



'''simple docstring'''





import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							lowerCAmelCase_		(	a_						):
    __UpperCAmelCase							=	(LMSDiscreteScheduler,)
    __UpperCAmelCase							=	10





    def 					__snake_case       (    self    :    Optional[Any],    **_snake_case    :    Any     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  ={
           '''num_train_timesteps''': 1_100,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_snake_case     )
       return config





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       for timesteps in [10, 50, 100, 1_000]:
          self.check_over_configs(num_train_timesteps=_snake_case     )





    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001],    [0.0002, 0.002, 0.02]     ):
          self.check_over_configs(beta_start=_snake_case,    beta_end=_snake_case     )





    def 					__snake_case       (    self    :    List[str]     ):



       '''simple docstring'''







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





    def 					__snake_case       (    self    :    Dict     ):



       '''simple docstring'''







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





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       for t in [0, 500, 800]:
          self.check_over_forward(time_step=_snake_case     )





    def 					__snake_case       (    self    :    Tuple     ):



       '''simple docstring'''







       snake_case   :							Dict						  =self.scheduler_classes[0]
       snake_case   :							str						  =self.get_scheduler_config()
       snake_case   :							Union[str, Any]						  =scheduler_class(**_snake_case     )

       scheduler.set_timesteps(self.num_inference_steps     )

       snake_case   :							Dict						  =self.dummy_model()
       snake_case   :							Dict						  =self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(scheduler.timesteps     ):
          snake_case   :							int						  =scheduler.scale_model_input(_snake_case,    _snake_case     )

          snake_case   :							List[Any]						  =model(_snake_case,    _snake_case     )

          snake_case   :							str						  =scheduler.step(_snake_case,    _snake_case,    _snake_case     )
          snake_case   :							List[Any]						  =output.prev_sample

       snake_case   :							int						  =torch.sum(torch.abs(_snake_case     )     )
       snake_case   :							Dict						  =torch.mean(torch.abs(_snake_case     )     )

       assert abs(result_sum.item() - 1006.388     ) < 1E-2
       assert abs(result_mean.item() - 1.31     ) < 1E-3





    def 					__snake_case       (    self    :    List[str]     ):



       '''simple docstring'''







       snake_case   :							str						  =self.scheduler_classes[0]
       snake_case   :							int						  =self.get_scheduler_config(prediction_type='''v_prediction'''     )
       snake_case   :							List[Any]						  =scheduler_class(**_snake_case     )

       scheduler.set_timesteps(self.num_inference_steps     )

       snake_case   :							List[str]						  =self.dummy_model()
       snake_case   :							int						  =self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(scheduler.timesteps     ):
          snake_case   :							Optional[int]						  =scheduler.scale_model_input(_snake_case,    _snake_case     )

          snake_case   :							Optional[int]						  =model(_snake_case,    _snake_case     )

          snake_case   :							Tuple						  =scheduler.step(_snake_case,    _snake_case,    _snake_case     )
          snake_case   :							Optional[int]						  =output.prev_sample

       snake_case   :							Dict						  =torch.sum(torch.abs(_snake_case     )     )
       snake_case   :							Optional[int]						  =torch.mean(torch.abs(_snake_case     )     )

       assert abs(result_sum.item() - 0.0017     ) < 1E-2
       assert abs(result_mean.item() - 2.2676E-06     ) < 1E-3





    def 					__snake_case       (    self    :    Tuple     ):



       '''simple docstring'''







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

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

       snake_case   :							str						  =self.dummy_model()
       snake_case   :							Dict						  =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       snake_case   :							Optional[int]						  =sample.to(_snake_case     )

       for i, t in enumerate(scheduler.timesteps     ):
          snake_case   :							Optional[Any]						  =scheduler.scale_model_input(_snake_case,    _snake_case     )

          snake_case   :							Any						  =model(_snake_case,    _snake_case     )

          snake_case   :							Optional[Any]						  =scheduler.step(_snake_case,    _snake_case,    _snake_case     )
          snake_case   :							Dict						  =output.prev_sample

       snake_case   :							Tuple						  =torch.sum(torch.abs(_snake_case     )     )
       snake_case   :							Any						  =torch.mean(torch.abs(_snake_case     )     )

       assert abs(result_sum.item() - 1006.388     ) < 1E-2
       assert abs(result_mean.item() - 1.31     ) < 1E-3






    def 					__snake_case       (    self    :    List[str]     ):



       '''simple docstring'''







       snake_case   :							Any						  =self.scheduler_classes[0]
       snake_case   :							List[str]						  =self.get_scheduler_config()
       snake_case   :							List[Any]						  =scheduler_class(**_snake_case,    use_karras_sigmas=_snake_case     )

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

       snake_case   :							Union[str, Any]						  =self.dummy_model()
       snake_case   :							Optional[Any]						  =self.dummy_sample_deter.to(_snake_case     ) * scheduler.init_noise_sigma
       snake_case   :							List[Any]						  =sample.to(_snake_case     )

       for t in scheduler.timesteps:
          snake_case   :							Optional[Any]						  =scheduler.scale_model_input(_snake_case,    _snake_case     )

          snake_case   :							List[str]						  =model(_snake_case,    _snake_case     )

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

       snake_case   :							Optional[Any]						  =torch.sum(torch.abs(_snake_case     )     )
       snake_case   :							str						  =torch.mean(torch.abs(_snake_case     )     )

       assert abs(result_sum.item() - 3812.9927     ) < 2E-2
       assert abs(result_mean.item() - 4.9648     ) < 1E-3










'''simple docstring'''





from __future__ import annotations

import random
import string





class 							lowerCAmelCase_		:





    def __init__(    self    :    Union[str, Any],    _snake_case    :    str | None = None     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =passcode or self.__passcode_creator()
       snake_case   :							List[str]						  =self.__make_key_list()
       snake_case   :							Dict						  =self.__make_shift_key()





    def __str__(    self    :    List[Any]     ):



       '''simple docstring'''







       return "".join(self.__passcode     )





    def 					__snake_case       (    self    :    int,    _snake_case    :    list[int]     ):



       '''simple docstring'''







       for i in range(1,    len(_snake_case     ),    2     ):
          iterlist[i] *= -1
       return iterlist





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							int						  =string.ascii_letters + string.digits
       snake_case   :							Union[str, Any]						  =[random.choice(_snake_case     ) for _ in range(random.randint(10,    20     )     )]
       return password





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							Dict						  =(
           string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
       )

       snake_case   :							Any						  =[]

       # creates points known as breakpoints to break the key_list_options at those
       # points and pivot each substring
       snake_case   :							Optional[int]						  =sorted(set(self.__passcode     )     )
       snake_case   :							list[str]						  =[]

       # algorithm for creating a new shuffled list, keys_l, out of key_list_options
       for i in key_list_options:
          temp_list.extend(_snake_case     )

          # checking breakpoints at which to pivot temporary sublist and add it into
          # keys_l
          if i in breakpoints or i == key_list_options[-1]:
             keys_l.extend(temp_list[::-1]     )
             temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
       return keys_l





    def 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       snake_case   :							str						  =sum(self.__neg_pos([ord(_snake_case     ) for x in self.__passcode]     )     )
       return num if num > 0 else len(self.__passcode     )





    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =''''''

       # decoding shift like Caesar cipher algorithm implementing negative shift or
       # reverse shift or left shift
       for i in encoded_message:
          snake_case   :							Optional[int]						  =self.__key_list.index(_snake_case     )
          decoded_message += self.__key_list[
              (position - self.__shift_key) % -len(self.__key_list     )
          ]

       return decoded_message






    def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							int						  =''''''

       # encoding shift like Caesar cipher algorithm implementing positive shift or
       # forward shift or right shift
       for i in plaintext:
          snake_case   :							int						  =self.__key_list.index(_snake_case     )
          encoded_message += self.__key_list[
              (position + self.__shift_key) % len(self.__key_list     )
          ]

       return encoded_message




def 		_a   (  lowerCamelCase_ = "Hello, this is a modified Caesar cipher"							):
   snake_case   :							List[Any]						  =ShuffledShiftCipher()
   return cipa.decrypt(cipa.encrypt(lowerCamelCase_							)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





