Optional[int]			      =	self.dummy_model()
         A_     :				List[Any]			      =	self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(a__       ):
            # 1. scale model input
            A_     :				Tuple			      =	scheduler.scale_model_input(a__      ,       a__       )

            # 2. predict noise residual
            A_     :				Tuple			      =	model(a__      ,       a__       )

            # 3. predict previous sample x_t-1
            A_     :				Tuple			      =	scheduler.step(a__      ,       a__      ,       a__      ,       generator=a__       ).prev_sample

            A_     :				Optional[int]			      =	pred_prev_sample

         A_     :				Optional[int]			      =	torch.sum(torch.abs(a__       )       )
         A_     :				Tuple			      =	torch.mean(torch.abs(a__       )       )

         assert abs(result_sum.item() - 192.7614       ) < 1E-2
         assert abs(result_mean.item() - 0.2510       ) < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	self.scheduler_classes[0]
         A_     :				int			      =	self.get_scheduler_config()
         A_     :				Union[str, Any]			      =	scheduler_class(**a__       )

         A_     :				Union[str, Any]			      =	[106, 0]
         scheduler.set_timesteps(timesteps=a__       )
         A_     :				List[Any]			      =	scheduler.timesteps

         A_     :				str			      =	torch.manual_seed(0       )

         A_     :				Optional[Any]			      =	self.dummy_model()
         A_     :				List[str]			      =	self.dummy_sample_deter * scheduler.init_noise_sigma

         for t in timesteps:
            # 1. scale model input
            A_     :				Dict			      =	scheduler.scale_model_input(a__      ,       a__       )

            # 2. predict noise residual
            A_     :				Tuple			      =	model(a__      ,       a__       )

            # 3. predict previous sample x_t-1
            A_     :				Union[str, Any]			      =	scheduler.step(a__      ,       a__      ,       a__      ,       generator=a__       ).prev_sample

            A_     :				Optional[Any]			      =	pred_prev_sample

         A_     :				Optional[int]			      =	torch.sum(torch.abs(a__       )       )
         A_     :				int			      =	torch.mean(torch.abs(a__       )       )

         assert abs(result_sum.item() - 347.6357       ) < 1E-2
         assert abs(result_mean.item() - 0.4527       ) < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	self.scheduler_classes[0]
         A_     :				Tuple			      =	self.get_scheduler_config()
         A_     :				Any			      =	scheduler_class(**a__       )

         A_     :				Tuple			      =	[39, 30, 12, 15, 0]

         with self.assertRaises(a__      ,       msg="""`timesteps` must be in descending order."""       ):
            scheduler.set_timesteps(timesteps=a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	self.scheduler_classes[0]
         A_     :				Optional[Any]			      =	self.get_scheduler_config()
         A_     :				Tuple			      =	scheduler_class(**a__       )

         A_     :				Any			      =	[39, 30, 12, 1, 0]
         A_     :				Optional[int]			      =	len(a__       )

         with self.assertRaises(a__      ,       msg="""Can only pass one of `num_inference_steps` or `timesteps`."""       ):
            scheduler.set_timesteps(num_inference_steps=a__      ,       timesteps=a__       )






      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	self.scheduler_classes[0]
         A_     :				Any			      =	self.get_scheduler_config()
         A_     :				List[Any]			      =	scheduler_class(**a__       )

         A_     :				str			      =	[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             a__      ,       msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""      ,       ):
            scheduler.set_timesteps(timesteps=a__       )













_lowerCAmelCase 				=     """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Any			      =	input("""Enter message: """     )
   A_     :				Dict			      =	input("""Enter key [alphanumeric]: """     )
   A_     :				Dict			      =	input("""Encrypt/Decrypt [e/d]: """     )

   if mode.lower().startswith("""e"""     ):
      A_     :				Union[str, Any]			      =	"""encrypt"""
      A_     :				Optional[int]			      =	encrypt_message(_lowerCAmelCase  ,_lowerCAmelCase     )
   elif mode.lower().startswith("""d"""     ):
      A_     :				str			      =	"""decrypt"""
      A_     :				Dict			      =	decrypt_message(_lowerCAmelCase  ,_lowerCAmelCase     )

   print(f"""\n{mode.title()}ed message:"""     )
   print(_lowerCAmelCase     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   return translate_message(_lowerCAmelCase  ,_lowerCAmelCase  ,"""encrypt"""     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   return translate_message(_lowerCAmelCase  ,_lowerCAmelCase  ,"""decrypt"""     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Dict			      =	[]
   A_     :				List[Any]			      =	0
   A_     :				int			      =	key.upper()

   for symbol in message:
      A_     :				Optional[int]			      =	LETTERS.find(symbol.upper()     )
      if num != -1:
         if mode == "encrypt":
            num += LETTERS.find(key[key_index]     )
         elif mode == "decrypt":
            num -= LETTERS.find(key[key_index]     )

         num %= len(_lowerCAmelCase     )

         if symbol.isupper():
            translated.append(LETTERS[num]     )
         elif symbol.islower():
            translated.append(LETTERS[num].lower()     )

         key_index += 1
         if key_index == len(_lowerCAmelCase     ):
            A_     :				Dict			      =	0
      else:
         translated.append(_lowerCAmelCase     )
   return "".join(_lowerCAmelCase     )


if __name__ == "__main__":
      main()






