

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class     SCREAMING_SNAKE_CASE__     (						__UpperCAmelCase		):





       '''simple docstring'''




       UpperCamelCase__ :     Dict				    =       (HeunDiscreteScheduler,)
       UpperCamelCase__ :     Dict				    =       1_0

       def UpperCAmelCase_						(						self       :						Union[str, Any]    ,		**lowerCAmelCase__       :						str							)			->    Dict:
             snake_case__					=   {
                 '''num_train_timesteps''': 1100,
                 '''beta_start''': 0.0_001,
                 '''beta_end''': 0.02,
                 '''beta_schedule''': '''linear''',
             }

             config.update(**lowercase_							)
             return config

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

             for timesteps in [10, 50, 100, 1000]:
                   self.check_over_configs(num_train_timesteps=lowercase_							)

       def UpperCAmelCase_						(						self       :						int							)			->    Any:

             for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]    ,		[0.0_002, 0.002, 0.02]							):
                   self.check_over_configs(beta_start=lowercase_    ,		beta_end=lowercase_							)

       def UpperCAmelCase_						(						self       :						Optional[int]							)			->    Optional[Any]:

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

       def UpperCAmelCase_						(						self       :						List[Any]							)			->    List[str]:

             for clip_sample_range in [1.0, 2.0, 3.0]:
                   self.check_over_configs(clip_sample_range=lowercase_    ,		clip_sample=lowercase_							)

       def UpperCAmelCase_						(						self       :						Optional[int]							)			->    Optional[int]:

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

       def UpperCAmelCase_						(						self       :						List[Any]							)			->    Tuple:
             snake_case__					=   self.scheduler_classes[0]
             snake_case__					=   self.get_scheduler_config()
             snake_case__					=   scheduler_class(**lowercase_							)

             scheduler.set_timesteps(self.num_inference_steps							)

             snake_case__					=   self.dummy_model()
             snake_case__					=   self.dummy_sample_deter * scheduler.init_noise_sigma
             snake_case__					=   sample.to(lowercase_							)

             for i, t in enumerate(scheduler.timesteps							):
                   snake_case__					=   scheduler.scale_model_input(lowercase_    ,		lowercase_							)

                   snake_case__					=   model(lowercase_    ,		lowercase_							)

                   snake_case__					=   scheduler.step(lowercase_    ,		lowercase_    ,		lowercase_							)
                   snake_case__					=   output.prev_sample

             snake_case__					=   torch.sum(torch.abs(lowercase_							)							)
             snake_case__					=   torch.mean(torch.abs(lowercase_							)							)

             if torch_device in ["cpu", "mps"]:
                   assert abs(result_sum.item() - 0.1_233							) < 1E-2
                   assert abs(result_mean.item() - 0.0_002							) < 1E-3
             else:
                   # CUDA
                   assert abs(result_sum.item() - 0.1_233							) < 1E-2
                   assert abs(result_mean.item() - 0.0_002							) < 1E-3

       def UpperCAmelCase_						(						self       :						Tuple							)			->    int:
             snake_case__					=   self.scheduler_classes[0]
             snake_case__					=   self.get_scheduler_config(prediction_type="""v_prediction"""							)
             snake_case__					=   scheduler_class(**lowercase_							)

             scheduler.set_timesteps(self.num_inference_steps							)

             snake_case__					=   self.dummy_model()
             snake_case__					=   self.dummy_sample_deter * scheduler.init_noise_sigma
             snake_case__					=   sample.to(lowercase_							)

             for i, t in enumerate(scheduler.timesteps							):
                   snake_case__					=   scheduler.scale_model_input(lowercase_    ,		lowercase_							)

                   snake_case__					=   model(lowercase_    ,		lowercase_							)

                   snake_case__					=   scheduler.step(lowercase_    ,		lowercase_    ,		lowercase_							)
                   snake_case__					=   output.prev_sample

             snake_case__					=   torch.sum(torch.abs(lowercase_							)							)
             snake_case__					=   torch.mean(torch.abs(lowercase_							)							)

             if torch_device in ["cpu", "mps"]:
                   assert abs(result_sum.item() - 4.6_934E-07							) < 1E-2
                   assert abs(result_mean.item() - 6.1_112E-10							) < 1E-3
             else:
                   # CUDA
                   assert abs(result_sum.item() - 4.693_428_650_170_972E-07							) < 1E-2
                   assert abs(result_mean.item() - 0.0_002							) < 1E-3

       def UpperCAmelCase_						(						self       :						Any							)			->    List[str]:
             snake_case__					=   self.scheduler_classes[0]
             snake_case__					=   self.get_scheduler_config()
             snake_case__					=   scheduler_class(**lowercase_							)

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

             snake_case__					=   self.dummy_model()
             snake_case__					=   self.dummy_sample_deter.to(lowercase_							) * scheduler.init_noise_sigma

             for t in scheduler.timesteps:
                   snake_case__					=   scheduler.scale_model_input(lowercase_    ,		lowercase_							)

                   snake_case__					=   model(lowercase_    ,		lowercase_							)

                   snake_case__					=   scheduler.step(lowercase_    ,		lowercase_    ,		lowercase_							)
                   snake_case__					=   output.prev_sample

             snake_case__					=   torch.sum(torch.abs(lowercase_							)							)
             snake_case__					=   torch.mean(torch.abs(lowercase_							)							)

             if str(lowercase_							).startswith("""cpu"""							):
                   # The following sum varies between 148 and 156 on mps. Why?
                   assert abs(result_sum.item() - 0.1_233							) < 1E-2
                   assert abs(result_mean.item() - 0.0_002							) < 1E-3
             elif str(lowercase_							).startswith("""mps"""							):
                   # Larger tolerance on mps
                   assert abs(result_mean.item() - 0.0_002							) < 1E-2
             else:
                   # CUDA
                   assert abs(result_sum.item() - 0.1_233							) < 1E-2
                   assert abs(result_mean.item() - 0.0_002							) < 1E-3

       def UpperCAmelCase_						(						self       :						Tuple							)			->    Optional[Any]:
             snake_case__					=   self.scheduler_classes[0]
             snake_case__					=   self.get_scheduler_config()
             snake_case__					=   scheduler_class(**lowercase_    ,		use_karras_sigmas=lowercase_							)

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

             snake_case__					=   self.dummy_model()
             snake_case__					=   self.dummy_sample_deter.to(lowercase_							) * scheduler.init_noise_sigma
             snake_case__					=   sample.to(lowercase_							)

             for t in scheduler.timesteps:
                   snake_case__					=   scheduler.scale_model_input(lowercase_    ,		lowercase_							)

                   snake_case__					=   model(lowercase_    ,		lowercase_							)

                   snake_case__					=   scheduler.step(lowercase_    ,		lowercase_    ,		lowercase_							)
                   snake_case__					=   output.prev_sample

             snake_case__					=   torch.sum(torch.abs(lowercase_							)							)
             snake_case__					=   torch.mean(torch.abs(lowercase_							)							)

             assert abs(result_sum.item() - 0.00_015							) < 1E-2
             assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07							) < 1E-2









lowerCAmelCase						:   Optional[Any]										=    1000





def 			_lowercase    (      __UpperCamelCase     :		float ):
      return x * x





def 			_lowercase    (      __UpperCamelCase     :		int   ,       __UpperCamelCase     :		float   ,       __UpperCamelCase     :		float   ,       __UpperCamelCase     :		int = 4 ):
      assert callable(
          __UpperCamelCase ), F'''the function(object) passed should be callable your input : {function}'''
      assert isinstance(__UpperCamelCase   ,       (float, int) ), F'''a should be float or integer your input : {a}'''
      assert isinstance(function(__UpperCamelCase )   ,       (float, int) ), (
          "the function should return integer or float return type of your function, "
          F'''{type(__UpperCamelCase )}'''
      )
      assert isinstance(__UpperCamelCase   ,       (float, int) ), F'''b should be float or integer your input : {b}'''
      assert (
          isinstance(__UpperCamelCase   ,       __UpperCamelCase ) 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

      snake_case__					=   (b - a) / N_STEPS
      snake_case__					=   function(__UpperCamelCase ) + function(__UpperCamelCase )

      for i in range(1   ,       __UpperCamelCase ):
            snake_case__					=   a + h * i
            result += function(__UpperCamelCase ) * (4 if i % 2 else 2)

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


if __name__ == "__main__":
       import doctest

       doctest.testmod()






