


"""simple docstring"""



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Any							  =  (CMStochasticIterativeScheduler,)
   __UpperCAmelCase			:	int							  =  10







   def 			lowerCamelCase		(     self						,     **UpperCamelCase__						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				Dict				 =   {
           "num_train_timesteps": 201,
           "sigma_min": 0.002,
           "sigma_max": 80.0,
       }

       config.update(**UpperCamelCase__						)
       return config







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				int				 =   10

       snake_case   :				int				 =   self.get_scheduler_config()
       snake_case   :				List[str]				 =   self.scheduler_classes[0](**UpperCamelCase__						)

       scheduler.set_timesteps(UpperCamelCase__						)

       snake_case   :				Optional[Any]				 =   scheduler.timesteps[0]
       snake_case   :				Optional[Any]				 =   scheduler.timesteps[1]

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

       snake_case   :				str				 =   scheduler.step(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						).prev_sample
       snake_case   :				Union[str, Any]				 =   scheduler.step(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						).prev_sample

       self.assertEqual(output_a.shape						,     sample.shape						)
       self.assertEqual(output_a.shape						,     output_a.shape						)







   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




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







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       for clip_denoised in [True, False]:
           self.check_over_configs(clip_denoised=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   self.scheduler_classes[0]
       snake_case   :				Tuple				 =   self.get_scheduler_config()
       snake_case   :				int				 =   scheduler_class(**UpperCamelCase__						)

       snake_case   :				int				 =   1
       scheduler.set_timesteps(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   scheduler.timesteps

       snake_case   :				Union[str, Any]				 =   torch.manual_seed(0						)

       snake_case   :				Optional[Any]				 =   self.dummy_model()
       snake_case   :				Any				 =   self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(UpperCamelCase__						):
           # 1. scale model input
           snake_case   :				List[Any]				 =   scheduler.scale_model_input(UpperCamelCase__						,     UpperCamelCase__						)

           # 2. predict noise residual
           snake_case   :				Any				 =   model(UpperCamelCase__						,     UpperCamelCase__						)

           # 3. predict previous sample x_t-1
           snake_case   :				Optional[Any]				 =   scheduler.step(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     generator=UpperCamelCase__						).prev_sample

           snake_case   :				Any				 =   pred_prev_sample

       snake_case   :				Union[str, Any]				 =   torch.sum(torch.abs(UpperCamelCase__						)						)
       snake_case   :				Union[str, Any]				 =   torch.mean(torch.abs(UpperCamelCase__						)						)

       assert abs(result_sum.item() - 192.7614						) < 1e-2
       assert abs(result_mean.item() - 0.2510						) < 1e-3







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				List[str]				 =   self.scheduler_classes[0]
       snake_case   :				Tuple				 =   self.get_scheduler_config()
       snake_case   :				List[Any]				 =   scheduler_class(**UpperCamelCase__						)

       snake_case   :				Dict				 =   [106, 0]
       scheduler.set_timesteps(timesteps=UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   scheduler.timesteps

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

       snake_case   :				Any				 =   self.dummy_model()
       snake_case   :				Union[str, Any]				 =   self.dummy_sample_deter * scheduler.init_noise_sigma

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

           # 2. predict noise residual
           snake_case   :				Any				 =   model(UpperCamelCase__						,     UpperCamelCase__						)

           # 3. predict previous sample x_t-1
           snake_case   :				Dict				 =   scheduler.step(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     generator=UpperCamelCase__						).prev_sample

           snake_case   :				Optional[Any]				 =   pred_prev_sample

       snake_case   :				Union[str, Any]				 =   torch.sum(torch.abs(UpperCamelCase__						)						)
       snake_case   :				Dict				 =   torch.mean(torch.abs(UpperCamelCase__						)						)

       assert abs(result_sum.item() - 347.6357						) < 1e-2
       assert abs(result_mean.item() - 0.4527						) < 1e-3







   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Any				 =   self.scheduler_classes[0]
       snake_case   :				str				 =   self.get_scheduler_config()
       snake_case   :				Tuple				 =   scheduler_class(**UpperCamelCase__						)

       snake_case   :				Dict				 =   [39, 30, 12, 15, 0]

       with self.assertRaises(UpperCamelCase__						,     msg="`timesteps` must be in descending order."						):
           scheduler.set_timesteps(timesteps=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Tuple				 =   self.scheduler_classes[0]
       snake_case   :				Union[str, Any]				 =   self.get_scheduler_config()
       snake_case   :				Optional[Any]				 =   scheduler_class(**UpperCamelCase__						)

       snake_case   :				Tuple				 =   [39, 30, 12, 1, 0]
       snake_case   :				str				 =   len(UpperCamelCase__						)

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







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   self.scheduler_classes[0]
       snake_case   :				int				 =   self.get_scheduler_config()
       snake_case   :				Any				 =   scheduler_class(**UpperCamelCase__						)

       snake_case   :				List[str]				 =   [scheduler.config.num_train_timesteps]

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









"""simple docstring"""



import string







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						str:


    """simple docstring"""





    snake_case   :				List[str]				 =   ""
    for i in sequence:
        snake_case   :				Optional[Any]				 =   ord(lowercase			)
        if 65 <= extract <= 90:
            output += chr(155 - extract			)
        elif 97 <= extract <= 122:
            output += chr(219 - extract			)
        else:
            output += i
    return output







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						str:


    """simple docstring"""





    snake_case   :				Dict				 =   string.ascii_letters
    snake_case   :				List[Any]				 =   string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
    return "".join(
        letters_reversed[letters.index(lowercase			)] if c in letters else c for c in sequence			)







def   __lowerCAmelCase						(						)     ->						None:


    """simple docstring"""





    from timeit import timeit

    print("Running performance benchmarks..."			)
    snake_case   :				Optional[int]				 =   "from string import printable ; from __main__ import atbash, atbash_slow"
    print(F'> atbash_slow(): {timeit("atbash_slow(printable)"						,							setup=lowercase			)} seconds'			)
    print(F'>      atbash(): {timeit("atbash(printable)"						,							setup=lowercase			)} seconds'			)


if __name__ == "__main__":
  for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
    print(F'''{example} encrypted in atbash: {atbash(example)}''')
  benchmark()





