'''simple docstring'''




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						str      			=		(DPMSolverMultistepScheduler,)
      __UpperCamelCase:						List[str]      			=		(("num_inference_steps", 2_5),)




      def   _A			( self		:       Any		,      **A		:       Tuple ):
             _UpperCAmelCase  :						Optional[Any]							   =					{
                 "num_train_timesteps": 1000,
                 "beta_start": 0.0_001,
                 "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(**A )
             return config




      def   _A			( self		:       str		,      A		:       Tuple=0		,      **A		:       Dict ):
             _UpperCAmelCase  :						List[Any]							   =					dict(self.forward_default_kwargs )
             _UpperCAmelCase  :						Optional[Any]							   =					kwargs.pop("num_inference_steps"		,      A )
             _UpperCAmelCase  :						Any							   =					self.dummy_sample
             _UpperCAmelCase  :						Union[str, Any]							   =					0.1 * sample
             _UpperCAmelCase  :						Tuple							   =					[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                    _UpperCAmelCase   ,						_UpperCAmelCase  :						Optional[Any]							   =					sample, sample
                    for t in range(A		,      time_step + scheduler.config.solver_order + 1 ):
                           _UpperCAmelCase  :						List[Any]							   =					scheduler.step(A		,      A		,      A		,      **A ).prev_sample
                           _UpperCAmelCase  :						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   _A			( self		:       Optional[Any] ):
             pass




      def   _A			( self		:       str		,      A		:       Union[str, Any]=0		,      **A		:       Tuple ):
             _UpperCAmelCase  :						List[Any]							   =					dict(self.forward_default_kwargs )
             _UpperCAmelCase  :						Dict							   =					kwargs.pop("num_inference_steps"		,      A )
             _UpperCAmelCase  :						Any							   =					self.dummy_sample
             _UpperCAmelCase  :						Optional[Any]							   =					0.1 * sample
             _UpperCAmelCase  :						Tuple							   =					[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(**A )
                    scheduler.set_timesteps(A )

                    # 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(A )
                           _UpperCAmelCase  :						Any							   =					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)
                           _UpperCAmelCase  :						Dict							   =					dummy_past_residuals[: new_scheduler.config.solver_order]

                    _UpperCAmelCase  :						Union[str, Any]							   =					scheduler.step(A		,      A		,      A		,      **A ).prev_sample
                    _UpperCAmelCase  :						List[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   _A			( self		:       List[Any]		,      A		:       Union[str, Any]=None		,      **A		:       Optional[int] ):
             if scheduler is None:
                    _UpperCAmelCase  :						List[Any]							   =					self.scheduler_classes[0]
                    _UpperCAmelCase  :						Optional[Any]							   =					self.get_scheduler_config(**A )
                    _UpperCAmelCase  :						List[str]							   =					scheduler_class(**A )

             _UpperCAmelCase  :						List[str]							   =					10
             _UpperCAmelCase  :						Tuple							   =					self.dummy_model()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.dummy_sample_deter
             scheduler.set_timesteps(A )

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

             return sample




      def   _A			( self		:       str ):
             _UpperCAmelCase  :						Optional[Any]							   =					dict(self.forward_default_kwargs )

             _UpperCAmelCase  :						List[Any]							   =					kwargs.pop("num_inference_steps"		,      A )

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

                    _UpperCAmelCase  :						Tuple							   =					self.dummy_sample
                    _UpperCAmelCase  :						Optional[int]							   =					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" ):
                           _UpperCAmelCase  :						Union[str, Any]							   =					num_inference_steps

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

                    _UpperCAmelCase  :						Dict							   =					scheduler.timesteps[5]
                    _UpperCAmelCase  :						Optional[Any]							   =					scheduler.timesteps[6]

                    _UpperCAmelCase  :						Optional[Any]							   =					scheduler.step(A		,      A		,      A		,      **A ).prev_sample
                    _UpperCAmelCase  :						Dict							   =					scheduler.step(A		,      A		,      A		,      **A ).prev_sample

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




      def   _A			( self		:       Optional[Any] ):
             for timesteps in [25, 50, 100, 999, 1000]:
                    self.check_over_configs(num_train_timesteps=A )




      def   _A			( self		:       Union[str, Any] ):
             self.check_over_configs(thresholding=A )
             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=A		,      prediction_type=A		,      sample_max_value=A		,      algorithm_type="dpmsolver++"		,      solver_order=A		,      solver_type=A		,      )




      def   _A			( self		:       List[Any] ):
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=A )




      def   _A			( self		:       Optional[Any] ):
             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=A		,      solver_type=A		,      prediction_type=A		,      algorithm_type=A		,      )
                                         _UpperCAmelCase  :						Tuple							   =					self.full_loop(
                                             solver_order=A		,      solver_type=A		,      prediction_type=A		,      algorithm_type=A		,      )
                                         assert not torch.isnan(A ).any(), "Samples have nan numbers"




      def   _A			( self		:       List[str] ):
             self.check_over_configs(lower_order_final=A )
             self.check_over_configs(lower_order_final=A )




      def   _A			( self		:       Tuple ):
             self.check_over_configs(lambda_min_clipped=-float("inf" ) )
             self.check_over_configs(lambda_min_clipped=-5.1 )




      def   _A			( self		:       List[str] ):
             self.check_over_configs(variance_type=A )
             self.check_over_configs(variance_type="learned_range" )




      def   _A			( self		:       Union[str, Any] ):
             for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                    self.check_over_forward(num_inference_steps=A		,      time_step=0 )




      def   _A			( self		:       Optional[Any] ):
             _UpperCAmelCase  :						List[str]							   =					self.full_loop()
             _UpperCAmelCase  :						Union[str, Any]							   =					torch.mean(torch.abs(A ) )

             assert abs(result_mean.item() - 0.3_301 ) < 1E-3




      def   _A			( self		:       Optional[Any] ):
             _UpperCAmelCase  :						List[Any]							   =					self.full_loop(thresholding=A		,      dynamic_thresholding_ratio=0.87		,      sample_max_value=0.5 )
             _UpperCAmelCase  :						str							   =					torch.mean(torch.abs(A ) )

             assert abs(result_mean.item() - 1.1_364 ) < 1E-3




      def   _A			( self		:       Dict ):
             _UpperCAmelCase  :						str							   =					self.full_loop(prediction_type="v_prediction" )
             _UpperCAmelCase  :						Union[str, Any]							   =					torch.mean(torch.abs(A ) )

             assert abs(result_mean.item() - 0.2_251 ) < 1E-3




      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						List[str]							   =					self.full_loop(prediction_type="v_prediction"		,      use_karras_sigmas=A )
             _UpperCAmelCase  :						str							   =					torch.mean(torch.abs(A ) )

             assert abs(result_mean.item() - 0.2_096 ) < 1E-3




      def   _A			( self		:       Optional[int] ):
             # make sure that iterating over schedulers with same config names gives same results
             # for defaults
             _UpperCAmelCase  :						Any							   =					DPMSolverMultistepScheduler(**self.get_scheduler_config() )
             _UpperCAmelCase  :						Union[str, Any]							   =					self.full_loop(scheduler=A )
             _UpperCAmelCase  :						Optional[Any]							   =					torch.mean(torch.abs(A ) )

             assert abs(result_mean.item() - 0.3_301 ) < 1E-3

             _UpperCAmelCase  :						Union[str, Any]							   =					DPMSolverSinglestepScheduler.from_config(scheduler.config )
             _UpperCAmelCase  :						Optional[Any]							   =					UniPCMultistepScheduler.from_config(scheduler.config )
             _UpperCAmelCase  :						str							   =					DEISMultistepScheduler.from_config(scheduler.config )
             _UpperCAmelCase  :						str							   =					DPMSolverMultistepScheduler.from_config(scheduler.config )

             _UpperCAmelCase  :						Union[str, Any]							   =					self.full_loop(scheduler=A )
             _UpperCAmelCase  :						Optional[Any]							   =					torch.mean(torch.abs(A ) )

             assert abs(result_mean.item() - 0.3_301 ) < 1E-3




      def   _A			( self		:       Any ):
             _UpperCAmelCase  :						Optional[Any]							   =					self.scheduler_classes[0]
             _UpperCAmelCase  :						Dict							   =					self.get_scheduler_config(thresholding=A		,      dynamic_thresholding_ratio=0 )
             _UpperCAmelCase  :						List[Any]							   =					scheduler_class(**A )

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

             for i, t in enumerate(scheduler.timesteps ):
                    _UpperCAmelCase  :						int							   =					model(A		,      A )
                    _UpperCAmelCase  :						int							   =					scheduler.step(A		,      A		,      A ).prev_sample

             assert sample.dtype == torch.floataa





      def   _A			( self		:       List[str]		,      **A		:       List[Any] ):
             for scheduler_class in self.scheduler_classes:
                    _UpperCAmelCase  :						int							   =					self.get_scheduler_config(**A )
                    _UpperCAmelCase  :						str							   =					scheduler_class(**A )

                    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 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   List[Any]					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						Union[str, Any]							   =					Version("1.0.0"					)
       assert (version == other) is expected_equality
       assert (version != other) is not expected_equality
       assert (hash(_UpperCAmelCase					) == hash(_UpperCAmelCase					)) is expected_equality

