"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			(DPMSolverMultistepScheduler,)
    _UpperCAmelCase       =			(("num_inference_steps", 2_5),)
    def 						_a							(  self					,       **__lowerCamelCase	):




         '''simple docstring'''



         __A					:					str								=		{
             'num_train_timesteps': 1000,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'solver_order': 2,
             'prediction_type': 'epsilon',
             'thresholding': False,
             'sample_max_value': 1.0,
             'algorithm_type': 'dpmsolver++',
             'solver_type': 'midpoint',
             'lower_order_final': False,
             'lambda_min_clipped': -float('inf'	),
             'variance_type': None,
         }

         config.update(**__lowerCamelCase	)
         return config
    def 						_a							(  self					,       __lowerCamelCase=0					,       **__lowerCamelCase	):




         '''simple docstring'''



         __A					:					int								=		dict(self.forward_default_kwargs	)
         __A					:					List[Any]								=		kwargs.pop('num_inference_steps'					,       __lowerCamelCase	)
         __A					:					Dict								=		self.dummy_sample
         __A					:					Dict								=		0.1 * sample
         __A					:					int								=		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

              __A						,		__A					:					Any								=		sample, sample
              for t in range(__lowerCamelCase					,       time_step + scheduler.config.solver_order + 1	):
                   __A					:					Any								=		scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       **__lowerCamelCase	).prev_sample
                   __A					:					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 						_a							(  self	):




         '''simple docstring'''



         pass
    def 						_a							(  self					,       __lowerCamelCase=0					,       **__lowerCamelCase	):




         '''simple docstring'''



         __A					:					Union[str, Any]								=		dict(self.forward_default_kwargs	)
         __A					:					int								=		kwargs.pop('num_inference_steps'					,       __lowerCamelCase	)
         __A					:					Any								=		self.dummy_sample
         __A					:					Dict								=		0.1 * sample
         __A					:					Union[str, Any]								=		[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
              __A					:					List[Any]								=		self.get_scheduler_config()
              __A					:					Any								=		scheduler_class(**__lowerCamelCase	)
              scheduler.set_timesteps(__lowerCamelCase	)

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(__lowerCamelCase	)
                   __A					:					Tuple								=		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)
                   __A					:					int								=		dummy_past_residuals[: new_scheduler.config.solver_order]

              __A					:					Tuple								=		scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       **__lowerCamelCase	).prev_sample
              __A					:					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 						_a							(  self					,       __lowerCamelCase=None					,       **__lowerCamelCase	):




         '''simple docstring'''



         if scheduler is None:
              __A					:					List[Any]								=		self.scheduler_classes[0]
              __A					:					Any								=		self.get_scheduler_config(**__lowerCamelCase	)
              __A					:					Any								=		scheduler_class(**__lowerCamelCase	)

         __A					:					List[str]								=		10
         __A					:					Any								=		self.dummy_model()
         __A					:					int								=		self.dummy_sample_deter
         scheduler.set_timesteps(__lowerCamelCase	)

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

         return sample
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		dict(self.forward_default_kwargs	)

         __A					:					Dict								=		kwargs.pop('num_inference_steps'					,       __lowerCamelCase	)

         for scheduler_class in self.scheduler_classes:
              __A					:					str								=		self.get_scheduler_config()
              __A					:					List[str]								=		scheduler_class(**__lowerCamelCase	)

              __A					:					Tuple								=		self.dummy_sample
              __A					:					str								=		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'	):
                   __A					:					List[Any]								=		num_inference_steps

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

              __A					:					Optional[Any]								=		scheduler.timesteps[5]
              __A					:					int								=		scheduler.timesteps[6]

              __A					:					Union[str, Any]								=		scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       **__lowerCamelCase	).prev_sample
              __A					:					List[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 						_a							(  self	):




         '''simple docstring'''



         for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=__lowerCamelCase	)
    def 						_a							(  self	):




         '''simple docstring'''



         self.check_over_configs(thresholding=__lowerCamelCase	)
         for order in [1, 2, 3]:
              for solver_type in ["midpoint", "heun"]:
                   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='dpmsolver++'					,       solver_order=__lowerCamelCase					,       solver_type=__lowerCamelCase					,       )
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



         for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
              for solver_type in ["midpoint", "heun"]:
                   for order in [1, 2, 3]:
                        for prediction_type in ["epsilon", "sample"]:
                             if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                  if order == 3:
                                       continue
                             else:
                                  self.check_over_configs(
                                      solver_order=__lowerCamelCase					,       solver_type=__lowerCamelCase					,       prediction_type=__lowerCamelCase					,       algorithm_type=__lowerCamelCase					,       )
                             __A					:					Dict								=		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 						_a							(  self	):




         '''simple docstring'''



         self.check_over_configs(lower_order_final=__lowerCamelCase	)
         self.check_over_configs(lower_order_final=__lowerCamelCase	)
    def 						_a							(  self	):




         '''simple docstring'''



         self.check_over_configs(lambda_min_clipped=-float('inf'	)	)
         self.check_over_configs(lambda_min_clipped=-5.1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self.check_over_configs(variance_type=__lowerCamelCase	)
         self.check_over_configs(variance_type='learned_range'	)
    def 						_a							(  self	):




         '''simple docstring'''



         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	):




         '''simple docstring'''



         __A					:					Optional[int]								=		self.full_loop()
         __A					:					List[Any]								=		torch.mean(torch.abs(__lowerCamelCase	)	)

         assert abs(result_mean.item() - 0.33_01	) < 1e-3
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.full_loop(thresholding=__lowerCamelCase					,       dynamic_thresholding_ratio=0.87					,       sample_max_value=0.5	)
         __A					:					str								=		torch.mean(torch.abs(__lowerCamelCase	)	)

         assert abs(result_mean.item() - 1.13_64	) < 1e-3
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Any								=		self.full_loop(prediction_type='v_prediction'	)
         __A					:					Tuple								=		torch.mean(torch.abs(__lowerCamelCase	)	)

         assert abs(result_mean.item() - 0.22_51	) < 1e-3
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		self.full_loop(prediction_type='v_prediction'					,       use_karras_sigmas=__lowerCamelCase	)
         __A					:					int								=		torch.mean(torch.abs(__lowerCamelCase	)	)

         assert abs(result_mean.item() - 0.20_96	) < 1e-3
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Any								=		DPMSolverMultistepScheduler(**self.get_scheduler_config()	)
         __A					:					Any								=		self.full_loop(scheduler=__lowerCamelCase	)
         __A					:					Any								=		torch.mean(torch.abs(__lowerCamelCase	)	)

         assert abs(result_mean.item() - 0.33_01	) < 1e-3

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

         __A					:					str								=		self.full_loop(scheduler=__lowerCamelCase	)
         __A					:					Optional[int]								=		torch.mean(torch.abs(__lowerCamelCase	)	)

         assert abs(result_mean.item() - 0.33_01	) < 1e-3
    def 						_a							(  self	):




         '''simple docstring'''



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

         __A					:					Any								=		10
         __A					:					List[str]								=		self.dummy_model()
         __A					:					Union[str, Any]								=		self.dummy_sample_deter.half()
         scheduler.set_timesteps(__lowerCamelCase	)

         for i, t in enumerate(scheduler.timesteps	):
              __A					:					Optional[int]								=		model(__lowerCamelCase					,       __lowerCamelCase	)
              __A					:					List[str]								=		scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	).prev_sample

         assert sample.dtype == torch.floataa

    def 						_a							(  self					,       **__lowerCamelCase	):




         '''simple docstring'''



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

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




"""simple docstring"""







import pytest

from datasets.utils.version import Version




@pytest.mark.parametrize(
    'other, expected_equality'  ,       [
        (Version('1.0.0'						), True),
        ('1.0.0', True),
        (Version('2.0.0'						), False),
        ('2.0.0', False),
        ('1', False),
        ('a', False),
        (1, False),
        (None, False),
    ]  ,       )
def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     List[str]:







     '''simple docstring'''


     __A					:					Tuple								=		Version('1.0.0'						)
     assert (version == other) is expected_equality
     assert (version != other) is not expected_equality
     assert (hash(_SCREAMING_SNAKE_CASE						) == hash(_SCREAMING_SNAKE_CASE						)) is expected_equality



