

from __future__ import annotations





def      snake_case_				(__A					:						list[int]					)      ->     bool:
   return len(set(__A					)					) == len(__A					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=(EulerDiscreteScheduler,)
  lowerCamelCase   :      Dict										=10







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							**lowerCAmelCase			:   Union[str, Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						{
         """num_train_timesteps""": 11_00,
         """beta_start""": 0.0001,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
     }

     config.update(**lowerCAmelCase   )
     return config







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Tuple:






     """simple docstring"""







     for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,							[0.0002, 0.002, 0.02]   ):
        self.check_over_configs(beta_start=lowerCAmelCase	,							beta_end=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     str:






     """simple docstring"""







     for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[Any]:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Union[str, Any]:






     """simple docstring"""







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

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      List[Any]    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Tuple    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      str    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						output.prev_sample

     __lowerCAmelCase  :      str    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      List[Any]    =						self.get_scheduler_config(prediction_type="""v_prediction"""   )
     __lowerCAmelCase  :      List[str]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      Any    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      int    =						self.dummy_model()
     __lowerCAmelCase  :      List[str]    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      List[Any]    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Any    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Any    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						output.prev_sample

     __lowerCAmelCase  :      List[str]    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 0.0002   ) < 1e-2
     assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







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

     scheduler.set_timesteps(self.num_inference_steps	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      Union[str, Any]    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      Dict    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      Dict    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      Union[str, Any]    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      int    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						output.prev_sample

     __lowerCAmelCase  :      int    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      str    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Optional[int]    =						self.get_scheduler_config()
     __lowerCAmelCase  :      List[Any]    =						scheduler_class(**lowerCAmelCase	,							use_karras_sigmas=lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      str    =						self.dummy_model()
     __lowerCAmelCase  :      str    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      int    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Tuple    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      List[Any]    =						output.prev_sample

     __lowerCAmelCase  :      Tuple    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Any    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 124.52_2994_9951_1719   ) < 1e-2
     assert abs(result_mean.item() - 0.1_6213_9326_3339_9963   ) < 1e-3


