"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      (UniPCMultistepScheduler,)
       SCREAMING_SNAKE_CASE_			=      (("num_inference_steps", 2_5),)







       def 		a_						( self,							**lowerCAmelCase__)      ->      List[Any]:
            snake_case_					       =					{
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'solver_order': 2,
                'solver_type': 'bh2',
            }

            config.update(**lowerCAmelCase__)
            return config







       def 		a_						( self,							lowerCAmelCase__=0,							**lowerCAmelCase__)      ->      Any:
            snake_case_					       =					dict(self.forward_default_kwargs)
            snake_case_					       =					kwargs.pop('num_inference_steps',							lowerCAmelCase__)
            snake_case_					       =					self.dummy_sample
            snake_case_					       =					0.1 * sample
            snake_case_					       =					[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)
                 scheduler.set_timesteps(lowerCAmelCase__)
                 # copy over dummy past residuals
                 snake_case_					       =					dummy_past_residuals[: scheduler.config.solver_order]

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

                 snake_case_       ,	snake_case_					       =					sample, sample
                 for t in range(lowerCAmelCase__,							time_step + scheduler.config.solver_order + 1):
                      snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample
                      snake_case_					       =					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 		a_						( self,							lowerCAmelCase__=0,							**lowerCAmelCase__)      ->      Dict:
            snake_case_					       =					dict(self.forward_default_kwargs)
            snake_case_					       =					kwargs.pop('num_inference_steps',							lowerCAmelCase__)
            snake_case_					       =					self.dummy_sample
            snake_case_					       =					0.1 * sample
            snake_case_					       =					[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config()
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)
                 scheduler.set_timesteps(lowerCAmelCase__)

                 # copy over dummy past residuals (must be after setting timesteps)
                 snake_case_					       =					dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__)
                      snake_case_					       =					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)
                      snake_case_					       =					dummy_past_residuals[: new_scheduler.config.solver_order]

                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample
                 snake_case_					       =					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 		a_						( self,							lowerCAmelCase__=None,							**lowerCAmelCase__)      ->      List[Any]:
            if scheduler is None:
                 snake_case_					       =					self.scheduler_classes[0]
                 snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)

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

            snake_case_					       =					10
            snake_case_					       =					self.dummy_model()
            snake_case_					       =					self.dummy_sample_deter
            scheduler.set_timesteps(lowerCAmelCase__)

            for i, t in enumerate(scheduler.timesteps):
                 snake_case_					       =					model(lowerCAmelCase__,							lowerCAmelCase__)
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__).prev_sample

            return sample







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					dict(self.forward_default_kwargs)

            snake_case_					       =					kwargs.pop('num_inference_steps',							lowerCAmelCase__)

            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config()
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)

                 snake_case_					       =					self.dummy_sample
                 snake_case_					       =					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'):
                      snake_case_					       =					num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 snake_case_					       =					[residual + 0.2, residual + 0.15, residual + 0.10]
                 snake_case_					       =					dummy_past_residuals[: scheduler.config.solver_order]

                 snake_case_					       =					scheduler.timesteps[5]
                 snake_case_					       =					scheduler.timesteps[6]

                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample

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







       def 		a_						( self)      ->      Tuple:
            # make sure that iterating over schedulers with same config names gives same results
            # for defaults
            snake_case_					       =					UniPCMultistepScheduler(**self.get_scheduler_config())
            snake_case_					       =					self.full_loop(scheduler=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2464) < 1e-3

            snake_case_					       =					DPMSolverSinglestepScheduler.from_config(scheduler.config)
            snake_case_					       =					DEISMultistepScheduler.from_config(scheduler.config)
            snake_case_					       =					DPMSolverMultistepScheduler.from_config(scheduler.config)
            snake_case_					       =					UniPCMultistepScheduler.from_config(scheduler.config)

            snake_case_					       =					self.full_loop(scheduler=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2464) < 1e-3







       def 		a_						( self)      ->      str:
            for timesteps in [25, 50, 100, 999, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCAmelCase__)







       def 		a_						( self)      ->      List[Any]:
            self.check_over_configs(thresholding=lowerCAmelCase__)
            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=lowerCAmelCase__,							prediction_type=lowerCAmelCase__,							sample_max_value=lowerCAmelCase__,							solver_order=lowerCAmelCase__,							solver_type=lowerCAmelCase__,							)







       def 		a_						( self)      ->      List[str]:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowerCAmelCase__)







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







       def 		a_						( self)      ->      Any:
            self.check_over_configs(lower_order_final=lowerCAmelCase__)
            self.check_over_configs(lower_order_final=lowerCAmelCase__)







       def 		a_						( self)      ->      int:
            for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                 self.check_over_forward(num_inference_steps=lowerCAmelCase__,							time_step=0)







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.full_loop()
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2464) < 1e-3







       def 		a_						( self)      ->      str:
            snake_case_					       =					self.full_loop(prediction_type='v_prediction')
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.1014) < 1e-3







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.scheduler_classes[0]
            snake_case_					       =					self.get_scheduler_config(thresholding=lowerCAmelCase__,							dynamic_thresholding_ratio=0)
            snake_case_					       =					scheduler_class(**lowerCAmelCase__)

            snake_case_					       =					10
            snake_case_					       =					self.dummy_model()
            snake_case_					       =					self.dummy_sample_deter.half()
            scheduler.set_timesteps(lowerCAmelCase__)

            for i, t in enumerate(scheduler.timesteps):
                 snake_case_					       =					model(lowerCAmelCase__,							lowerCAmelCase__)
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__).prev_sample

            assert sample.dtype == torch.floataa







       def 		a_						( self,							**lowerCAmelCase__)      ->      str:
            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)

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





from math import isclose, sqrt


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[str]		     =					point_y / 4 / point_x
  A_      :				Union[str, Any]		     =					2 * normal_gradient / (1 + normal_gradient * normal_gradient)
  A_      :				int		     =					(1 - normal_gradient * normal_gradient) / (
      1 + normal_gradient * normal_gradient
  )
  A_      :				List[str]		     =					(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

  # to find the next point, solve the simultaeneous equations:
  # y^2 + 4x^2 = 100
  # y - b = m * (x - a)
  # ==> A x^2 + B x + C = 0
  A_      :				List[str]		     =					outgoing_gradient**2 + 4
  A_      :				List[str]		     =					2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
  A_      :				Any		     =					(point_y - outgoing_gradient * point_x) ** 2 - 100

  A_      :				str		     =					(
      -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
  ) / (2 * quadratic_term)
  A_      :				Any		     =					(
      -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
  ) / (2 * quadratic_term)

  # two solutions, one of which is our input point
  A_      :				int		     =					x_minus if isclose(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) else x_plus
  A_      :				Any		     =					point_y + outgoing_gradient * (next_x - point_x)

  return next_x, next_y, outgoing_gradient


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE = 1.4 ,			SCREAMING_SNAKE_CASE = -9.6       ):
  A_      :				int		     =					0
  A_      :				float		     =					first_x_coord
  A_      :				float		     =					first_y_coord
  A_      :				float		     =					(1_0.1 - point_y) / (0.0 - point_x)

  while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
    A_     ,       A_     ,       A_      :				List[str]		     =					next_point(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    num_reflections += 1

  return num_reflections


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




