


"""simple docstring"""





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 						snake_case_(				a__	):
     __UpperCamelCase     =    (DPMSolverSinglestepScheduler,)
     __UpperCamelCase     =    (('''num_inference_steps''', 25),)






     def    lowerCamelCase__   (		self     :    Any    , **UpperCamelCase_     :    List[str] ):
           lowerCAmelCase    :		Union[str, Any]				=      {
               '''num_train_timesteps''': 1_0_0_0,
               '''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''',
               '''lambda_min_clipped''': -float('''inf''' ),
               '''variance_type''': None,
           }

           config.update(**UpperCamelCase_ )
           return config






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    Optional[Any]=0    , **UpperCamelCase_     :    Tuple ):
           lowerCAmelCase    :		Dict				=      dict(self.forward_default_kwargs )
           lowerCAmelCase    :		int				=      kwargs.pop('''num_inference_steps'''    , UpperCamelCase_ )
           lowerCAmelCase    :		int				=      self.dummy_sample
           lowerCAmelCase    :		List[Any]				=      0.1 * sample
           lowerCAmelCase    :		int				=      [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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






     def    lowerCamelCase__   (		self     :    Tuple ):
           pass






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    str=0    , **UpperCamelCase_     :    Union[str, Any] ):
           lowerCAmelCase    :		str				=      dict(self.forward_default_kwargs )
           lowerCAmelCase    :		str				=      kwargs.pop('''num_inference_steps'''    , UpperCamelCase_ )
           lowerCAmelCase    :		Dict				=      self.dummy_sample
           lowerCAmelCase    :		Tuple				=      0.1 * sample
           lowerCAmelCase    :		str				=      [residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCAmelCase    :		str				=      self.get_scheduler_config()
                 lowerCAmelCase    :		Tuple				=      scheduler_class(**UpperCamelCase_ )
                 scheduler.set_timesteps(UpperCamelCase_ )

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

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

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

                 lowerCAmelCase    :		int				=      scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample
                 lowerCAmelCase    :		str				=      new_scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample

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






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    List[Any]=None    , **UpperCamelCase_     :    List[Any] ):
           if scheduler is None:
                 lowerCAmelCase    :		Tuple				=      self.scheduler_classes[0]
                 lowerCAmelCase    :		Dict				=      self.get_scheduler_config(**UpperCamelCase_ )
                 lowerCAmelCase    :		Optional[int]				=      scheduler_class(**UpperCamelCase_ )

           lowerCAmelCase    :		Optional[int]				=      self.scheduler_classes[0]
           lowerCAmelCase    :		Dict				=      self.get_scheduler_config(**UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      scheduler_class(**UpperCamelCase_ )

           lowerCAmelCase    :		Optional[int]				=      1_0
           lowerCAmelCase    :		Dict				=      self.dummy_model()
           lowerCAmelCase    :		Tuple				=      self.dummy_sample_deter
           scheduler.set_timesteps(UpperCamelCase_ )

           for i, t in enumerate(scheduler.timesteps ):
                 lowerCAmelCase    :		Optional[int]				=      model(UpperCamelCase_    , UpperCamelCase_ )
                 lowerCAmelCase    :		List[Any]				=      scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ ).prev_sample

           return sample






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		str				=      DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
           lowerCAmelCase    :		Any				=      5_0
           lowerCAmelCase    :		Tuple				=      self.dummy_model()
           lowerCAmelCase    :		Dict				=      self.dummy_sample_deter
           scheduler.set_timesteps(UpperCamelCase_ )

           # make sure that the first t is uneven
           for i, t in enumerate(scheduler.timesteps[3:] ):
                 lowerCAmelCase    :		int				=      model(UpperCamelCase_    , UpperCamelCase_ )
                 lowerCAmelCase    :		Union[str, Any]				=      scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ ).prev_sample

           lowerCAmelCase    :		List[Any]				=      torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_mean.item() - 0.2_574 ) < 1E-3






     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           lowerCAmelCase    :		str				=      DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
           lowerCAmelCase    :		Tuple				=      self.full_loop(scheduler=UpperCamelCase_ )
           lowerCAmelCase    :		str				=      torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_mean.item() - 0.2_791 ) < 1E-3

           lowerCAmelCase    :		Any				=      DEISMultistepScheduler.from_config(scheduler.config )
           lowerCAmelCase    :		int				=      DPMSolverMultistepScheduler.from_config(scheduler.config )
           lowerCAmelCase    :		Any				=      UniPCMultistepScheduler.from_config(scheduler.config )
           lowerCAmelCase    :		Any				=      DPMSolverSinglestepScheduler.from_config(scheduler.config )

           lowerCAmelCase    :		Union[str, Any]				=      self.full_loop(scheduler=UpperCamelCase_ )
           lowerCAmelCase    :		Tuple				=      torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_mean.item() - 0.2_791 ) < 1E-3






     def    lowerCamelCase__   (		self     :    str ):
           self.check_over_configs(thresholding=UpperCamelCase_ )
           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=UpperCamelCase_    , prediction_type=UpperCamelCase_    , sample_max_value=UpperCamelCase_    , algorithm_type='''dpmsolver++'''    , solver_order=UpperCamelCase_    , solver_type=UpperCamelCase_    , )






     def    lowerCamelCase__   (		self     :    Tuple ):
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                 for solver_type in ["midpoint", "heun"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       solver_order=UpperCamelCase_    , solver_type=UpperCamelCase_    , prediction_type=UpperCamelCase_    , algorithm_type=UpperCamelCase_    , )
                                   lowerCAmelCase    :		List[Any]				=      self.full_loop(
                                       solver_order=UpperCamelCase_    , solver_type=UpperCamelCase_    , prediction_type=UpperCamelCase_    , algorithm_type=UpperCamelCase_    , )
                                   assert not torch.isnan(UpperCamelCase_ ).any(), "Samples have nan numbers"






     def    lowerCamelCase__   (		self     :    Any ):
           self.check_over_configs(lower_order_final=UpperCamelCase_ )
           self.check_over_configs(lower_order_final=UpperCamelCase_ )






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






     def    lowerCamelCase__   (		self     :    Dict ):
           self.check_over_configs(variance_type=UpperCamelCase_ )
           self.check_over_configs(variance_type='''learned_range''' )






     def    lowerCamelCase__   (		self     :    Any ):
           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=UpperCamelCase_    , time_step=0 )






     def    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase    :		List[Any]				=      self.full_loop()
           lowerCAmelCase    :		Union[str, Any]				=      torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_mean.item() - 0.2_791 ) < 1E-3






     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Union[str, Any]				=      self.full_loop(use_karras_sigmas=UpperCamelCase_ )
           lowerCAmelCase    :		List[str]				=      torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_mean.item() - 0.2_248 ) < 1E-3






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		List[Any]				=      self.full_loop(prediction_type='''v_prediction''' )
           lowerCAmelCase    :		int				=      torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_mean.item() - 0.1_453 ) < 1E-3






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		List[Any]				=      self.full_loop(prediction_type='''v_prediction'''    , use_karras_sigmas=UpperCamelCase_ )
           lowerCAmelCase    :		Dict				=      torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_mean.item() - 0.0_649 ) < 1E-3






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		Tuple				=      self.scheduler_classes[0]
           lowerCAmelCase    :		str				=      self.get_scheduler_config(thresholding=UpperCamelCase_    , dynamic_thresholding_ratio=0 )
           lowerCAmelCase    :		Any				=      scheduler_class(**UpperCamelCase_ )

           lowerCAmelCase    :		Dict				=      1_0
           lowerCAmelCase    :		Dict				=      self.dummy_model()
           lowerCAmelCase    :		str				=      self.dummy_sample_deter.half()
           scheduler.set_timesteps(UpperCamelCase_ )

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

           assert sample.dtype == torch.floataa








from __future__ import annotations

import numpy as np







def 				lowerCAmelCase_	(     snake_case_     ):
      return np.maximum(0,snake_case_     )


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]

