






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 			UpperCAmelCase_						(					lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Any           =(DEISMultistepScheduler,)
      UpperCamelCase_				:					int           =(('num_inference_steps', 25),)
      def     UpperCAmelCase						(				self    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          UpperCamelCase       :Union[str, Any]								=   {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''solver_order''': 2,
          }

          config.update(**__lowerCAmelCase )
          return config
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_=0    ,				**SCREAMING_SNAKE_CASE_ )				->      Tuple:
          UpperCamelCase       :Optional[int]								=   dict(self.forward_default_kwargs )
          UpperCamelCase       :int								=   kwargs.pop('''num_inference_steps'''    ,				__lowerCAmelCase )
          UpperCamelCase       :Optional[int]								=   self.dummy_sample
          UpperCamelCase       :Optional[int]								=   0.1 * sample
          UpperCamelCase       :Any								=   [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase       :Tuple								=   self.get_scheduler_config(**__lowerCAmelCase )
              UpperCamelCase       :Any								=   scheduler_class(**__lowerCAmelCase )
              scheduler.set_timesteps(__lowerCAmelCase )
              # copy over dummy past residuals
              UpperCamelCase       :Dict								=   dummy_past_residuals[: scheduler.config.solver_order]

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

              UpperCamelCase							,   UpperCamelCase       :List[str]								=   sample, sample
              for t in range(__lowerCAmelCase    ,				time_step + scheduler.config.solver_order + 1 ):
                  UpperCamelCase       :Any								=   scheduler.step(__lowerCAmelCase    ,				__lowerCAmelCase    ,				__lowerCAmelCase    ,				**__lowerCAmelCase ).prev_sample
                  UpperCamelCase       :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"
      def     UpperCAmelCase						(				self )				->      Dict:
          pass
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_=0    ,				**SCREAMING_SNAKE_CASE_ )				->      Dict:
          UpperCamelCase       :Union[str, Any]								=   dict(self.forward_default_kwargs )
          UpperCamelCase       :Any								=   kwargs.pop('''num_inference_steps'''    ,				__lowerCAmelCase )
          UpperCamelCase       :List[str]								=   self.dummy_sample
          UpperCamelCase       :Optional[int]								=   0.1 * sample
          UpperCamelCase       :Optional[int]								=   [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase       :int								=   self.get_scheduler_config()
              UpperCamelCase       :Optional[Any]								=   scheduler_class(**__lowerCAmelCase )
              scheduler.set_timesteps(__lowerCAmelCase )

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(__lowerCAmelCase )
                  UpperCamelCase       :Any								=   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)
                  UpperCamelCase       :List[Any]								=   dummy_past_residuals[: new_scheduler.config.solver_order]

              UpperCamelCase       :Optional[Any]								=   scheduler.step(__lowerCAmelCase    ,				__lowerCAmelCase    ,				__lowerCAmelCase    ,				**__lowerCAmelCase ).prev_sample
              UpperCamelCase       :Optional[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    ,				SCREAMING_SNAKE_CASE_=None    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:



          if scheduler is None:
              UpperCamelCase       :Any								=   self.scheduler_classes[0]
              UpperCamelCase       :Optional[int]								=   self.get_scheduler_config(**__lowerCAmelCase )
              UpperCamelCase       :Optional[Any]								=   scheduler_class(**__lowerCAmelCase )

          UpperCamelCase       :int								=   self.scheduler_classes[0]
          UpperCamelCase       :str								=   self.get_scheduler_config(**__lowerCAmelCase )
          UpperCamelCase       :Optional[Any]								=   scheduler_class(**__lowerCAmelCase )

          UpperCamelCase       :Dict								=   10
          UpperCamelCase       :Any								=   self.dummy_model()
          UpperCamelCase       :List[str]								=   self.dummy_sample_deter
          scheduler.set_timesteps(__lowerCAmelCase )

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

          return sample
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          UpperCamelCase       :Union[str, Any]								=   dict(self.forward_default_kwargs )

          UpperCamelCase       :Any								=   kwargs.pop('''num_inference_steps'''    ,				__lowerCAmelCase )

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase       :List[str]								=   self.get_scheduler_config()
              UpperCamelCase       :Tuple								=   scheduler_class(**__lowerCAmelCase )

              UpperCamelCase       :Dict								=   self.dummy_sample
              UpperCamelCase       :Tuple								=   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''' ):
                  UpperCamelCase       :Dict								=   num_inference_steps

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

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

              UpperCamelCase       :Optional[int]								=   scheduler.step(__lowerCAmelCase    ,				__lowerCAmelCase    ,				__lowerCAmelCase    ,				**__lowerCAmelCase ).prev_sample
              UpperCamelCase       :str								=   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 )				->      Dict:
          UpperCamelCase       :List[Any]								=   DEISMultistepScheduler(**self.get_scheduler_config() )
          UpperCamelCase       :Union[str, Any]								=   self.full_loop(scheduler=__lowerCAmelCase )
          UpperCamelCase       :Optional[Any]								=   torch.mean(torch.abs(__lowerCAmelCase ) )

          assert abs(result_mean.item() - 0.2_3916 ) < 1e-3

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

          UpperCamelCase       :List[str]								=   self.full_loop(scheduler=__lowerCAmelCase )
          UpperCamelCase       :Dict								=   torch.mean(torch.abs(__lowerCAmelCase ) )

          assert abs(result_mean.item() - 0.2_3916 ) < 1e-3
      def     UpperCAmelCase						(				self )				->      List[str]:



          for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
      def     UpperCAmelCase						(				self )				->      Optional[Any]:
          self.check_over_configs(thresholding=__lowerCAmelCase )
          for order in [1, 2, 3]:
              for solver_type in ["logrho"]:
                  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    ,				algorithm_type='''deis'''    ,				solver_order=__lowerCAmelCase    ,				solver_type=__lowerCAmelCase    ,				)
      def     UpperCAmelCase						(				self )				->      Optional[Any]:



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



          for algorithm_type in ["deis"]:
              for solver_type in ["logrho"]:
                  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    ,				algorithm_type=__lowerCAmelCase    ,				)
                          UpperCamelCase       :Optional[Any]								=   self.full_loop(
                              solver_order=__lowerCAmelCase    ,				solver_type=__lowerCAmelCase    ,				prediction_type=__lowerCAmelCase    ,				algorithm_type=__lowerCAmelCase    ,				)
                          assert not torch.isnan(__lowerCAmelCase ).any(), "Samples have nan numbers"
      def     UpperCAmelCase						(				self )				->      int:
          self.check_over_configs(lower_order_final=__lowerCAmelCase )
          self.check_over_configs(lower_order_final=__lowerCAmelCase )
      def     UpperCAmelCase						(				self )				->      List[str]:



          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     UpperCAmelCase						(				self )				->      Any:
          UpperCamelCase       :List[Any]								=   self.full_loop()
          UpperCamelCase       :List[str]								=   torch.mean(torch.abs(__lowerCAmelCase ) )

          assert abs(result_mean.item() - 0.2_3916 ) < 1e-3
      def     UpperCAmelCase						(				self )				->      int:
          UpperCamelCase       :int								=   self.full_loop(prediction_type='''v_prediction''' )
          UpperCamelCase       :Any								=   torch.mean(torch.abs(__lowerCAmelCase ) )

          assert abs(result_mean.item() - 0.091 ) < 1e-3




      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          UpperCamelCase       :Union[str, Any]								=   self.scheduler_classes[0]
          UpperCamelCase       :str								=   self.get_scheduler_config(thresholding=__lowerCAmelCase    ,				dynamic_thresholding_ratio=0 )
          UpperCamelCase       :Optional[int]								=   scheduler_class(**__lowerCAmelCase )

          UpperCamelCase       :Optional[Any]								=   10
          UpperCamelCase       :Union[str, Any]								=   self.dummy_model()
          UpperCamelCase       :List[Any]								=   self.dummy_sample_deter.half()
          scheduler.set_timesteps(__lowerCAmelCase )

          for i, t in enumerate(scheduler.timesteps ):
              UpperCamelCase       :List[str]								=   model(__lowerCAmelCase    ,				__lowerCAmelCase )
              UpperCamelCase       :str								=   scheduler.step(__lowerCAmelCase    ,				__lowerCAmelCase    ,				__lowerCAmelCase ).prev_sample

          assert sample.dtype == torch.floataa






"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase    :       str					     =list[list[float | int]]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Matrix					,		lowerCAmelCase__     :Matrix		) ->    Matrix:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(size + 1		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for row in range(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	matrix[row][col]

            lowercase														=	vector[row][0]

      lowercase														=	0
      lowercase														=	0
      while row < size and col < size:
            # pivoting
            lowercase														=	max((abs(augmented[rowa][col]		), rowa) for rowa in range(lowerCAmelCase__					,		lowerCAmelCase__		)		)[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  lowercase							,							lowercase														=	augmented[pivot_row], augmented[row]

            for rowa in range(row + 1					,		lowerCAmelCase__		):
                  lowercase														=	augmented[rowa][col] / augmented[row][col]
                  lowercase														=	0
                  for cola in range(col + 1					,		size + 1		):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1					,		lowerCAmelCase__		):
            for row in range(lowerCAmelCase__		):
                  lowercase														=	augmented[row][col] / augmented[col][col]
                  for cola in range(lowerCAmelCase__					,		size + 1		):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row]					,		1_0		)] for row in range(lowerCAmelCase__		)
      ]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]		) ->    Callable[[int], int]:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	[[0] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for x_val, y_val in enumerate(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	(x_val + 1) ** (size - col - 1)
            lowercase														=	y_val

      lowercase														=	solve(lowerCAmelCase__					,		lowerCAmelCase__		)

      def interpolated_func(lowerCAmelCase__     :int		) -> int:
            return sum(
                round(coeffs[x_val][0]		) * (var ** (size - x_val - 1))
                for x_val in range(lowerCAmelCase__		)		)

      return interpolated_func


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int		) ->    int:
      '''simple docstring'''


      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**1_0
      )


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Callable[[int], int] = question_function					,		lowerCAmelCase__     :int = 1_0		) ->    int:
      '''simple docstring'''


      lowercase														=	[func(lowerCAmelCase__		) for x_val in range(1					,		order + 1		)]

      lowercase														=	[
          interpolate(data_points[:max_coeff]		) for max_coeff in range(1					,		order + 1		)
      ]

      lowercase														=	0
      lowercase														=	42
      lowercase														=	42

      for poly in polynomials:
            lowercase														=	1
            while func(lowerCAmelCase__		) == poly(lowerCAmelCase__		):
                  x_val += 1

            ret += poly(lowerCAmelCase__		)

      return ret


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



