

'''simple docstring'''

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest




@require_torchsde
class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  (DPMSolverSDEScheduler,)
     UpperCAmelCase_						:     int  		=  10


     def        UpperCAmelCase_	(		self			:					Tuple					,      **lowercase_			:					Tuple	)						->     List[Any]:
      UpperCAmelCase	:     str						 =			{
          'num_train_timesteps': 1_100,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'noise_sampler_seed': 0,
      }

      config.update(**lowerCamelCase_	)
      return config


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     str:

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


     def        UpperCAmelCase_	(		self			:					Any	)						->     int:

      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=lowerCamelCase_					,      beta_end=lowerCamelCase_	)


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     List[Any]:

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


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     int:

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


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Dict:
      UpperCAmelCase	:     List[str]						 =			self.scheduler_classes[0]
      UpperCAmelCase	:     str						 =			self.get_scheduler_config()
      UpperCAmelCase	:     Dict						 =			scheduler_class(**lowerCamelCase_	)

      scheduler.set_timesteps(self.num_inference_steps	)

      UpperCAmelCase	:     List[str]						 =			self.dummy_model()
      UpperCAmelCase	:     Optional[Any]						 =			self.dummy_sample_deter * scheduler.init_noise_sigma
      UpperCAmelCase	:     int						 =			sample.to(lowerCamelCase_	)

      for i, t in enumerate(scheduler.timesteps	):
       UpperCAmelCase	:     Union[str, Any]						 =			scheduler.scale_model_input(lowerCamelCase_					,      lowerCamelCase_	)

       UpperCAmelCase	:     Tuple						 =			model(lowerCamelCase_					,      lowerCamelCase_	)

       UpperCAmelCase	:     str						 =			scheduler.step(lowerCamelCase_					,      lowerCamelCase_					,      lowerCamelCase_	)
       UpperCAmelCase	:     Dict						 =			output.prev_sample

      UpperCAmelCase	:     Optional[Any]						 =			torch.sum(torch.abs(lowerCamelCase_	)	)
      UpperCAmelCase	:     str						 =			torch.mean(torch.abs(lowerCamelCase_	)	)

      if torch_device in ["mps"]:
       assert abs(result_sum.item() - 167.47_8210_4492_1875	) < 1E-2
       assert abs(result_mean.item() - 0.2178_7059_6456_5277	) < 1E-3
      elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 171.59_3521_1181_6406	) < 1E-2
       assert abs(result_mean.item() - 0.2_2342_9068_9229_9652	) < 1E-3
      else:
       assert abs(result_sum.item() - 162.52_3834_2285_1562	) < 1E-2
       assert abs(result_mean.item() - 0.211_6195_7085_1326	) < 1E-3


     def        UpperCAmelCase_	(		self			:					Any	)						->     int:
      UpperCAmelCase	:     int						 =			self.scheduler_classes[0]
      UpperCAmelCase	:     Dict						 =			self.get_scheduler_config(prediction_type='v_prediction'	)
      UpperCAmelCase	:     List[Any]						 =			scheduler_class(**lowerCamelCase_	)

      scheduler.set_timesteps(self.num_inference_steps	)

      UpperCAmelCase	:     Dict						 =			self.dummy_model()
      UpperCAmelCase	:     int						 =			self.dummy_sample_deter * scheduler.init_noise_sigma
      UpperCAmelCase	:     Union[str, Any]						 =			sample.to(lowerCamelCase_	)

      for i, t in enumerate(scheduler.timesteps	):
       UpperCAmelCase	:     Union[str, Any]						 =			scheduler.scale_model_input(lowerCamelCase_					,      lowerCamelCase_	)

       UpperCAmelCase	:     Tuple						 =			model(lowerCamelCase_					,      lowerCamelCase_	)

       UpperCAmelCase	:     int						 =			scheduler.step(lowerCamelCase_					,      lowerCamelCase_					,      lowerCamelCase_	)
       UpperCAmelCase	:     Any						 =			output.prev_sample

      UpperCAmelCase	:     str						 =			torch.sum(torch.abs(lowerCamelCase_	)	)
      UpperCAmelCase	:     Any						 =			torch.mean(torch.abs(lowerCamelCase_	)	)

      if torch_device in ["mps"]:
       assert abs(result_sum.item() - 124.77_1492_0043_9453	) < 1E-2
       assert abs(result_mean.item() - 0.1_6226_2890_1481_6284	) < 1E-3
      elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 128.1_6633_6059_5703	) < 1E-2
       assert abs(result_mean.item() - 0.1_6688_3260_0116_7297	) < 1E-3
      else:
       assert abs(result_sum.item() - 119.8_4875_4882_8125	) < 1E-2
       assert abs(result_mean.item() - 0.1560_5306_6253_6621	) < 1E-3


     def        UpperCAmelCase_	(		self			:					Any	)						->     Optional[int]:
      UpperCAmelCase	:     List[str]						 =			self.scheduler_classes[0]
      UpperCAmelCase	:     Optional[Any]						 =			self.get_scheduler_config()
      UpperCAmelCase	:     List[Any]						 =			scheduler_class(**lowerCamelCase_	)

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

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

      for t in scheduler.timesteps:
       UpperCAmelCase	:     int						 =			scheduler.scale_model_input(lowerCamelCase_					,      lowerCamelCase_	)

       UpperCAmelCase	:     Optional[Any]						 =			model(lowerCamelCase_					,      lowerCamelCase_	)

       UpperCAmelCase	:     Tuple						 =			scheduler.step(lowerCamelCase_					,      lowerCamelCase_					,      lowerCamelCase_	)
       UpperCAmelCase	:     Optional[int]						 =			output.prev_sample

      UpperCAmelCase	:     Optional[Any]						 =			torch.sum(torch.abs(lowerCamelCase_	)	)
      UpperCAmelCase	:     Dict						 =			torch.mean(torch.abs(lowerCamelCase_	)	)

      if torch_device in ["mps"]:
       assert abs(result_sum.item() - 167.46_9573_9746_0938	) < 1E-2
       assert abs(result_mean.item() - 0.2_1805_9346_0798_2635	) < 1E-3
      elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 171.59_3536_3769_5312	) < 1E-2
       assert abs(result_mean.item() - 0.2_2342_9083_8241_5771	) < 1E-3
      else:
       assert abs(result_sum.item() - 162.52_3834_2285_1562	) < 1E-2
       assert abs(result_mean.item() - 0.211_6195_7085_1326	) < 1E-3




     def        UpperCAmelCase_	(		self			:					int	)						->     List[Any]:
      UpperCAmelCase	:     Dict						 =			self.scheduler_classes[0]
      UpperCAmelCase	:     Dict						 =			self.get_scheduler_config()
      UpperCAmelCase	:     Union[str, Any]						 =			scheduler_class(**lowerCamelCase_					,      use_karras_sigmas=lowerCamelCase_	)

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

      UpperCAmelCase	:     Dict						 =			self.dummy_model()
      UpperCAmelCase	:     Optional[Any]						 =			self.dummy_sample_deter.to(lowerCamelCase_	) * scheduler.init_noise_sigma
      UpperCAmelCase	:     Optional[Any]						 =			sample.to(lowerCamelCase_	)

      for t in scheduler.timesteps:
       UpperCAmelCase	:     str						 =			scheduler.scale_model_input(lowerCamelCase_					,      lowerCamelCase_	)

       UpperCAmelCase	:     Optional[int]						 =			model(lowerCamelCase_					,      lowerCamelCase_	)

       UpperCAmelCase	:     List[str]						 =			scheduler.step(lowerCamelCase_					,      lowerCamelCase_					,      lowerCamelCase_	)
       UpperCAmelCase	:     Dict						 =			output.prev_sample

      UpperCAmelCase	:     Union[str, Any]						 =			torch.sum(torch.abs(lowerCamelCase_	)	)
      UpperCAmelCase	:     Any						 =			torch.mean(torch.abs(lowerCamelCase_	)	)

      if torch_device in ["mps"]:
       assert abs(result_sum.item() - 176.66_9741_3574_2188	) < 1E-2
       assert abs(result_mean.item() - 0.2_3003_8727_3098_1811	) < 1E-2
      elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 177.63_6535_6445_3125	) < 1E-2
       assert abs(result_mean.item() - 0.2_3003_8727_3098_1811	) < 1E-2
      else:
       assert abs(result_sum.item() - 170.3_1352_2338_8672	) < 1E-2
       assert abs(result_mean.item() - 0.2_3003_8727_3098_1811	) < 1E-2







'''simple docstring'''

import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

lowercase__         =      3




def 				UpperCamelCase(					UpperCAmelCase_     ):
 print('Generating primitive root of p'     )
 while True:
  UpperCAmelCase	:     Union[str, Any]						 =			random.randrange(3  ,	UpperCAmelCase_     )
  if pow(UpperCAmelCase_  ,	2  ,	UpperCAmelCase_     ) == 1:
   continue
  if pow(UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ) == 1:
   continue
  return g




def 				UpperCamelCase(					UpperCAmelCase_     ):
 print('Generating prime p...'     )
 UpperCAmelCase	:     str						 =			rabin_miller.generate_large_prime(UpperCAmelCase_     )  # select large prime number.
 UpperCAmelCase	:     List[str]						 =			primitive_root(UpperCAmelCase_     )  # one primitive root on modulo p.
 UpperCAmelCase	:     List[Any]						 =			random.randrange(3  ,	UpperCAmelCase_     )  # private_key -> have to be greater than 2 for safety.
 UpperCAmelCase	:     List[Any]						 =			cryptomath.find_mod_inverse(pow(UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     Tuple						 =			(key_size, e_a, e_a, p)
 UpperCAmelCase	:     Optional[int]						 =			(key_size, d)

 return public_key, private_key




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if os.path.exists(F"""{name}_pubkey.txt"""     ) or os.path.exists(F"""{name}_privkey.txt"""     ):
  print('\nWARNING:'     )
  print(
      F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
      'Use a different name or delete these files and re-run this program.'     )
  sys.exit()

 UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			generate_key(UpperCAmelCase_     )
 print(F"""\nWriting public key to file {name}_pubkey.txt..."""     )
 with open(F"""{name}_pubkey.txt"""  ,	'w'     ) as fo:
  fo.write(F"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""     )

 print(F"""Writing private key to file {name}_privkey.txt..."""     )
 with open(F"""{name}_privkey.txt"""  ,	'w'     ) as fo:
  fo.write(F"""{private_key[0]},{private_key[1]}"""     )




def 				UpperCamelCase(					):
 print('Making key files...'     )
 make_key_files('elgamal'  ,	20_48     )
 print('Key files generation successful'     )


if __name__ == "__main__":
 main()




