


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class        A_					(       a__    ):




      '''simple docstring'''


      __snake_case					       =   (LMSDiscreteScheduler,)
      __snake_case					       =   10




      def      _snake_case	(				self:       int						,      **a:       Tuple				):
       __lowerCamelCase		:							int							=						{
           'num_train_timesteps': 1100,
           'beta_start': 0.0_0_0_1,
           'beta_end': 0.0_2,
           'beta_schedule': 'linear',
       }

       config.update(**_snake_case				)
       return config




      def      _snake_case	(				self:       Any				):






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




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






       for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,      [0.0_0_0_2, 0.0_0_2, 0.0_2]				):
        self.check_over_configs(beta_start=_snake_case						,      beta_end=_snake_case				)




      def      _snake_case	(				self:       List[Any]				):






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




      def      _snake_case	(				self:       List[str]				):






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




      def      _snake_case	(				self:       List[Any]				):






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




      def      _snake_case	(				self:       Tuple				):
       __lowerCamelCase		:							List[Any]							=						self.scheduler_classes[0]
       __lowerCamelCase		:							Optional[int]							=						self.get_scheduler_config()
       __lowerCamelCase		:							Optional[Any]							=						scheduler_class(**_snake_case				)

       scheduler.set_timesteps(self.num_inference_steps				)

       __lowerCamelCase		:							Optional[int]							=						self.dummy_model()
       __lowerCamelCase		:							int							=						self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(scheduler.timesteps				):
        __lowerCamelCase		:							Tuple							=						scheduler.scale_model_input(_snake_case						,      _snake_case				)

        __lowerCamelCase		:							Tuple							=						model(_snake_case						,      _snake_case				)

        __lowerCamelCase		:							Tuple							=						scheduler.step(_snake_case						,      _snake_case						,      _snake_case				)
        __lowerCamelCase		:							List[Any]							=						output.prev_sample

       __lowerCamelCase		:							List[str]							=						torch.sum(torch.abs(_snake_case				)				)
       __lowerCamelCase		:							Tuple							=						torch.mean(torch.abs(_snake_case				)				)

       assert abs(result_sum.item() - 1006.388				) < 1e-2
       assert abs(result_mean.item() - 1.3_1				) < 1e-3




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

       scheduler.set_timesteps(self.num_inference_steps				)

       __lowerCamelCase		:							Tuple							=						self.dummy_model()
       __lowerCamelCase		:							str							=						self.dummy_sample_deter * scheduler.init_noise_sigma

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

        __lowerCamelCase		:							Tuple							=						model(_snake_case						,      _snake_case				)

        __lowerCamelCase		:							Tuple							=						scheduler.step(_snake_case						,      _snake_case						,      _snake_case				)
        __lowerCamelCase		:							Any							=						output.prev_sample

       __lowerCamelCase		:							Dict							=						torch.sum(torch.abs(_snake_case				)				)
       __lowerCamelCase		:							Any							=						torch.mean(torch.abs(_snake_case				)				)

       assert abs(result_sum.item() - 0.0_0_1_7				) < 1e-2
       assert abs(result_mean.item() - 2.2676e-06				) < 1e-3




      def      _snake_case	(				self:       Optional[int]				):
       __lowerCamelCase		:							List[str]							=						self.scheduler_classes[0]
       __lowerCamelCase		:							Optional[int]							=						self.get_scheduler_config()
       __lowerCamelCase		:							Tuple							=						scheduler_class(**_snake_case				)

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

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

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

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

        __lowerCamelCase		:							Optional[int]							=						scheduler.step(_snake_case						,      _snake_case						,      _snake_case				)
        __lowerCamelCase		:							Dict							=						output.prev_sample

       __lowerCamelCase		:							Any							=						torch.sum(torch.abs(_snake_case				)				)
       __lowerCamelCase		:							str							=						torch.mean(torch.abs(_snake_case				)				)

       assert abs(result_sum.item() - 1006.388				) < 1e-2
       assert abs(result_mean.item() - 1.3_1				) < 1e-3




      def      _snake_case	(				self:       List[str]				):
       __lowerCamelCase		:							Dict							=						self.scheduler_classes[0]
       __lowerCamelCase		:							List[Any]							=						self.get_scheduler_config()
       __lowerCamelCase		:							Optional[Any]							=						scheduler_class(**_snake_case						,      use_karras_sigmas=_snake_case				)

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

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

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

        __lowerCamelCase		:							Tuple							=						model(_snake_case						,      _snake_case				)

        __lowerCamelCase		:							str							=						scheduler.step(_snake_case						,      _snake_case						,      _snake_case				)
        __lowerCamelCase		:							Any							=						output.prev_sample

       __lowerCamelCase		:							List[Any]							=						torch.sum(torch.abs(_snake_case				)				)
       __lowerCamelCase		:							Dict							=						torch.mean(torch.abs(_snake_case				)				)

       assert abs(result_sum.item() - 3812.9927				) < 2e-2
       assert abs(result_mean.item() - 4.9_6_4_8				) < 1e-3








"""simple docstring"""
import os
import sys
import time

from . import transposition_cipher as trans_cipher

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      'Prehistoric Men.txt'
 lowerCAmelCase  							=      'Output.txt'
 lowerCAmelCase  							=      int(input('Enter key: '       )       )
 lowerCAmelCase  							=      input('Encrypt/Decrypt [e/d]: '       )

 if not os.path.exists(_UpperCAmelCase       ):
  print(F'File {input_file} does not exist. Quitting...'       )
  sys.exit()
 if os.path.exists(_UpperCAmelCase       ):
  print(F'Overwrite {output_file}? [y/n]'       )
  lowerCAmelCase  							=      input('> '       )
  if not response.lower().startswith('y'       ):
   sys.exit()

 lowerCAmelCase  							=      time.time()
 if mode.lower().startswith('e'       ):
  with open(_UpperCAmelCase       ) as f:
   lowerCAmelCase  							=      f.read()
  lowerCAmelCase  							=      trans_cipher.encrypt_message(_UpperCAmelCase   ,			_UpperCAmelCase       )
 elif mode.lower().startswith('d'       ):
  with open(_UpperCAmelCase       ) as f:
   lowerCAmelCase  							=      f.read()
  lowerCAmelCase  							=      trans_cipher.decrypt_message(_UpperCAmelCase   ,			_UpperCAmelCase       )

 with open(_UpperCAmelCase   ,			'w'       ) as output_obj:
  output_obj.write(_UpperCAmelCase       )

 lowerCAmelCase  							=      round(time.time() - start_time   ,			2       )
 print(('Done (', total_time, 'seconds )')       )


if __name__ == "__main__":
       main()





