





"""simple docstring"""





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       Optional[Any]         =       (UniPCMultistepScheduler,)
      _A     :       Union[str, Any]         =       (('num_inference_steps', 25),)

      def       A_					(							self       :      int						,				**__lowercase       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						{
            '''num_train_timesteps''': 1_000,
            '''beta_start''': 0.0_0_0_1,
            '''beta_end''': 0.0_2,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
            '''solver_type''': '''bh2''',
        }

        config.update(**__lowercase )
        return config

      def       A_					(							self       :      str						,				__lowercase       :      Union[str, Any]=0						,				**__lowercase       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						dict(self.forward_default_kwargs )
        __UpperCAmelCase    :						Optional[Any]       =						kwargs.pop('''num_inference_steps'''						,				__lowercase )
        __UpperCAmelCase    :						Tuple       =						self.dummy_sample
        __UpperCAmelCase    :						Tuple       =						0.1 * sample
        __UpperCAmelCase    :						Any       =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

        for scheduler_class in self.scheduler_classes:
          __UpperCAmelCase    :						int       =						self.get_scheduler_config(**__lowercase )
          __UpperCAmelCase    :						int       =						scheduler_class(**__lowercase )
          scheduler.set_timesteps(__lowercase )
          # copy over dummy past residuals
          __UpperCAmelCase    :						Union[str, Any]       =						dummy_past_residuals[: scheduler.config.solver_order]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(__lowercase )
            __UpperCAmelCase    :						int       =						scheduler_class.from_pretrained(__lowercase )
            new_scheduler.set_timesteps(__lowercase )
            # copy over dummy past residuals
            __UpperCAmelCase    :						Tuple       =						dummy_past_residuals[: new_scheduler.config.solver_order]

          __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						sample, sample
          for t in range(__lowercase						,				time_step + scheduler.config.solver_order + 1 ):
            __UpperCAmelCase    :						Any       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase						,				**__lowercase ).prev_sample
            __UpperCAmelCase    :						Optional[int]       =						new_scheduler.step(__lowercase						,				__lowercase						,				__lowercase						,				**__lowercase ).prev_sample

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

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      int=0						,				**__lowercase       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						dict(self.forward_default_kwargs )
        __UpperCAmelCase    :						Tuple       =						kwargs.pop('''num_inference_steps'''						,				__lowercase )
        __UpperCAmelCase    :						Tuple       =						self.dummy_sample
        __UpperCAmelCase    :						Union[str, Any]       =						0.1 * sample
        __UpperCAmelCase    :						Union[str, Any]       =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

        for scheduler_class in self.scheduler_classes:
          __UpperCAmelCase    :						List[Any]       =						self.get_scheduler_config()
          __UpperCAmelCase    :						str       =						scheduler_class(**__lowercase )
          scheduler.set_timesteps(__lowercase )

          # copy over dummy past residuals (must be after setting timesteps)
          __UpperCAmelCase    :						Union[str, Any]       =						dummy_past_residuals[: scheduler.config.solver_order]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(__lowercase )
            __UpperCAmelCase    :						List[Any]       =						scheduler_class.from_pretrained(__lowercase )
            # copy over dummy past residuals
            new_scheduler.set_timesteps(__lowercase )

            # copy over dummy past residual (must be after setting timesteps)
            __UpperCAmelCase    :						Optional[int]       =						dummy_past_residuals[: new_scheduler.config.solver_order]

          __UpperCAmelCase    :						Union[str, Any]       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase						,				**__lowercase ).prev_sample
          __UpperCAmelCase    :						Dict       =						new_scheduler.step(__lowercase						,				__lowercase						,				__lowercase						,				**__lowercase ).prev_sample

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

      def       A_					(							self       :      Tuple						,				__lowercase       :      Union[str, Any]=None						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        if scheduler is None:
          __UpperCAmelCase    :						List[str]       =						self.scheduler_classes[0]
          __UpperCAmelCase    :						Dict       =						self.get_scheduler_config(**__lowercase )
          __UpperCAmelCase    :						Tuple       =						scheduler_class(**__lowercase )

        __UpperCAmelCase    :						str       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						List[Any]       =						self.get_scheduler_config(**__lowercase )
        __UpperCAmelCase    :						Tuple       =						scheduler_class(**__lowercase )

        __UpperCAmelCase    :						Any       =						10
        __UpperCAmelCase    :						Optional[int]       =						self.dummy_model()
        __UpperCAmelCase    :						Dict       =						self.dummy_sample_deter
        scheduler.set_timesteps(__lowercase )

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

        return sample

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						dict(self.forward_default_kwargs )

        __UpperCAmelCase    :						Any       =						kwargs.pop('''num_inference_steps'''						,				__lowercase )

        for scheduler_class in self.scheduler_classes:
          __UpperCAmelCase    :						Any       =						self.get_scheduler_config()
          __UpperCAmelCase    :						Union[str, Any]       =						scheduler_class(**__lowercase )

          __UpperCAmelCase    :						Optional[int]       =						self.dummy_sample
          __UpperCAmelCase    :						int       =						0.1 * sample

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

          # copy over dummy past residuals (must be done after set_timesteps)
          __UpperCAmelCase    :						int       =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
          __UpperCAmelCase    :						str       =						dummy_past_residuals[: scheduler.config.solver_order]

          __UpperCAmelCase    :						List[str]       =						scheduler.timesteps[5]
          __UpperCAmelCase    :						Union[str, Any]       =						scheduler.timesteps[6]

          __UpperCAmelCase    :						Dict       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase						,				**__lowercase ).prev_sample
          __UpperCAmelCase    :						Tuple       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase						,				**__lowercase ).prev_sample

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

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


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						UniPCMultistepScheduler(**self.get_scheduler_config() )
        __UpperCAmelCase    :						int       =						self.full_loop(scheduler=__lowercase )
        __UpperCAmelCase    :						Any       =						torch.mean(torch.abs(__lowercase ) )

        assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3

        __UpperCAmelCase    :						List[Any]       =						DPMSolverSinglestepScheduler.from_config(scheduler.config )
        __UpperCAmelCase    :						Optional[int]       =						DEISMultistepScheduler.from_config(scheduler.config )
        __UpperCAmelCase    :						Optional[Any]       =						DPMSolverMultistepScheduler.from_config(scheduler.config )
        __UpperCAmelCase    :						List[str]       =						UniPCMultistepScheduler.from_config(scheduler.config )

        __UpperCAmelCase    :						Optional[int]       =						self.full_loop(scheduler=__lowercase )
        __UpperCAmelCase    :						Union[str, Any]       =						torch.mean(torch.abs(__lowercase ) )

        assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3

      def       A_					(							self       :      int ):


        '''simple docstring'''




        for timesteps in [25, 50, 100, 999, 1_000]:
          self.check_over_configs(num_train_timesteps=__lowercase )

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        self.check_over_configs(thresholding=__lowercase )
        for order in [1, 2, 3]:
          for solver_type in ["bh1", "bh2"]:
            for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample"]:
                self.check_over_configs(
                    thresholding=__lowercase						,				prediction_type=__lowercase						,				sample_max_value=__lowercase						,				solver_order=__lowercase						,				solver_type=__lowercase						,				)

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




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

      def       A_					(							self       :      int ):


        '''simple docstring'''




        for solver_type in ["bh1", "bh2"]:
          for order in [1, 2, 3]:
            for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  solver_order=__lowercase						,				solver_type=__lowercase						,				prediction_type=__lowercase						,				)
              __UpperCAmelCase    :						Tuple       =						self.full_loop(
                  solver_order=__lowercase						,				solver_type=__lowercase						,				prediction_type=__lowercase						,				)
              assert not torch.isnan(__lowercase ).any(), "Samples have nan numbers"

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        self.check_over_configs(lower_order_final=__lowercase )
        self.check_over_configs(lower_order_final=__lowercase )

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
          self.check_over_forward(num_inference_steps=__lowercase						,				time_step=0 )

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						self.full_loop()
        __UpperCAmelCase    :						List[str]       =						torch.mean(torch.abs(__lowercase ) )

        assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						self.full_loop(prediction_type='''v_prediction''' )
        __UpperCAmelCase    :						int       =						torch.mean(torch.abs(__lowercase ) )

        assert abs(result_mean.item() - 0.1_0_1_4 ) < 1e-3

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


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						List[Any]       =						self.get_scheduler_config(thresholding=__lowercase						,				dynamic_thresholding_ratio=0 )
        __UpperCAmelCase    :						Union[str, Any]       =						scheduler_class(**__lowercase )

        __UpperCAmelCase    :						Tuple       =						10
        __UpperCAmelCase    :						int       =						self.dummy_model()
        __UpperCAmelCase    :						int       =						self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowercase )

        for i, t in enumerate(scheduler.timesteps ):
          __UpperCAmelCase    :						Dict       =						model(__lowercase						,				__lowercase )
          __UpperCAmelCase    :						Dict       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase ).prev_sample

        assert sample.dtype == torch.floataa







      def       A_					(							self       :      Optional[int]						,				**__lowercase       :      Dict ):


        '''simple docstring'''




        for scheduler_class in self.scheduler_classes:
          __UpperCAmelCase    :						str       =						self.get_scheduler_config(**__lowercase )
          __UpperCAmelCase    :						Dict       =						scheduler_class(**__lowercase )

          scheduler.set_timesteps(scheduler.config.num_train_timesteps )
          assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps






"""simple docstring"""





def       lowerCamelCase_       (   )						->str:





  """simple docstring"""


  for n in range(1  ,			1_00_00_00  ):
    yield n * (n + 1) // 2


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						1
  __UpperCAmelCase    :						Any       =						2
  while i * i <= n:
    __UpperCAmelCase    :						Tuple       =						0
    while n % i == 0:
      n //= i
      multiplicity += 1
    divisors_count *= multiplicity + 1
    i += 1
  if n > 1:
    divisors_count *= 2
  return divisors_count


def       lowerCamelCase_       (   )						->Optional[int]:





  """simple docstring"""


  return next(i for i in triangle_number_generator() if count_divisors(UpperCAmelCase_  ) > 5_00  )


if __name__ == "__main__":
  print(solution())