


"""simple docstring"""





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class      _lowercase (     lowerCAmelCase						):
      _a   :     Tuple								=   (UniPCMultistepScheduler,)
      _a   :     Optional[Any]								=   (('''num_inference_steps''', 25),)


      def _UpperCamelCase						(    self				:	List[Any]							,   **a				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    List[str]  			={
                '''num_train_timesteps''': 1_0_0_0,
                '''beta_start''': 0.0_0_0_1,
                '''beta_end''': 0.0_2,
                '''beta_schedule''': '''linear''',
                '''solver_order''': 2,
                '''solver_type''': '''bh2''',
            }

            config.update(**a						)
            return config


      def _UpperCamelCase						(    self				:	Any							,   a				:	Union[str, Any]=0							,   **a				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=dict(self.forward_default_kwargs						)
            __snake_case      :    str  			=kwargs.pop('''num_inference_steps'''							,   a						)
            __snake_case      :    str  			=self.dummy_sample
            __snake_case      :    Optional[Any]  			=0.1 * sample
            __snake_case      :    List[str]  			=[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(a						)
                        __snake_case      :    Optional[Any]  			=scheduler_class.from_pretrained(a						)
                        new_scheduler.set_timesteps(a						)
                        # copy over dummy past residuals
                        __snake_case      :    Union[str, Any]  			=dummy_past_residuals[: new_scheduler.config.solver_order]

                  __snake_case      :    Union[str, Any]  			=sample, sample
                  for t in range(a							,   time_step + scheduler.config.solver_order + 1						):
                        __snake_case      :    Optional[Any]  			=scheduler.step(a							,   a							,   a							,   **a						).prev_sample
                        __snake_case      :    str  			=new_scheduler.step(a							,   a							,   a							,   **a						).prev_sample

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


      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	Optional[Any]=0							,   **a				:	Optional[int]						):





            """simple docstring"""







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

            for scheduler_class in self.scheduler_classes:
                  __snake_case      :    Tuple  			=self.get_scheduler_config()
                  __snake_case      :    Tuple  			=scheduler_class(**a						)
                  scheduler.set_timesteps(a						)

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(a						)
                        __snake_case      :    str  			=scheduler_class.from_pretrained(a						)
                        # copy over dummy past residuals
                        new_scheduler.set_timesteps(a						)

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

                  __snake_case      :    Optional[Any]  			=scheduler.step(a							,   a							,   a							,   **a						).prev_sample
                  __snake_case      :    Dict  			=new_scheduler.step(a							,   a							,   a							,   **a						).prev_sample

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


      def _UpperCamelCase						(    self				:	str							,   a				:	List[Any]=None							,   **a				:	List[str]						):





            """simple docstring"""







            if scheduler is None:
                  __snake_case      :    List[str]  			=self.scheduler_classes[0]
                  __snake_case      :    int  			=self.get_scheduler_config(**a						)
                  __snake_case      :    List[str]  			=scheduler_class(**a						)

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

            __snake_case      :    str  			=1_0
            __snake_case      :    str  			=self.dummy_model()
            __snake_case      :    Optional[Any]  			=self.dummy_sample_deter
            scheduler.set_timesteps(a						)

            for i, t in enumerate(scheduler.timesteps						):
                  __snake_case      :    Union[str, Any]  			=model(a							,   a						)
                  __snake_case      :    Any  			=scheduler.step(a							,   a							,   a						).prev_sample

            return sample


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







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

            __snake_case      :    Tuple  			=kwargs.pop('''num_inference_steps'''							,   a						)

            for scheduler_class in self.scheduler_classes:
                  __snake_case      :    int  			=self.get_scheduler_config()
                  __snake_case      :    Any  			=scheduler_class(**a						)

                  __snake_case      :    List[str]  			=self.dummy_sample
                  __snake_case      :    Dict  			=0.1 * sample

                  if num_inference_steps is not None and hasattr(a							,   '''set_timesteps'''						):
                        scheduler.set_timesteps(a						)
                  elif num_inference_steps is not None and not hasattr(a							,   '''set_timesteps'''						):
                        __snake_case      :    Optional[Any]  			=num_inference_steps

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

                  __snake_case      :    Any  			=scheduler.timesteps[5]
                  __snake_case      :    int  			=scheduler.timesteps[6]

                  __snake_case      :    int  			=scheduler.step(a							,   a							,   a							,   **a						).prev_sample
                  __snake_case      :    Optional[int]  			=scheduler.step(a							,   a							,   a							,   **a						).prev_sample

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


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    str  			=UniPCMultistepScheduler(**self.get_scheduler_config()						)
            __snake_case      :    Optional[Any]  			=self.full_loop(scheduler=a						)
            __snake_case      :    Tuple  			=torch.mean(torch.abs(a						)						)

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

            __snake_case      :    List[Any]  			=DPMSolverSinglestepScheduler.from_config(scheduler.config						)
            __snake_case      :    Dict  			=DEISMultistepScheduler.from_config(scheduler.config						)
            __snake_case      :    Tuple  			=DPMSolverMultistepScheduler.from_config(scheduler.config						)
            __snake_case      :    Optional[int]  			=UniPCMultistepScheduler.from_config(scheduler.config						)

            __snake_case      :    Any  			=self.full_loop(scheduler=a						)
            __snake_case      :    Optional[int]  			=torch.mean(torch.abs(a						)						)

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


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                  self.check_over_configs(num_train_timesteps=a						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            self.check_over_configs(thresholding=a						)
            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=a							,   prediction_type=a							,   sample_max_value=a							,   solver_order=a							,   solver_type=a							,   )


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Optional[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=a							,   solver_type=a							,   prediction_type=a							,   )
                              __snake_case      :    Optional[int]  			=self.full_loop(
                                  solver_order=a							,   solver_type=a							,   prediction_type=a							,   )
                              assert not torch.isnan(a						).any(), "Samples have nan numbers"


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





            """simple docstring"""







            self.check_over_configs(lower_order_final=a						)
            self.check_over_configs(lower_order_final=a						)


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                  self.check_over_forward(num_inference_steps=a							,   time_step=0						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    int  			=self.full_loop()
            __snake_case      :    str  			=torch.mean(torch.abs(a						)						)

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


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Dict  			=self.full_loop(prediction_type='''v_prediction'''						)
            __snake_case      :    List[Any]  			=torch.mean(torch.abs(a						)						)

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


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    str  			=self.scheduler_classes[0]
            __snake_case      :    Tuple  			=self.get_scheduler_config(thresholding=a							,   dynamic_thresholding_ratio=0						)
            __snake_case      :    Union[str, Any]  			=scheduler_class(**a						)

            __snake_case      :    Optional[int]  			=1_0
            __snake_case      :    int  			=self.dummy_model()
            __snake_case      :    int  			=self.dummy_sample_deter.half()
            scheduler.set_timesteps(a						)

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

            assert sample.dtype == torch.floataa







      def _UpperCamelCase						(    self				:	int							,   **a				:	Optional[int]						):





            """simple docstring"""







            for scheduler_class in self.scheduler_classes:
                  __snake_case      :    Tuple  			=self.get_scheduler_config(**a						)
                  __snake_case      :    str  			=scheduler_class(**a						)

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










"""simple docstring"""





from __future__ import annotations



def     __lowercase					(    a :				int       ,		a :				int )					->					list[str]:
      if partitions <= 0:
            raise ValueError('''partitions must be a positive number!''' )
      if partitions > number_of_bytes:
            raise ValueError('''partitions can not > number_of_bytes!''' )
      __snake_case      :    List[str]  			=number_of_bytes // partitions
      __snake_case      :    str  			=[]
      for i in range(a ):
            __snake_case      :    Optional[Any]  			=i * bytes_per_partition + 1
            __snake_case      :    Any  			=(
                number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
            )
            allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
      return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()


