
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





   '''simple docstring'''
   __UpperCamelCase	:					Any				       =					(KDPMaAncestralDiscreteScheduler,)
   __UpperCamelCase	:					int				       =					10


   def 				__magic_name__       (      self  :							Tuple    ,    **lowerCAmelCase_  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						Any	    = {
          '''num_train_timesteps''': 1_1_0_0,
          '''beta_start''': 0.0001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
      }

      config.update(**lowerCAmelCase_  )
      return config


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,    [0.0002, 0.002, 0.02]  ):
         self.check_over_configs(beta_start=lowerCAmelCase_    ,    beta_end=lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Optional[Any]  ):

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


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

      """simple docstring"""
      if torch_device == "mps":
         return
      _A:						int	    = self.scheduler_classes[0]
      _A:						List[str]	    = self.get_scheduler_config()
      _A:						Tuple	    = scheduler_class(**lowerCAmelCase_  )

      scheduler.set_timesteps(self.num_inference_steps  )

      _A:						List[str]	    = torch.manual_seed(0  )

      _A:						Any	    = self.dummy_model()
      _A:						List[Any]	    = self.dummy_sample_deter * scheduler.init_noise_sigma
      _A:						List[str]	    = sample.to(lowerCAmelCase_  )

      for i, t in enumerate(scheduler.timesteps  ):
         _A:						Tuple	    = scheduler.scale_model_input(lowerCAmelCase_    ,    lowerCAmelCase_  )

         _A:						List[Any]	    = model(lowerCAmelCase_    ,    lowerCAmelCase_  )

         _A:						Tuple	    = scheduler.step(lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_    ,    generator=lowerCAmelCase_  )
         _A:						Dict	    = output.prev_sample

      _A:						List[str]	    = torch.sum(torch.abs(lowerCAmelCase_  )  )
      _A:						List[Any]	    = torch.mean(torch.abs(lowerCAmelCase_  )  )

      assert abs(result_sum.item() - 13849.3877  ) < 1e-2
      assert abs(result_mean.item() - 18.0331  ) < 5e-3


   def 				__magic_name__       (      self  :							Optional[int]  ):

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


   def 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      if torch_device == "mps":
         return
      _A:						List[Any]	    = self.scheduler_classes[0]
      _A:						str	    = self.get_scheduler_config(prediction_type='''v_prediction'''  )
      _A:						Union[str, Any]	    = scheduler_class(**lowerCAmelCase_  )

      scheduler.set_timesteps(self.num_inference_steps  )

      _A:						int	    = self.dummy_model()
      _A:						List[Any]	    = self.dummy_sample_deter * scheduler.init_noise_sigma
      _A:						Optional[Any]	    = sample.to(lowerCAmelCase_  )

      _A:						List[str]	    = torch.manual_seed(0  )

      for i, t in enumerate(scheduler.timesteps  ):
         _A:						Union[str, Any]	    = scheduler.scale_model_input(lowerCAmelCase_    ,    lowerCAmelCase_  )

         _A:						Any	    = model(lowerCAmelCase_    ,    lowerCAmelCase_  )

         _A:						Optional[int]	    = scheduler.step(lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_    ,    generator=lowerCAmelCase_  )
         _A:						Union[str, Any]	    = output.prev_sample

      _A:						Any	    = torch.sum(torch.abs(lowerCAmelCase_  )  )
      _A:						Optional[int]	    = torch.mean(torch.abs(lowerCAmelCase_  )  )

      assert abs(result_sum.item() - 328.9970  ) < 1e-2
      assert abs(result_mean.item() - 0.4284  ) < 1e-3


   def 				__magic_name__       (      self  :							List[str]  ):

      """simple docstring"""
      if torch_device == "mps":
         return
      _A:						Dict	    = self.scheduler_classes[0]
      _A:						Optional[Any]	    = self.get_scheduler_config()
      _A:						str	    = scheduler_class(**lowerCAmelCase_  )

      scheduler.set_timesteps(self.num_inference_steps    ,    device=lowerCAmelCase_  )
      _A:						List[Any]	    = torch.manual_seed(0  )

      _A:						Union[str, Any]	    = self.dummy_model()
      _A:						Dict	    = self.dummy_sample_deter.to(lowerCAmelCase_  ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         _A:						Tuple	    = scheduler.scale_model_input(lowerCAmelCase_    ,    lowerCAmelCase_  )

         _A:						Any	    = model(lowerCAmelCase_    ,    lowerCAmelCase_  )

         _A:						Tuple	    = scheduler.step(lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_    ,    generator=lowerCAmelCase_  )
         _A:						Any	    = output.prev_sample

      _A:						str	    = torch.sum(torch.abs(lowerCAmelCase_  )  )
      _A:						Dict	    = torch.mean(torch.abs(lowerCAmelCase_  )  )

      assert abs(result_sum.item() - 13849.3818  ) < 1e-1
      assert abs(result_mean.item() - 18.0331  ) < 1e-3





UpperCAmelCase__	:  str      =   1000






def 						lowerCamelCase__							(      a   )   ->     float:
   return x * x






def 						lowerCamelCase__							(      a	,	a	,	a	,	a = 4   )   ->     float:
   assert callable(
       a   ), f"""the function(object) passed should be callable your input : {function}"""
   assert isinstance(a	,	(float, int)   ), f"""a should be float or integer your input : {a}"""
   assert isinstance(function(a   )	,	(float, int)   ), (
       "the function should return integer or float return type of your function, "
       f"""{type(a   )}"""
   )
   assert isinstance(a	,	(float, int)   ), f"""b should be float or integer your input : {b}"""
   assert (
       isinstance(a	,	a   ) and precision > 0
   ), f"""precision should be positive integer your input : {precision}"""

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   _A:						int	    = (b - a) / N_STEPS
   _A:						Any	    = function(a   ) + function(a   )

   for i in range(1	,	a   ):
      _A:						List[Any]	    = a + h * i
      result += function(a   ) * (4 if i % 2 else 2)

   result *= h / 3
   return round(a	,	a   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



