import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   (IPNDMScheduler,)
    __a						=   (("num_inference_steps", 50),)
    def     UpperCamelCase_					(	self			,			**lowerCAmelCase )							->       Dict:
     SCREAMING_SNAKE_CASE__: Optional[int]=   {'''num_train_timesteps''': 1000}
     config.update(**lowerCAmelCase )
     return config
    def     UpperCamelCase_					(	self			,			lowerCAmelCase=0			,			**lowerCAmelCase )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   dict(self.forward_default_kwargs )
     SCREAMING_SNAKE_CASE__: Optional[int]=   kwargs.pop('''num_inference_steps'''			,			lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: List[Any]=   self.dummy_sample
     SCREAMING_SNAKE_CASE__: Tuple=   0.1 * sample
     SCREAMING_SNAKE_CASE__: Any=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
      SCREAMING_SNAKE_CASE__: Tuple=   self.get_scheduler_config(**lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Any=   scheduler_class(**lowerCAmelCase )
      scheduler.set_timesteps(lowerCAmelCase )
      # copy over dummy past residuals
      SCREAMING_SNAKE_CASE__: Optional[Any]=   dummy_past_residuals[:]

      if time_step is None:
       SCREAMING_SNAKE_CASE__: List[Any]=   scheduler.timesteps[len(scheduler.timesteps ) // 2]

      with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(lowerCAmelCase )
       SCREAMING_SNAKE_CASE__: Any=   scheduler_class.from_pretrained(lowerCAmelCase )
       new_scheduler.set_timesteps(lowerCAmelCase )
       # copy over dummy past residuals
       SCREAMING_SNAKE_CASE__: Dict=   dummy_past_residuals[:]

      SCREAMING_SNAKE_CASE__: Union[str, Any]=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample
      SCREAMING_SNAKE_CASE__: Optional[int]=   new_scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample

      assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

      SCREAMING_SNAKE_CASE__: List[str]=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample
      SCREAMING_SNAKE_CASE__: str=   new_scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample

      assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     pass
    def     UpperCamelCase_					(	self			,			lowerCAmelCase=0			,			**lowerCAmelCase )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: List[Any]=   dict(self.forward_default_kwargs )
     SCREAMING_SNAKE_CASE__: Optional[int]=   kwargs.pop('''num_inference_steps'''			,			lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Tuple=   self.dummy_sample
     SCREAMING_SNAKE_CASE__: str=   0.1 * sample
     SCREAMING_SNAKE_CASE__: Dict=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
      SCREAMING_SNAKE_CASE__: Optional[int]=   self.get_scheduler_config()
      SCREAMING_SNAKE_CASE__: Tuple=   scheduler_class(**lowerCAmelCase )
      scheduler.set_timesteps(lowerCAmelCase )

      # copy over dummy past residuals (must be after setting timesteps)
      SCREAMING_SNAKE_CASE__: List[Any]=   dummy_past_residuals[:]

      if time_step is None:
       SCREAMING_SNAKE_CASE__: Tuple=   scheduler.timesteps[len(scheduler.timesteps ) // 2]

      with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(lowerCAmelCase )
       SCREAMING_SNAKE_CASE__: int=   scheduler_class.from_pretrained(lowerCAmelCase )
       # copy over dummy past residuals
       new_scheduler.set_timesteps(lowerCAmelCase )

       # copy over dummy past residual (must be after setting timesteps)
       SCREAMING_SNAKE_CASE__: int=   dummy_past_residuals[:]

      SCREAMING_SNAKE_CASE__: Optional[Any]=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample
      SCREAMING_SNAKE_CASE__: List[str]=   new_scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample

      assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

      SCREAMING_SNAKE_CASE__: Tuple=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample
      SCREAMING_SNAKE_CASE__: List[Any]=   new_scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample

      assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
    def     UpperCamelCase_					(	self			,			**lowerCAmelCase )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: List[str]=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__: List[Any]=   self.get_scheduler_config(**lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: int=   scheduler_class(**lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: List[str]=   10
     SCREAMING_SNAKE_CASE__: Dict=   self.dummy_model()
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.dummy_sample_deter
     scheduler.set_timesteps(lowerCAmelCase )

     for i, t in enumerate(scheduler.timesteps ):
      SCREAMING_SNAKE_CASE__: str=   model(lowerCAmelCase			,			lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase ).prev_sample

     for i, t in enumerate(scheduler.timesteps ):
      SCREAMING_SNAKE_CASE__: List[Any]=   model(lowerCAmelCase			,			lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase ).prev_sample

     return sample
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   dict(self.forward_default_kwargs )

     SCREAMING_SNAKE_CASE__: List[Any]=   kwargs.pop('''num_inference_steps'''			,			lowerCAmelCase )

     for scheduler_class in self.scheduler_classes:
      SCREAMING_SNAKE_CASE__: List[Any]=   self.get_scheduler_config()
      SCREAMING_SNAKE_CASE__: int=   scheduler_class(**lowerCAmelCase )

      SCREAMING_SNAKE_CASE__: Any=   self.dummy_sample
      SCREAMING_SNAKE_CASE__: Any=   0.1 * sample

      if num_inference_steps is not None and hasattr(lowerCAmelCase			,			'''set_timesteps''' ):
       scheduler.set_timesteps(lowerCAmelCase )
      elif num_inference_steps is not None and not hasattr(lowerCAmelCase			,			'''set_timesteps''' ):
       SCREAMING_SNAKE_CASE__: Optional[int]=   num_inference_steps

      # copy over dummy past residuals (must be done after set_timesteps)
      SCREAMING_SNAKE_CASE__: List[str]=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
      SCREAMING_SNAKE_CASE__: List[Any]=   dummy_past_residuals[:]

      SCREAMING_SNAKE_CASE__: List[Any]=   scheduler.timesteps[5]
      SCREAMING_SNAKE_CASE__: str=   scheduler.timesteps[6]

      SCREAMING_SNAKE_CASE__: List[str]=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample
      SCREAMING_SNAKE_CASE__: Any=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample

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

      SCREAMING_SNAKE_CASE__: Union[str, Any]=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample
      SCREAMING_SNAKE_CASE__: Any=   scheduler.step(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase ).prev_sample

      self.assertEqual(output_a.shape			,			sample.shape )
      self.assertEqual(output_a.shape			,			output_a.shape )
    def     UpperCamelCase_					(	self )							->       str:
     for timesteps in [100, 1000]:
      self.check_over_configs(num_train_timesteps=lowerCAmelCase			,			time_step=lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     for t, num_inference_steps in zip([1, 5, 10]			,			[10, 50, 100] ):
      self.check_over_forward(num_inference_steps=lowerCAmelCase			,			time_step=lowerCAmelCase )



    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.full_loop()
     SCREAMING_SNAKE_CASE__: int=   torch.mean(torch.abs(lowerCAmelCase ) )

     assert abs(result_mean.item() - 2540529 ) < 10








'''simple docstring'''






def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if divisor % 5 == 0 or divisor % 2 == 0:
            return 0
      _lowerCAmelCase					=				1
      _lowerCAmelCase					=				1
      while repunit:
            _lowerCAmelCase					=				(10 * repunit + 1) % divisor
            repunit_index += 1
      return repunit_index
def   UpperCamelCase__ (						lowerCAmelCase = 1_00_00_00    ):





      """simple docstring"""


      _lowerCAmelCase					=				limit - 1
      if divisor % 2 == 0:
            divisor += 1
      while least_divisible_repunit(lowerCAmelCase    ) <= limit:
            divisor += 2
      return divisor


if __name__ == "__main__":
      print(F"""{solution() = }""")






