'''simple docstring'''




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class    a__				(       lowerCamelCase__			):
  lowerCamelCase  :					Tuple							=(DDPMScheduler,)






  def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    **a		:		int   ):





      """simple docstring"""



      __lowerCamelCase          =  {
          '''num_train_timesteps''': 10_00,
          '''beta_start''': 0.00_01,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
          '''variance_type''': '''fixed_small''',
          '''clip_sample''': True,
      }

      config.update(**__lowerCamelCase   )
      return config






  def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





      """simple docstring"""



      for timesteps in [1, 5, 1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=__lowerCamelCase   )






  def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





      """simple docstring"""



      for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,    [0.0_02, 0.02, 0.2, 2]   ):
          self.check_over_configs(beta_start=__lowerCamelCase  ,    beta_end=__lowerCamelCase   )






  def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





      """simple docstring"""



      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=__lowerCamelCase   )






  def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





      """simple docstring"""



      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=__lowerCamelCase   )






  def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





      """simple docstring"""



      for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=__lowerCamelCase   )






  def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





      """simple docstring"""



      self.check_over_configs(thresholding=__lowerCamelCase   )
      for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=__lowerCamelCase  ,    prediction_type=__lowerCamelCase  ,    sample_max_value=__lowerCamelCase  ,    )






  def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





      """simple docstring"""



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






  def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





      """simple docstring"""



      for t in [0, 5_00, 9_99]:
          self.check_over_forward(time_step=__lowerCamelCase   )






  def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





      """simple docstring"""



      __lowerCamelCase          =  self.scheduler_classes[0]
      __lowerCamelCase          =  self.get_scheduler_config()
      __lowerCamelCase          =  scheduler_class(**__lowerCamelCase   )

      assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_09_79   )   ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.02   )   ) < 1e-5






  def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





      """simple docstring"""



      __lowerCamelCase          =  self.scheduler_classes[0]
      __lowerCamelCase          =  self.get_scheduler_config()
      __lowerCamelCase          =  scheduler_class(**__lowerCamelCase   )

      __lowerCamelCase          =  len(__lowerCamelCase   )

      __lowerCamelCase          =  self.dummy_model()
      __lowerCamelCase          =  self.dummy_sample_deter
      __lowerCamelCase          =  torch.manual_seed(0   )

      for t in reversed(range(__lowerCamelCase   )   ):
          # 1. predict noise residual
          __lowerCamelCase          =  model(__lowerCamelCase  ,    __lowerCamelCase   )

          # 2. predict previous mean of sample x_t-1
          __lowerCamelCase          =  scheduler.step(__lowerCamelCase  ,    __lowerCamelCase  ,    __lowerCamelCase  ,    generator=__lowerCamelCase   ).prev_sample

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          __lowerCamelCase          =  pred_prev_sample

      __lowerCamelCase          =  torch.sum(torch.abs(__lowerCamelCase   )   )
      __lowerCamelCase          =  torch.mean(torch.abs(__lowerCamelCase   )   )

      assert abs(result_sum.item() - 2_58.96_06   ) < 1e-2
      assert abs(result_mean.item() - 0.33_72   ) < 1e-3






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





      """simple docstring"""



      __lowerCamelCase          =  self.scheduler_classes[0]
      __lowerCamelCase          =  self.get_scheduler_config(prediction_type='''v_prediction'''   )
      __lowerCamelCase          =  scheduler_class(**__lowerCamelCase   )

      __lowerCamelCase          =  len(__lowerCamelCase   )

      __lowerCamelCase          =  self.dummy_model()
      __lowerCamelCase          =  self.dummy_sample_deter
      __lowerCamelCase          =  torch.manual_seed(0   )

      for t in reversed(range(__lowerCamelCase   )   ):
          # 1. predict noise residual
          __lowerCamelCase          =  model(__lowerCamelCase  ,    __lowerCamelCase   )

          # 2. predict previous mean of sample x_t-1
          __lowerCamelCase          =  scheduler.step(__lowerCamelCase  ,    __lowerCamelCase  ,    __lowerCamelCase  ,    generator=__lowerCamelCase   ).prev_sample

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          __lowerCamelCase          =  pred_prev_sample

      __lowerCamelCase          =  torch.sum(torch.abs(__lowerCamelCase   )   )
      __lowerCamelCase          =  torch.mean(torch.abs(__lowerCamelCase   )   )

      assert abs(result_sum.item() - 2_02.02_96   ) < 1e-2
      assert abs(result_mean.item() - 0.26_31   ) < 1e-3






  def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





      """simple docstring"""



      __lowerCamelCase          =  self.scheduler_classes[0]
      __lowerCamelCase          =  self.get_scheduler_config()
      __lowerCamelCase          =  scheduler_class(**__lowerCamelCase   )

      __lowerCamelCase          =  [1_00, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=__lowerCamelCase   )

      __lowerCamelCase          =  scheduler.timesteps

      for i, timestep in enumerate(__lowerCamelCase   ):
          if i == len(__lowerCamelCase   ) - 1:
              __lowerCamelCase          =  -1
          else:
              __lowerCamelCase          =  timesteps[i + 1]

          __lowerCamelCase          =  scheduler.previous_timestep(__lowerCamelCase   )
          __lowerCamelCase          =  prev_t.item()

          self.assertEqual(__lowerCamelCase  ,    __lowerCamelCase   )






  def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





      """simple docstring"""



      __lowerCamelCase          =  self.scheduler_classes[0]
      __lowerCamelCase          =  self.get_scheduler_config()
      __lowerCamelCase          =  scheduler_class(**__lowerCamelCase   )

      __lowerCamelCase          =  [1_00, 87, 50, 51, 0]

      with self.assertRaises(__lowerCamelCase  ,    msg='''`custom_timesteps` must be in descending order.'''   ):
          scheduler.set_timesteps(timesteps=__lowerCamelCase   )






  def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





      """simple docstring"""



      __lowerCamelCase          =  self.scheduler_classes[0]
      __lowerCamelCase          =  self.get_scheduler_config()
      __lowerCamelCase          =  scheduler_class(**__lowerCamelCase   )

      __lowerCamelCase          =  [1_00, 87, 50, 1, 0]
      __lowerCamelCase          =  len(__lowerCamelCase   )

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






  def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





      """simple docstring"""



      __lowerCamelCase          =  self.scheduler_classes[0]
      __lowerCamelCase          =  self.get_scheduler_config()
      __lowerCamelCase          =  scheduler_class(**__lowerCamelCase   )

      __lowerCamelCase          =  [scheduler.config.num_train_timesteps]

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

'''simple docstring'''




__UpperCAmelCase												="ABCDEFGHIJKLMNOPQRSTUVWXYZ"






def __lowerCAmelCase			(							)     ->   None:
    __lowerCamelCase          =  input('''Enter message: '''    )
    __lowerCamelCase          =  input('''Enter key [alphanumeric]: '''    )
    __lowerCamelCase          =  input('''Encrypt/Decrypt [e/d]: '''    )

    if mode.lower().startswith('''e'''    ):
        __lowerCamelCase          =  '''encrypt'''
        __lowerCamelCase          =  encrypt_message(UpperCamelCase__    ,  UpperCamelCase__    )
    elif mode.lower().startswith('''d'''    ):
        __lowerCamelCase          =  '''decrypt'''
        __lowerCamelCase          =  decrypt_message(UpperCamelCase__    ,  UpperCamelCase__    )

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






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    return translate_message(UpperCamelCase__    ,  UpperCamelCase__    ,  '''encrypt'''    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    return translate_message(UpperCamelCase__    ,  UpperCamelCase__    ,  '''decrypt'''    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  0
    __lowerCamelCase          =  key.upper()

    for symbol in message:
        __lowerCamelCase          =  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(UpperCamelCase__    )

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

            key_index += 1
            if key_index == len(UpperCamelCase__    ):
                __lowerCamelCase          =  0
        else:
            translated.append(UpperCamelCase__    )
    return "".join(UpperCamelCase__    )


if __name__ == "__main__":
  main()
